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doc_30400 | See Migration guide for more details. tf.compat.v1.math.argmin
tf.compat.v1.argmin(
input, axis=None, name=None, dimension=None, output_type=tf.dtypes.int64
)
Warning: SOME ARGUMENTS ARE DEPRECATED: (dimension). They will be removed in a future version. Instructions for updating: Use the axis argument instead Note that in case of ties the identity of the return value is not guaranteed. Usage: import tensorflow as tf
a = [1, 10, 26.9, 2.8, 166.32, 62.3]
b = tf.math.argmin(input = a)
c = tf.keras.backend.eval(b)
# c = 0
# here a[0] = 1 which is the smallest element of a across axis 0
Args
input A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64, bool.
axis A Tensor. Must be one of the following types: int32, int64. int32 or int64, must be in the range [-rank(input), rank(input)). Describes which axis of the input Tensor to reduce across. For vectors, use axis = 0.
output_type An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int64.
name A name for the operation (optional).
Returns A Tensor of type output_type. | |
doc_30401 | See Migration guide for more details. tf.compat.v1.raw_ops.SnapshotDatasetV2
tf.raw_ops.SnapshotDatasetV2(
input_dataset, path, reader_func_other_args, shard_func_other_args,
output_types, output_shapes, reader_func, shard_func, compression='',
reader_prefix='', writer_prefix='', name=None
)
This dataset attempts to determine whether a valid snapshot exists at the snapshot_path, and reads from the snapshot in lieu of using input_dataset. If not, it will run the preprocessing pipeline as usual, and write out a snapshot of the data processed for future use.
Args
input_dataset A Tensor of type variant. A variant tensor representing the input dataset.
path A Tensor of type string. The path we should write snapshots to / read snapshots from.
reader_func_other_args A list of Tensor objects.
shard_func_other_args A list of Tensor objects.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
reader_func A function decorated with @Defun. Optional. A function to control how to read data from snapshot shards.
shard_func A function decorated with @Defun. Optional. A function to control how to shard data when writing a snapshot.
compression An optional string. Defaults to "". The type of compression to be applied to the saved snapshot files.
reader_prefix An optional string. Defaults to "".
writer_prefix An optional string. Defaults to "".
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_30402 | Use the indexes option instead. The newer indexes option provides more functionality than index_together. index_together may be deprecated in the future. Sets of field names that, taken together, are indexed: index_together = [
["pub_date", "deadline"],
]
This list of fields will be indexed together (i.e. the appropriate CREATE INDEX statement will be issued.) For convenience, index_together can be a single list when dealing with a single set of fields: index_together = ["pub_date", "deadline"] | |
doc_30403 |
Draw samples from a standard Gamma distribution. Samples are drawn from a Gamma distribution with specified parameters, shape (sometimes designated “k”) and scale=1. Note New code should use the standard_gamma method of a default_rng() instance instead; please see the Quick Start. Parameters
shapefloat or array_like of floats
Parameter, must be non-negative.
sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a single value is returned if shape is a scalar. Otherwise, np.array(shape).size samples are drawn. Returns
outndarray or scalar
Drawn samples from the parameterized standard gamma distribution. See also scipy.stats.gamma
probability density function, distribution or cumulative density function, etc. Generator.standard_gamma
which should be used for new code. Notes The probability density for the Gamma distribution is \[p(x) = x^{k-1}\frac{e^{-x/\theta}}{\theta^k\Gamma(k)},\] where \(k\) is the shape and \(\theta\) the scale, and \(\Gamma\) is the Gamma function. The Gamma distribution is often used to model the times to failure of electronic components, and arises naturally in processes for which the waiting times between Poisson distributed events are relevant. References 1
Weisstein, Eric W. “Gamma Distribution.” From MathWorld–A Wolfram Web Resource. http://mathworld.wolfram.com/GammaDistribution.html 2
Wikipedia, “Gamma distribution”, https://en.wikipedia.org/wiki/Gamma_distribution Examples Draw samples from the distribution: >>> shape, scale = 2., 1. # mean and width
>>> s = np.random.standard_gamma(shape, 1000000)
Display the histogram of the samples, along with the probability density function: >>> import matplotlib.pyplot as plt
>>> import scipy.special as sps
>>> count, bins, ignored = plt.hist(s, 50, density=True)
>>> y = bins**(shape-1) * ((np.exp(-bins/scale))/
... (sps.gamma(shape) * scale**shape))
>>> plt.plot(bins, y, linewidth=2, color='r')
>>> plt.show() | |
doc_30404 | Local objects cannot manage themselves. For that you need a local manager. You can pass a local manager multiple locals or add them later y appending them to manager.locals. Every time the manager cleans up, it will clean up all the data left in the locals for this context. Changed in version 2.0: ident_func is deprecated and will be removed in Werkzeug 2.1. Changelog Changed in version 0.7: The ident_func parameter was added. Changed in version 0.6.1: The release_local() function can be used instead of a manager. Parameters
locals (Optional[Iterable[Union[werkzeug.local.Local, werkzeug.local.LocalStack]]]) –
ident_func (None) – Return type
None
cleanup()
Manually clean up the data in the locals for this context. Call this at the end of the request or use make_middleware(). Return type
None
get_ident()
Return the context identifier the local objects use internally for this context. You cannot override this method to change the behavior but use it to link other context local objects (such as SQLAlchemy’s scoped sessions) to the Werkzeug locals. Deprecated since version 2.0: Will be removed in Werkzeug 2.1. Changelog Changed in version 0.7: You can pass a different ident function to the local manager that will then be propagated to all the locals passed to the constructor. Return type
int
make_middleware(app)
Wrap a WSGI application so that cleaning up happens after request end. Parameters
app (WSGIApplication) – Return type
WSGIApplication
middleware(func)
Like make_middleware but for decorating functions. Example usage: @manager.middleware
def application(environ, start_response):
...
The difference to make_middleware is that the function passed will have all the arguments copied from the inner application (name, docstring, module). Parameters
func (WSGIApplication) – Return type
WSGIApplication | |
doc_30405 |
Return the minimum value along an axis. Parameters
See `amin` for complete descriptions.
See also
amin, ndarray.min
Notes This is the same as ndarray.min, but returns a matrix object where ndarray.min would return an ndarray. Examples >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, -1, -2, -3],
[ -4, -5, -6, -7],
[ -8, -9, -10, -11]])
>>> x.min()
-11
>>> x.min(0)
matrix([[ -8, -9, -10, -11]])
>>> x.min(1)
matrix([[ -3],
[ -7],
[-11]]) | |
doc_30406 | See Migration guide for more details. tf.compat.v1.raw_ops.TakeWhileDataset
tf.raw_ops.TakeWhileDataset(
input_dataset, other_arguments, predicate, output_types, output_shapes,
name=None
)
The predicate function must return a scalar boolean and accept the following arguments: One tensor for each component of an element of input_dataset. One tensor for each value in other_arguments.
Args
input_dataset A Tensor of type variant.
other_arguments A list of Tensor objects. A list of tensors, typically values that were captured when building a closure for predicate.
predicate A function decorated with @Defun. A function returning a scalar boolean.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_30407 |
Return the timedelta in nanoseconds (ns), for internal compatibility. Returns
int
Timedelta in nanoseconds. Examples
>>> td = pd.Timedelta('1 days 42 ns')
>>> td.delta
86400000000042
>>> td = pd.Timedelta('3 s')
>>> td.delta
3000000000
>>> td = pd.Timedelta('3 ms 5 us')
>>> td.delta
3005000
>>> td = pd.Timedelta(42, unit='ns')
>>> td.delta
42 | |
doc_30408 |
The transmute method is the very core of the ArrowStyle class and must be overridden in the subclasses. It receives the path object along which the arrow will be drawn, and the mutation_size, with which the arrow head etc. will be scaled. The linewidth may be used to adjust the path so that it does not pass beyond the given points. It returns a tuple of a Path instance and a boolean. The boolean value indicate whether the path can be filled or not. The return value can also be a list of paths and list of booleans of a same length. | |
doc_30409 |
Apply trees in the forest to X, return leaf indices. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix. Returns
X_leavesndarray of shape (n_samples, n_estimators)
For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in. | |
doc_30410 | Returns the indices of the upper triangular part of a row by col matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates. Indices are ordered based on rows and then columns. The upper triangular part of the matrix is defined as the elements on and above the diagonal. The argument offset controls which diagonal to consider. If offset = 0, all elements on and above the main diagonal are retained. A positive value excludes just as many diagonals above the main diagonal, and similarly a negative value includes just as many diagonals below the main diagonal. The main diagonal are the set of indices {(i,i)}\lbrace (i, i) \rbrace for i∈[0,min{d1,d2}−1]i \in [0, \min\{d_{1}, d_{2}\} - 1] where d1,d2d_{1}, d_{2} are the dimensions of the matrix. Note When running on CUDA, row * col must be less than 2592^{59} to prevent overflow during calculation. Parameters
row (int) – number of rows in the 2-D matrix.
col (int) – number of columns in the 2-D matrix.
offset (int) – diagonal offset from the main diagonal. Default: if not provided, 0. Keyword Arguments
dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, torch.long.
device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
layout (torch.layout, optional) – currently only support torch.strided. Example::
>>> a = torch.triu_indices(3, 3)
>>> a
tensor([[0, 0, 0, 1, 1, 2],
[0, 1, 2, 1, 2, 2]])
>>> a = torch.triu_indices(4, 3, -1)
>>> a
tensor([[0, 0, 0, 1, 1, 1, 2, 2, 3],
[0, 1, 2, 0, 1, 2, 1, 2, 2]])
>>> a = torch.triu_indices(4, 3, 1)
>>> a
tensor([[0, 0, 1],
[1, 2, 2]]) | |
doc_30411 | See Migration guide for more details. tf.compat.v1.raw_ops.BatchIFFT
tf.raw_ops.BatchIFFT(
input, name=None
)
Args
input A Tensor of type complex64.
name A name for the operation (optional).
Returns A Tensor of type complex64. | |
doc_30412 | Set the frame rate to n. Changed in version 3.2: A non-integral input to this method is rounded to the nearest integer. | |
doc_30413 | tf.add_n Compat aliases for migration See Migration guide for more details. tf.compat.v1.add_n, tf.compat.v1.math.add_n
tf.math.add_n(
inputs, name=None
)
tf.math.add_n performs the same operation as tf.math.accumulate_n, but it waits for all of its inputs to be ready before beginning to sum. This buffering can result in higher memory consumption when inputs are ready at different times, since the minimum temporary storage required is proportional to the input size rather than the output size. This op does not broadcast its inputs. If you need broadcasting, use tf.math.add (or the + operator) instead. For example:
a = tf.constant([[3, 5], [4, 8]])
b = tf.constant([[1, 6], [2, 9]])
tf.math.add_n([a, b, a])
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 7, 16],
[10, 25]], dtype=int32)>
Args
inputs A list of tf.Tensor or tf.IndexedSlices objects, each with the same shape and type. tf.IndexedSlices objects will be converted into dense tensors prior to adding.
name A name for the operation (optional).
Returns A tf.Tensor of the same shape and type as the elements of inputs.
Raises
ValueError If inputs don't all have same shape and dtype or the shape cannot be inferred. | |
doc_30414 | The maximum pixel value of the band (excluding the “no data” value). | |
doc_30415 | tf.compat.v1.estimator.tpu.TPUConfig(
iterations_per_loop=2, num_shards=None, num_cores_per_replica=None,
per_host_input_for_training=True, tpu_job_name=None,
initial_infeed_sleep_secs=None, input_partition_dims=None,
eval_training_input_configuration=InputPipelineConfig.PER_HOST_V1,
experimental_host_call_every_n_steps=1,
experimental_allow_per_host_v2_parallel_get_next=False,
experimental_feed_hook=None
)
Args
iterations_per_loop This is the number of train steps running in TPU system before returning to CPU host for each Session.run. This means global step is increased iterations_per_loop times in one Session.run. It is recommended to be set as number of global steps for next checkpoint. Note that in evaluation don't use this value, instead we run total eval steps on TPU for a single Session.run. [Experimental]: iterations_per_loop can be specified as a time interval. To specify N seconds in one Session.run, one can specify it as Ns and substitute the N with the N with the number of desired seconds. Alternatively, the unit of time can also be specified in minutes or hours, e.g. 3600s or 60m or 1h.
num_shards (Deprecated, ignored by TPUEstimator). The number of model replicas in the system. For non-model-parallelism case, this number equals the total number of TPU cores. For model-parallelism, the total number of TPU cores equals num_cores_per_replica * num_shards.
num_cores_per_replica Defaults to None, which disables model parallelism. An integer which describes the number of TPU cores per model replica. This is required by model-parallelism which enables partitioning the model to multiple cores. Currently num_cores_per_replica must be 1, 2, 4, or 8.
per_host_input_for_training If True, for PER_HOST_V1, the input_fn is invoked once on each host, and the number of hosts must be smaller or equal to the number of replicas. For PER_HOST_V2, the input_fn is invoked once for each host (if the number of hosts is less than the number of replicas) or replica (if the number of replicas is less than the number of hosts. With the per-core input pipeline configuration, it is invoked once for each core. With a global batch size train_batch_size in TPUEstimator constructor, the batch size for each shard is train_batch_size // #hosts in the True or PER_HOST_V1 mode. In PER_HOST_V2 mode, it is train_batch_size // #cores. In BROADCAST mode, input_fn is only invoked once on host 0 and the tensors are broadcasted to all other replicas. The batch size equals to train_batch_size. With the per-core input pipeline configuration, the shard batch size is also train_batch_size // #cores. Note: per_host_input_for_training==PER_SHARD_V1 only supports mode.TRAIN.
tpu_job_name The name of the TPU job. Typically, this name is auto-inferred within TPUEstimator, however when using ClusterSpec propagation in more esoteric cluster configurations, you may need to specify the job name as a string.
initial_infeed_sleep_secs The number of seconds the infeed thread should wait before enqueueing the first batch. This helps avoid timeouts for models that require a long compilation time.
input_partition_dims A nested list to describe the partition dims for all the tensors from input_fn(). The structure of input_partition_dims must match the structure of features and labels from input_fn(). The total number of partitions must match num_cores_per_replica. For example, if input_fn() returns two tensors: images with shape [N, H, W, C] and labels [N]. input_partition_dims = [[1, 2, 2, 1], None] will split the images to 4 pieces and feed into 4 TPU cores. labels tensor are directly broadcasted to all the TPU cores since the partition dims is None. Current limitations: This feature is only supported with the PER_HOST_V2 input mode.
eval_training_input_configuration If SLICED, input_fn is only invoked once on host 0 and the tensors are broadcasted to all other replicas. Unlike per_host_input_for_training=BROADCAST, each replica will only get a slice of the data instead of a whole copy. If PER_HOST_V1, the behaviour is determined by per_host_input_for_training.
experimental_host_call_every_n_steps Within a training loop, this argument sets how often host calls are performed during training. Host calls will be evaluated every n steps within a training loop where n is the value of this argument.
experimental_allow_per_host_v2_parallel_get_next When enabled, allows concurrent execution of dataset get next calls when using PER_HOST_V2 input. May result in a performance increase for models with a small step time, but as a consequence TPUEstimator may non-deterministically distribute batches to different cores, rather than guaranteeing round robin behavior.
experimental_feed_hook This is a class which user can provide to the TPU estimator to override the default TPUInfeedOutfeedSessionHook implementation and add customized implementatioin to handle infeed outfeed logic. If given class is None, TPU estimator uses default TPUInfeedOutfeedSessionHook implementation in tpu_estimator.py. If not None, TPU estimator uses this customized tpu infeed outfeed session hook class rather to override the default one.
Raises
ValueError If num_cores_per_replica is not 1, 2, 4, 8, ..., 128.
Attributes
iterations_per_loop
num_shards
num_cores_per_replica
per_host_input_for_training
tpu_job_name
initial_infeed_sleep_secs
input_partition_dims
eval_training_input_configuration
experimental_host_call_every_n_steps
experimental_allow_per_host_v2_parallel_get_next
experimental_feed_hook | |
doc_30416 |
Alias for get_edgecolor. | |
doc_30417 |
Like Artist.get_window_extent, but includes any clipping. Parameters
rendererRendererBase subclass
renderer that will be used to draw the figures (i.e. fig.canvas.get_renderer()) Returns
Bbox
The enclosing bounding box (in figure pixel coordinates). | |
doc_30418 | tf.estimator.classifier_parse_example_spec(
feature_columns, label_key, label_dtype=tf.dtypes.int64, label_default=None,
weight_column=None
)
If users keep data in tf.Example format, they need to call tf.parse_example with a proper feature spec. There are two main things that this utility helps: Users need to combine parsing spec of features with labels and weights (if any) since they are all parsed from same tf.Example instance. This utility combines these specs. It is difficult to map expected label by a classifier such as DNNClassifier to corresponding tf.parse_example spec. This utility encodes it by getting related information from users (key, dtype). Example output of parsing spec: # Define features and transformations
feature_b = tf.feature_column.numeric_column(...)
feature_c_bucketized = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column("feature_c"), ...)
feature_a_x_feature_c = tf.feature_column.crossed_column(
columns=["feature_a", feature_c_bucketized], ...)
feature_columns = [feature_b, feature_c_bucketized, feature_a_x_feature_c]
parsing_spec = tf.estimator.classifier_parse_example_spec(
feature_columns, label_key='my-label', label_dtype=tf.string)
# For the above example, classifier_parse_example_spec would return the dict:
assert parsing_spec == {
"feature_a": parsing_ops.VarLenFeature(tf.string),
"feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
"feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
"my-label" : parsing_ops.FixedLenFeature([1], dtype=tf.string)
}
Example usage with a classifier: feature_columns = # define features via tf.feature_column
estimator = DNNClassifier(
n_classes=1000,
feature_columns=feature_columns,
weight_column='example-weight',
label_vocabulary=['photos', 'keep', ...],
hidden_units=[256, 64, 16])
# This label configuration tells the classifier the following:
# * weights are retrieved with key 'example-weight'
# * label is string and can be one of the following ['photos', 'keep', ...]
# * integer id for label 'photos' is 0, 'keep' is 1, ...
# Input builders
def input_fn_train(): # Returns a tuple of features and labels.
features = tf.contrib.learn.read_keyed_batch_features(
file_pattern=train_files,
batch_size=batch_size,
# creates parsing configuration for tf.parse_example
features=tf.estimator.classifier_parse_example_spec(
feature_columns,
label_key='my-label',
label_dtype=tf.string,
weight_column='example-weight'),
reader=tf.RecordIOReader)
labels = features.pop('my-label')
return features, labels
estimator.train(input_fn=input_fn_train)
Args
feature_columns An iterable containing all feature columns. All items should be instances of classes derived from FeatureColumn.
label_key A string identifying the label. It means tf.Example stores labels with this key.
label_dtype A tf.dtype identifies the type of labels. By default it is tf.int64. If user defines a label_vocabulary, this should be set as tf.string. tf.float32 labels are only supported for binary classification.
label_default used as label if label_key does not exist in given tf.Example. An example usage: let's say label_key is 'clicked' and tf.Example contains clicked data only for positive examples in following format key:clicked, value:1. This means that if there is no data with key 'clicked' it should count as negative example by setting label_deafault=0. Type of this value should be compatible with label_dtype.
weight_column A string or a NumericColumn created by tf.feature_column.numeric_column defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the features. If it is a NumericColumn, raw tensor is fetched by key weight_column.key, then weight_column.normalizer_fn is applied on it to get weight tensor.
Returns A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.
Raises
ValueError If label is used in feature_columns.
ValueError If weight_column is used in feature_columns.
ValueError If any of the given feature_columns is not a _FeatureColumn instance.
ValueError If weight_column is not a NumericColumn instance.
ValueError if label_key is None. | |
doc_30419 | Disable compression on the SSL channel. This is useful if the application protocol supports its own compression scheme. This option is only available with OpenSSL 1.0.0 and later. New in version 3.3. | |
doc_30420 |
Return terms per document with nonzero entries in X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Document-term matrix. Returns
X_invlist of arrays of shape (n_samples,)
List of arrays of terms. | |
doc_30421 |
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for X.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
scorefloat
Mean accuracy of self.predict(X) wrt. y. | |
doc_30422 |
Bases: matplotlib.mathtext.MathtextBackend [Deprecated] Store information to write a mathtext rendering to the SVG backend. Notes Deprecated since version 3.4. get_results(box, used_characters)[source]
Return a backend-specific tuple to return to the backend after all processing is done.
render_glyph(ox, oy, info)[source]
Draw a glyph described by info to the reference point (ox, oy).
render_rect_filled(x1, y1, x2, y2)[source]
Draw a filled black rectangle from (x1, y1) to (x2, y2). | |
doc_30423 | Clears the local variables of all the stack frames in a traceback tb by calling the clear() method of each frame object. New in version 3.4. | |
doc_30424 | Object that is greater than anything (except itself). Used to test mixed type comparison. | |
doc_30425 | See Migration guide for more details. tf.compat.v1.raw_ops.CollectiveBcastSend
tf.raw_ops.CollectiveBcastSend(
input, group_size, group_key, instance_key, shape,
communication_hint='auto', timeout_seconds=0, name=None
)
Args
input A Tensor. Must be one of the following types: bool, float32, half, float64, int32, int64.
group_size An int.
group_key An int.
instance_key An int.
shape A tf.TensorShape or list of ints.
communication_hint An optional string. Defaults to "auto".
timeout_seconds An optional float. Defaults to 0.
name A name for the operation (optional).
Returns A Tensor. Has the same type as input. | |
doc_30426 | See Migration guide for more details. tf.compat.v1.train.ServerDef
Attributes
cluster ClusterDef cluster
cluster_device_filters ClusterDeviceFilters cluster_device_filters
default_session_config ConfigProto default_session_config
job_name string job_name
port int32 port
protocol string protocol
task_index int32 task_index | |
doc_30427 | This method accepts a coroutine that can be used as a cleanup function. | |
doc_30428 | Set up clear text data connection. | |
doc_30429 |
Predict classes at each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. New in version 0.24. Parameters
Xarray-like of shape (n_samples, n_features)
The input samples. Yields
ygenerator of ndarray of shape (n_samples,)
The predicted classes of the input samples, for each iteration. | |
doc_30430 | Depth limit on the creation of recursive representations. The default is 6. | |
doc_30431 | See Migration guide for more details. tf.compat.v1.keras.preprocessing.sequence.skipgrams
tf.keras.preprocessing.sequence.skipgrams(
sequence, vocabulary_size, window_size=4, negative_samples=1.0, shuffle=True,
categorical=False, sampling_table=None, seed=None
)
This function transforms a sequence of word indexes (list of integers) into tuples of words of the form: (word, word in the same window), with label 1 (positive samples). (word, random word from the vocabulary), with label 0 (negative samples). Read more about Skipgram in this gnomic paper by Mikolov et al.: Efficient Estimation of Word Representations in Vector Space
Arguments
sequence A word sequence (sentence), encoded as a list of word indices (integers). If using a sampling_table, word indices are expected to match the rank of the words in a reference dataset (e.g. 10 would encode the 10-th most frequently occurring token). Note that index 0 is expected to be a non-word and will be skipped.
vocabulary_size Int, maximum possible word index + 1
window_size Int, size of sampling windows (technically half-window). The window of a word w_i will be [i - window_size, i + window_size+1].
negative_samples Float >= 0. 0 for no negative (i.e. random) samples. 1 for same number as positive samples.
shuffle Whether to shuffle the word couples before returning them.
categorical bool. if False, labels will be integers (eg. [0, 1, 1 .. ]), if True, labels will be categorical, e.g. [[1,0],[0,1],[0,1] .. ].
sampling_table 1D array of size vocabulary_size where the entry i encodes the probability to sample a word of rank i.
seed Random seed.
Returns couples, labels: where couples are int pairs and labels are either 0 or 1.
Note: By convention, index 0 in the vocabulary is a non-word and will be skipped. | |
doc_30432 |
Check if the Index has duplicate values. Returns
bool
Whether or not the Index has duplicate values. Examples
>>> idx = pd.Index([1, 5, 7, 7])
>>> idx.has_duplicates
True
>>> idx = pd.Index([1, 5, 7])
>>> idx.has_duplicates
False
>>> idx = pd.Index(["Watermelon", "Orange", "Apple",
... "Watermelon"]).astype("category")
>>> idx.has_duplicates
True
>>> idx = pd.Index(["Orange", "Apple",
... "Watermelon"]).astype("category")
>>> idx.has_duplicates
False | |
doc_30433 | operator.__mul__(a, b)
Return a * b, for a and b numbers. | |
doc_30434 |
Predict target for X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
**predict_paramsdict of str -> obj
Parameters to the predict called by the final_estimator. Note that this may be used to return uncertainties from some estimators with return_std or return_cov. Be aware that it will only accounts for uncertainty in the final estimator. Returns
y_predndarray of shape (n_samples,) or (n_samples, n_output)
Predicted targets. | |
doc_30435 | See Migration guide for more details. tf.compat.v1.raw_ops.ApplyAdadelta
tf.raw_ops.ApplyAdadelta(
var, accum, accum_update, lr, rho, epsilon, grad, use_locking=False, name=None
)
accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update;
Args
var A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64. Should be from a Variable().
accum A mutable Tensor. Must have the same type as var. Should be from a Variable().
accum_update A mutable Tensor. Must have the same type as var. Should be from a Variable().
lr A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
rho A Tensor. Must have the same type as var. Decay factor. Must be a scalar.
epsilon A Tensor. Must have the same type as var. Constant factor. Must be a scalar.
grad A Tensor. Must have the same type as var. The gradient.
use_locking An optional bool. Defaults to False. If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
name A name for the operation (optional).
Returns A mutable Tensor. Has the same type as var. | |
doc_30436 |
Predict the closest cluster each sample in X belongs to. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
New data to predict. If a sparse matrix is provided, it will be converted into a sparse csr_matrix. Returns
labelsndarray of shape (n_samples,)
Cluster labels. | |
doc_30437 | A generic version of collections.abc.Reversible. Deprecated since version 3.9: collections.abc.Reversible now supports []. See PEP 585 and Generic Alias Type. | |
doc_30438 | The total number of tests run so far. | |
doc_30439 | tf.experimental.numpy.sign(
x, out=None, where=None, **kwargs
)
See the NumPy documentation for numpy.sign. | |
doc_30440 | Sets the locale for category to the default setting. The default setting is determined by calling getdefaultlocale(). category defaults to LC_ALL. | |
doc_30441 |
Return a copy of a with only the first character of each element capitalized. Calls str.capitalize element-wise. For 8-bit strings, this method is locale-dependent. Parameters
aarray_like of str or unicode
Input array of strings to capitalize. Returns
outndarray
Output array of str or unicode, depending on input types See also str.capitalize
Examples >>> c = np.array(['a1b2','1b2a','b2a1','2a1b'],'S4'); c
array(['a1b2', '1b2a', 'b2a1', '2a1b'],
dtype='|S4')
>>> np.char.capitalize(c)
array(['A1b2', '1b2a', 'B2a1', '2a1b'],
dtype='|S4') | |
doc_30442 | When rotating, rotate the current log. The default implementation calls the ‘rotator’ attribute of the handler, if it’s callable, passing the source and dest arguments to it. If the attribute isn’t callable (the default is None), the source is simply renamed to the destination. Parameters
source – The source filename. This is normally the base filename, e.g. ‘test.log’.
dest – The destination filename. This is normally what the source is rotated to, e.g. ‘test.log.1’. New in version 3.3. | |
doc_30443 | Schedule the execution of a Coroutines. Return a Task object. Third-party event loops can use their own subclass of Task for interoperability. In this case, the result type is a subclass of Task. If the name argument is provided and not None, it is set as the name of the task using Task.set_name(). Changed in version 3.8: Added the name parameter. | |
doc_30444 | Return True if strings a and b are equal, otherwise False, in such a way as to reduce the risk of timing attacks. See hmac.compare_digest() for additional details. | |
doc_30445 | Standard input stream (StreamWriter) or None if the process was created with stdin=None. | |
doc_30446 | tf.compat.v1.train.Optimizer(
use_locking, name
)
This class defines the API to add Ops to train a model. You never use this class directly, but instead instantiate one of its subclasses such as GradientDescentOptimizer, AdagradOptimizer, or MomentumOptimizer. Usage # Create an optimizer with the desired parameters.
opt = GradientDescentOptimizer(learning_rate=0.1)
# Add Ops to the graph to minimize a cost by updating a list of variables.
# "cost" is a Tensor, and the list of variables contains tf.Variable
# objects.
opt_op = opt.minimize(cost, var_list=<list of variables>)
In the training program you will just have to run the returned Op. # Execute opt_op to do one step of training:
opt_op.run()
Processing gradients before applying them. Calling minimize() takes care of both computing the gradients and applying them to the variables. If you want to process the gradients before applying them you can instead use the optimizer in three steps: Compute the gradients with compute_gradients(). Process the gradients as you wish. Apply the processed gradients with apply_gradients(). Example: # Create an optimizer.
opt = GradientDescentOptimizer(learning_rate=0.1)
# Compute the gradients for a list of variables.
grads_and_vars = opt.compute_gradients(loss, <list of variables>)
# grads_and_vars is a list of tuples (gradient, variable). Do whatever you
# need to the 'gradient' part, for example cap them, etc.
capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars]
# Ask the optimizer to apply the capped gradients.
opt.apply_gradients(capped_grads_and_vars)
Gating Gradients Both minimize() and compute_gradients() accept a gate_gradients argument that controls the degree of parallelism during the application of the gradients. The possible values are: GATE_NONE, GATE_OP, and GATE_GRAPH. GATE_NONE: Compute and apply gradients in parallel. This provides the maximum parallelism in execution, at the cost of some non-reproducibility in the results. For example the two gradients of matmul depend on the input values: With GATE_NONE one of the gradients could be applied to one of the inputs before the other gradient is computed resulting in non-reproducible results. GATE_OP: For each Op, make sure all gradients are computed before they are used. This prevents race conditions for Ops that generate gradients for multiple inputs where the gradients depend on the inputs. GATE_GRAPH: Make sure all gradients for all variables are computed before any one of them is used. This provides the least parallelism but can be useful if you want to process all gradients before applying any of them. Slots Some optimizer subclasses, such as MomentumOptimizer and AdagradOptimizer allocate and manage additional variables associated with the variables to train. These are called Slots. Slots have names and you can ask the optimizer for the names of the slots that it uses. Once you have a slot name you can ask the optimizer for the variable it created to hold the slot value. This can be useful if you want to log debug a training algorithm, report stats about the slots, etc.
Args
use_locking Bool. If True apply use locks to prevent concurrent updates to variables.
name A non-empty string. The name to use for accumulators created for the optimizer.
Raises
ValueError If name is malformed. Methods apply_gradients View source
apply_gradients(
grads_and_vars, global_step=None, name=None
)
Apply gradients to variables. This is the second part of minimize(). It returns an Operation that applies gradients.
Args
grads_and_vars List of (gradient, variable) pairs as returned by compute_gradients().
global_step Optional Variable to increment by one after the variables have been updated.
name Optional name for the returned operation. Default to the name passed to the Optimizer constructor.
Returns An Operation that applies the specified gradients. If global_step was not None, that operation also increments global_step.
Raises
TypeError If grads_and_vars is malformed.
ValueError If none of the variables have gradients.
RuntimeError If you should use _distributed_apply() instead. compute_gradients View source
compute_gradients(
loss, var_list=None, gate_gradients=GATE_OP, aggregation_method=None,
colocate_gradients_with_ops=False, grad_loss=None
)
Compute gradients of loss for the variables in var_list. This is the first part of minimize(). It returns a list of (gradient, variable) pairs where "gradient" is the gradient for "variable". Note that "gradient" can be a Tensor, an IndexedSlices, or None if there is no gradient for the given variable.
Args
loss A Tensor containing the value to minimize or a callable taking no arguments which returns the value to minimize. When eager execution is enabled it must be a callable.
var_list Optional list or tuple of tf.Variable to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES.
gate_gradients How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
aggregation_method Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.
colocate_gradients_with_ops If True, try colocating gradients with the corresponding op.
grad_loss Optional. A Tensor holding the gradient computed for loss.
Returns A list of (gradient, variable) pairs. Variable is always present, but gradient can be None.
Raises
TypeError If var_list contains anything else than Variable objects.
ValueError If some arguments are invalid.
RuntimeError If called with eager execution enabled and loss is not callable. Eager Compatibility When eager execution is enabled, gate_gradients, aggregation_method, and colocate_gradients_with_ops are ignored. get_name View source
get_name()
get_slot View source
get_slot(
var, name
)
Return a slot named name created for var by the Optimizer. Some Optimizer subclasses use additional variables. For example Momentum and Adagrad use variables to accumulate updates. This method gives access to these Variable objects if for some reason you need them. Use get_slot_names() to get the list of slot names created by the Optimizer.
Args
var A variable passed to minimize() or apply_gradients().
name A string.
Returns The Variable for the slot if it was created, None otherwise.
get_slot_names View source
get_slot_names()
Return a list of the names of slots created by the Optimizer. See get_slot().
Returns A list of strings.
minimize View source
minimize(
loss, global_step=None, var_list=None, gate_gradients=GATE_OP,
aggregation_method=None, colocate_gradients_with_ops=False, name=None,
grad_loss=None
)
Add operations to minimize loss by updating var_list. This method simply combines calls compute_gradients() and apply_gradients(). If you want to process the gradient before applying them call compute_gradients() and apply_gradients() explicitly instead of using this function.
Args
loss A Tensor containing the value to minimize.
global_step Optional Variable to increment by one after the variables have been updated.
var_list Optional list or tuple of Variable objects to update to minimize loss. Defaults to the list of variables collected in the graph under the key GraphKeys.TRAINABLE_VARIABLES.
gate_gradients How to gate the computation of gradients. Can be GATE_NONE, GATE_OP, or GATE_GRAPH.
aggregation_method Specifies the method used to combine gradient terms. Valid values are defined in the class AggregationMethod.
colocate_gradients_with_ops If True, try colocating gradients with the corresponding op.
name Optional name for the returned operation.
grad_loss Optional. A Tensor holding the gradient computed for loss.
Returns An Operation that updates the variables in var_list. If global_step was not None, that operation also increments global_step.
Raises
ValueError If some of the variables are not Variable objects. Eager Compatibility When eager execution is enabled, loss should be a Python function that takes no arguments and computes the value to be minimized. Minimization (and gradient computation) is done with respect to the elements of var_list if not None, else with respect to any trainable variables created during the execution of the loss function. gate_gradients, aggregation_method, colocate_gradients_with_ops and grad_loss are ignored when eager execution is enabled. variables View source
variables()
A list of variables which encode the current state of Optimizer. Includes slot variables and additional global variables created by the optimizer in the current default graph.
Returns A list of variables.
Class Variables
GATE_GRAPH 2
GATE_NONE 0
GATE_OP 1 | |
doc_30447 | See Migration guide for more details. tf.compat.v1.raw_ops.RequantizationRange
tf.raw_ops.RequantizationRange(
input, input_min, input_max, name=None
)
Given a quantized tensor described by (input, input_min, input_max), outputs a range that covers the actual values present in that tensor. This op is typically used to produce the requested_output_min and requested_output_max for Requantize.
Args
input A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
input_min A Tensor of type float32. The float value that the minimum quantized input value represents.
input_max A Tensor of type float32. The float value that the maximum quantized input value represents.
name A name for the operation (optional).
Returns A tuple of Tensor objects (output_min, output_max). output_min A Tensor of type float32.
output_max A Tensor of type float32. | |
doc_30448 | Splits a tensor into multiple sub-tensors, all of which are views of input, along dimension dim according to the indices or number of sections specified by indices_or_sections. This function is based on NumPy’s numpy.array_split(). Parameters
input (Tensor) – the tensor to split
indices_or_sections (Tensor, int or list or tuple of python:ints) –
If indices_or_sections is an integer n or a zero dimensional long tensor with value n, input is split into n sections along dimension dim. If input is divisible by n along dimension dim, each section will be of equal size, input.size(dim) / n. If input is not divisible by n, the sizes of the first int(input.size(dim) % n) sections will have size int(input.size(dim) / n) + 1, and the rest will have size int(input.size(dim) / n). If indices_or_sections is a list or tuple of ints, or a one-dimensional long tensor, then input is split along dimension dim at each of the indices in the list, tuple or tensor. For instance, indices_or_sections=[2, 3] and dim=0 would result in the tensors input[:2], input[2:3], and input[3:]. If indices_or_sections is a tensor, it must be a zero-dimensional or one-dimensional long tensor on the CPU.
dim (int, optional) – dimension along which to split the tensor. Default: 0
Example::
>>> x = torch.arange(8)
>>> torch.tensor_split(x, 3)
(tensor([0, 1, 2]), tensor([3, 4, 5]), tensor([6, 7]))
>>> x = torch.arange(7)
>>> torch.tensor_split(x, 3)
(tensor([0, 1, 2]), tensor([3, 4]), tensor([5, 6]))
>>> torch.tensor_split(x, (1, 6))
(tensor([0]), tensor([1, 2, 3, 4, 5]), tensor([6]))
>>> x = torch.arange(14).reshape(2, 7)
>>> x
tensor([[ 0, 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12, 13]])
>>> torch.tensor_split(x, 3, dim=1)
(tensor([[0, 1, 2],
[7, 8, 9]]),
tensor([[ 3, 4],
[10, 11]]),
tensor([[ 5, 6],
[12, 13]]))
>>> torch.tensor_split(x, (1, 6), dim=1)
(tensor([[0],
[7]]),
tensor([[ 1, 2, 3, 4, 5],
[ 8, 9, 10, 11, 12]]),
tensor([[ 6],
[13]])) | |
doc_30449 | This generator function uses the co_firstlineno and co_lnotab attributes of the code object code to find the offsets which are starts of lines in the source code. They are generated as (offset, lineno) pairs. See Objects/lnotab_notes.txt for the co_lnotab format and how to decode it. Changed in version 3.6: Line numbers can be decreasing. Before, they were always increasing. | |
doc_30450 | The “ctime” as reported by the operating system. On some systems (like Unix) is the time of the last metadata change, and, on others (like Windows), is the creation time (see platform documentation for details). | |
doc_30451 |
Plot the cross-spectral density. The cross spectral density \(P_{xy}\) by Welch's average periodogram method. The vectors x and y are divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. noverlap gives the length of the overlap between segments. The product of the direct FFTs of x and y are averaged over each segment to compute \(P_{xy}\), with a scaling to correct for power loss due to windowing. If len(x) < NFFT or len(y) < NFFT, they will be zero padded to NFFT. Parameters
x, y1-D arrays or sequences
Arrays or sequences containing the data.
Fsfloat, default: 2
The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit.
windowcallable or ndarray, default: window_hanning
A function or a vector of length NFFT. To create window vectors see window_hanning, window_none, numpy.blackman, numpy.hamming, numpy.bartlett, scipy.signal, scipy.signal.get_window, etc. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment.
sides{'default', 'onesided', 'twosided'}, optional
Which sides of the spectrum to return. 'default' is one-sided for real data and two-sided for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided.
pad_toint, optional
The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT
NFFTint, default: 256
The number of data points used in each block for the FFT. A power 2 is most efficient. This should NOT be used to get zero padding, or the scaling of the result will be incorrect; use pad_to for this instead.
detrend{'none', 'mean', 'linear'} or callable, default: 'none'
The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the detrend parameter is a vector, in Matplotlib it is a function. The mlab module defines detrend_none, detrend_mean, and detrend_linear, but you can use a custom function as well. You can also use a string to choose one of the functions: 'none' calls detrend_none. 'mean' calls detrend_mean. 'linear' calls detrend_linear.
scale_by_freqbool, default: True
Whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility.
noverlapint, default: 0 (no overlap)
The number of points of overlap between segments.
Fcint, default: 0
The center frequency of x, which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband.
return_linebool, default: False
Whether to include the line object plotted in the returned values. Returns
Pxy1-D array
The values for the cross spectrum \(P_{xy}\) before scaling (complex valued).
freqs1-D array
The frequencies corresponding to the elements in Pxy.
lineLine2D
The line created by this function. Only returned if return_line is True. Other Parameters
dataindexable object, optional
If given, the following parameters also accept a string s, which is interpreted as data[s] (unless this raises an exception): x, y **kwargs
Keyword arguments control the Line2D properties:
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha scalar or None
animated bool
antialiased or aa bool
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
color or c color
dash_capstyle CapStyle or {'butt', 'projecting', 'round'}
dash_joinstyle JoinStyle or {'miter', 'round', 'bevel'}
dashes sequence of floats (on/off ink in points) or (None, None)
data (2, N) array or two 1D arrays
drawstyle or ds {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'
figure Figure
fillstyle {'full', 'left', 'right', 'bottom', 'top', 'none'}
gid str
in_layout bool
label object
linestyle or ls {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw float
marker marker style string, Path or MarkerStyle
markeredgecolor or mec color
markeredgewidth or mew float
markerfacecolor or mfc color
markerfacecoloralt or mfcalt color
markersize or ms float
markevery None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]
path_effects AbstractPathEffect
picker float or callable[[Artist, Event], tuple[bool, dict]]
pickradius float
rasterized bool
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
solid_capstyle CapStyle or {'butt', 'projecting', 'round'}
solid_joinstyle JoinStyle or {'miter', 'round', 'bevel'}
transform unknown
url str
visible bool
xdata 1D array
ydata 1D array
zorder float See also psd
is equivalent to setting y = x. Notes For plotting, the power is plotted as \(10 \log_{10}(P_{xy})\) for decibels, though \(P_{xy}\) itself is returned. References Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986)
Examples using matplotlib.axes.Axes.csd
CSD Demo | |
doc_30452 | Output of the child process if it was captured by run() or check_output(). Otherwise, None. | |
doc_30453 | When the noqiflush() routine is used, normal flush of input and output queues associated with the INTR, QUIT and SUSP characters will not be done. You may want to call noqiflush() in a signal handler if you want output to continue as though the interrupt had not occurred, after the handler exits. | |
doc_30454 | See Migration guide for more details. tf.compat.v1.raw_ops.LMDBDataset
tf.raw_ops.LMDBDataset(
filenames, output_types, output_shapes, name=None
)
The Lightning Memory-Mapped Database Manager, or LMDB, is an embedded binary key-value database. This dataset can read the contents of LMDB database files, the names of which generally have the .mdb suffix. Each output element consists of a key-value pair represented as a pair of scalar string Tensors, where the first Tensor contains the key and the second Tensor contains the value. LMDB uses different file formats on big- and little-endian machines. LMDBDataset can only read files in the format of the host machine.
Args
filenames A Tensor of type string. A scalar or a vector containing the name(s) of the binary file(s) to be read.
output_types A list of tf.DTypes that has length >= 1.
output_shapes A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
name A name for the operation (optional).
Returns A Tensor of type variant. | |
doc_30455 |
This loss combines a Sigmoid layer and the BCELoss in one single class. This version is more numerically stable than using a plain Sigmoid followed by a BCELoss as, by combining the operations into one layer, we take advantage of the log-sum-exp trick for numerical stability. The unreduced (i.e. with reduction set to 'none') loss can be described as: ℓ(x,y)=L={l1,…,lN}⊤,ln=−wn[yn⋅logσ(xn)+(1−yn)⋅log(1−σ(xn))],\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad l_n = - w_n \left[ y_n \cdot \log \sigma(x_n) + (1 - y_n) \cdot \log (1 - \sigma(x_n)) \right],
where NN is the batch size. If reduction is not 'none' (default 'mean'), then ℓ(x,y)={mean(L),if reduction=‘mean’;sum(L),if reduction=‘sum’.\ell(x, y) = \begin{cases} \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} \end{cases}
This is used for measuring the error of a reconstruction in for example an auto-encoder. Note that the targets t[i] should be numbers between 0 and 1. It’s possible to trade off recall and precision by adding weights to positive examples. In the case of multi-label classification the loss can be described as: ℓc(x,y)=Lc={l1,c,…,lN,c}⊤,ln,c=−wn,c[pcyn,c⋅logσ(xn,c)+(1−yn,c)⋅log(1−σ(xn,c))],\ell_c(x, y) = L_c = \{l_{1,c},\dots,l_{N,c}\}^\top, \quad l_{n,c} = - w_{n,c} \left[ p_c y_{n,c} \cdot \log \sigma(x_{n,c}) + (1 - y_{n,c}) \cdot \log (1 - \sigma(x_{n,c})) \right],
where cc is the class number (c>1c > 1 for multi-label binary classification, c=1c = 1 for single-label binary classification), nn is the number of the sample in the batch and pcp_c is the weight of the positive answer for the class cc . pc>1p_c > 1 increases the recall, pc<1p_c < 1 increases the precision. For example, if a dataset contains 100 positive and 300 negative examples of a single class, then pos_weight for the class should be equal to 300100=3\frac{300}{100}=3 . The loss would act as if the dataset contains 3×100=3003\times 100=300 positive examples. Examples: >>> target = torch.ones([10, 64], dtype=torch.float32) # 64 classes, batch size = 10
>>> output = torch.full([10, 64], 1.5) # A prediction (logit)
>>> pos_weight = torch.ones([64]) # All weights are equal to 1
>>> criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight)
>>> criterion(output, target) # -log(sigmoid(1.5))
tensor(0.2014)
Parameters
weight (Tensor, optional) – a manual rescaling weight given to the loss of each batch element. If given, has to be a Tensor of size nbatch.
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
pos_weight (Tensor, optional) – a weight of positive examples. Must be a vector with length equal to the number of classes. Shape:
Input: (N,∗)(N, *) where ∗* means, any number of additional dimensions Target: (N,∗)(N, *) , same shape as the input Output: scalar. If reduction is 'none', then (N,∗)(N, *) , same shape as input. Examples: >>> loss = nn.BCEWithLogitsLoss()
>>> input = torch.randn(3, requires_grad=True)
>>> target = torch.empty(3).random_(2)
>>> output = loss(input, target)
>>> output.backward() | |
doc_30456 | turtle.lt(angle)
Parameters
angle – a number (integer or float) Turn turtle left by angle units. (Units are by default degrees, but can be set via the degrees() and radians() functions.) Angle orientation depends on the turtle mode, see mode(). >>> turtle.heading()
22.0
>>> turtle.left(45)
>>> turtle.heading()
67.0 | |
doc_30457 | 'blogs.blog': lambda o: "/blogs/%s/" % o.slug,
'news.story': lambda o: "/stories/%s/%s/" % (o.pub_year, o.slug),
}
The model name used in this setting should be all lowercase, regardless of the case of the actual model class name. ADMINS Default: [] (Empty list) A list of all the people who get code error notifications. When DEBUG=False and AdminEmailHandler is configured in LOGGING (done by default), Django emails these people the details of exceptions raised in the request/response cycle. Each item in the list should be a tuple of (Full name, email address). Example: [('John', 'john@example.com'), ('Mary', 'mary@example.com')]
ALLOWED_HOSTS Default: [] (Empty list) A list of strings representing the host/domain names that this Django site can serve. This is a security measure to prevent HTTP Host header attacks, which are possible even under many seemingly-safe web server configurations. Values in this list can be fully qualified names (e.g. 'www.example.com'), in which case they will be matched against the request’s Host header exactly (case-insensitive, not including port). A value beginning with a period can be used as a subdomain wildcard: '.example.com' will match example.com, www.example.com, and any other subdomain of example.com. A value of '*' will match anything; in this case you are responsible to provide your own validation of the Host header (perhaps in a middleware; if so this middleware must be listed first in MIDDLEWARE). Django also allows the fully qualified domain name (FQDN) of any entries. Some browsers include a trailing dot in the Host header which Django strips when performing host validation. If the Host header (or X-Forwarded-Host if USE_X_FORWARDED_HOST is enabled) does not match any value in this list, the django.http.HttpRequest.get_host() method will raise SuspiciousOperation. When DEBUG is True and ALLOWED_HOSTS is empty, the host is validated against ['.localhost', '127.0.0.1', '[::1]']. ALLOWED_HOSTS is also checked when running tests. This validation only applies via get_host(); if your code accesses the Host header directly from request.META you are bypassing this security protection. APPEND_SLASH Default: True When set to True, if the request URL does not match any of the patterns in the URLconf and it doesn’t end in a slash, an HTTP redirect is issued to the same URL with a slash appended. Note that the redirect may cause any data submitted in a POST request to be lost. The APPEND_SLASH setting is only used if CommonMiddleware is installed (see Middleware). See also PREPEND_WWW. CACHES Default: {
'default': {
'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',
}
}
A dictionary containing the settings for all caches to be used with Django. It is a nested dictionary whose contents maps cache aliases to a dictionary containing the options for an individual cache. The CACHES setting must configure a default cache; any number of additional caches may also be specified. If you are using a cache backend other than the local memory cache, or you need to define multiple caches, other options will be required. The following cache options are available. BACKEND Default: '' (Empty string) The cache backend to use. The built-in cache backends are: 'django.core.cache.backends.db.DatabaseCache' 'django.core.cache.backends.dummy.DummyCache' 'django.core.cache.backends.filebased.FileBasedCache' 'django.core.cache.backends.locmem.LocMemCache' 'django.core.cache.backends.memcached.PyMemcacheCache' 'django.core.cache.backends.memcached.PyLibMCCache' 'django.core.cache.backends.redis.RedisCache' You can use a cache backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path of a cache backend class (i.e. mypackage.backends.whatever.WhateverCache). Changed in Django 3.2: The PyMemcacheCache backend was added. Changed in Django 4.0: The RedisCache backend was added. KEY_FUNCTION A string containing a dotted path to a function (or any callable) that defines how to compose a prefix, version and key into a final cache key. The default implementation is equivalent to the function: def make_key(key, key_prefix, version):
return ':'.join([key_prefix, str(version), key])
You may use any key function you want, as long as it has the same argument signature. See the cache documentation for more information. KEY_PREFIX Default: '' (Empty string) A string that will be automatically included (prepended by default) to all cache keys used by the Django server. See the cache documentation for more information. LOCATION Default: '' (Empty string) The location of the cache to use. This might be the directory for a file system cache, a host and port for a memcache server, or an identifying name for a local memory cache. e.g.: CACHES = {
'default': {
'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',
'LOCATION': '/var/tmp/django_cache',
}
}
OPTIONS Default: None Extra parameters to pass to the cache backend. Available parameters vary depending on your cache backend. Some information on available parameters can be found in the cache arguments documentation. For more information, consult your backend module’s own documentation. TIMEOUT Default: 300 The number of seconds before a cache entry is considered stale. If the value of this setting is None, cache entries will not expire. A value of 0 causes keys to immediately expire (effectively “don’t cache”). VERSION Default: 1 The default version number for cache keys generated by the Django server. See the cache documentation for more information. CACHE_MIDDLEWARE_ALIAS Default: 'default' The cache connection to use for the cache middleware. CACHE_MIDDLEWARE_KEY_PREFIX Default: '' (Empty string) A string which will be prefixed to the cache keys generated by the cache middleware. This prefix is combined with the KEY_PREFIX setting; it does not replace it. See Django’s cache framework. CACHE_MIDDLEWARE_SECONDS Default: 600 The default number of seconds to cache a page for the cache middleware. See Django’s cache framework. CSRF_COOKIE_AGE Default: 31449600 (approximately 1 year, in seconds) The age of CSRF cookies, in seconds. The reason for setting a long-lived expiration time is to avoid problems in the case of a user closing a browser or bookmarking a page and then loading that page from a browser cache. Without persistent cookies, the form submission would fail in this case. Some browsers (specifically Internet Explorer) can disallow the use of persistent cookies or can have the indexes to the cookie jar corrupted on disk, thereby causing CSRF protection checks to (sometimes intermittently) fail. Change this setting to None to use session-based CSRF cookies, which keep the cookies in-memory instead of on persistent storage. CSRF_COOKIE_DOMAIN Default: None The domain to be used when setting the CSRF cookie. This can be useful for easily allowing cross-subdomain requests to be excluded from the normal cross site request forgery protection. It should be set to a string such as ".example.com" to allow a POST request from a form on one subdomain to be accepted by a view served from another subdomain. Please note that the presence of this setting does not imply that Django’s CSRF protection is safe from cross-subdomain attacks by default - please see the CSRF limitations section. CSRF_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the CSRF cookie. If this is set to True, client-side JavaScript will not be able to access the CSRF cookie. Designating the CSRF cookie as HttpOnly doesn’t offer any practical protection because CSRF is only to protect against cross-domain attacks. If an attacker can read the cookie via JavaScript, they’re already on the same domain as far as the browser knows, so they can do anything they like anyway. (XSS is a much bigger hole than CSRF.) Although the setting offers little practical benefit, it’s sometimes required by security auditors. If you enable this and need to send the value of the CSRF token with an AJAX request, your JavaScript must pull the value from a hidden CSRF token form input instead of from the cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. CSRF_COOKIE_NAME Default: 'csrftoken' The name of the cookie to use for the CSRF authentication token. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Cross Site Request Forgery protection. CSRF_COOKIE_PATH Default: '/' The path set on the CSRF cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own CSRF cookie. CSRF_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the CSRF cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. CSRF_COOKIE_SECURE Default: False Whether to use a secure cookie for the CSRF cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent with an HTTPS connection. CSRF_USE_SESSIONS Default: False Whether to store the CSRF token in the user’s session instead of in a cookie. It requires the use of django.contrib.sessions. Storing the CSRF token in a cookie (Django’s default) is safe, but storing it in the session is common practice in other web frameworks and therefore sometimes demanded by security auditors. Since the default error views require the CSRF token, SessionMiddleware must appear in MIDDLEWARE before any middleware that may raise an exception to trigger an error view (such as PermissionDenied) if you’re using CSRF_USE_SESSIONS. See Middleware ordering. CSRF_FAILURE_VIEW Default: 'django.views.csrf.csrf_failure' A dotted path to the view function to be used when an incoming request is rejected by the CSRF protection. The function should have this signature: def csrf_failure(request, reason=""):
...
where reason is a short message (intended for developers or logging, not for end users) indicating the reason the request was rejected. It should return an HttpResponseForbidden. django.views.csrf.csrf_failure() accepts an additional template_name parameter that defaults to '403_csrf.html'. If a template with that name exists, it will be used to render the page. CSRF_HEADER_NAME Default: 'HTTP_X_CSRFTOKEN' The name of the request header used for CSRF authentication. As with other HTTP headers in request.META, the header name received from the server is normalized by converting all characters to uppercase, replacing any hyphens with underscores, and adding an 'HTTP_' prefix to the name. For example, if your client sends a 'X-XSRF-TOKEN' header, the setting should be 'HTTP_X_XSRF_TOKEN'. CSRF_TRUSTED_ORIGINS Default: [] (Empty list) A list of trusted origins for unsafe requests (e.g. POST). For requests that include the Origin header, Django’s CSRF protection requires that header match the origin present in the Host header. For a secure unsafe request that doesn’t include the Origin header, the request must have a Referer header that matches the origin present in the Host header. These checks prevent, for example, a POST request from subdomain.example.com from succeeding against api.example.com. If you need cross-origin unsafe requests, continuing the example, add 'https://subdomain.example.com' to this list (and/or http://... if requests originate from an insecure page). The setting also supports subdomains, so you could add 'https://*.example.com', for example, to allow access from all subdomains of example.com. Changed in Django 4.0: The values in older versions must only include the hostname (possibly with a leading dot) and not the scheme or an asterisk. Also, Origin header checking isn’t performed in older versions. DATABASES Default: {} (Empty dictionary) A dictionary containing the settings for all databases to be used with Django. It is a nested dictionary whose contents map a database alias to a dictionary containing the options for an individual database. The DATABASES setting must configure a default database; any number of additional databases may also be specified. The simplest possible settings file is for a single-database setup using SQLite. This can be configured using the following: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': 'mydatabase',
}
}
When connecting to other database backends, such as MariaDB, MySQL, Oracle, or PostgreSQL, additional connection parameters will be required. See the ENGINE setting below on how to specify other database types. This example is for PostgreSQL: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'mydatabase',
'USER': 'mydatabaseuser',
'PASSWORD': 'mypassword',
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
The following inner options that may be required for more complex configurations are available: ATOMIC_REQUESTS Default: False Set this to True to wrap each view in a transaction on this database. See Tying transactions to HTTP requests. AUTOCOMMIT Default: True Set this to False if you want to disable Django’s transaction management and implement your own. ENGINE Default: '' (Empty string) The database backend to use. The built-in database backends are: 'django.db.backends.postgresql' 'django.db.backends.mysql' 'django.db.backends.sqlite3' 'django.db.backends.oracle' You can use a database backend that doesn’t ship with Django by setting ENGINE to a fully-qualified path (i.e. mypackage.backends.whatever). HOST Default: '' (Empty string) Which host to use when connecting to the database. An empty string means localhost. Not used with SQLite. If this value starts with a forward slash ('/') and you’re using MySQL, MySQL will connect via a Unix socket to the specified socket. For example: "HOST": '/var/run/mysql'
If you’re using MySQL and this value doesn’t start with a forward slash, then this value is assumed to be the host. If you’re using PostgreSQL, by default (empty HOST), the connection to the database is done through UNIX domain sockets (‘local’ lines in pg_hba.conf). If your UNIX domain socket is not in the standard location, use the same value of unix_socket_directory from postgresql.conf. If you want to connect through TCP sockets, set HOST to ‘localhost’ or ‘127.0.0.1’ (‘host’ lines in pg_hba.conf). On Windows, you should always define HOST, as UNIX domain sockets are not available. NAME Default: '' (Empty string) The name of the database to use. For SQLite, it’s the full path to the database file. When specifying the path, always use forward slashes, even on Windows (e.g. C:/homes/user/mysite/sqlite3.db). CONN_MAX_AGE Default: 0 The lifetime of a database connection, as an integer of seconds. Use 0 to close database connections at the end of each request — Django’s historical behavior — and None for unlimited persistent connections. OPTIONS Default: {} (Empty dictionary) Extra parameters to use when connecting to the database. Available parameters vary depending on your database backend. Some information on available parameters can be found in the Database Backends documentation. For more information, consult your backend module’s own documentation. PASSWORD Default: '' (Empty string) The password to use when connecting to the database. Not used with SQLite. PORT Default: '' (Empty string) The port to use when connecting to the database. An empty string means the default port. Not used with SQLite. TIME_ZONE Default: None A string representing the time zone for this database connection or None. This inner option of the DATABASES setting accepts the same values as the general TIME_ZONE setting. When USE_TZ is True and this option is set, reading datetimes from the database returns aware datetimes in this time zone instead of UTC. When USE_TZ is False, it is an error to set this option.
If the database backend doesn’t support time zones (e.g. SQLite, MySQL, Oracle), Django reads and writes datetimes in local time according to this option if it is set and in UTC if it isn’t. Changing the connection time zone changes how datetimes are read from and written to the database. If Django manages the database and you don’t have a strong reason to do otherwise, you should leave this option unset. It’s best to store datetimes in UTC because it avoids ambiguous or nonexistent datetimes during daylight saving time changes. Also, receiving datetimes in UTC keeps datetime arithmetic simple — there’s no need to consider potential offset changes over a DST transition. If you’re connecting to a third-party database that stores datetimes in a local time rather than UTC, then you must set this option to the appropriate time zone. Likewise, if Django manages the database but third-party systems connect to the same database and expect to find datetimes in local time, then you must set this option.
If the database backend supports time zones (e.g. PostgreSQL), the TIME_ZONE option is very rarely needed. It can be changed at any time; the database takes care of converting datetimes to the desired time zone. Setting the time zone of the database connection may be useful for running raw SQL queries involving date/time functions provided by the database, such as date_trunc, because their results depend on the time zone. However, this has a downside: receiving all datetimes in local time makes datetime arithmetic more tricky — you must account for possible offset changes over DST transitions. Consider converting to local time explicitly with AT TIME ZONE in raw SQL queries instead of setting the TIME_ZONE option. DISABLE_SERVER_SIDE_CURSORS Default: False Set this to True if you want to disable the use of server-side cursors with QuerySet.iterator(). Transaction pooling and server-side cursors describes the use case. This is a PostgreSQL-specific setting. USER Default: '' (Empty string) The username to use when connecting to the database. Not used with SQLite. TEST Default: {} (Empty dictionary) A dictionary of settings for test databases; for more details about the creation and use of test databases, see The test database. Here’s an example with a test database configuration: DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'USER': 'mydatabaseuser',
'NAME': 'mydatabase',
'TEST': {
'NAME': 'mytestdatabase',
},
},
}
The following keys in the TEST dictionary are available: CHARSET Default: None The character set encoding used to create the test database. The value of this string is passed directly through to the database, so its format is backend-specific. Supported by the PostgreSQL (postgresql) and MySQL (mysql) backends. COLLATION Default: None The collation order to use when creating the test database. This value is passed directly to the backend, so its format is backend-specific. Only supported for the mysql backend (see the MySQL manual for details). DEPENDENCIES Default: ['default'], for all databases other than default, which has no dependencies. The creation-order dependencies of the database. See the documentation on controlling the creation order of test databases for details. MIGRATE Default: True When set to False, migrations won’t run when creating the test database. This is similar to setting None as a value in MIGRATION_MODULES, but for all apps. MIRROR Default: None The alias of the database that this database should mirror during testing. This setting exists to allow for testing of primary/replica (referred to as master/slave by some databases) configurations of multiple databases. See the documentation on testing primary/replica configurations for details. NAME Default: None The name of database to use when running the test suite. If the default value (None) is used with the SQLite database engine, the tests will use a memory resident database. For all other database engines the test database will use the name 'test_' + DATABASE_NAME. See The test database. SERIALIZE Boolean value to control whether or not the default test runner serializes the database into an in-memory JSON string before running tests (used to restore the database state between tests if you don’t have transactions). You can set this to False to speed up creation time if you don’t have any test classes with serialized_rollback=True. Deprecated since version 4.0: This setting is deprecated as it can be inferred from the databases with the serialized_rollback option enabled. TEMPLATE This is a PostgreSQL-specific setting. The name of a template (e.g. 'template0') from which to create the test database. CREATE_DB Default: True This is an Oracle-specific setting. If it is set to False, the test tablespaces won’t be automatically created at the beginning of the tests or dropped at the end. CREATE_USER Default: True This is an Oracle-specific setting. If it is set to False, the test user won’t be automatically created at the beginning of the tests and dropped at the end. USER Default: None This is an Oracle-specific setting. The username to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will use 'test_' + USER. PASSWORD Default: None This is an Oracle-specific setting. The password to use when connecting to the Oracle database that will be used when running tests. If not provided, Django will generate a random password. ORACLE_MANAGED_FILES Default: False This is an Oracle-specific setting. If set to True, Oracle Managed Files (OMF) tablespaces will be used. DATAFILE and DATAFILE_TMP will be ignored. TBLSPACE Default: None This is an Oracle-specific setting. The name of the tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER. TBLSPACE_TMP Default: None This is an Oracle-specific setting. The name of the temporary tablespace that will be used when running tests. If not provided, Django will use 'test_' + USER + '_temp'. DATAFILE Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE. If not provided, Django will use TBLSPACE + '.dbf'. DATAFILE_TMP Default: None This is an Oracle-specific setting. The name of the datafile to use for the TBLSPACE_TMP. If not provided, Django will use TBLSPACE_TMP + '.dbf'. DATAFILE_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE is allowed to grow to. DATAFILE_TMP_MAXSIZE Default: '500M' This is an Oracle-specific setting. The maximum size that the DATAFILE_TMP is allowed to grow to. DATAFILE_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE. DATAFILE_TMP_SIZE Default: '50M' This is an Oracle-specific setting. The initial size of the DATAFILE_TMP. DATAFILE_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE is extended when more space is required. DATAFILE_TMP_EXTSIZE Default: '25M' This is an Oracle-specific setting. The amount by which the DATAFILE_TMP is extended when more space is required. DATA_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size in bytes that a request body may be before a SuspiciousOperation (RequestDataTooBig) is raised. The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data. You can set this to None to disable the check. Applications that are expected to receive unusually large form posts should tune this setting. The amount of request data is correlated to the amount of memory needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. See also FILE_UPLOAD_MAX_MEMORY_SIZE. DATA_UPLOAD_MAX_NUMBER_FIELDS Default: 1000 The maximum number of parameters that may be received via GET or POST before a SuspiciousOperation (TooManyFields) is raised. You can set this to None to disable the check. Applications that are expected to receive an unusually large number of form fields should tune this setting. The number of request parameters is correlated to the amount of time needed to process the request and populate the GET and POST dictionaries. Large requests could be used as a denial-of-service attack vector if left unchecked. Since web servers don’t typically perform deep request inspection, it’s not possible to perform a similar check at that level. DATABASE_ROUTERS Default: [] (Empty list) The list of routers that will be used to determine which database to use when performing a database query. See the documentation on automatic database routing in multi database configurations. DATE_FORMAT Default: 'N j, Y' (e.g. Feb. 4, 2003) The default formatting to use for displaying date fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATETIME_FORMAT, TIME_FORMAT and SHORT_DATE_FORMAT. DATE_INPUT_FORMATS Default: [
'%Y-%m-%d', '%m/%d/%Y', '%m/%d/%y', # '2006-10-25', '10/25/2006', '10/25/06'
'%b %d %Y', '%b %d, %Y', # 'Oct 25 2006', 'Oct 25, 2006'
'%d %b %Y', '%d %b, %Y', # '25 Oct 2006', '25 Oct, 2006'
'%B %d %Y', '%B %d, %Y', # 'October 25 2006', 'October 25, 2006'
'%d %B %Y', '%d %B, %Y', # '25 October 2006', '25 October, 2006'
]
A list of formats that will be accepted when inputting data on a date field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATETIME_INPUT_FORMATS and TIME_INPUT_FORMATS. DATETIME_FORMAT Default: 'N j, Y, P' (e.g. Feb. 4, 2003, 4 p.m.) The default formatting to use for displaying datetime fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT, TIME_FORMAT and SHORT_DATETIME_FORMAT. DATETIME_INPUT_FORMATS Default: [
'%Y-%m-%d %H:%M:%S', # '2006-10-25 14:30:59'
'%Y-%m-%d %H:%M:%S.%f', # '2006-10-25 14:30:59.000200'
'%Y-%m-%d %H:%M', # '2006-10-25 14:30'
'%m/%d/%Y %H:%M:%S', # '10/25/2006 14:30:59'
'%m/%d/%Y %H:%M:%S.%f', # '10/25/2006 14:30:59.000200'
'%m/%d/%Y %H:%M', # '10/25/2006 14:30'
'%m/%d/%y %H:%M:%S', # '10/25/06 14:30:59'
'%m/%d/%y %H:%M:%S.%f', # '10/25/06 14:30:59.000200'
'%m/%d/%y %H:%M', # '10/25/06 14:30'
]
A list of formats that will be accepted when inputting data on a datetime field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. Date-only formats are not included as datetime fields will automatically try DATE_INPUT_FORMATS in last resort. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and TIME_INPUT_FORMATS. DEBUG Default: False A boolean that turns on/off debug mode. Never deploy a site into production with DEBUG turned on. One of the main features of debug mode is the display of detailed error pages. If your app raises an exception when DEBUG is True, Django will display a detailed traceback, including a lot of metadata about your environment, such as all the currently defined Django settings (from settings.py). As a security measure, Django will not include settings that might be sensitive, such as SECRET_KEY. Specifically, it will exclude any setting whose name includes any of the following: 'API' 'KEY' 'PASS' 'SECRET' 'SIGNATURE' 'TOKEN' Note that these are partial matches. 'PASS' will also match PASSWORD, just as 'TOKEN' will also match TOKENIZED and so on. Still, note that there are always going to be sections of your debug output that are inappropriate for public consumption. File paths, configuration options and the like all give attackers extra information about your server. It is also important to remember that when running with DEBUG turned on, Django will remember every SQL query it executes. This is useful when you’re debugging, but it’ll rapidly consume memory on a production server. Finally, if DEBUG is False, you also need to properly set the ALLOWED_HOSTS setting. Failing to do so will result in all requests being returned as “Bad Request (400)”. Note The default settings.py file created by django-admin
startproject sets DEBUG = True for convenience. DEBUG_PROPAGATE_EXCEPTIONS Default: False If set to True, Django’s exception handling of view functions (handler500, or the debug view if DEBUG is True) and logging of 500 responses (django.request) is skipped and exceptions propagate upward. This can be useful for some test setups. It shouldn’t be used on a live site unless you want your web server (instead of Django) to generate “Internal Server Error” responses. In that case, make sure your server doesn’t show the stack trace or other sensitive information in the response. DECIMAL_SEPARATOR Default: '.' (Dot) Default decimal separator used when formatting decimal numbers. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. DEFAULT_AUTO_FIELD New in Django 3.2. Default: 'django.db.models.AutoField' Default primary key field type to use for models that don’t have a field with primary_key=True. Migrating auto-created through tables The value of DEFAULT_AUTO_FIELD will be respected when creating new auto-created through tables for many-to-many relationships. Unfortunately, the primary keys of existing auto-created through tables cannot currently be updated by the migrations framework. This means that if you switch the value of DEFAULT_AUTO_FIELD and then generate migrations, the primary keys of the related models will be updated, as will the foreign keys from the through table, but the primary key of the auto-created through table will not be migrated. In order to address this, you should add a RunSQL operation to your migrations to perform the required ALTER TABLE step. You can check the existing table name through sqlmigrate, dbshell, or with the field’s remote_field.through._meta.db_table property. Explicitly defined through models are already handled by the migrations system. Allowing automatic migrations for the primary key of existing auto-created through tables may be implemented at a later date. DEFAULT_CHARSET Default: 'utf-8' Default charset to use for all HttpResponse objects, if a MIME type isn’t manually specified. Used when constructing the Content-Type header. DEFAULT_EXCEPTION_REPORTER Default: 'django.views.debug.ExceptionReporter' Default exception reporter class to be used if none has been assigned to the HttpRequest instance yet. See Custom error reports. DEFAULT_EXCEPTION_REPORTER_FILTER Default: 'django.views.debug.SafeExceptionReporterFilter' Default exception reporter filter class to be used if none has been assigned to the HttpRequest instance yet. See Filtering error reports. DEFAULT_FILE_STORAGE Default: 'django.core.files.storage.FileSystemStorage' Default file storage class to be used for any file-related operations that don’t specify a particular storage system. See Managing files. DEFAULT_FROM_EMAIL Default: 'webmaster@localhost' Default email address to use for various automated correspondence from the site manager(s). This doesn’t include error messages sent to ADMINS and MANAGERS; for that, see SERVER_EMAIL. DEFAULT_INDEX_TABLESPACE Default: '' (Empty string) Default tablespace to use for indexes on fields that don’t specify one, if the backend supports it (see Tablespaces). DEFAULT_TABLESPACE Default: '' (Empty string) Default tablespace to use for models that don’t specify one, if the backend supports it (see Tablespaces). DISALLOWED_USER_AGENTS Default: [] (Empty list) List of compiled regular expression objects representing User-Agent strings that are not allowed to visit any page, systemwide. Use this for bots/crawlers. This is only used if CommonMiddleware is installed (see Middleware). EMAIL_BACKEND Default: 'django.core.mail.backends.smtp.EmailBackend' The backend to use for sending emails. For the list of available backends see Sending email. EMAIL_FILE_PATH Default: Not defined The directory used by the file email backend to store output files. EMAIL_HOST Default: 'localhost' The host to use for sending email. See also EMAIL_PORT. EMAIL_HOST_PASSWORD Default: '' (Empty string) Password to use for the SMTP server defined in EMAIL_HOST. This setting is used in conjunction with EMAIL_HOST_USER when authenticating to the SMTP server. If either of these settings is empty, Django won’t attempt authentication. See also EMAIL_HOST_USER. EMAIL_HOST_USER Default: '' (Empty string) Username to use for the SMTP server defined in EMAIL_HOST. If empty, Django won’t attempt authentication. See also EMAIL_HOST_PASSWORD. EMAIL_PORT Default: 25 Port to use for the SMTP server defined in EMAIL_HOST. EMAIL_SUBJECT_PREFIX Default: '[Django] ' Subject-line prefix for email messages sent with django.core.mail.mail_admins or django.core.mail.mail_managers. You’ll probably want to include the trailing space. EMAIL_USE_LOCALTIME Default: False Whether to send the SMTP Date header of email messages in the local time zone (True) or in UTC (False). EMAIL_USE_TLS Default: False Whether to use a TLS (secure) connection when talking to the SMTP server. This is used for explicit TLS connections, generally on port 587. If you are experiencing hanging connections, see the implicit TLS setting EMAIL_USE_SSL. EMAIL_USE_SSL Default: False Whether to use an implicit TLS (secure) connection when talking to the SMTP server. In most email documentation this type of TLS connection is referred to as SSL. It is generally used on port 465. If you are experiencing problems, see the explicit TLS setting EMAIL_USE_TLS. Note that EMAIL_USE_TLS/EMAIL_USE_SSL are mutually exclusive, so only set one of those settings to True. EMAIL_SSL_CERTFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted certificate chain file to use for the SSL connection. EMAIL_SSL_KEYFILE Default: None If EMAIL_USE_SSL or EMAIL_USE_TLS is True, you can optionally specify the path to a PEM-formatted private key file to use for the SSL connection. Note that setting EMAIL_SSL_CERTFILE and EMAIL_SSL_KEYFILE doesn’t result in any certificate checking. They’re passed to the underlying SSL connection. Please refer to the documentation of Python’s ssl.wrap_socket() function for details on how the certificate chain file and private key file are handled. EMAIL_TIMEOUT Default: None Specifies a timeout in seconds for blocking operations like the connection attempt. FILE_UPLOAD_HANDLERS Default: [
'django.core.files.uploadhandler.MemoryFileUploadHandler',
'django.core.files.uploadhandler.TemporaryFileUploadHandler',
]
A list of handlers to use for uploading. Changing this setting allows complete customization – even replacement – of Django’s upload process. See Managing files for details. FILE_UPLOAD_MAX_MEMORY_SIZE Default: 2621440 (i.e. 2.5 MB). The maximum size (in bytes) that an upload will be before it gets streamed to the file system. See Managing files for details. See also DATA_UPLOAD_MAX_MEMORY_SIZE. FILE_UPLOAD_DIRECTORY_PERMISSIONS Default: None The numeric mode to apply to directories created in the process of uploading files. This setting also determines the default permissions for collected static directories when using the collectstatic management command. See collectstatic for details on overriding it. This value mirrors the functionality and caveats of the FILE_UPLOAD_PERMISSIONS setting. FILE_UPLOAD_PERMISSIONS Default: 0o644 The numeric mode (i.e. 0o644) to set newly uploaded files to. For more information about what these modes mean, see the documentation for os.chmod(). If None, you’ll get operating-system dependent behavior. On most platforms, temporary files will have a mode of 0o600, and files saved from memory will be saved using the system’s standard umask. For security reasons, these permissions aren’t applied to the temporary files that are stored in FILE_UPLOAD_TEMP_DIR. This setting also determines the default permissions for collected static files when using the collectstatic management command. See collectstatic for details on overriding it. Warning Always prefix the mode with 0o . If you’re not familiar with file modes, please note that the 0o prefix is very important: it indicates an octal number, which is the way that modes must be specified. If you try to use 644, you’ll get totally incorrect behavior. FILE_UPLOAD_TEMP_DIR Default: None The directory to store data to (typically files larger than FILE_UPLOAD_MAX_MEMORY_SIZE) temporarily while uploading files. If None, Django will use the standard temporary directory for the operating system. For example, this will default to /tmp on *nix-style operating systems. See Managing files for details. FIRST_DAY_OF_WEEK Default: 0 (Sunday) A number representing the first day of the week. This is especially useful when displaying a calendar. This value is only used when not using format internationalization, or when a format cannot be found for the current locale. The value must be an integer from 0 to 6, where 0 means Sunday, 1 means Monday and so on. FIXTURE_DIRS Default: [] (Empty list) List of directories searched for fixture files, in addition to the fixtures directory of each application, in search order. Note that these paths should use Unix-style forward slashes, even on Windows. See Providing data with fixtures and Fixture loading. FORCE_SCRIPT_NAME Default: None If not None, this will be used as the value of the SCRIPT_NAME environment variable in any HTTP request. This setting can be used to override the server-provided value of SCRIPT_NAME, which may be a rewritten version of the preferred value or not supplied at all. It is also used by django.setup() to set the URL resolver script prefix outside of the request/response cycle (e.g. in management commands and standalone scripts) to generate correct URLs when SCRIPT_NAME is not /. FORM_RENDERER Default: 'django.forms.renderers.DjangoTemplates' The class that renders forms and form widgets. It must implement the low-level render API. Included form renderers are:
'django.forms.renderers.DjangoTemplates'
'django.forms.renderers.Jinja2'
FORMAT_MODULE_PATH Default: None A full Python path to a Python package that contains custom format definitions for project locales. If not None, Django will check for a formats.py file, under the directory named as the current locale, and will use the formats defined in this file. For example, if FORMAT_MODULE_PATH is set to mysite.formats, and current language is en (English), Django will expect a directory tree like: mysite/
formats/
__init__.py
en/
__init__.py
formats.py
You can also set this setting to a list of Python paths, for example: FORMAT_MODULE_PATH = [
'mysite.formats',
'some_app.formats',
]
When Django searches for a certain format, it will go through all given Python paths until it finds a module that actually defines the given format. This means that formats defined in packages farther up in the list will take precedence over the same formats in packages farther down. Available formats are: DATE_FORMAT DATE_INPUT_FORMATS
DATETIME_FORMAT, DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS YEAR_MONTH_FORMAT IGNORABLE_404_URLS Default: [] (Empty list) List of compiled regular expression objects describing URLs that should be ignored when reporting HTTP 404 errors via email (see How to manage error reporting). Regular expressions are matched against request's full paths (including query string, if any). Use this if your site does not provide a commonly requested file such as favicon.ico or robots.txt. This is only used if BrokenLinkEmailsMiddleware is enabled (see Middleware). INSTALLED_APPS Default: [] (Empty list) A list of strings designating all applications that are enabled in this Django installation. Each string should be a dotted Python path to: an application configuration class (preferred), or a package containing an application. Learn more about application configurations. Use the application registry for introspection Your code should never access INSTALLED_APPS directly. Use django.apps.apps instead. Application names and labels must be unique in INSTALLED_APPS Application names — the dotted Python path to the application package — must be unique. There is no way to include the same application twice, short of duplicating its code under another name. Application labels — by default the final part of the name — must be unique too. For example, you can’t include both django.contrib.auth and myproject.auth. However, you can relabel an application with a custom configuration that defines a different label. These rules apply regardless of whether INSTALLED_APPS references application configuration classes or application packages. When several applications provide different versions of the same resource (template, static file, management command, translation), the application listed first in INSTALLED_APPS has precedence. INTERNAL_IPS Default: [] (Empty list) A list of IP addresses, as strings, that: Allow the debug() context processor to add some variables to the template context. Can use the admindocs bookmarklets even if not logged in as a staff user. Are marked as “internal” (as opposed to “EXTERNAL”) in AdminEmailHandler emails. LANGUAGE_CODE Default: 'en-us' A string representing the language code for this installation. This should be in standard language ID format. For example, U.S. English is "en-us". See also the list of language identifiers and Internationalization and localization. USE_I18N must be active for this setting to have any effect. It serves two purposes: If the locale middleware isn’t in use, it decides which translation is served to all users. If the locale middleware is active, it provides a fallback language in case the user’s preferred language can’t be determined or is not supported by the website. It also provides the fallback translation when a translation for a given literal doesn’t exist for the user’s preferred language. See How Django discovers language preference for more details. LANGUAGE_COOKIE_AGE Default: None (expires at browser close) The age of the language cookie, in seconds. LANGUAGE_COOKIE_DOMAIN Default: None The domain to use for the language cookie. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies that have the old domain will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting) and to add a middleware that copies the value from the old cookie to a new one and then deletes the old one. LANGUAGE_COOKIE_HTTPONLY Default: False Whether to use HttpOnly flag on the language cookie. If this is set to True, client-side JavaScript will not be able to access the language cookie. See SESSION_COOKIE_HTTPONLY for details on HttpOnly. LANGUAGE_COOKIE_NAME Default: 'django_language' The name of the cookie to use for the language cookie. This can be whatever you want (as long as it’s different from the other cookie names in your application). See Internationalization and localization. LANGUAGE_COOKIE_PATH Default: '/' The path set on the language cookie. This should either match the URL path of your Django installation or be a parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths and each instance will only see its own language cookie. Be cautious when updating this setting on a production site. If you update this setting to use a deeper path than it previously used, existing user cookies that have the old path will not be updated. This will result in site users being unable to switch the language as long as these cookies persist. The only safe and reliable option to perform the switch is to change the language cookie name permanently (via the LANGUAGE_COOKIE_NAME setting), and to add a middleware that copies the value from the old cookie to a new one and then deletes the one. LANGUAGE_COOKIE_SAMESITE Default: None The value of the SameSite flag on the language cookie. This flag prevents the cookie from being sent in cross-site requests. See SESSION_COOKIE_SAMESITE for details about SameSite. LANGUAGE_COOKIE_SECURE Default: False Whether to use a secure cookie for the language cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. LANGUAGES Default: A list of all available languages. This list is continually growing and including a copy here would inevitably become rapidly out of date. You can see the current list of translated languages by looking in django/conf/global_settings.py. The list is a list of two-tuples in the format (language code, language name) – for example, ('ja', 'Japanese'). This specifies which languages are available for language selection. See Internationalization and localization. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, you can mark the language names as translation strings using the gettext_lazy() function. Here’s a sample settings file: from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('de', _('German')),
('en', _('English')),
]
LANGUAGES_BIDI Default: A list of all language codes that are written right-to-left. You can see the current list of these languages by looking in django/conf/global_settings.py. The list contains language codes for languages that are written right-to-left. Generally, the default value should suffice. Only set this setting if you want to restrict language selection to a subset of the Django-provided languages. If you define a custom LANGUAGES setting, the list of bidirectional languages may contain language codes which are not enabled on a given site. LOCALE_PATHS Default: [] (Empty list) A list of directories where Django looks for translation files. See How Django discovers translations. Example: LOCALE_PATHS = [
'/home/www/project/common_files/locale',
'/var/local/translations/locale',
]
Django will look within each of these paths for the <locale_code>/LC_MESSAGES directories containing the actual translation files. LOGGING Default: A logging configuration dictionary. A data structure containing configuration information. The contents of this data structure will be passed as the argument to the configuration method described in LOGGING_CONFIG. Among other things, the default logging configuration passes HTTP 500 server errors to an email log handler when DEBUG is False. See also Configuring logging. You can see the default logging configuration by looking in django/utils/log.py. LOGGING_CONFIG Default: 'logging.config.dictConfig' A path to a callable that will be used to configure logging in the Django project. Points at an instance of Python’s dictConfig configuration method by default. If you set LOGGING_CONFIG to None, the logging configuration process will be skipped. MANAGERS Default: [] (Empty list) A list in the same format as ADMINS that specifies who should get broken link notifications when BrokenLinkEmailsMiddleware is enabled. MEDIA_ROOT Default: '' (Empty string) Absolute filesystem path to the directory that will hold user-uploaded files. Example: "/var/www/example.com/media/" See also MEDIA_URL. Warning MEDIA_ROOT and STATIC_ROOT must have different values. Before STATIC_ROOT was introduced, it was common to rely or fallback on MEDIA_ROOT to also serve static files; however, since this can have serious security implications, there is a validation check to prevent it. MEDIA_URL Default: '' (Empty string) URL that handles the media served from MEDIA_ROOT, used for managing stored files. It must end in a slash if set to a non-empty value. You will need to configure these files to be served in both development and production environments. If you want to use {{ MEDIA_URL }} in your templates, add 'django.template.context_processors.media' in the 'context_processors' option of TEMPLATES. Example: "http://media.example.com/" Warning There are security risks if you are accepting uploaded content from untrusted users! See the security guide’s topic on User-uploaded content for mitigation details. Warning MEDIA_URL and STATIC_URL must have different values. See MEDIA_ROOT for more details. Note If MEDIA_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. MIDDLEWARE Default: None A list of middleware to use. See Middleware. MIGRATION_MODULES Default: {} (Empty dictionary) A dictionary specifying the package where migration modules can be found on a per-app basis. The default value of this setting is an empty dictionary, but the default package name for migration modules is migrations. Example: {'blog': 'blog.db_migrations'}
In this case, migrations pertaining to the blog app will be contained in the blog.db_migrations package. If you provide the app_label argument, makemigrations will automatically create the package if it doesn’t already exist. When you supply None as a value for an app, Django will consider the app as an app without migrations regardless of an existing migrations submodule. This can be used, for example, in a test settings file to skip migrations while testing (tables will still be created for the apps’ models). To disable migrations for all apps during tests, you can set the MIGRATE to False instead. If MIGRATION_MODULES is used in your general project settings, remember to use the migrate --run-syncdb option if you want to create tables for the app. MONTH_DAY_FORMAT Default: 'F j' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the month and day are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given day displays the day and month. Different locales have different formats. For example, U.S. English would say “January 1,” whereas Spanish might say “1 Enero.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and YEAR_MONTH_FORMAT. NUMBER_GROUPING Default: 0 Number of digits grouped together on the integer part of a number. Common use is to display a thousand separator. If this setting is 0, then no grouping will be applied to the number. If this setting is greater than 0, then THOUSAND_SEPARATOR will be used as the separator between those groups. Some locales use non-uniform digit grouping, e.g. 10,00,00,000 in en_IN. For this case, you can provide a sequence with the number of digit group sizes to be applied. The first number defines the size of the group preceding the decimal delimiter, and each number that follows defines the size of preceding groups. If the sequence is terminated with -1, no further grouping is performed. If the sequence terminates with a 0, the last group size is used for the remainder of the number. Example tuple for en_IN: NUMBER_GROUPING = (3, 2, 0)
Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also DECIMAL_SEPARATOR, THOUSAND_SEPARATOR and USE_THOUSAND_SEPARATOR. PREPEND_WWW Default: False Whether to prepend the “www.” subdomain to URLs that don’t have it. This is only used if CommonMiddleware is installed (see Middleware). See also APPEND_SLASH. ROOT_URLCONF Default: Not defined A string representing the full Python import path to your root URLconf, for example "mydjangoapps.urls". Can be overridden on a per-request basis by setting the attribute urlconf on the incoming HttpRequest object. See How Django processes a request for details. SECRET_KEY Default: '' (Empty string) A secret key for a particular Django installation. This is used to provide cryptographic signing, and should be set to a unique, unpredictable value. django-admin startproject automatically adds a randomly-generated SECRET_KEY to each new project. Uses of the key shouldn’t assume that it’s text or bytes. Every use should go through force_str() or force_bytes() to convert it to the desired type. Django will refuse to start if SECRET_KEY is not set. Warning Keep this value secret. Running Django with a known SECRET_KEY defeats many of Django’s security protections, and can lead to privilege escalation and remote code execution vulnerabilities. The secret key is used for: All sessions if you are using any other session backend than django.contrib.sessions.backends.cache, or are using the default get_session_auth_hash(). All messages if you are using CookieStorage or FallbackStorage. All PasswordResetView tokens. Any usage of cryptographic signing, unless a different key is provided. If you rotate your secret key, all of the above will be invalidated. Secret keys are not used for passwords of users and key rotation will not affect them. Note The default settings.py file created by django-admin
startproject creates a unique SECRET_KEY for convenience. SECURE_CONTENT_TYPE_NOSNIFF Default: True If True, the SecurityMiddleware sets the X-Content-Type-Options: nosniff header on all responses that do not already have it. SECURE_CROSS_ORIGIN_OPENER_POLICY New in Django 4.0. Default: 'same-origin' Unless set to None, the SecurityMiddleware sets the Cross-Origin Opener Policy header on all responses that do not already have it to the value provided. SECURE_HSTS_INCLUDE_SUBDOMAINS Default: False If True, the SecurityMiddleware adds the includeSubDomains directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. Warning Setting this incorrectly can irreversibly (for the value of SECURE_HSTS_SECONDS) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_HSTS_PRELOAD Default: False If True, the SecurityMiddleware adds the preload directive to the HTTP Strict Transport Security header. It has no effect unless SECURE_HSTS_SECONDS is set to a non-zero value. SECURE_HSTS_SECONDS Default: 0 If set to a non-zero integer value, the SecurityMiddleware sets the HTTP Strict Transport Security header on all responses that do not already have it. Warning Setting this incorrectly can irreversibly (for some time) break your site. Read the HTTP Strict Transport Security documentation first. SECURE_PROXY_SSL_HEADER Default: None A tuple representing an HTTP header/value combination that signifies a request is secure. This controls the behavior of the request object’s is_secure() method. By default, is_secure() determines if a request is secure by confirming that a requested URL uses https://. This method is important for Django’s CSRF protection, and it may be used by your own code or third-party apps. If your Django app is behind a proxy, though, the proxy may be “swallowing” whether the original request uses HTTPS or not. If there is a non-HTTPS connection between the proxy and Django then is_secure() would always return False – even for requests that were made via HTTPS by the end user. In contrast, if there is an HTTPS connection between the proxy and Django then is_secure() would always return True – even for requests that were made originally via HTTP. In this situation, configure your proxy to set a custom HTTP header that tells Django whether the request came in via HTTPS, and set SECURE_PROXY_SSL_HEADER so that Django knows what header to look for. Set a tuple with two elements – the name of the header to look for and the required value. For example: SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https')
This tells Django to trust the X-Forwarded-Proto header that comes from our proxy, and any time its value is 'https', then the request is guaranteed to be secure (i.e., it originally came in via HTTPS). You should only set this setting if you control your proxy or have some other guarantee that it sets/strips this header appropriately. Note that the header needs to be in the format as used by request.META – all caps and likely starting with HTTP_. (Remember, Django automatically adds 'HTTP_' to the start of x-header names before making the header available in request.META.) Warning Modifying this setting can compromise your site’s security. Ensure you fully understand your setup before changing it. Make sure ALL of the following are true before setting this (assuming the values from the example above): Your Django app is behind a proxy. Your proxy strips the X-Forwarded-Proto header from all incoming requests. In other words, if end users include that header in their requests, the proxy will discard it. Your proxy sets the X-Forwarded-Proto header and sends it to Django, but only for requests that originally come in via HTTPS. If any of those are not true, you should keep this setting set to None and find another way of determining HTTPS, perhaps via custom middleware. SECURE_REDIRECT_EXEMPT Default: [] (Empty list) If a URL path matches a regular expression in this list, the request will not be redirected to HTTPS. The SecurityMiddleware strips leading slashes from URL paths, so patterns shouldn’t include them, e.g. SECURE_REDIRECT_EXEMPT = [r'^no-ssl/$', …]. If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_REFERRER_POLICY Default: 'same-origin' If configured, the SecurityMiddleware sets the Referrer Policy header on all responses that do not already have it to the value provided. SECURE_SSL_HOST Default: None If a string (e.g. secure.example.com), all SSL redirects will be directed to this host rather than the originally-requested host (e.g. www.example.com). If SECURE_SSL_REDIRECT is False, this setting has no effect. SECURE_SSL_REDIRECT Default: False If True, the SecurityMiddleware redirects all non-HTTPS requests to HTTPS (except for those URLs matching a regular expression listed in SECURE_REDIRECT_EXEMPT). Note If turning this to True causes infinite redirects, it probably means your site is running behind a proxy and can’t tell which requests are secure and which are not. Your proxy likely sets a header to indicate secure requests; you can correct the problem by finding out what that header is and configuring the SECURE_PROXY_SSL_HEADER setting accordingly. SERIALIZATION_MODULES Default: Not defined A dictionary of modules containing serializer definitions (provided as strings), keyed by a string identifier for that serialization type. For example, to define a YAML serializer, use: SERIALIZATION_MODULES = {'yaml': 'path.to.yaml_serializer'}
SERVER_EMAIL Default: 'root@localhost' The email address that error messages come from, such as those sent to ADMINS and MANAGERS. Why are my emails sent from a different address? This address is used only for error messages. It is not the address that regular email messages sent with send_mail() come from; for that, see DEFAULT_FROM_EMAIL. SHORT_DATE_FORMAT Default: 'm/d/Y' (e.g. 12/31/2003) An available formatting that can be used for displaying date fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATETIME_FORMAT. SHORT_DATETIME_FORMAT Default: 'm/d/Y P' (e.g. 12/31/2003 4 p.m.) An available formatting that can be used for displaying datetime fields on templates. Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT and SHORT_DATE_FORMAT. SIGNING_BACKEND Default: 'django.core.signing.TimestampSigner' The backend used for signing cookies and other data. See also the Cryptographic signing documentation. SILENCED_SYSTEM_CHECKS Default: [] (Empty list) A list of identifiers of messages generated by the system check framework (i.e. ["models.W001"]) that you wish to permanently acknowledge and ignore. Silenced checks will not be output to the console. See also the System check framework documentation. TEMPLATES Default: [] (Empty list) A list containing the settings for all template engines to be used with Django. Each item of the list is a dictionary containing the options for an individual engine. Here’s a setup that tells the Django template engine to load templates from the templates subdirectory inside each installed application: TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'APP_DIRS': True,
},
]
The following options are available for all backends. BACKEND Default: Not defined The template backend to use. The built-in template backends are: 'django.template.backends.django.DjangoTemplates' 'django.template.backends.jinja2.Jinja2' You can use a template backend that doesn’t ship with Django by setting BACKEND to a fully-qualified path (i.e. 'mypackage.whatever.Backend'). NAME Default: see below The alias for this particular template engine. It’s an identifier that allows selecting an engine for rendering. Aliases must be unique across all configured template engines. It defaults to the name of the module defining the engine class, i.e. the next to last piece of BACKEND, when it isn’t provided. For example if the backend is 'mypackage.whatever.Backend' then its default name is 'whatever'. DIRS Default: [] (Empty list) Directories where the engine should look for template source files, in search order. APP_DIRS Default: False Whether the engine should look for template source files inside installed applications. Note The default settings.py file created by django-admin
startproject sets 'APP_DIRS': True. OPTIONS Default: {} (Empty dict) Extra parameters to pass to the template backend. Available parameters vary depending on the template backend. See DjangoTemplates and Jinja2 for the options of the built-in backends. TEST_RUNNER Default: 'django.test.runner.DiscoverRunner' The name of the class to use for starting the test suite. See Using different testing frameworks. TEST_NON_SERIALIZED_APPS Default: [] (Empty list) In order to restore the database state between tests for TransactionTestCases and database backends without transactions, Django will serialize the contents of all apps when it starts the test run so it can then reload from that copy before running tests that need it. This slows down the startup time of the test runner; if you have apps that you know don’t need this feature, you can add their full names in here (e.g. 'django.contrib.contenttypes') to exclude them from this serialization process. THOUSAND_SEPARATOR Default: ',' (Comma) Default thousand separator used when formatting numbers. This setting is used only when USE_THOUSAND_SEPARATOR is True and NUMBER_GROUPING is greater than 0. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See also NUMBER_GROUPING, DECIMAL_SEPARATOR and USE_THOUSAND_SEPARATOR. TIME_FORMAT Default: 'P' (e.g. 4 p.m.) The default formatting to use for displaying time fields in any part of the system. Note that if USE_L10N is set to True, then the locale-dictated format has higher precedence and will be applied instead. See allowed date format strings. See also DATE_FORMAT and DATETIME_FORMAT. TIME_INPUT_FORMATS Default: [
'%H:%M:%S', # '14:30:59'
'%H:%M:%S.%f', # '14:30:59.000200'
'%H:%M', # '14:30'
]
A list of formats that will be accepted when inputting data on a time field. Formats will be tried in order, using the first valid one. Note that these format strings use Python’s datetime module syntax, not the format strings from the date template filter. When USE_L10N is True, the locale-dictated format has higher precedence and will be applied instead. See also DATE_INPUT_FORMATS and DATETIME_INPUT_FORMATS. TIME_ZONE Default: 'America/Chicago' A string representing the time zone for this installation. See the list of time zones. Note Since Django was first released with the TIME_ZONE set to 'America/Chicago', the global setting (used if nothing is defined in your project’s settings.py) remains 'America/Chicago' for backwards compatibility. New project templates default to 'UTC'. Note that this isn’t necessarily the time zone of the server. For example, one server may serve multiple Django-powered sites, each with a separate time zone setting. When USE_TZ is False, this is the time zone in which Django will store all datetimes. When USE_TZ is True, this is the default time zone that Django will use to display datetimes in templates and to interpret datetimes entered in forms. On Unix environments (where time.tzset() is implemented), Django sets the os.environ['TZ'] variable to the time zone you specify in the TIME_ZONE setting. Thus, all your views and models will automatically operate in this time zone. However, Django won’t set the TZ environment variable if you’re using the manual configuration option as described in manually configuring settings. If Django doesn’t set the TZ environment variable, it’s up to you to ensure your processes are running in the correct environment. Note Django cannot reliably use alternate time zones in a Windows environment. If you’re running Django on Windows, TIME_ZONE must be set to match the system time zone. USE_DEPRECATED_PYTZ New in Django 4.0. Default: False A boolean that specifies whether to use pytz, rather than zoneinfo, as the default time zone implementation. Deprecated since version 4.0: This transitional setting is deprecated. Support for using pytz will be removed in Django 5.0. USE_I18N Default: True A boolean that specifies whether Django’s translation system should be enabled. This provides a way to turn it off, for performance. If this is set to False, Django will make some optimizations so as not to load the translation machinery. See also LANGUAGE_CODE, USE_L10N and USE_TZ. Note The default settings.py file created by django-admin
startproject includes USE_I18N = True for convenience. USE_L10N Default: True A boolean that specifies if localized formatting of data will be enabled by default or not. If this is set to True, e.g. Django will display numbers and dates using the format of the current locale. See also LANGUAGE_CODE, USE_I18N and USE_TZ. Changed in Django 4.0: In older versions, the default value is False. Deprecated since version 4.0: This setting is deprecated. Starting with Django 5.0, localized formatting of data will always be enabled. For example Django will display numbers and dates using the format of the current locale. USE_THOUSAND_SEPARATOR Default: False A boolean that specifies whether to display numbers using a thousand separator. When set to True and USE_L10N is also True, Django will format numbers using the NUMBER_GROUPING and THOUSAND_SEPARATOR settings. These settings may also be dictated by the locale, which takes precedence. See also DECIMAL_SEPARATOR, NUMBER_GROUPING and THOUSAND_SEPARATOR. USE_TZ Default: False Note In Django 5.0, the default value will change from False to True. A boolean that specifies if datetimes will be timezone-aware by default or not. If this is set to True, Django will use timezone-aware datetimes internally. When USE_TZ is False, Django will use naive datetimes in local time, except when parsing ISO 8601 formatted strings, where timezone information will always be retained if present. See also TIME_ZONE, USE_I18N and USE_L10N. Note The default settings.py file created by django-admin startproject includes USE_TZ = True for convenience. USE_X_FORWARDED_HOST Default: False A boolean that specifies whether to use the X-Forwarded-Host header in preference to the Host header. This should only be enabled if a proxy which sets this header is in use. This setting takes priority over USE_X_FORWARDED_PORT. Per RFC 7239#section-5.3, the X-Forwarded-Host header can include the port number, in which case you shouldn’t use USE_X_FORWARDED_PORT. USE_X_FORWARDED_PORT Default: False A boolean that specifies whether to use the X-Forwarded-Port header in preference to the SERVER_PORT META variable. This should only be enabled if a proxy which sets this header is in use. USE_X_FORWARDED_HOST takes priority over this setting. WSGI_APPLICATION Default: None The full Python path of the WSGI application object that Django’s built-in servers (e.g. runserver) will use. The django-admin
startproject management command will create a standard wsgi.py file with an application callable in it, and point this setting to that application. If not set, the return value of django.core.wsgi.get_wsgi_application() will be used. In this case, the behavior of runserver will be identical to previous Django versions. YEAR_MONTH_FORMAT Default: 'F Y' The default formatting to use for date fields on Django admin change-list pages – and, possibly, by other parts of the system – in cases when only the year and month are displayed. For example, when a Django admin change-list page is being filtered by a date drilldown, the header for a given month displays the month and the year. Different locales have different formats. For example, U.S. English would say “January 2006,” whereas another locale might say “2006/January.” Note that if USE_L10N is set to True, then the corresponding locale-dictated format has higher precedence and will be applied. See allowed date format strings. See also DATE_FORMAT, DATETIME_FORMAT, TIME_FORMAT and MONTH_DAY_FORMAT. X_FRAME_OPTIONS Default: 'DENY' The default value for the X-Frame-Options header used by XFrameOptionsMiddleware. See the clickjacking protection documentation. Auth Settings for django.contrib.auth. AUTHENTICATION_BACKENDS Default: ['django.contrib.auth.backends.ModelBackend'] A list of authentication backend classes (as strings) to use when attempting to authenticate a user. See the authentication backends documentation for details. AUTH_USER_MODEL Default: 'auth.User' The model to use to represent a User. See Substituting a custom User model. Warning You cannot change the AUTH_USER_MODEL setting during the lifetime of a project (i.e. once you have made and migrated models that depend on it) without serious effort. It is intended to be set at the project start, and the model it refers to must be available in the first migration of the app that it lives in. See Substituting a custom User model for more details. LOGIN_REDIRECT_URL Default: '/accounts/profile/' The URL or named URL pattern where requests are redirected after login when the LoginView doesn’t get a next GET parameter. LOGIN_URL Default: '/accounts/login/' The URL or named URL pattern where requests are redirected for login when using the login_required() decorator, LoginRequiredMixin, or AccessMixin. LOGOUT_REDIRECT_URL Default: None The URL or named URL pattern where requests are redirected after logout if LogoutView doesn’t have a next_page attribute. If None, no redirect will be performed and the logout view will be rendered. PASSWORD_RESET_TIMEOUT Default: 259200 (3 days, in seconds) The number of seconds a password reset link is valid for. Used by the PasswordResetConfirmView. Note Reducing the value of this timeout doesn’t make any difference to the ability of an attacker to brute-force a password reset token. Tokens are designed to be safe from brute-forcing without any timeout. This timeout exists to protect against some unlikely attack scenarios, such as someone gaining access to email archives that may contain old, unused password reset tokens. PASSWORD_HASHERS See How Django stores passwords. Default: [
'django.contrib.auth.hashers.PBKDF2PasswordHasher',
'django.contrib.auth.hashers.PBKDF2SHA1PasswordHasher',
'django.contrib.auth.hashers.Argon2PasswordHasher',
'django.contrib.auth.hashers.BCryptSHA256PasswordHasher',
]
AUTH_PASSWORD_VALIDATORS Default: [] (Empty list) The list of validators that are used to check the strength of user’s passwords. See Password validation for more details. By default, no validation is performed and all passwords are accepted. Messages Settings for django.contrib.messages. MESSAGE_LEVEL Default: messages.INFO Sets the minimum message level that will be recorded by the messages framework. See message levels for more details. Important If you override MESSAGE_LEVEL in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_LEVEL = message_constants.DEBUG
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. MESSAGE_STORAGE Default: 'django.contrib.messages.storage.fallback.FallbackStorage' Controls where Django stores message data. Valid values are: 'django.contrib.messages.storage.fallback.FallbackStorage' 'django.contrib.messages.storage.session.SessionStorage' 'django.contrib.messages.storage.cookie.CookieStorage' See message storage backends for more details. The backends that use cookies – CookieStorage and FallbackStorage – use the value of SESSION_COOKIE_DOMAIN, SESSION_COOKIE_SECURE and SESSION_COOKIE_HTTPONLY when setting their cookies. MESSAGE_TAGS Default: {
messages.DEBUG: 'debug',
messages.INFO: 'info',
messages.SUCCESS: 'success',
messages.WARNING: 'warning',
messages.ERROR: 'error',
}
This sets the mapping of message level to message tag, which is typically rendered as a CSS class in HTML. If you specify a value, it will extend the default. This means you only have to specify those values which you need to override. See Displaying messages above for more details. Important If you override MESSAGE_TAGS in your settings file and rely on any of the built-in constants, you must import the constants module directly to avoid the potential for circular imports, e.g.: from django.contrib.messages import constants as message_constants
MESSAGE_TAGS = {message_constants.INFO: ''}
If desired, you may specify the numeric values for the constants directly according to the values in the above constants table. Sessions Settings for django.contrib.sessions. SESSION_CACHE_ALIAS Default: 'default' If you’re using cache-based session storage, this selects the cache to use. SESSION_COOKIE_AGE Default: 1209600 (2 weeks, in seconds) The age of session cookies, in seconds. SESSION_COOKIE_DOMAIN Default: None The domain to use for session cookies. Set this to a string such as "example.com" for cross-domain cookies, or use None for a standard domain cookie. To use cross-domain cookies with CSRF_USE_SESSIONS, you must include a leading dot (e.g. ".example.com") to accommodate the CSRF middleware’s referer checking. Be cautious when updating this setting on a production site. If you update this setting to enable cross-domain cookies on a site that previously used standard domain cookies, existing user cookies will be set to the old domain. This may result in them being unable to log in as long as these cookies persist. This setting also affects cookies set by django.contrib.messages. SESSION_COOKIE_HTTPONLY Default: True Whether to use HttpOnly flag on the session cookie. If this is set to True, client-side JavaScript will not be able to access the session cookie. HttpOnly is a flag included in a Set-Cookie HTTP response header. It’s part of the RFC 6265#section-4.1.2.6 standard for cookies and can be a useful way to mitigate the risk of a client-side script accessing the protected cookie data. This makes it less trivial for an attacker to escalate a cross-site scripting vulnerability into full hijacking of a user’s session. There aren’t many good reasons for turning this off. Your code shouldn’t read session cookies from JavaScript. SESSION_COOKIE_NAME Default: 'sessionid' The name of the cookie to use for sessions. This can be whatever you want (as long as it’s different from the other cookie names in your application). SESSION_COOKIE_PATH Default: '/' The path set on the session cookie. This should either match the URL path of your Django installation or be parent of that path. This is useful if you have multiple Django instances running under the same hostname. They can use different cookie paths, and each instance will only see its own session cookie. SESSION_COOKIE_SAMESITE Default: 'Lax' The value of the SameSite flag on the session cookie. This flag prevents the cookie from being sent in cross-site requests thus preventing CSRF attacks and making some methods of stealing session cookie impossible. Possible values for the setting are:
'Strict': prevents the cookie from being sent by the browser to the target site in all cross-site browsing context, even when following a regular link. For example, for a GitHub-like website this would mean that if a logged-in user follows a link to a private GitHub project posted on a corporate discussion forum or email, GitHub will not receive the session cookie and the user won’t be able to access the project. A bank website, however, most likely doesn’t want to allow any transactional pages to be linked from external sites so the 'Strict' flag would be appropriate.
'Lax' (default): provides a balance between security and usability for websites that want to maintain user’s logged-in session after the user arrives from an external link. In the GitHub scenario, the session cookie would be allowed when following a regular link from an external website and be blocked in CSRF-prone request methods (e.g. POST).
'None' (string): the session cookie will be sent with all same-site and cross-site requests.
False: disables the flag. Note Modern browsers provide a more secure default policy for the SameSite flag and will assume Lax for cookies without an explicit value set. SESSION_COOKIE_SECURE Default: False Whether to use a secure cookie for the session cookie. If this is set to True, the cookie will be marked as “secure”, which means browsers may ensure that the cookie is only sent under an HTTPS connection. Leaving this setting off isn’t a good idea because an attacker could capture an unencrypted session cookie with a packet sniffer and use the cookie to hijack the user’s session. SESSION_ENGINE Default: 'django.contrib.sessions.backends.db' Controls where Django stores session data. Included engines are: 'django.contrib.sessions.backends.db' 'django.contrib.sessions.backends.file' 'django.contrib.sessions.backends.cache' 'django.contrib.sessions.backends.cached_db' 'django.contrib.sessions.backends.signed_cookies' See Configuring the session engine for more details. SESSION_EXPIRE_AT_BROWSER_CLOSE Default: False Whether to expire the session when the user closes their browser. See Browser-length sessions vs. persistent sessions. SESSION_FILE_PATH Default: None If you’re using file-based session storage, this sets the directory in which Django will store session data. When the default value (None) is used, Django will use the standard temporary directory for the system. SESSION_SAVE_EVERY_REQUEST Default: False Whether to save the session data on every request. If this is False (default), then the session data will only be saved if it has been modified – that is, if any of its dictionary values have been assigned or deleted. Empty sessions won’t be created, even if this setting is active. SESSION_SERIALIZER Default: 'django.contrib.sessions.serializers.JSONSerializer' Full import path of a serializer class to use for serializing session data. Included serializers are: 'django.contrib.sessions.serializers.PickleSerializer' 'django.contrib.sessions.serializers.JSONSerializer' See Session serialization for details, including a warning regarding possible remote code execution when using PickleSerializer. Sites Settings for django.contrib.sites. SITE_ID Default: Not defined The ID, as an integer, of the current site in the django_site database table. This is used so that application data can hook into specific sites and a single database can manage content for multiple sites. Static Files Settings for django.contrib.staticfiles. STATIC_ROOT Default: None The absolute path to the directory where collectstatic will collect static files for deployment. Example: "/var/www/example.com/static/" If the staticfiles contrib app is enabled (as in the default project template), the collectstatic management command will collect static files into this directory. See the how-to on managing static files for more details about usage. Warning This should be an initially empty destination directory for collecting your static files from their permanent locations into one directory for ease of deployment; it is not a place to store your static files permanently. You should do that in directories that will be found by staticfiles’s finders, which by default, are 'static/' app sub-directories and any directories you include in STATICFILES_DIRS). STATIC_URL Default: None URL to use when referring to static files located in STATIC_ROOT. Example: "static/" or "http://static.example.com/" If not None, this will be used as the base path for asset definitions (the Media class) and the staticfiles app. It must end in a slash if set to a non-empty value. You may need to configure these files to be served in development and will definitely need to do so in production. Note If STATIC_URL is a relative path, then it will be prefixed by the server-provided value of SCRIPT_NAME (or / if not set). This makes it easier to serve a Django application in a subpath without adding an extra configuration to the settings. STATICFILES_DIRS Default: [] (Empty list) This setting defines the additional locations the staticfiles app will traverse if the FileSystemFinder finder is enabled, e.g. if you use the collectstatic or findstatic management command or use the static file serving view. This should be set to a list of strings that contain full paths to your additional files directory(ies) e.g.: STATICFILES_DIRS = [
"/home/special.polls.com/polls/static",
"/home/polls.com/polls/static",
"/opt/webfiles/common",
]
Note that these paths should use Unix-style forward slashes, even on Windows (e.g. "C:/Users/user/mysite/extra_static_content"). Prefixes (optional) In case you want to refer to files in one of the locations with an additional namespace, you can optionally provide a prefix as (prefix, path) tuples, e.g.: STATICFILES_DIRS = [
# ...
("downloads", "/opt/webfiles/stats"),
]
For example, assuming you have STATIC_URL set to 'static/', the collectstatic management command would collect the “stats” files in a 'downloads' subdirectory of STATIC_ROOT. This would allow you to refer to the local file '/opt/webfiles/stats/polls_20101022.tar.gz' with '/static/downloads/polls_20101022.tar.gz' in your templates, e.g.: <a href="{% static 'downloads/polls_20101022.tar.gz' %}">
STATICFILES_STORAGE Default: 'django.contrib.staticfiles.storage.StaticFilesStorage' The file storage engine to use when collecting static files with the collectstatic management command. A ready-to-use instance of the storage backend defined in this setting can be found at django.contrib.staticfiles.storage.staticfiles_storage. For an example, see Serving static files from a cloud service or CDN. STATICFILES_FINDERS Default: [
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
]
The list of finder backends that know how to find static files in various locations. The default will find files stored in the STATICFILES_DIRS setting (using django.contrib.staticfiles.finders.FileSystemFinder) and in a static subdirectory of each app (using django.contrib.staticfiles.finders.AppDirectoriesFinder). If multiple files with the same name are present, the first file that is found will be used. One finder is disabled by default: django.contrib.staticfiles.finders.DefaultStorageFinder. If added to your STATICFILES_FINDERS setting, it will look for static files in the default file storage as defined by the DEFAULT_FILE_STORAGE setting. Note When using the AppDirectoriesFinder finder, make sure your apps can be found by staticfiles by adding the app to the INSTALLED_APPS setting of your site. Static file finders are currently considered a private interface, and this interface is thus undocumented. Core Settings Topical Index Cache CACHES CACHE_MIDDLEWARE_ALIAS CACHE_MIDDLEWARE_KEY_PREFIX CACHE_MIDDLEWARE_SECONDS Database DATABASES DATABASE_ROUTERS DEFAULT_INDEX_TABLESPACE DEFAULT_TABLESPACE Debugging DEBUG DEBUG_PROPAGATE_EXCEPTIONS Email ADMINS DEFAULT_CHARSET DEFAULT_FROM_EMAIL EMAIL_BACKEND EMAIL_FILE_PATH EMAIL_HOST EMAIL_HOST_PASSWORD EMAIL_HOST_USER EMAIL_PORT EMAIL_SSL_CERTFILE EMAIL_SSL_KEYFILE EMAIL_SUBJECT_PREFIX EMAIL_TIMEOUT EMAIL_USE_LOCALTIME EMAIL_USE_TLS MANAGERS SERVER_EMAIL Error reporting DEFAULT_EXCEPTION_REPORTER DEFAULT_EXCEPTION_REPORTER_FILTER IGNORABLE_404_URLS MANAGERS SILENCED_SYSTEM_CHECKS File uploads DEFAULT_FILE_STORAGE FILE_UPLOAD_HANDLERS FILE_UPLOAD_MAX_MEMORY_SIZE FILE_UPLOAD_PERMISSIONS FILE_UPLOAD_TEMP_DIR MEDIA_ROOT MEDIA_URL Forms FORM_RENDERER Globalization (i18n/l10n) DATE_FORMAT DATE_INPUT_FORMATS DATETIME_FORMAT DATETIME_INPUT_FORMATS DECIMAL_SEPARATOR FIRST_DAY_OF_WEEK FORMAT_MODULE_PATH LANGUAGE_CODE LANGUAGE_COOKIE_AGE LANGUAGE_COOKIE_DOMAIN LANGUAGE_COOKIE_HTTPONLY LANGUAGE_COOKIE_NAME LANGUAGE_COOKIE_PATH LANGUAGE_COOKIE_SAMESITE LANGUAGE_COOKIE_SECURE LANGUAGES LANGUAGES_BIDI LOCALE_PATHS MONTH_DAY_FORMAT NUMBER_GROUPING SHORT_DATE_FORMAT SHORT_DATETIME_FORMAT THOUSAND_SEPARATOR TIME_FORMAT TIME_INPUT_FORMATS TIME_ZONE USE_I18N USE_L10N USE_THOUSAND_SEPARATOR USE_TZ YEAR_MONTH_FORMAT HTTP DATA_UPLOAD_MAX_MEMORY_SIZE DATA_UPLOAD_MAX_NUMBER_FIELDS DEFAULT_CHARSET DISALLOWED_USER_AGENTS FORCE_SCRIPT_NAME INTERNAL_IPS MIDDLEWARE Security SECURE_CONTENT_TYPE_NOSNIFF SECURE_CROSS_ORIGIN_OPENER_POLICY SECURE_HSTS_INCLUDE_SUBDOMAINS SECURE_HSTS_PRELOAD SECURE_HSTS_SECONDS SECURE_PROXY_SSL_HEADER SECURE_REDIRECT_EXEMPT SECURE_REFERRER_POLICY SECURE_SSL_HOST SECURE_SSL_REDIRECT SIGNING_BACKEND USE_X_FORWARDED_HOST USE_X_FORWARDED_PORT WSGI_APPLICATION Logging LOGGING LOGGING_CONFIG Models ABSOLUTE_URL_OVERRIDES FIXTURE_DIRS INSTALLED_APPS Security Cross Site Request Forgery Protection CSRF_COOKIE_DOMAIN CSRF_COOKIE_NAME CSRF_COOKIE_PATH CSRF_COOKIE_SAMESITE CSRF_COOKIE_SECURE CSRF_FAILURE_VIEW CSRF_HEADER_NAME CSRF_TRUSTED_ORIGINS CSRF_USE_SESSIONS SECRET_KEY X_FRAME_OPTIONS Serialization DEFAULT_CHARSET SERIALIZATION_MODULES Templates TEMPLATES Testing Database: TEST
TEST_NON_SERIALIZED_APPS TEST_RUNNER URLs APPEND_SLASH PREPEND_WWW ROOT_URLCONF | |
doc_30458 | Store a file in binary transfer mode. cmd should be an appropriate STOR command: "STOR filename". fp is a file object (opened in binary mode) which is read until EOF using its read() method in blocks of size blocksize to provide the data to be stored. The blocksize argument defaults to 8192. callback is an optional single parameter callable that is called on each block of data after it is sent. rest means the same thing as in the transfercmd() method. Changed in version 3.2: rest parameter added. | |
doc_30459 |
Compute eigenvalues of structure tensor. Parameters
Axxndarray
Element of the structure tensor for each pixel in the input image.
Axyndarray
Element of the structure tensor for each pixel in the input image.
Ayyndarray
Element of the structure tensor for each pixel in the input image. Returns
l1ndarray
Larger eigen value for each input matrix.
l2ndarray
Smaller eigen value for each input matrix. Examples >>> from skimage.feature import structure_tensor, structure_tensor_eigvals
>>> square = np.zeros((5, 5))
>>> square[2, 2] = 1
>>> Arr, Arc, Acc = structure_tensor(square, sigma=0.1, order='rc')
>>> structure_tensor_eigvals(Acc, Arc, Arr)[0]
array([[0., 0., 0., 0., 0.],
[0., 2., 4., 2., 0.],
[0., 4., 0., 4., 0.],
[0., 2., 4., 2., 0.],
[0., 0., 0., 0., 0.]]) | |
doc_30460 | class tkinter.colorchooser.Chooser(master=None, **options)
tkinter.colorchooser.askcolor(color=None, **options)
Create a color choosing dialog. A call to this method will show the window, wait for the user to make a selection, and return the selected color (or None) to the caller.
See also
Module tkinter.commondialog
Tkinter standard dialog module | |
doc_30461 |
Draw the text instance. Parameters
gcGraphicsContextBase
The graphics context.
xfloat
The x location of the text in display coords.
yfloat
The y location of the text baseline in display coords.
sstr
The text string.
propmatplotlib.font_manager.FontProperties
The font properties.
anglefloat
The rotation angle in degrees anti-clockwise.
mtextmatplotlib.text.Text
The original text object to be rendered. Notes Note for backend implementers: When you are trying to determine if you have gotten your bounding box right (which is what enables the text layout/alignment to work properly), it helps to change the line in text.py: if 0: bbox_artist(self, renderer)
to if 1, and then the actual bounding box will be plotted along with your text. | |
doc_30462 | tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver, variable_partitioner=None
)
Parameter server training is a common data-parallel method to scale up a machine learning model on multiple machines. A parameter server training cluster consists of workers and parameter servers. Variables are created on parameter servers and they are read and updated by workers in each step. By default, workers read and update these variables independently without synchronizing with each other. Under this configuration, it is known as asynchronous training. In TensorFlow 2, we recommend a central coordiantion-based architecture for parameter server training, where workers and parameter servers run a tf.distribute.Server and there is another task that creates resources on workers and parameter servers, dispatches functions, and coordinates the training. We refer to this task as “coordinator”. The coordinator uses a tf.distribute.experimental.coordinator.ClusterCoordinator to coordinate the cluster, and a tf.distribute.experimental.ParameterServerStrategy to define variables on parameter servers and computation on workers. For the training to work, the coordinator dispatches tf.functions to be executed on remote workers. Upon receiving requests from the coordinator, a worker executes the tf.function by reading the variables from parameter servers, executing the ops, and updating the variables on the parameter servers. Each of the worker only processes the requests from the coordinator, and communicates with parameter servers, without direct interactions with other workers in the cluster. As a result, failures of some workers do not prevent the cluster from continuing the work, and this allows the cluster to train with instances that can be occasionally unavailable (e.g. preemptible or spot instances). The coordinator and parameter servers though, must be available at all times for the cluster to make progress. Note that the coordinator is not one of the training workers. Instead, it creates resources such as variables and datasets, dispatchs tf.functions, saving checkpoints and so on. In addition to workers, parameter servers and the coordinator, an optional evaluator can be run on the side that periodically reads the checkpoints saved by the coordinator and runs evaluations against each checkpoint. tf.distribute.experimental.ParameterServerStrategy has to work in conjunction with a tf.distribute.experimental.coordinator.ClusterCoordinator object. Standalone usage of tf.distribute.experimental.ParameterServerStrategy without central coordination is not supported at this time. Example code for coordinator Here's an example usage of the API, with a custom training loop to train a model. This code snippet is intended to be run on (the only) one task that is designated as the coordinator. Note that cluster_resolver, variable_partitioner, and dataset_fn arguments are explained in the following "Cluster setup", "Variable partitioning", and "Dataset preparation" sections. # Set the environment variable to allow reporting worker and ps failure to the
# coordinator. This a short-term workaround.
os.environ["GRPC_FAIL_FAST"] = "use_caller"
# Prepare a strategy to use with the cluster and variable partitioning info.
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver=...,
variable_partitioner=...)
coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
strategy=strategy)
# Prepare a distribute dataset that will place datasets on the workers.
distributed_dataset = coordinator.create_per_worker_dataset(dataset_fn=...)
with strategy.scope():
model = ...
optimizer, metrics = ... # Keras optimizer/metrics are great choices
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
checkpoint_manager = tf.train.CheckpointManager(
checkpoint, checkpoint_dir, max_to_keep=2)
# `load_checkpoint` infers initial epoch from `optimizer.iterations`.
initial_epoch = load_checkpoint(checkpoint_manager) or 0
@tf.function
def worker_fn(iterator):
def replica_fn(inputs):
batch_data, labels = inputs
# calculate gradient, applying gradient, metrics update etc.
strategy.run(replica_fn, args=(next(iterator),))
for epoch in range(initial_epoch, num_epoch):
distributed_iterator = iter(distributed_dataset) # Reset iterator state.
for step in range(steps_per_epoch):
# Asynchronously schedule the `worker_fn` to be executed on an arbitrary
# worker. This call returns immediately.
coordinator.schedule(worker_fn, args=(distributed_iterator,))
# `join` blocks until all scheduled `worker_fn`s finish execution. Once it
# returns, we can read the metrics and save checkpoints as needed.
coordinator.join()
logging.info('Metric result: %r', metrics.result())
train_accuracy.reset_states()
checkpoint_manager.save()
Example code for worker and parameter servers In addition to the coordinator, there should be tasks designated as "worker" or "ps". They should run the following code to start a TensorFlow server, waiting for coordinator's requests: # Set the environment variable to allow reporting worker and ps failure to the
# coordinator.
os.environ["GRPC_FAIL_FAST"] = "use_caller"
# Provide a `tf.distribute.cluster_resolver.ClusterResolver` that serves
# the cluster information. See below "Cluster setup" section.
cluster_resolver = ...
server = tf.distribute.Server(
cluster_resolver.cluster_spec(),
job_name=cluster_resolver.task_type,
task_index=cluster_resolver.task_id,
protocol="grpc")
# Blocking the process that starts a server from exiting.
server.join()
Cluster setup In order for the tasks in the cluster to know other tasks' addresses, a tf.distribute.cluster_resolver.ClusterResolver is required to be used in coordinator, worker, and ps. The tf.distribute.cluster_resolver.ClusterResolver is responsible for providing the cluster information, as well as the task type and id of the current task. See tf.distribute.cluster_resolver.ClusterResolver for more information. If TF_CONFIG environment variable is set, a tf.distribute.cluster_resolver.TFConfigClusterResolver should be used as well. Note that for legacy reason, on some platform, "chief" is used as the task type for the coordinator, as the following example demonstrates. Here we set TF_CONFIG for the task designated as a parameter server (task type "ps") and index 1 (the second task), in a cluster with 1 chief, 2 parameter servers, and 3 workers. Note that the it needs to be set before the use of tf.distribute.cluster_resolver.TFConfigClusterResolver. Example code for cluster setup: os.environ['TF_CONFIG'] = '''
{
"cluster": {
"chief": ["chief.example.com:2222"],
"ps": ["ps0.example.com:2222", "ps1.example.com:2222"],
"worker": ["worker0.example.com:2222", "worker1.example.com:2222",
"worker2.example.com:2222"]
},
"task": {
"type": "ps",
"index": 1
}
}
'''
If you prefer to run the same binary for all tasks, you will need to let the binary branch into different roles at the beginning of the program: os.environ["GRPC_FAIL_FAST"] = "use_caller"
cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
# If coordinator, create a strategy and start the training program.
if cluster_resolver.task_type == 'chief':
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver)
...
# If worker/ps, create a server
elif cluster_resolver.task_type in ("worker", "ps"):
server = tf.distribute.Server(...)
...
Alternatively, you can also start a bunch of TensorFlow servers in advance and connect to them later. The coordinator can be in the same cluster or on any machine that has connectivity to workers and parameter server. This is covered in our guide and tutorial. Variable creation with strategy.scope() tf.distribute.experimental.ParameterServerStrategy follows the tf.distribute API contract where variable creation is expected to be inside the context manager returned by strategy.scope(), in order to be correctly placed on parameter servers in a round-robin manner: # In this example, we're assuming having 3 ps.
strategy = tf.distribute.experimental.ParameterServerStrategy(
cluster_resolver=...)
coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(
strategy=strategy)
# Variables should be created inside scope to be placed on parameter servers.
# If created outside scope such as `v1` here, it would be placed on the
# coordinator.
v1 = tf.Variable(initial_value=0.0)
with strategy.scope():
v2 = tf.Variable(initial_value=1.0)
v3 = tf.Variable(initial_value=2.0)
v4 = tf.Variable(initial_value=3.0)
v5 = tf.Variable(initial_value=4.0)
# v2 through v5 are created in scope and are distributed on parameter servers.
# Default placement is round-robin but the order should not be relied on.
assert v2.device == "/job:ps/replica:0/task:0/device:CPU:0"
assert v3.device == "/job:ps/replica:0/task:1/device:CPU:0"
assert v4.device == "/job:ps/replica:0/task:2/device:CPU:0"
assert v5.device == "/job:ps/replica:0/task:0/device:CPU:0"
See distribute.Strategy.scope for more information. Variable partitioning Having dedicated servers to store variables means being able to divide up, or "shard" the variables across the ps. Partitioning large variable among ps is a commonly used technique to boost training throughput and mitigate memory constraints. It enables parallel computations and updates on different shards of a variable, and often yields better load balancing across parameter servers . Without sharding, models with large variables (e.g, embeddings) that can't fit into one machine's memory would otherwise be unable to train. With tf.distribute.experimental.ParameterServerStrategy, if a variable_partitioner is provided to __init__ and certain conditions are satisfied, the resulting variables created in scope are sharded across the parameter servers, in a round-robin fashion. The variable reference returned from tf.Variable becomes a type that serves as the container of the sharded variables. One can access variables attribute of this container for the actual variable components. If building model with tf.Module or Keras, the variable components are collected in the variables alike attributes. class Dense(tf.Module):
def __init__(self, name=None):
super().__init__(name=name)
self.w = tf.Variable(tf.random.normal([100, 10]), name='w')
def __call__(self, x):
return x * self.w
# Partition the dense layer into 2 shards.
variable_partitioiner = (
tf.distribute.experimental.partitioners.FixedShardsPartitioner(
num_shards = 2))
strategy = ParameterServerStrategy(cluster_resolver=...,
variable_partitioner = variable_partitioner)
with strategy.scope():
dense = Dense()
assert len(dense.variables) == 2
assert isinstance(dense.variables[0], tf.Variable)
assert isinstance(dense.variables[1], tf.Variable)
assert dense.variables[0].name == "w/part_0"
assert dense.variables[1].name == "w/part_1"
The sharded variable container can be converted to a Tensor via tf.convert_to_tensor. This means the container can be directly used in most Python Ops where such Tensor convertion automatically happens. For example in the above code snippet, x * self.w would implicitly apply the said tensor convertion. Note that such convertion can be expensive, as the variable components need to be transferred from multiple parameter servers to where the value is used. tf.nn.embedding_lookup on the other hand doesn't apply the tensor convertion , and performs parallel lookups on the variable components instead. This is crutial to scale up embedding lookups when the embedding table variable is large. When a partitioned variable is saved to SavedModel, it will be saved as if it is one single variable. This improves serving efficiency by eliminating a number of Ops that handle the partiton aspects. Known limitations of variable partitioning: Number of parttions must not change across Checkpoint save/load. After saving partitioned variables to a SavedModel, the SavedModel can't be loaded via tf.saved_model.load. Partition variable doesn't directly work with tf.GradientTape, please use the variables attributes to get the actual variable components and use them in gradient APIs instead. Dataset preparation With tf.distribute.experimental.ParameterServerStrategy, a dataset is created in each of the workers to be used for training. This is done by creating a dataset_fn that takes no argument and returns a tf.data.Dataset, and passing the dataset_fn into tf.distribute.experimental.coordinator. ClusterCoordinator.create_per_worker_dataset. We recommend the dataset to be shuffled and repeated to have the examples run through the training as evenly as possible. def dataset_fn():
filenames = ...
dataset = tf.data.Dataset.from_tensor_slices(filenames)
# Dataset is recommended to be shuffled, and repeated.
return dataset.shuffle(buffer_size=...).repeat().batch(batch_size=...)
coordinator =
tf.distribute.experimental.coordinator.ClusterCoordinator(strategy=...)
distributed_dataset = coordinator.create_per_worker_dataset(dataset_fn)
Limitations tf.distribute.experimental.ParameterServerStrategy in TF2 is experimental, and the API is subject to further changes. tf.distribute.experimental.ParameterServerStrategy does not yet support training with GPU(s). This is a feature request being developed. tf.distribute.experimental.ParameterServerStrategy only supports custom training loop API currently in TF2. Usage of it with Keras compile/fit API is being developed. tf.distribute.experimental.ParameterServerStrategy must be used with tf.distribute.experimental.coordinator.ClusterCoordinator.
Args
cluster_resolver a tf.distribute.cluster_resolver.ClusterResolver object.
variable_partitioner a distribute.experimental.partitioners.Partitioner that specifies how to partition variables. If None, variables will not be partitioned. Predefined partitioners in tf.distribute.experimental.partitioners can be used for this argument. A commonly used partitioner is MinSizePartitioner(min_shard_bytes = 256 << 10, max_shards = num_ps), which allocates at least 256K per shard, and each ps gets at most one shard. variable_partitioner will be called for each variable created under strategy scope to instruct how the variable should be partitioned. Variables that have only one partition along the partitioning axis (i.e., no need for partition) will be created as normal tf.Variable. Only the first / outermost axis partitioning is supported. Div partition strategy is used to partition variables. Assuming we assign consecutive integer ids along the first axis of a variable, then ids are assigned to shards in a contiguous manner, while attempting to keep each shard size identical. If the ids do not evenly divide the number of shards, each of the first several shards will be assigned one more id. For instance, a variable whose first dimension is 13 has 13 ids, and they are split across 5 shards as: [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]. Variables created under strategy.extended.colocate_vars_with will not be partitioned.
Attributes
cluster_resolver Returns the cluster resolver associated with this strategy. In general, when using a multi-worker tf.distribute strategy such as tf.distribute.experimental.MultiWorkerMirroredStrategy or tf.distribute.TPUStrategy(), there is a tf.distribute.cluster_resolver.ClusterResolver associated with the strategy used, and such an instance is returned by this property. Strategies that intend to have an associated tf.distribute.cluster_resolver.ClusterResolver must set the relevant attribute, or override this property; otherwise, None is returned by default. Those strategies should also provide information regarding what is returned by this property. Single-worker strategies usually do not have a tf.distribute.cluster_resolver.ClusterResolver, and in those cases this property will return None. The tf.distribute.cluster_resolver.ClusterResolver may be useful when the user needs to access information such as the cluster spec, task type or task id. For example,
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["localhost:12345", "localhost:23456"],
'ps': ["localhost:34567"]
},
'task': {'type': 'worker', 'index': 0}
})
# This implicitly uses TF_CONFIG for the cluster and current task info.
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
...
if strategy.cluster_resolver.task_type == 'worker':
# Perform something that's only applicable on workers. Since we set this
# as a worker above, this block will run on this particular instance.
elif strategy.cluster_resolver.task_type == 'ps':
# Perform something that's only applicable on parameter servers. Since we
# set this as a worker above, this block will not run on this particular
# instance.
For more information, please see tf.distribute.cluster_resolver.ClusterResolver's API docstring.
extended tf.distribute.StrategyExtended with additional methods.
num_replicas_in_sync Returns number of replicas over which gradients are aggregated. Methods distribute_datasets_from_function View source
distribute_datasets_from_function(
dataset_fn, options=None
)
Distributes tf.data.Dataset instances created by calls to dataset_fn. The argument dataset_fn that users pass in is an input function that has a tf.distribute.InputContext argument and returns a tf.data.Dataset instance. It is expected that the returned dataset from dataset_fn is already batched by per-replica batch size (i.e. global batch size divided by the number of replicas in sync) and sharded. tf.distribute.Strategy.distribute_datasets_from_function does not batch or shard the tf.data.Dataset instance returned from the input function. dataset_fn will be called on the CPU device of each of the workers and each generates a dataset where every replica on that worker will dequeue one batch of inputs (i.e. if a worker has two replicas, two batches will be dequeued from the Dataset every step). This method can be used for several purposes. First, it allows you to specify your own batching and sharding logic. (In contrast, tf.distribute.experimental_distribute_dataset does batching and sharding for you.) For example, where experimental_distribute_dataset is unable to shard the input files, this method might be used to manually shard the dataset (avoiding the slow fallback behavior in experimental_distribute_dataset). In cases where the dataset is infinite, this sharding can be done by creating dataset replicas that differ only in their random seed. The dataset_fn should take an tf.distribute.InputContext instance where information about batching and input replication can be accessed. You can use element_spec property of the tf.distribute.DistributedDataset returned by this API to query the tf.TypeSpec of the elements returned by the iterator. This can be used to set the input_signature property of a tf.function. Follow tf.distribute.DistributedDataset.element_spec to see an example. Key Point: The tf.data.Dataset returned by dataset_fn should have a per-replica batch size, unlike experimental_distribute_dataset, which uses the global batch size. This may be computed using input_context.get_per_replica_batch_size.
Note: If you are using TPUStrategy, the order in which the data is processed by the workers when using tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.distribute_datasets_from_function is not guaranteed. This is typically required if you are using tf.distribute to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to this snippet for an example of how to order outputs.
Note: Stateful dataset transformations are currently not supported with tf.distribute.experimental_distribute_dataset or tf.distribute.distribute_datasets_from_function. Any stateful ops that the dataset may have are currently ignored. For example, if your dataset has a map_fn that uses tf.random.uniform to rotate an image, then you have a dataset graph that depends on state (i.e the random seed) on the local machine where the python process is being executed.
For a tutorial on more usage and properties of this method, refer to the tutorial on distributed input). If you are interested in last partial batch handling, read this section.
Args
dataset_fn A function taking a tf.distribute.InputContext instance and returning a tf.data.Dataset.
options tf.distribute.InputOptions used to control options on how this dataset is distributed.
Returns A tf.distribute.DistributedDataset.
experimental_distribute_dataset View source
experimental_distribute_dataset(
dataset, options=None
)
Creates tf.distribute.DistributedDataset from tf.data.Dataset. The returned tf.distribute.DistributedDataset can be iterated over similar to regular datasets. NOTE: The user cannot add any more transformations to a tf.distribute.DistributedDataset. You can only create an iterator or examine the tf.TypeSpec of the data generated by it. See API docs of tf.distribute.DistributedDataset to learn more. The following is an example:
global_batch_size = 2
# Passing the devices is optional.
strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
# Create a dataset
dataset = tf.data.Dataset.range(4).batch(global_batch_size)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)
@tf.function
def replica_fn(input):
return input*2
result = []
# Iterate over the `tf.distribute.DistributedDataset`
for x in dist_dataset:
# process dataset elements
result.append(strategy.run(replica_fn, args=(x,)))
print(result)
[PerReplica:{
0: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([0])>,
1: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([2])>
}, PerReplica:{
0: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([4])>,
1: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([6])>
}]
Three key actions happending under the hood of this method are batching, sharding, and prefetching. In the code snippet above, dataset is batched by global_batch_size, and calling experimental_distribute_dataset on it rebatches dataset to a new batch size that is equal to the global batch size divided by the number of replicas in sync. We iterate through it using a Pythonic for loop. x is a tf.distribute.DistributedValues containing data for all replicas, and each replica gets data of the new batch size. tf.distribute.Strategy.run will take care of feeding the right per-replica data in x to the right replica_fn executed on each replica. Sharding contains autosharding across multiple workers and within every worker. First, in multi-worker distributed training (i.e. when you use tf.distribute.experimental.MultiWorkerMirroredStrategy or tf.distribute.TPUStrategy), autosharding a dataset over a set of workers means that each worker is assigned a subset of the entire dataset (if the right tf.data.experimental.AutoShardPolicy is set). This is to ensure that at each step, a global batch size of non-overlapping dataset elements will be processed by each worker. Autosharding has a couple of different options that can be specified using tf.data.experimental.DistributeOptions. Then, sharding within each worker means the method will split the data among all the worker devices (if more than one a present). This will happen regardless of multi-worker autosharding.
Note: for autosharding across multiple workers, the default mode is tf.data.experimental.AutoShardPolicy.AUTO. This mode will attempt to shard the input dataset by files if the dataset is being created out of reader datasets (e.g. tf.data.TFRecordDataset, tf.data.TextLineDataset, etc.) or otherwise shard the dataset by data, where each of the workers will read the entire dataset and only process the shard assigned to it. However, if you have less than one input file per worker, we suggest that you disable dataset autosharding across workers by setting the tf.data.experimental.DistributeOptions.auto_shard_policy to be tf.data.experimental.AutoShardPolicy.OFF.
By default, this method adds a prefetch transformation at the end of the user provided tf.data.Dataset instance. The argument to the prefetch transformation which is buffer_size is equal to the number of replicas in sync. If the above batch splitting and dataset sharding logic is undesirable, please use tf.distribute.Strategy.distribute_datasets_from_function instead, which does not do any automatic batching or sharding for you.
Note: If you are using TPUStrategy, the order in which the data is processed by the workers when using tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.distribute_datasets_from_function is not guaranteed. This is typically required if you are using tf.distribute to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to this snippet for an example of how to order outputs.
Note: Stateful dataset transformations are currently not supported with tf.distribute.experimental_distribute_dataset or tf.distribute.distribute_datasets_from_function. Any stateful ops that the dataset may have are currently ignored. For example, if your dataset has a map_fn that uses tf.random.uniform to rotate an image, then you have a dataset graph that depends on state (i.e the random seed) on the local machine where the python process is being executed.
For a tutorial on more usage and properties of this method, refer to the tutorial on distributed input. If you are interested in last partial batch handling, read this section.
Args
dataset tf.data.Dataset that will be sharded across all replicas using the rules stated above.
options tf.distribute.InputOptions used to control options on how this dataset is distributed.
Returns A tf.distribute.DistributedDataset.
experimental_distribute_values_from_function View source
experimental_distribute_values_from_function(
value_fn
)
Generates tf.distribute.DistributedValues from value_fn. This function is to generate tf.distribute.DistributedValues to pass into run, reduce, or other methods that take distributed values when not using datasets.
Args
value_fn The function to run to generate values. It is called for each replica with tf.distribute.ValueContext as the sole argument. It must return a Tensor or a type that can be converted to a Tensor.
Returns A tf.distribute.DistributedValues containing a value for each replica.
Example usage: Return constant value per replica:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def value_fn(ctx):
return tf.constant(1.)
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
local_result = strategy.experimental_local_results(distributed_values)
local_result
(<tf.Tensor: shape=(), dtype=float32, numpy=1.0>,
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>)
Distribute values in array based on replica_id:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
array_value = np.array([3., 2., 1.])
def value_fn(ctx):
return array_value[ctx.replica_id_in_sync_group]
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
local_result = strategy.experimental_local_results(distributed_values)
local_result
(3.0, 2.0)
Specify values using num_replicas_in_sync:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def value_fn(ctx):
return ctx.num_replicas_in_sync
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
local_result = strategy.experimental_local_results(distributed_values)
local_result
(2, 2)
Place values on devices and distribute: strategy = tf.distribute.TPUStrategy()
worker_devices = strategy.extended.worker_devices
multiple_values = []
for i in range(strategy.num_replicas_in_sync):
with tf.device(worker_devices[i]):
multiple_values.append(tf.constant(1.0))
def value_fn(ctx):
return multiple_values[ctx.replica_id_in_sync_group]
distributed_values = strategy.
experimental_distribute_values_from_function(
value_fn)
experimental_local_results View source
experimental_local_results(
value
)
Returns the list of all local per-replica values contained in value.
Note: This only returns values on the worker initiated by this client. When using a tf.distribute.Strategy like tf.distribute.experimental.MultiWorkerMirroredStrategy, each worker will be its own client, and this function will only return values computed on that worker.
Args
value A value returned by experimental_run(), run(), extended.call_for_each_replica(), or a variable created in scope.
Returns A tuple of values contained in value. If value represents a single value, this returns (value,).
gather View source
gather(
value, axis
)
Gather value across replicas along axis to the current device. Given a tf.distribute.DistributedValues or tf.Tensor-like object value, this API gathers and concatenates value across replicas along the axis-th dimension. The result is copied to the "current" device which would typically be the CPU of the worker on which the program is running. For tf.distribute.TPUStrategy, it is the first TPU host. For multi-client MultiWorkerMirroredStrategy, this is CPU of each worker. This API can only be called in the cross-replica context. For a counterpart in the replica context, see tf.distribute.ReplicaContext.all_gather.
Note: For all strategies except tf.distribute.TPUStrategy, the input value on different replicas must have the same rank, and their shapes must be the same in all dimensions except the axis-th dimension. In other words, their shapes cannot be different in a dimension d where d does not equal to the axis argument. For example, given a tf.distribute.DistributedValues with component tensors of shape (1, 2, 3) and (1, 3, 3) on two replicas, you can call gather(..., axis=1, ...) on it, but not gather(..., axis=0, ...) or gather(..., axis=2, ...). However, for tf.distribute.TPUStrategy.gather, all tensors must have exactly the same rank and same shape.
Note: Given a tf.distribute.DistributedValues value, its component tensors must have a non-zero rank. Otherwise, consider using tf.expand_dims before gathering them.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
# A DistributedValues with component tensor of shape (2, 1) on each replica
distributed_values = strategy.experimental_distribute_values_from_function(lambda _: tf.identity(tf.constant([[1], [2]])))
@tf.function
def run():
return strategy.gather(distributed_values, axis=0)
run()
<tf.Tensor: shape=(4, 1), dtype=int32, numpy=
array([[1],
[2],
[1],
[2]], dtype=int32)>
Consider the following example for more combinations:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1", "GPU:2", "GPU:3"])
single_tensor = tf.reshape(tf.range(6), shape=(1,2,3))
distributed_values = strategy.experimental_distribute_values_from_function(lambda _: tf.identity(single_tensor))
@tf.function
def run(axis):
return strategy.gather(distributed_values, axis=axis)
axis=0
run(axis)
<tf.Tensor: shape=(4, 2, 3), dtype=int32, numpy=
array([[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]]], dtype=int32)>
axis=1
run(axis)
<tf.Tensor: shape=(1, 8, 3), dtype=int32, numpy=
array([[[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5]]], dtype=int32)>
axis=2
run(axis)
<tf.Tensor: shape=(1, 2, 12), dtype=int32, numpy=
array([[[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5, 3, 4, 5]]], dtype=int32)>
Args
value a tf.distribute.DistributedValues instance, e.g. returned by Strategy.run, to be combined into a single tensor. It can also be a regular tensor when used with tf.distribute.OneDeviceStrategy or the default strategy. The tensors that constitute the DistributedValues can only be dense tensors with non-zero rank, NOT a tf.IndexedSlices.
axis 0-D int32 Tensor. Dimension along which to gather. Must be in the range [0, rank(value)).
Returns A Tensor that's the concatenation of value across replicas along axis dimension.
reduce View source
reduce(
reduce_op, value, axis
)
Reduce value across replicas and return result on current device.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def step_fn():
i = tf.distribute.get_replica_context().replica_id_in_sync_group
return tf.identity(i)
per_replica_result = strategy.run(step_fn)
total = strategy.reduce("SUM", per_replica_result, axis=None)
total
<tf.Tensor: shape=(), dtype=int32, numpy=1>
To see how this would look with multiple replicas, consider the same example with MirroredStrategy with 2 GPUs: strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
def step_fn():
i = tf.distribute.get_replica_context().replica_id_in_sync_group
return tf.identity(i)
per_replica_result = strategy.run(step_fn)
# Check devices on which per replica result is:
strategy.experimental_local_results(per_replica_result)[0].device
# /job:localhost/replica:0/task:0/device:GPU:0
strategy.experimental_local_results(per_replica_result)[1].device
# /job:localhost/replica:0/task:0/device:GPU:1
total = strategy.reduce("SUM", per_replica_result, axis=None)
# Check device on which reduced result is:
total.device
# /job:localhost/replica:0/task:0/device:CPU:0
This API is typically used for aggregating the results returned from different replicas, for reporting etc. For example, loss computed from different replicas can be averaged using this API before printing.
Note: The result is copied to the "current" device - which would typically be the CPU of the worker on which the program is running. For TPUStrategy, it is the first TPU host. For multi client MultiWorkerMirroredStrategy, this is CPU of each worker.
There are a number of different tf.distribute APIs for reducing values across replicas:
tf.distribute.ReplicaContext.all_reduce: This differs from Strategy.reduce in that it is for replica context and does not copy the results to the host device. all_reduce should be typically used for reductions inside the training step such as gradients.
tf.distribute.StrategyExtended.reduce_to and tf.distribute.StrategyExtended.batch_reduce_to: These APIs are more advanced versions of Strategy.reduce as they allow customizing the destination of the result. They are also called in cross replica context. What should axis be? Given a per-replica value returned by run, say a per-example loss, the batch will be divided across all the replicas. This function allows you to aggregate across replicas and optionally also across batch elements by specifying the axis parameter accordingly. For example, if you have a global batch size of 8 and 2 replicas, values for examples [0, 1, 2, 3] will be on replica 0 and [4, 5, 6, 7] will be on replica 1. With axis=None, reduce will aggregate only across replicas, returning [0+4, 1+5, 2+6, 3+7]. This is useful when each replica is computing a scalar or some other value that doesn't have a "batch" dimension (like a gradient or loss). strategy.reduce("sum", per_replica_result, axis=None)
Sometimes, you will want to aggregate across both the global batch and all replicas. You can get this behavior by specifying the batch dimension as the axis, typically axis=0. In this case it would return a scalar 0+1+2+3+4+5+6+7. strategy.reduce("sum", per_replica_result, axis=0)
If there is a last partial batch, you will need to specify an axis so that the resulting shape is consistent across replicas. So if the last batch has size 6 and it is divided into [0, 1, 2, 3] and [4, 5], you would get a shape mismatch unless you specify axis=0. If you specify tf.distribute.ReduceOp.MEAN, using axis=0 will use the correct denominator of 6. Contrast this with computing reduce_mean to get a scalar value on each replica and this function to average those means, which will weigh some values 1/8 and others 1/4.
Args
reduce_op a tf.distribute.ReduceOp value specifying how values should be combined. Allows using string representation of the enum such as "SUM", "MEAN".
value a tf.distribute.DistributedValues instance, e.g. returned by Strategy.run, to be combined into a single tensor. It can also be a regular tensor when used with OneDeviceStrategy or default strategy.
axis specifies the dimension to reduce along within each replica's tensor. Should typically be set to the batch dimension, or None to only reduce across replicas (e.g. if the tensor has no batch dimension).
Returns A Tensor.
run View source
run(
fn, args=(), kwargs=None, options=None
)
Invokes fn on each replica, with the given arguments. This method is the primary way to distribute your computation with a tf.distribute object. It invokes fn on each replica. If args or kwargs have tf.distribute.DistributedValues, such as those produced by a tf.distribute.DistributedDataset from tf.distribute.Strategy.experimental_distribute_dataset or tf.distribute.Strategy.distribute_datasets_from_function, when fn is executed on a particular replica, it will be executed with the component of tf.distribute.DistributedValues that correspond to that replica. fn is invoked under a replica context. fn may call tf.distribute.get_replica_context() to access members such as all_reduce. Please see the module-level docstring of tf.distribute for the concept of replica context. All arguments in args or kwargs should either be Python values of a nested structure of tensors, e.g. a list of tensors, in which case args and kwargs will be passed to the fn invoked on each replica. Or args or kwargs can be tf.distribute.DistributedValues containing tensors or composite tensors, i.e. tf.compat.v1.TensorInfo.CompositeTensor, in which case each fn call will get the component of a tf.distribute.DistributedValues corresponding to its replica. Key Point: Depending on the implementation of tf.distribute.Strategy and whether eager execution is enabled, fn may be called one or more times. If fn is annotated with tf.function or tf.distribute.Strategy.run is called inside a tf.function (eager execution is disabled inside a tf.function by default), fn is called once per replica to generate a Tensorflow graph, which will then be reused for execution with new inputs. Otherwise, if eager execution is enabled, fn will be called once per replica every step just like regular python code. Example usage: Constant tensor input.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
tensor_input = tf.constant(3.0)
@tf.function
def replica_fn(input):
return input*2.0
result = strategy.run(replica_fn, args=(tensor_input,))
result
PerReplica:{
0: <tf.Tensor: shape=(), dtype=float32, numpy=6.0>,
1: <tf.Tensor: shape=(), dtype=float32, numpy=6.0>
}
DistributedValues input.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
@tf.function
def run():
def value_fn(value_context):
return value_context.num_replicas_in_sync
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
def replica_fn2(input):
return input*2
return strategy.run(replica_fn2, args=(distributed_values,))
result = run()
result
<tf.Tensor: shape=(), dtype=int32, numpy=4>
Use tf.distribute.ReplicaContext to allreduce values.
strategy = tf.distribute.MirroredStrategy(["gpu:0", "gpu:1"])
@tf.function
def run():
def value_fn(value_context):
return tf.constant(value_context.replica_id_in_sync_group)
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
def replica_fn(input):
return tf.distribute.get_replica_context().all_reduce("sum", input)
return strategy.run(replica_fn, args=(distributed_values,))
result = run()
result
PerReplica:{
0: <tf.Tensor: shape=(), dtype=int32, numpy=1>,
1: <tf.Tensor: shape=(), dtype=int32, numpy=1>
}
Args
fn The function to run on each replica.
args Optional positional arguments to fn. Its element can be a Python value, a tensor or a tf.distribute.DistributedValues.
kwargs Optional keyword arguments to fn. Its element can be a Python value, a tensor or a tf.distribute.DistributedValues.
options An optional instance of tf.distribute.RunOptions specifying the options to run fn.
Returns Merged return value of fn across replicas. The structure of the return value is the same as the return value from fn. Each element in the structure can either be tf.distribute.DistributedValues, Tensor objects, or Tensors (for example, if running on a single replica).
scope View source
scope()
Context manager to make the strategy current and distribute variables. This method returns a context manager, and is used as follows:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
# Variable created inside scope:
with strategy.scope():
mirrored_variable = tf.Variable(1.)
mirrored_variable
MirroredVariable:{
0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>,
1: <tf.Variable 'Variable/replica_1:0' shape=() dtype=float32, numpy=1.0>
}
# Variable created outside scope:
regular_variable = tf.Variable(1.)
regular_variable
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
What happens when Strategy.scope is entered?
strategy is installed in the global context as the "current" strategy. Inside this scope, tf.distribute.get_strategy() will now return this strategy. Outside this scope, it returns the default no-op strategy. Entering the scope also enters the "cross-replica context". See tf.distribute.StrategyExtended for an explanation on cross-replica and replica contexts. Variable creation inside scope is intercepted by the strategy. Each strategy defines how it wants to affect the variable creation. Sync strategies like MirroredStrategy, TPUStrategy and MultiWorkerMiroredStrategy create variables replicated on each replica, whereas ParameterServerStrategy creates variables on the parameter servers. This is done using a custom tf.variable_creator_scope. In some strategies, a default device scope may also be entered: in MultiWorkerMiroredStrategy, a default device scope of "/CPU:0" is entered on each worker.
Note: Entering a scope does not automatically distribute a computation, except in the case of high level training framework like keras model.fit. If you're not using model.fit, you need to use strategy.run API to explicitly distribute that computation. See an example in the custom training loop tutorial.
What should be in scope and what should be outside? There are a number of requirements on what needs to happen inside the scope. However, in places where we have information about which strategy is in use, we often enter the scope for the user, so they don't have to do it explicitly (i.e. calling those either inside or outside the scope is OK). Anything that creates variables that should be distributed variables must be in strategy.scope. This can be either by directly putting it in scope, or relying on another API like strategy.run or model.fit to enter it for you. Any variable that is created outside scope will not be distributed and may have performance implications. Common things that create variables in TF: models, optimizers, metrics. These should always be created inside the scope. Another source of variable creation can be a checkpoint restore - when variables are created lazily. Note that any variable created inside a strategy captures the strategy information. So reading and writing to these variables outside the strategy.scope can also work seamlessly, without the user having to enter the scope. Some strategy APIs (such as strategy.run and strategy.reduce) which require to be in a strategy's scope, enter the scope for you automatically, which means when using those APIs you don't need to enter the scope yourself. When a tf.keras.Model is created inside a strategy.scope, we capture this information. When high level training frameworks methods such as model.compile, model.fit etc are then called on this model, we automatically enter the scope, as well as use this strategy to distribute the training etc. See detailed example in distributed keras tutorial. Note that simply calling the model(..) is not impacted - only high level training framework APIs are. model.compile, model.fit, model.evaluate, model.predict and model.save can all be called inside or outside the scope. The following can be either inside or outside the scope: Creating the input datasets Defining tf.functions that represent your training step Saving APIs such as tf.saved_model.save. Loading creates variables, so that should go inside the scope if you want to train the model in a distributed way. Checkpoint saving. As mentioned above - checkpoint.restore may sometimes need to be inside scope if it creates variables.
Returns A context manager. | |
doc_30463 |
Set the edgecolor(s) of the collection. Parameters
ccolor or list of colors or 'face'
The collection edgecolor(s). If a sequence, the patches cycle through it. If 'face', match the facecolor. | |
doc_30464 |
If created from a 64-bit integer, it represents an offset from 1970-01-01T00:00:00. If created from string, the string can be in ISO 8601 date or datetime format. >>> np.datetime64(10, 'Y')
numpy.datetime64('1980')
>>> np.datetime64('1980', 'Y')
numpy.datetime64('1980')
>>> np.datetime64(10, 'D')
numpy.datetime64('1970-01-11')
See Datetimes and Timedeltas for more information. Character code
'M' | |
doc_30465 | See Migration guide for more details. tf.compat.v1.raw_ops.DecodeProtoV2
tf.raw_ops.DecodeProtoV2(
bytes, message_type, field_names, output_types,
descriptor_source='local://', message_format='binary',
sanitize=False, name=None
)
The decode_proto op extracts fields from a serialized protocol buffers message into tensors. The fields in field_names are decoded and converted to the corresponding output_types if possible. A message_type name must be provided to give context for the field names. The actual message descriptor can be looked up either in the linked-in descriptor pool or a filename provided by the caller using the descriptor_source attribute. Each output tensor is a dense tensor. This means that it is padded to hold the largest number of repeated elements seen in the input minibatch. (The shape is also padded by one to prevent zero-sized dimensions). The actual repeat counts for each example in the minibatch can be found in the sizes output. In many cases the output of decode_proto is fed immediately into tf.squeeze if missing values are not a concern. When using tf.squeeze, always pass the squeeze dimension explicitly to avoid surprises. For the most part, the mapping between Proto field types and TensorFlow dtypes is straightforward. However, there are a few special cases: A proto field that contains a submessage or group can only be converted to DT_STRING (the serialized submessage). This is to reduce the complexity of the API. The resulting string can be used as input to another instance of the decode_proto op. TensorFlow lacks support for unsigned integers. The ops represent uint64 types as a DT_INT64 with the same twos-complement bit pattern (the obvious way). Unsigned int32 values can be represented exactly by specifying type DT_INT64, or using twos-complement if the caller specifies DT_INT32 in the output_types attribute. Both binary and text proto serializations are supported, and can be chosen using the format attribute. The descriptor_source attribute selects the source of protocol descriptors to consult when looking up message_type. This may be: An empty string or "local://", in which case protocol descriptors are created for C++ (not Python) proto definitions linked to the binary. A file, in which case protocol descriptors are created from the file, which is expected to contain a FileDescriptorSet serialized as a string. NOTE: You can build a descriptor_source file using the --descriptor_set_out and --include_imports options to the protocol compiler protoc. A "bytes://", in which protocol descriptors are created from <bytes>, which is expected to be a FileDescriptorSet serialized as a string.
Args
bytes A Tensor of type string. Tensor of serialized protos with shape batch_shape.
message_type A string. Name of the proto message type to decode.
field_names A list of strings. List of strings containing proto field names. An extension field can be decoded by using its full name, e.g. EXT_PACKAGE.EXT_FIELD_NAME.
output_types A list of tf.DTypes. List of TF types to use for the respective field in field_names.
descriptor_source An optional string. Defaults to "local://". Either the special value local:// or a path to a file containing a serialized FileDescriptorSet.
message_format An optional string. Defaults to "binary". Either binary or text.
sanitize An optional bool. Defaults to False. Whether to sanitize the result or not.
name A name for the operation (optional).
Returns A tuple of Tensor objects (sizes, values). sizes A Tensor of type int32.
values A list of Tensor objects of type output_types. | |
doc_30466 |
Set the width of the box. Parameters
widthfloat | |
doc_30467 |
Return a dictionary of all the properties of the artist. | |
doc_30468 |
Set the linestyle(s) for the collection.
linestyle description
'-' or 'solid' solid line
'--' or 'dashed' dashed line
'-.' or 'dashdot' dash-dotted line
':' or 'dotted' dotted line Alternatively a dash tuple of the following form can be provided: (offset, onoffseq),
where onoffseq is an even length tuple of on and off ink in points. Parameters
lsstr or tuple or list thereof
Valid values for individual linestyles include {'-', '--', '-.', ':', '', (offset, on-off-seq)}. See Line2D.set_linestyle for a complete description. | |
doc_30469 | Signals the end of an element in non-namespace mode. The name parameter contains the name of the element type, just as with the startElement() event. | |
doc_30470 |
Get the width, height, and descent (offset from the bottom to the baseline), in display coords, of the string s with FontProperties prop. | |
doc_30471 | Like max(self, other) except that the context rounding rule is applied before returning and that NaN values are either signaled or ignored (depending on the context and whether they are signaling or quiet). | |
doc_30472 |
Immunohistochemical (IHC) staining with hematoxylin counterstaining. This picture shows colonic glands where the IHC expression of FHL2 protein is revealed with DAB. Hematoxylin counterstaining is applied to enhance the negative parts of the tissue. This image was acquired at the Center for Microscopy And Molecular Imaging (CMMI). No known copyright restrictions. Returns
immunohistochemistry(512, 512, 3) uint8 ndarray
Immunohistochemistry image. | |
doc_30473 | class sklearn.model_selection.LeaveOneGroupOut [source]
Leave One Group Out cross-validator Provides train/test indices to split data according to a third-party provided group. This group information can be used to encode arbitrary domain specific stratifications of the samples as integers. For instance the groups could be the year of collection of the samples and thus allow for cross-validation against time-based splits. Read more in the User Guide. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeaveOneGroupOut
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 1, 2])
>>> groups = np.array([1, 1, 2, 2])
>>> logo = LeaveOneGroupOut()
>>> logo.get_n_splits(X, y, groups)
2
>>> logo.get_n_splits(groups=groups) # 'groups' is always required
2
>>> print(logo)
LeaveOneGroupOut()
>>> for train_index, test_index in logo.split(X, y, groups):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
... print(X_train, X_test, y_train, y_test)
TRAIN: [2 3] TEST: [0 1]
[[5 6]
[7 8]] [[1 2]
[3 4]] [1 2] [1 2]
TRAIN: [0 1] TEST: [2 3]
[[1 2]
[3 4]] [[5 6]
[7 8]] [1 2] [1 2]
Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generate indices to split data into training and test set.
get_n_splits(X=None, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,), default=None
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. Yields
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split. | |
doc_30474 | class BaseDatabaseSchemaEditor
Django’s migration system is split into two parts; the logic for calculating and storing what operations should be run (django.db.migrations), and the database abstraction layer that turns things like “create a model” or “delete a field” into SQL - which is the job of the SchemaEditor. It’s unlikely that you will want to interact directly with SchemaEditor as a normal developer using Django, but if you want to write your own migration system, or have more advanced needs, it’s a lot nicer than writing SQL. Each database backend in Django supplies its own version of SchemaEditor, and it’s always accessible via the connection.schema_editor() context manager: with connection.schema_editor() as schema_editor:
schema_editor.delete_model(MyModel)
It must be used via the context manager as this allows it to manage things like transactions and deferred SQL (like creating ForeignKey constraints). It exposes all possible operations as methods, that should be called in the order you wish changes to be applied. Some possible operations or types of change are not possible on all databases - for example, MyISAM does not support foreign key constraints. If you are writing or maintaining a third-party database backend for Django, you will need to provide a SchemaEditor implementation in order to work with Django’s migration functionality - however, as long as your database is relatively standard in its use of SQL and relational design, you should be able to subclass one of the built-in Django SchemaEditor classes and tweak the syntax a little. Methods execute()
BaseDatabaseSchemaEditor.execute(sql, params=())
Executes the SQL statement passed in, with parameters if supplied. This is a wrapper around the normal database cursors that allows capture of the SQL to a .sql file if the user wishes. create_model()
BaseDatabaseSchemaEditor.create_model(model)
Creates a new table in the database for the provided model, along with any unique constraints or indexes it requires. delete_model()
BaseDatabaseSchemaEditor.delete_model(model)
Drops the model’s table in the database along with any unique constraints or indexes it has. add_index()
BaseDatabaseSchemaEditor.add_index(model, index)
Adds index to model’s table. remove_index()
BaseDatabaseSchemaEditor.remove_index(model, index)
Removes index from model’s table. add_constraint()
BaseDatabaseSchemaEditor.add_constraint(model, constraint)
Adds constraint to model’s table. remove_constraint()
BaseDatabaseSchemaEditor.remove_constraint(model, constraint)
Removes constraint from model’s table. alter_unique_together()
BaseDatabaseSchemaEditor.alter_unique_together(model, old_unique_together, new_unique_together)
Changes a model’s unique_together value; this will add or remove unique constraints from the model’s table until they match the new value. alter_index_together()
BaseDatabaseSchemaEditor.alter_index_together(model, old_index_together, new_index_together)
Changes a model’s index_together value; this will add or remove indexes from the model’s table until they match the new value. alter_db_table()
BaseDatabaseSchemaEditor.alter_db_table(model, old_db_table, new_db_table)
Renames the model’s table from old_db_table to new_db_table. alter_db_tablespace()
BaseDatabaseSchemaEditor.alter_db_tablespace(model, old_db_tablespace, new_db_tablespace)
Moves the model’s table from one tablespace to another. add_field()
BaseDatabaseSchemaEditor.add_field(model, field)
Adds a column (or sometimes multiple) to the model’s table to represent the field. This will also add indexes or a unique constraint if the field has db_index=True or unique=True. If the field is a ManyToManyField without a value for through, instead of creating a column, it will make a table to represent the relationship. If through is provided, it is a no-op. If the field is a ForeignKey, this will also add the foreign key constraint to the column. remove_field()
BaseDatabaseSchemaEditor.remove_field(model, field)
Removes the column(s) representing the field from the model’s table, along with any unique constraints, foreign key constraints, or indexes caused by that field. If the field is a ManyToManyField without a value for through, it will remove the table created to track the relationship. If through is provided, it is a no-op. alter_field()
BaseDatabaseSchemaEditor.alter_field(model, old_field, new_field, strict=False)
This transforms the field on the model from the old field to the new one. This includes changing the name of the column (the db_column attribute), changing the type of the field (if the field class changes), changing the NULL status of the field, adding or removing field-only unique constraints and indexes, changing primary key, and changing the destination of ForeignKey constraints. The most common transformation this cannot do is transforming a ManyToManyField into a normal Field or vice-versa; Django cannot do this without losing data, and so it will refuse to do it. Instead, remove_field() and add_field() should be called separately. If the database has the supports_combined_alters, Django will try and do as many of these in a single database call as possible; otherwise, it will issue a separate ALTER statement for each change, but will not issue ALTERs where no change is required. Attributes All attributes should be considered read-only unless stated otherwise. connection
SchemaEditor.connection
A connection object to the database. A useful attribute of the connection is alias which can be used to determine the name of the database being accessed. This is useful when doing data migrations for migrations with multiple databases. | |
doc_30475 | Compare two operands using their abstract representation rather than their numerical value. Similar to the compare() method, but the result gives a total ordering on Decimal instances. Two Decimal instances with the same numeric value but different representations compare unequal in this ordering: >>> Decimal('12.0').compare_total(Decimal('12'))
Decimal('-1')
Quiet and signaling NaNs are also included in the total ordering. The result of this function is Decimal('0') if both operands have the same representation, Decimal('-1') if the first operand is lower in the total order than the second, and Decimal('1') if the first operand is higher in the total order than the second operand. See the specification for details of the total order. This operation is unaffected by context and is quiet: no flags are changed and no rounding is performed. As an exception, the C version may raise InvalidOperation if the second operand cannot be converted exactly. | |
doc_30476 | Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices, the order of the corresponding innermost dimension within sorted_sequence would be preserved. Return a new tensor with the same size as values. If right is False (default), then the left boundary of sorted_sequence is closed. More formally, the returned index satisfies the following rules:
sorted_sequence right returned index satisfies
1-D False sorted_sequence[i-1] < values[m][n]...[l][x] <= sorted_sequence[i]
1-D True sorted_sequence[i-1] <= values[m][n]...[l][x] < sorted_sequence[i]
N-D False sorted_sequence[m][n]...[l][i-1] < values[m][n]...[l][x] <= sorted_sequence[m][n]...[l][i]
N-D True sorted_sequence[m][n]...[l][i-1] <= values[m][n]...[l][x] < sorted_sequence[m][n]...[l][i] Parameters
sorted_sequence (Tensor) – N-D or 1-D tensor, containing monotonically increasing sequence on the innermost dimension.
values (Tensor or Scalar) – N-D tensor or a Scalar containing the search value(s). Keyword Arguments
out_int32 (bool, optional) – indicate the output data type. torch.int32 if True, torch.int64 otherwise. Default value is False, i.e. default output data type is torch.int64.
right (bool, optional) – if False, return the first suitable location that is found. If True, return the last such index. If no suitable index found, return 0 for non-numerical value (eg. nan, inf) or the size of innermost dimension within sorted_sequence (one pass the last index of the innermost dimension). In other words, if False, gets the lower bound index for each value in values on the corresponding innermost dimension of the sorted_sequence. If True, gets the upper bound index instead. Default value is False.
out (Tensor, optional) – the output tensor, must be the same size as values if provided. Note If your use case is always 1-D sorted sequence, torch.bucketize() is preferred, because it has fewer dimension checks resulting in slightly better performance. Example: >>> sorted_sequence = torch.tensor([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]])
>>> sorted_sequence
tensor([[ 1, 3, 5, 7, 9],
[ 2, 4, 6, 8, 10]])
>>> values = torch.tensor([[3, 6, 9], [3, 6, 9]])
>>> values
tensor([[3, 6, 9],
[3, 6, 9]])
>>> torch.searchsorted(sorted_sequence, values)
tensor([[1, 3, 4],
[1, 2, 4]])
>>> torch.searchsorted(sorted_sequence, values, right=True)
tensor([[2, 3, 5],
[1, 3, 4]])
>>> sorted_sequence_1d = torch.tensor([1, 3, 5, 7, 9])
>>> sorted_sequence_1d
tensor([1, 3, 5, 7, 9])
>>> torch.searchsorted(sorted_sequence_1d, values)
tensor([[1, 3, 4],
[1, 3, 4]]) | |
doc_30477 |
Set the colormap to 'hsv'. This changes the default colormap as well as the colormap of the current image if there is one. See help(colormaps) for more information. | |
doc_30478 | When this namespace is specified, the name string is an X.500 DN in DER or a text output format. | |
doc_30479 |
Convert the object to a JSON string. Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps. Parameters
path_or_buf:str, path object, file-like object, or None, default None
String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string.
orient:str
Indication of expected JSON string format.
Series:
default is ‘index’ allowed values are: {‘split’, ‘records’, ‘index’, ‘table’}.
DataFrame:
default is ‘columns’ allowed values are: {‘split’, ‘records’, ‘index’, ‘columns’, ‘values’, ‘table’}.
The format of the JSON string:
‘split’ : dict like {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]} ‘records’ : list like [{column -> value}, … , {column -> value}] ‘index’ : dict like {index -> {column -> value}} ‘columns’ : dict like {column -> {index -> value}} ‘values’ : just the values array ‘table’ : dict like {‘schema’: {schema}, ‘data’: {data}} Describing the data, where data component is like orient='records'.
date_format:{None, ‘epoch’, ‘iso’}
Type of date conversion. ‘epoch’ = epoch milliseconds, ‘iso’ = ISO8601. The default depends on the orient. For orient='table', the default is ‘iso’. For all other orients, the default is ‘epoch’.
double_precision:int, default 10
The number of decimal places to use when encoding floating point values.
force_ascii:bool, default True
Force encoded string to be ASCII.
date_unit:str, default ‘ms’ (milliseconds)
The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.
default_handler:callable, default None
Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.
lines:bool, default False
If ‘orient’ is ‘records’ write out line-delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list-like.
compression:str or dict, default ‘infer’
For on-the-fly compression of the output data. If ‘infer’ and ‘path_or_buf’ path-like, then detect compression from the following extensions: ‘.gz’, ‘.bz2’, ‘.zip’, ‘.xz’, or ‘.zst’ (otherwise no compression). Set to None for no compression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, or zstandard.ZstdDecompressor, respectively. As an example, the following could be passed for faster compression and to create a reproducible gzip archive: compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}. Changed in version 1.4.0: Zstandard support.
index:bool, default True
Whether to include the index values in the JSON string. Not including the index (index=False) is only supported when orient is ‘split’ or ‘table’.
indent:int, optional
Length of whitespace used to indent each record. New in version 1.0.0.
storage_options:dict, optional
Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec. Please see fsspec and urllib for more details. New in version 1.2.0. Returns
None or str
If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None. See also read_json
Convert a JSON string to pandas object. Notes The behavior of indent=0 varies from the stdlib, which does not indent the output but does insert newlines. Currently, indent=0 and the default indent=None are equivalent in pandas, though this may change in a future release. orient='table' contains a ‘pandas_version’ field under ‘schema’. This stores the version of pandas used in the latest revision of the schema. Examples
>>> import json
>>> df = pd.DataFrame(
... [["a", "b"], ["c", "d"]],
... index=["row 1", "row 2"],
... columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)
{
"columns": [
"col 1",
"col 2"
],
"index": [
"row 1",
"row 2"
],
"data": [
[
"a",
"b"
],
[
"c",
"d"
]
]
}
Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.
>>> result = df.to_json(orient="records")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)
[
{
"col 1": "a",
"col 2": "b"
},
{
"col 1": "c",
"col 2": "d"
}
]
Encoding/decoding a Dataframe using 'index' formatted JSON:
>>> result = df.to_json(orient="index")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)
{
"row 1": {
"col 1": "a",
"col 2": "b"
},
"row 2": {
"col 1": "c",
"col 2": "d"
}
}
Encoding/decoding a Dataframe using 'columns' formatted JSON:
>>> result = df.to_json(orient="columns")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)
{
"col 1": {
"row 1": "a",
"row 2": "c"
},
"col 2": {
"row 1": "b",
"row 2": "d"
}
}
Encoding/decoding a Dataframe using 'values' formatted JSON:
>>> result = df.to_json(orient="values")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)
[
[
"a",
"b"
],
[
"c",
"d"
]
]
Encoding with Table Schema:
>>> result = df.to_json(orient="table")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4)
{
"schema": {
"fields": [
{
"name": "index",
"type": "string"
},
{
"name": "col 1",
"type": "string"
},
{
"name": "col 2",
"type": "string"
}
],
"primaryKey": [
"index"
],
"pandas_version": "1.4.0"
},
"data": [
{
"index": "row 1",
"col 1": "a",
"col 2": "b"
},
{
"index": "row 2",
"col 1": "c",
"col 2": "d"
}
]
} | |
doc_30480 | Elements are counted from an iterable or added-in from another mapping (or counter). Like dict.update() but adds counts instead of replacing them. Also, the iterable is expected to be a sequence of elements, not a sequence of (key, value) pairs. | |
doc_30481 |
Set both the edgecolor and the facecolor. Parameters
ccolor or list of rgba tuples
See also
Collection.set_facecolor, Collection.set_edgecolor
For setting the edge or face color individually. | |
doc_30482 |
Return v=0, nth=1. | |
doc_30483 |
Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one. The transformation is given by (when axis=0): X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min
where min, max = feature_range. The transformation is calculated as (when axis=0): X_scaled = scale * X + min - X.min(axis=0) * scale
where scale = (max - min) / (X.max(axis=0) - X.min(axis=0))
This transformation is often used as an alternative to zero mean, unit variance scaling. Read more in the User Guide. New in version 0.17: minmax_scale function interface to MinMaxScaler. Parameters
Xarray-like of shape (n_samples, n_features)
The data.
feature_rangetuple (min, max), default=(0, 1)
Desired range of transformed data.
axisint, default=0
Axis used to scale along. If 0, independently scale each feature, otherwise (if 1) scale each sample.
copybool, default=True
Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array). Returns
X_trndarray of shape (n_samples, n_features)
The transformed data. Warning Risk of data leak Do not use minmax_scale unless you know what you are doing. A common mistake is to apply it to the entire data before splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend using MinMaxScaler within a Pipeline in order to prevent most risks of data leaking: pipe = make_pipeline(MinMaxScaler(), LogisticRegression()). See also
MinMaxScaler
Performs scaling to a given range using the Transformer API (e.g. as part of a preprocessing Pipeline). Notes For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py. | |
doc_30484 | AxesDivider(axes[, xref, yref]) Divider based on the pre-existing axes.
AxesLocator(axes_divider, nx, ny[, nx1, ny1]) A callable object which returns the position and size of a given AxesDivider cell.
Divider(fig, pos, horizontal, vertical[, ...]) An Axes positioning class.
HBoxDivider(fig, *args[, horizontal, ...]) A SubplotDivider for laying out axes horizontally, while ensuring that they have equal heights.
SubplotDivider(fig, *args[, horizontal, ...]) The Divider class whose rectangle area is specified as a subplot geometry.
VBoxDivider(fig, *args[, horizontal, ...]) A SubplotDivider for laying out axes vertically, while ensuring that they have equal widths. Functions
make_axes_area_auto_adjustable(ax[, ...])
make_axes_locatable(axes) | |
doc_30485 | Registers the given model class (or iterable of classes) with the given admin_class. admin_class defaults to ModelAdmin (the default admin options). If keyword arguments are given – e.g. list_display – they’ll be applied as options to the admin class. Raises ImproperlyConfigured if a model is abstract. and django.contrib.admin.sites.AlreadyRegistered if a model is already registered. | |
doc_30486 | Nonzero if a DST timezone is defined. See note below. | |
doc_30487 | Receive notification of a processing instruction. The Parser will invoke this method once for each processing instruction found: note that processing instructions may occur before or after the main document element. A SAX parser should never report an XML declaration (XML 1.0, section 2.8) or a text declaration (XML 1.0, section 4.3.1) using this method. | |
doc_30488 |
Predict targets of given samples. Parameters
Xarray-like of shape (n_samples, n_features)
Samples.
copybool, default=True
Whether to copy X and Y, or perform in-place normalization. Notes This call requires the estimation of a matrix of shape (n_features, n_targets), which may be an issue in high dimensional space. | |
doc_30489 |
Set multiple properties at once. Supported properties are
Property Description
agg_filter a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha array-like or scalar or None
animated bool
antialiased or aa or antialiaseds bool or list of bools
array array-like or None
capstyle CapStyle or {'butt', 'projecting', 'round'}
clim (vmin: float, vmax: float)
clip_box Bbox
clip_on bool
clip_path Patch or (Path, Transform) or None
cmap Colormap or str or None
color color or list of rgba tuples
edgecolor or ec or edgecolors color or list of colors or 'face'
facecolor or facecolors or fc color or list of colors
figure Figure
gid str
hatch {'/', '\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
in_layout bool
joinstyle JoinStyle or {'miter', 'round', 'bevel'}
label object
linestyle or dashes or linestyles or ls str or tuple or list thereof
linewidth or linewidths or lw float or list of floats
norm Normalize or None
offset_transform Transform
offsets (N, 2) or (2,) array-like
path_effects AbstractPathEffect
picker None or bool or float or callable
pickradius float
rasterized bool
sizes ndarray or None
sketch_params (scale: float, length: float, randomness: float)
snap bool or None
transform Transform
url str
urls list of str or None
visible bool
zorder float | |
doc_30490 |
Map values using an input mapping or function. Parameters
mapper:function, dict, or Series
Mapping correspondence.
na_action:{None, ‘ignore’}
If ‘ignore’, propagate NA values, without passing them to the mapping correspondence. Returns
applied:Union[Index, MultiIndex], inferred
The output of the mapping function applied to the index. If the function returns a tuple with more than one element a MultiIndex will be returned. | |
doc_30491 |
Applies local response normalization over an input signal composed of several input planes, where channels occupy the second dimension. Applies normalization across channels. bc=ac(k+αn∑c′=max(0,c−n/2)min(N−1,c+n/2)ac′2)−βb_{c} = a_{c}\left(k + \frac{\alpha}{n} \sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta}
Parameters
size – amount of neighbouring channels used for normalization
alpha – multiplicative factor. Default: 0.0001
beta – exponent. Default: 0.75
k – additive factor. Default: 1 Shape:
Input: (N,C,∗)(N, C, *)
Output: (N,C,∗)(N, C, *) (same shape as input) Examples: >>> lrn = nn.LocalResponseNorm(2)
>>> signal_2d = torch.randn(32, 5, 24, 24)
>>> signal_4d = torch.randn(16, 5, 7, 7, 7, 7)
>>> output_2d = lrn(signal_2d)
>>> output_4d = lrn(signal_4d) | |
doc_30492 | Path to a custom template, used by changelist_view(). | |
doc_30493 | sklearn.metrics.average_precision_score(y_true, y_score, *, average='macro', pos_label=1, sample_weight=None) [source]
Compute average precision (AP) from prediction scores. AP summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the weight: \[\text{AP} = \sum_n (R_n - R_{n-1}) P_n\] where \(P_n\) and \(R_n\) are the precision and recall at the nth threshold [1]. This implementation is not interpolated and is different from computing the area under the precision-recall curve with the trapezoidal rule, which uses linear interpolation and can be too optimistic. Note: this implementation is restricted to the binary classification task or multilabel classification task. Read more in the User Guide. Parameters
y_truendarray of shape (n_samples,) or (n_samples, n_classes)
True binary labels or binary label indicators.
y_scorendarray of shape (n_samples,) or (n_samples, n_classes)
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by decision_function on some classifiers).
average{‘micro’, ‘samples’, ‘weighted’, ‘macro’} or None, default=’macro’
If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:
'micro':
Calculate metrics globally by considering each element of the label indicator matrix as a label.
'macro':
Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
'weighted':
Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label).
'samples':
Calculate metrics for each instance, and find their average. Will be ignored when y_true is binary.
pos_labelint or str, default=1
The label of the positive class. Only applied to binary y_true. For multilabel-indicator y_true, pos_label is fixed to 1.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
average_precisionfloat
See also
roc_auc_score
Compute the area under the ROC curve.
precision_recall_curve
Compute precision-recall pairs for different probability thresholds. Notes Changed in version 0.19: Instead of linearly interpolating between operating points, precisions are weighted by the change in recall since the last operating point. References
1
Wikipedia entry for the Average precision Examples >>> import numpy as np
>>> from sklearn.metrics import average_precision_score
>>> y_true = np.array([0, 0, 1, 1])
>>> y_scores = np.array([0.1, 0.4, 0.35, 0.8])
>>> average_precision_score(y_true, y_scores)
0.83...
Examples using sklearn.metrics.average_precision_score
Precision-Recall | |
doc_30494 | Return the Graph underlying this GraphModule | |
doc_30495 |
[Deprecated] Implement the default Matplotlib key bindings defined at Navigation keyboard shortcuts. Notes Deprecated since version 3.4. | |
doc_30496 | Generic element structure builder. This builder converts a sequence of start, data, end, comment and pi method calls to a well-formed element structure. You can use this class to build an element structure using a custom XML parser, or a parser for some other XML-like format. element_factory, when given, must be a callable accepting two positional arguments: a tag and a dict of attributes. It is expected to return a new element instance. The comment_factory and pi_factory functions, when given, should behave like the Comment() and ProcessingInstruction() functions to create comments and processing instructions. When not given, the default factories will be used. When insert_comments and/or insert_pis is true, comments/pis will be inserted into the tree if they appear within the root element (but not outside of it).
close()
Flushes the builder buffers, and returns the toplevel document element. Returns an Element instance.
data(data)
Adds text to the current element. data is a string. This should be either a bytestring, or a Unicode string.
end(tag)
Closes the current element. tag is the element name. Returns the closed element.
start(tag, attrs)
Opens a new element. tag is the element name. attrs is a dictionary containing element attributes. Returns the opened element.
comment(text)
Creates a comment with the given text. If insert_comments is true, this will also add it to the tree. New in version 3.8.
pi(target, text)
Creates a comment with the given target name and text. If insert_pis is true, this will also add it to the tree. New in version 3.8.
In addition, a custom TreeBuilder object can provide the following methods:
doctype(name, pubid, system)
Handles a doctype declaration. name is the doctype name. pubid is the public identifier. system is the system identifier. This method does not exist on the default TreeBuilder class. New in version 3.2.
start_ns(prefix, uri)
Is called whenever the parser encounters a new namespace declaration, before the start() callback for the opening element that defines it. prefix is '' for the default namespace and the declared namespace prefix name otherwise. uri is the namespace URI. New in version 3.8.
end_ns(prefix)
Is called after the end() callback of an element that declared a namespace prefix mapping, with the name of the prefix that went out of scope. New in version 3.8. | |
doc_30497 |
Load and return the digits dataset (classification). Each datapoint is a 8x8 image of a digit.
Classes 10
Samples per class ~180
Samples total 1797
Dimensionality 64
Features integers 0-16 Read more in the User Guide. Parameters
n_classint, default=10
The number of classes to return. Between 0 and 10.
return_X_ybool, default=False
If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object. New in version 0.18.
as_framebool, default=False
If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below. New in version 0.23. Returns
dataBunch
Dictionary-like object, with the following attributes.
data{ndarray, dataframe} of shape (1797, 64)
The flattened data matrix. If as_frame=True, data will be a pandas DataFrame. target: {ndarray, Series} of shape (1797,)
The classification target. If as_frame=True, target will be a pandas Series. feature_names: list
The names of the dataset columns. target_names: list
The names of target classes. New in version 0.20. frame: DataFrame of shape (1797, 65)
Only present when as_frame=True. DataFrame with data and target. New in version 0.23. images: {ndarray} of shape (1797, 8, 8)
The raw image data. DESCR: str
The full description of the dataset.
(data, target)tuple if return_X_y is True
New in version 0.18. This is a copy of the test set of the UCI ML hand-written digits datasets
https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
Examples To load the data and visualize the images: >>> from sklearn.datasets import load_digits
>>> digits = load_digits()
>>> print(digits.data.shape)
(1797, 64)
>>> import matplotlib.pyplot as plt
>>> plt.gray()
>>> plt.matshow(digits.images[0])
>>> plt.show() | |
doc_30498 |
Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. | |
doc_30499 | Return the maximum peak-peak value in the sound fragment. |
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