BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def dtype(self): """Data-type of the array's elements. Returns ------- numpy.dtype This NDArray's data type. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x.dtype <type 'numpy.float32'> >>> y = mx.nd.zeros((2,3), dtype='int...
def _fresh_grad(self): """Whether this array's corresponding gradient array (registered via `autograd.mark_variables`) has been updated by `autograd.backward` since last reset. `_fresh_grad` need to be manually set to False after consuming gradient (usually after updating this ...
def asnumpy(self): """Returns a ``numpy.ndarray`` object with value copied from this array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.asnumpy() >>> type(y) <type 'numpy.ndarray'> >>> y array([[ 1., 1., 1.], [ 1., 1., ...
def astype(self, dtype, copy=True): """Returns a copy of the array after casting to a specified type. Parameters ---------- dtype : numpy.dtype or str The type of the returned array. copy : bool Default `True`. By default, astype always returns a newly ...
def copyto(self, other): """Copies the value of this array to another array. If ``other`` is a ``NDArray`` object, then ``other.shape`` and ``self.shape`` should be the same. This function copies the value from ``self`` to ``other``. If ``other`` is a context, a new ``NDArray``...
def as_in_context(self, context): """Returns an array on the target device with the same value as this array. If the target context is the same as ``self.context``, then ``self`` is returned. Otherwise, a copy is made. Parameters ---------- context : Context ...
def attach_grad(self, grad_req='write', stype=None): """Attach a gradient buffer to this NDArray, so that `backward` can compute gradient with respect to it. Parameters ---------- grad_req : {'write', 'add', 'null'} How gradient will be accumulated. - 'wr...
def grad(self): """Returns gradient buffer attached to this NDArray.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl))) if hdl.value is None: return None return _ndarray_cls(hdl)
def detach(self): """Returns a new NDArray, detached from the current graph.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl))) return _ndarray_cls(hdl)
def backward(self, out_grad=None, retain_graph=False, train_mode=True): """Compute the gradients of this NDArray w.r.t variables. Parameters ---------- out_grad : NDArray, optional Gradient with respect to head. retain_graph : bool, optional Whether to re...
def build(self, align_path): """ Build the align array """ file = open(align_path, 'r') lines = file.readlines() file.close() # words: list([op, ed, word]) words = [] for line in lines: _op, _ed, word = line.strip().split(' ') ...
def sentence(self, padding=75): """ Get sentence """ vec = word_to_vector(self.sentence_str) vec += [-1] * (padding - self.sentence_length) return np.array(vec, dtype=np.int32)
def word(self, _id, padding=75): """ Get words """ word = self.words[_id][2] vec = word_to_vector(word) vec += [-1] * (padding - len(vec)) return np.array(vec, dtype=np.int32)
def word_frame_pos(self, _id): """ Get the position of words """ left = int(self.words[_id][0]/1000) right = max(left+1, int(self.words[_id][1]/1000)) return (left, right)
def prepare_sparse_params(self, param_rowids): '''Prepares the module for processing a data batch by pulling row_sparse parameters from kvstore to all devices based on rowids. Parameters ---------- param_rowids : dict of str to NDArray of list of NDArrays ''' if ...
def save_params(self, fname): """Saves model parameters to file. Parameters ---------- fname : str Path to output param file. Examples -------- >>> # An example of saving module parameters. >>> mod.save_params('myfile') """ arg_...
def get_params_from_kv(self, arg_params, aux_params): """ Copy data from kvstore to `arg_params` and `aux_params`. Parameters ---------- arg_params : list of NDArray Target parameter arrays. aux_params : list of NDArray Target aux arrays. Notes ...
def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None): """Clips gradient norm. The norm is computed over all gradients together, as if they were concatenated into a single vector. Gradients are modified in-place. The method is first used in `[ICML2013] On the ...
def rescale_grad(self, scale=None, param_name=None): """ Rescale the gradient of provided parameters by a certain scale """ if scale is None or param_name is None: return param_idx = self._exec_group.param_names.index(param_name) grad_vals = self._exec_group.grad_arrays[param...
def factorization_machine_model(factor_size, num_features, lr_mult_config, wd_mult_config, init_config): """ builds factorization machine network with proper formulation: y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2) """ x = mx.symbol.Variable("...
def batchify(data, batch_size): """Reshape data into (num_example, batch_size)""" nbatch = data.shape[0] // batch_size data = data[:nbatch * batch_size] data = data.reshape((batch_size, nbatch)).T return data
def tokenize(self, path): """Tokenizes a text file.""" assert os.path.exists(path) # Add words to the dictionary with open(path, 'r') as f: tokens = 0 for line in f: words = line.split() + ['<eos>'] tokens += len(words) ...
def _build_doc(func_name, desc, arg_names, arg_types, arg_desc, key_var_num_args=None, ret_type=None): """Build docstring for symbolic functions.""" param_str = _build_param_doc(arg_names, arg_types, arg_desc) if key_v...
def get_output_shape(sym, input_shapes): """Get user friendly information of the output shapes.""" _, s_outputs, _ = sym.infer_shape(input_shapes) return dict(zip(sym.list_outputs(), s_outputs))
def num_gpus(): """Query CUDA for the number of GPUs present. Raises ------ Will raise an exception on any CUDA error. Returns ------- count : int The number of GPUs. """ count = ctypes.c_int() check_call(_LIB.MXGetGPUCount(ctypes.byref(count))) return count.value
def gpu_memory_info(device_id=0): """Query CUDA for the free and total bytes of GPU global memory. Parameters ---------- device_id : int, optional The device id of the GPU device. Raises ------ Will raise an exception on any CUDA error. Returns ------- (free, total) : ...
def current_context(): """Returns the current context. By default, `mx.cpu()` is used for all the computations and it can be overridden by using `with mx.Context(x)` statement where x can be cpu(device_id) or gpu(device_id). Examples ------- >>> mx.current_context() cpu(0) >>> with...
def _list_audio_files(self, root, skip_rows=0): """Populates synsets - a map of index to label for the data items. Populates the data in the dataset, making tuples of (data, label) """ self.synsets = [] self.items = [] if not self._train_csv: # The audio files...
def transform_first(self, fn, lazy=False): """Returns a new dataset with the first element of each sample transformed by the transformer function `fn`. This is useful, for example, when you only want to transform data while keeping label as is. lazy=False is passed to transform_...
def config_cython(): """Try to configure cython and return cython configuration""" if not with_cython: return [] # pylint: disable=unreachable if os.name == 'nt': print("WARNING: Cython is not supported on Windows, will compile without cython module") return [] try: ...
def _compose(self, args, *kwargs): """Compose symbol on inputs. This call mutates the current symbol. Parameters ---------- args: provide positional arguments kwargs: provide keyword arguments Returns ------- the resul...
def _set_attr(self, kwargs): """Set the attribute of the symbol. Parameters ---------- kwargs The attributes to set """ keys = c_str_array(kwargs.keys()) vals = c_str_array([str(s) for s in kwargs.values()]) num_args = mx_uint(len(kwargs))...
def get_config(network, data_shape, **kwargs): """Configuration factory for various networks Parameters ---------- network : str base network name, such as vgg_reduced, inceptionv3, resnet... data_shape : int input data dimension kwargs : dict extra arguments """ ...
def get_symbol_train(network, data_shape, **kwargs): """Wrapper for get symbol for train Parameters ---------- network : str name for the base network symbol data_shape : int input shape kwargs : dict see symbol_builder.get_symbol_train for more details """ if ne...
def _set_trainer(self, trainer): """ Set the trainer this parameter is associated with. """ # trainer cannot be replaced for sparse params if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer: raise RuntimeError( "Failed to set th...
def _get_row_sparse(self, arr_list, ctx, row_id): """ Get row_sparse data from row_sparse parameters based on row_id. """ # get row sparse params based on row ids if not isinstance(row_id, ndarray.NDArray): raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id...
def _load_init(self, data, ctx): """(Re)initializes by loading from data.""" if self.shape: for self_dim, data_dim in zip(self.shape, data.shape): assert self_dim in (0, data_dim), \ "Failed loading Parameter '%s' from saved params: " \ ...
def _finish_deferred_init(self): """Finishes deferred initialization.""" if not self._deferred_init: return init, ctx, default_init, data = self._deferred_init self._deferred_init = () assert self.shape is not None and np.prod(self.shape) > 0, \ "Cannot in...
def _init_impl(self, data, ctx_list): """Sets data and grad.""" self._ctx_list = list(ctx_list) self._ctx_map = [[], []] for i, ctx in enumerate(self._ctx_list): dev_list = self._ctx_map[ctx.device_typeid&1] while len(dev_list) <= ctx.device_id: de...
def _init_grad(self): """Initialize grad buffers.""" if self.grad_req == 'null': self._grad = None return self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context, stype=self._grad_stype) for i in self._data] au...
def _reduce(self): """Reduce data from multiple context to cpu.""" ctx = context.cpu() if self._stype == 'default': block = self.list_data() data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block) else: # fetch all rows for 'row_sparse' para...
def initialize(self, init=None, ctx=None, default_init=initializer.Uniform(), force_reinit=False): """Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API. Parameters ---------- init : Initializer The initializer to use. Overrid...
def reset_ctx(self, ctx): """Re-assign Parameter to other contexts. Parameters ---------- ctx : Context or list of Context, default ``context.current_context()``. Assign Parameter to given context. If ctx is a list of Context, a copy will be made for each context...
def set_data(self, data): """Sets this parameter's value on all contexts.""" self.shape = data.shape if self._data is None: assert self._deferred_init, \ "Parameter '%s' has not been initialized"%self.name self._deferred_init = self._deferred_init[:3] + (...
def row_sparse_data(self, row_id): """Returns a copy of the 'row_sparse' parameter on the same context as row_id's. The copy only retains rows whose ids occur in provided row ids. The parameter must have been initialized on this context before. Parameters ---------- row_...
def list_row_sparse_data(self, row_id): """Returns copies of the 'row_sparse' parameter on all contexts, in the same order as creation. The copy only retains rows whose ids occur in provided row ids. The parameter must have been initialized before. Parameters ---------- ...
def data(self, ctx=None): """Returns a copy of this parameter on one context. Must have been initialized on this context before. For sparse parameters, use :py:meth:`Parameter.row_sparse_data` instead. Parameters ---------- ctx : Context Desired context. ...
def list_data(self): """Returns copies of this parameter on all contexts, in the same order as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data` instead. Returns ------- list of NDArrays """ if self._stype != 'default': ...
def grad(self, ctx=None): """Returns a gradient buffer for this parameter on one context. Parameters ---------- ctx : Context Desired context. """ if self._data is not None and self._grad is None: raise RuntimeError( "Cannot get gr...
def list_grad(self): """Returns gradient buffers on all contexts, in the same order as :py:meth:`values`.""" if self._data is not None and self._grad is None: raise RuntimeError( "Cannot get gradient array for Parameter '%s' " \ "because grad_req='null...
def list_ctx(self): """Returns a list of contexts this parameter is initialized on.""" if self._data is None: if self._deferred_init: return self._deferred_init[1] raise RuntimeError("Parameter '%s' has not been initialized"%self.name) return self._ctx_lis...
def zero_grad(self): """Sets gradient buffer on all contexts to 0. No action is taken if parameter is uninitialized or doesn't require gradient.""" if self._grad is None: return for i in self._grad: ndarray.zeros_like(i, out=i)
def var(self): """Returns a symbol representing this parameter.""" if self._var is None: self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype, lr_mult=self.lr_mult, wd_mult=self.wd_mult, init=self.init, sty...
def cast(self, dtype): """Cast data and gradient of this Parameter to a new data type. Parameters ---------- dtype : str or numpy.dtype The new data type. """ self.dtype = dtype if self._data is None: return with autograd.pause(): ...
def get(self, name, **kwargs): """Retrieves a :py:class:`Parameter` with name ``self.prefix+name``. If not found, :py:func:`get` will first try to retrieve it from "shared" dict. If still not found, :py:func:`get` will create a new :py:class:`Parameter` with key-word arguments and insert...
def get_constant(self, name, value=None): """Retrieves a :py:class:`.Constant` with name ``self.prefix+name``. If not found, :py:func:`get` will first try to retrieve it from "shared" dict. If still not found, :py:func:`get` will create a new :py:class:`.Constant` with key-word arguments...
def update(self, other): """Copies all Parameters in ``other`` to self.""" for k, v in other.items(): if k in self._params: assert self._params[k] is v, \ "Cannot update self with other because they have different " \ "Parameters with t...
def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False, force_reinit=False): """Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray` API. It has no effect when using :py:class:`Symbol` API. Parameters ---------- ...
def setattr(self, name, value): """Set an attribute to a new value for all Parameters. For example, set grad_req to null if you don't need gradient w.r.t a model's Parameters:: model.collect_params().setattr('grad_req', 'null') or change the learning rate multiplier:: ...
def save(self, filename, strip_prefix=''): """Save parameters to file. Parameters ---------- filename : str Path to parameter file. strip_prefix : str, default '' Strip prefix from parameter names before saving. """ arg_dict = {} f...
def load(self, filename, ctx=None, allow_missing=False, ignore_extra=False, restore_prefix=''): """Load parameters from file. Parameters ---------- filename : str Path to parameter file. ctx : Context or list of Context Context(s) initialize ...
def _make_torch_function(handle): """Create a Torch function from the FunctionHandle.""" # Get the property of function n_used_vars = mx_uint() n_scalars = mx_uint() n_mutate_vars = mx_uint() type_mask = ctypes.c_int() check_call(_LIB.MXFuncDescribe( handle, ctypes.byref(n_us...
def _init_torch_module(): """List and add all the torch backed ndarray functions to current module.""" plist = ctypes.POINTER(FunctionHandle)() size = ctypes.c_uint() check_call(_LIB.MXListFunctions(ctypes.byref(size), ctypes.byref(plist))) module_obj = sys.modul...
def inception_v3(pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""Inception v3 model from `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_ paper. Parameters ---------- pretrained : bool, de...
def pack(header, s): """Pack a string into MXImageRecord. Parameters ---------- header : IRHeader Header of the image record. ``header.label`` can be a number or an array. See more detail in ``IRHeader``. s : str Raw image string to be packed. Returns ------- s ...
def unpack(s): """Unpack a MXImageRecord to string. Parameters ---------- s : str String buffer from ``MXRecordIO.read``. Returns ------- header : IRHeader Header of the image record. s : str Unpacked string. Examples -------- >>> record = mx.record...
def unpack_img(s, iscolor=-1): """Unpack a MXImageRecord to image. Parameters ---------- s : str String buffer from ``MXRecordIO.read``. iscolor : int Image format option for ``cv2.imdecode``. Returns ------- header : IRHeader Header of the image record. img...
def pack_img(header, img, quality=95, img_fmt='.jpg'): """Pack an image into ``MXImageRecord``. Parameters ---------- header : IRHeader Header of the image record. ``header.label`` can be a number or an array. See more detail in ``IRHeader``. img : numpy.ndarray Image to be ...
def open(self): """Opens the record file.""" if self.flag == "w": check_call(_LIB.MXRecordIOWriterCreate(self.uri, ctypes.byref(self.handle))) self.writable = True elif self.flag == "r": check_call(_LIB.MXRecordIOReaderCreate(self.uri, ctypes.byref(self.handle...
def _check_pid(self, allow_reset=False): """Check process id to ensure integrity, reset if in new process.""" if not self.pid == current_process().pid: if allow_reset: self.reset() else: raise RuntimeError("Forbidden operation in multiple processes...
def close(self): """Closes the record file.""" if not self.is_open: return if self.writable: check_call(_LIB.MXRecordIOWriterFree(self.handle)) else: check_call(_LIB.MXRecordIOReaderFree(self.handle)) self.is_open = False self.pid = Non...
def write(self, buf): """Inserts a string buffer as a record. Examples --------- >>> record = mx.recordio.MXRecordIO('tmp.rec', 'w') >>> for i in range(5): ... record.write('record_%d'%i) >>> record.close() Parameters ---------- buf : ...
def read(self): """Returns record as a string. Examples --------- >>> record = mx.recordio.MXRecordIO('tmp.rec', 'r') >>> for i in range(5): ... item = record.read() ... print(item) record_0 record_1 record_2 record_3 ...
def close(self): """Closes the record file.""" if not self.is_open: return super(MXIndexedRecordIO, self).close() self.fidx.close()
def seek(self, idx): """Sets the current read pointer position. This function is internally called by `read_idx(idx)` to find the current reader pointer position. It doesn't return anything.""" assert not self.writable self._check_pid(allow_reset=True) pos = ctypes.c_siz...
def tell(self): """Returns the current position of write head. Examples --------- >>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w') >>> print(record.tell()) 0 >>> for i in range(5): ... record.write_idx(i, 'record_%d'%i) .....
def write_idx(self, idx, buf): """Inserts input record at given index. Examples --------- >>> for i in range(5): ... record.write_idx(i, 'record_%d'%i) >>> record.close() Parameters ---------- idx : int Index of a file. bu...
def _add_new_columns(dataframe, metrics): """Add new metrics as new columns to selected pandas dataframe. Parameters ---------- dataframe : pandas.DataFrame Selected dataframe needs to be modified. metrics : metric.EvalMetric New metrics to be added. """ #TODO(leodirac): we ...
def args_wrapper(*args): """Generates callback arguments for model.fit() for a set of callback objects. Callback objects like PandasLogger(), LiveLearningCurve() get passed in. This assembles all their callback arguments. """ out = defaultdict(list) for callback in args: callback_ar...
def append_metrics(self, metrics, df_name): """Append new metrics to selected dataframes. Parameters ---------- metrics : metric.EvalMetric New metrics to be added. df_name : str Name of the dataframe to be modified. """ dataframe = self._...
def train_cb(self, param): """Callback funtion for training. """ if param.nbatch % self.frequent == 0: self._process_batch(param, 'train')
def _process_batch(self, param, dataframe): """Update parameters for selected dataframe after a completed batch Parameters ---------- dataframe : pandas.DataFrame Selected dataframe needs to be modified. """ now = time.time() if param.eval_metric is no...
def epoch_cb(self): """Callback function after each epoch. Now it records each epoch time and append it to epoch dataframe. """ metrics = {} metrics['elapsed'] = self.elapsed() now = datetime.datetime.now() metrics['epoch_time'] = now - self.last_epoch_time ...
def _push_render(self): """Render the plot with bokeh.io and push to notebook. """ bokeh.io.push_notebook(handle=self.handle) self.last_update = time.time()
def _process_batch(self, param, df_name): """Update selected dataframe after a completed batch Parameters ---------- df_name : str Selected dataframe name needs to be modified. """ if param.eval_metric is not None: metrics = dict(param.eval_metric....
def build_vocab(nested_list): """ :param nested_list: list of list of string :return: dictionary mapping from string to int, inverse of that dictionary """ # Build vocabulary word_counts = Counter(itertools.chain(*nested_list)) # Mapping from index to label vocabulary_inv = [x[0] for x ...
def build_iters(data_dir, max_records, train_fraction, batch_size, buckets=None): """ Reads a csv of sentences/tag sequences into a pandas dataframe. Converts into X = array(list(int)) & Y = array(list(int)) Splits into training and test sets Builds dictionaries mapping from index labels to labels/ ...
def sym_gen(seq_len): """ Build NN symbol depending on the length of the input sequence """ sentence_shape = train_iter.provide_data[0][1] char_sentence_shape = train_iter.provide_data[1][1] entities_shape = train_iter.provide_label[0][1] X_sent = mx.symbol.Variable(train_iter.provide_data[...
def rand_zipfian(true_classes, num_sampled, range_max): """Draw random samples from an approximately log-uniform or Zipfian distribution. This operation randomly samples num_sampled candidates the range of integers [0, range_max). The elements of sampled_candidates are drawn with replacement from the bas...
def foreach(body, data, init_states, name="foreach"): """Run a for loop with user-defined computation over Symbols on dimension 0. This operator simulates a for loop and body has the computation for an iteration of the for loop. It runs the computation in body on each slice from the input NDArrays. ...
def while_loop(cond, func, loop_vars, max_iterations=None, name="while_loop"): """Run a while loop with user-defined computation and loop condition. This operator simulates a while loop which iterately does customized computation as long as the condition is satisfied. `loop_vars` is a Symbol or nested...
def cond(pred, then_func, else_func, name="cond"): """Run an if-then-else using user-defined condition and computation This operator simulates a if-like branch which chooses to do one of the two customized computations according to the specified condition. `pred` is a scalar MXNet Symbol, indicati...
def _index_unknown_and_reserved_tokens(self, unknown_token, reserved_tokens): """Indexes unknown and reserved tokens.""" self._unknown_token = unknown_token # Thus, constants.UNKNOWN_IDX must be 0. self._idx_to_token = [unknown_token] if reserved_tokens is None: sel...
def _index_counter_keys(self, counter, unknown_token, reserved_tokens, most_freq_count, min_freq): """Indexes keys of `counter`. Indexes keys of `counter` according to frequency thresholds such as `most_freq_count` and `min_freq`. """ assert isinsta...
def to_indices(self, tokens): """Converts tokens to indices according to the vocabulary. Parameters ---------- tokens : str or list of strs A source token or tokens to be converted. Returns ------- int or list of ints A token index or a...
def to_tokens(self, indices): """Converts token indices to tokens according to the vocabulary. Parameters ---------- indices : int or list of ints A source token index or token indices to be converted. Returns ------- str or list of strs ...
def _make_io_iterator(handle): """Create an io iterator by handle.""" name = ctypes.c_char_p() desc = ctypes.c_char_p() num_args = mx_uint() arg_names = ctypes.POINTER(ctypes.c_char_p)() arg_types = ctypes.POINTER(ctypes.c_char_p)() arg_descs = ctypes.POINTER(ctypes.c_char_p)() check_ca...
def _init_io_module(): """List and add all the data iterators to current module.""" plist = ctypes.POINTER(ctypes.c_void_p)() size = ctypes.c_uint() check_call(_LIB.MXListDataIters(ctypes.byref(size), ctypes.byref(plist))) module_obj = sys.modules[__name__] for i in range(size.value): hd...
def get_list(shapes, types): """Get DataDesc list from attribute lists. Parameters ---------- shapes : a tuple of (name_, shape_) types : a tuple of (name_, np.dtype) """ if types is not None: type_dict = dict(types) return [DataDesc(x[0]...
def next(self): """Get next data batch from iterator. Returns ------- DataBatch The data of next batch. Raises ------ StopIteration If the end of the data is reached. """ if self.iter_next(): return DataBatch(d...

def dtype(self): """Data-type of the array's elements. Returns ------- numpy.dtype This NDArray's data type. Examples -------- >>> x = mx.nd.zeros((2,3)) >>> x.dtype <type 'numpy.float32'> >>> y = mx.nd.zeros((2,3), dtype='int...

def _fresh_grad(self): """Whether this array's corresponding gradient array (registered via `autograd.mark_variables`) has been updated by `autograd.backward` since last reset. `_fresh_grad` need to be manually set to False after consuming gradient (usually after updating this ...

def asnumpy(self): """Returns a ``numpy.ndarray`` object with value copied from this array. Examples -------- >>> x = mx.nd.ones((2,3)) >>> y = x.asnumpy() >>> type(y) <type 'numpy.ndarray'> >>> y array([[ 1., 1., 1.], [ 1., 1., ...

def astype(self, dtype, copy=True): """Returns a copy of the array after casting to a specified type. Parameters ---------- dtype : numpy.dtype or str The type of the returned array. copy : bool Default `True`. By default, astype always returns a newly ...

def copyto(self, other): """Copies the value of this array to another array. If ``other`` is a ``NDArray`` object, then ``other.shape`` and ``self.shape`` should be the same. This function copies the value from ``self`` to ``other``. If ``other`` is a context, a new ``NDArray``...

def as_in_context(self, context): """Returns an array on the target device with the same value as this array. If the target context is the same as ``self.context``, then ``self`` is returned. Otherwise, a copy is made. Parameters ---------- context : Context ...

def attach_grad(self, grad_req='write', stype=None): """Attach a gradient buffer to this NDArray, so that `backward` can compute gradient with respect to it. Parameters ---------- grad_req : {'write', 'add', 'null'} How gradient will be accumulated. - 'wr...

def grad(self): """Returns gradient buffer attached to this NDArray.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayGetGrad(self.handle, ctypes.byref(hdl))) if hdl.value is None: return None return _ndarray_cls(hdl)

def detach(self): """Returns a new NDArray, detached from the current graph.""" from . import _ndarray_cls hdl = NDArrayHandle() check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl))) return _ndarray_cls(hdl)

def backward(self, out_grad=None, retain_graph=False, train_mode=True): """Compute the gradients of this NDArray w.r.t variables. Parameters ---------- out_grad : NDArray, optional Gradient with respect to head. retain_graph : bool, optional Whether to re...

def build(self, align_path): """ Build the align array """ file = open(align_path, 'r') lines = file.readlines() file.close() # words: list([op, ed, word]) words = [] for line in lines: _op, _ed, word = line.strip().split(' ') ...

def sentence(self, padding=75): """ Get sentence """ vec = word_to_vector(self.sentence_str) vec += [-1] * (padding - self.sentence_length) return np.array(vec, dtype=np.int32)

def word(self, _id, padding=75): """ Get words """ word = self.words[_id][2] vec = word_to_vector(word) vec += [-1] * (padding - len(vec)) return np.array(vec, dtype=np.int32)

def word_frame_pos(self, _id): """ Get the position of words """ left = int(self.words[_id][0]/1000) right = max(left+1, int(self.words[_id][1]/1000)) return (left, right)

def prepare_sparse_params(self, param_rowids): '''Prepares the module for processing a data batch by pulling row_sparse parameters from kvstore to all devices based on rowids. Parameters ---------- param_rowids : dict of str to NDArray of list of NDArrays ''' if ...

def save_params(self, fname): """Saves model parameters to file. Parameters ---------- fname : str Path to output param file. Examples -------- >>> # An example of saving module parameters. >>> mod.save_params('myfile') """ arg_...

def get_params_from_kv(self, arg_params, aux_params): """ Copy data from kvstore to `arg_params` and `aux_params`. Parameters ---------- arg_params : list of NDArray Target parameter arrays. aux_params : list of NDArray Target aux arrays. Notes ...

def clip_by_global_norm_per_ctx(self, max_norm=1.0, param_names=None): """Clips gradient norm. The norm is computed over all gradients together, as if they were concatenated into a single vector. Gradients are modified in-place. The method is first used in `[ICML2013] On the ...

def rescale_grad(self, scale=None, param_name=None): """ Rescale the gradient of provided parameters by a certain scale """ if scale is None or param_name is None: return param_idx = self._exec_group.param_names.index(param_name) grad_vals = self._exec_group.grad_arrays[param...

def factorization_machine_model(factor_size, num_features, lr_mult_config, wd_mult_config, init_config): """ builds factorization machine network with proper formulation: y = w_0 \sum(x_i w_i) + 0.5(\sum\sum<v_i,v_j>x_ix_j - \sum<v_iv_i>x_i^2) """ x = mx.symbol.Variable("...

def batchify(data, batch_size): """Reshape data into (num_example, batch_size)""" nbatch = data.shape[0] // batch_size data = data[:nbatch * batch_size] data = data.reshape((batch_size, nbatch)).T return data

def tokenize(self, path): """Tokenizes a text file.""" assert os.path.exists(path) # Add words to the dictionary with open(path, 'r') as f: tokens = 0 for line in f: words = line.split() + ['<eos>'] tokens += len(words) ...

def _build_doc(func_name, desc, arg_names, arg_types, arg_desc, key_var_num_args=None, ret_type=None): """Build docstring for symbolic functions.""" param_str = _build_param_doc(arg_names, arg_types, arg_desc) if key_v...

def get_output_shape(sym, **input_shapes): """Get user friendly information of the output shapes.""" _, s_outputs, _ = sym.infer_shape(**input_shapes) return dict(zip(sym.list_outputs(), s_outputs))

def num_gpus(): """Query CUDA for the number of GPUs present. Raises ------ Will raise an exception on any CUDA error. Returns ------- count : int The number of GPUs. """ count = ctypes.c_int() check_call(_LIB.MXGetGPUCount(ctypes.byref(count))) return count.value

def gpu_memory_info(device_id=0): """Query CUDA for the free and total bytes of GPU global memory. Parameters ---------- device_id : int, optional The device id of the GPU device. Raises ------ Will raise an exception on any CUDA error. Returns ------- (free, total) : ...

def current_context(): """Returns the current context. By default, `mx.cpu()` is used for all the computations and it can be overridden by using `with mx.Context(x)` statement where x can be cpu(device_id) or gpu(device_id). Examples ------- >>> mx.current_context() cpu(0) >>> with...

def _list_audio_files(self, root, skip_rows=0): """Populates synsets - a map of index to label for the data items. Populates the data in the dataset, making tuples of (data, label) """ self.synsets = [] self.items = [] if not self._train_csv: # The audio files...

def transform_first(self, fn, lazy=False): """Returns a new dataset with the first element of each sample transformed by the transformer function `fn`. This is useful, for example, when you only want to transform data while keeping label as is. lazy=False is passed to transform_...

def config_cython(): """Try to configure cython and return cython configuration""" if not with_cython: return [] # pylint: disable=unreachable if os.name == 'nt': print("WARNING: Cython is not supported on Windows, will compile without cython module") return [] try: ...

def _compose(self, *args, **kwargs): """Compose symbol on inputs. This call mutates the current symbol. Parameters ---------- args: provide positional arguments kwargs: provide keyword arguments Returns ------- the resul...

def _set_attr(self, **kwargs): """Set the attribute of the symbol. Parameters ---------- **kwargs The attributes to set """ keys = c_str_array(kwargs.keys()) vals = c_str_array([str(s) for s in kwargs.values()]) num_args = mx_uint(len(kwargs))...

def get_config(network, data_shape, **kwargs): """Configuration factory for various networks Parameters ---------- network : str base network name, such as vgg_reduced, inceptionv3, resnet... data_shape : int input data dimension kwargs : dict extra arguments """ ...

def get_symbol_train(network, data_shape, **kwargs): """Wrapper for get symbol for train Parameters ---------- network : str name for the base network symbol data_shape : int input shape kwargs : dict see symbol_builder.get_symbol_train for more details """ if ne...

def _set_trainer(self, trainer): """ Set the trainer this parameter is associated with. """ # trainer cannot be replaced for sparse params if self._stype != 'default' and self._trainer and trainer and self._trainer is not trainer: raise RuntimeError( "Failed to set th...

def _get_row_sparse(self, arr_list, ctx, row_id): """ Get row_sparse data from row_sparse parameters based on row_id. """ # get row sparse params based on row ids if not isinstance(row_id, ndarray.NDArray): raise TypeError("row_id must have NDArray type, but %s is given"%(type(row_id...

def _load_init(self, data, ctx): """(Re)initializes by loading from data.""" if self.shape: for self_dim, data_dim in zip(self.shape, data.shape): assert self_dim in (0, data_dim), \ "Failed loading Parameter '%s' from saved params: " \ ...

def _finish_deferred_init(self): """Finishes deferred initialization.""" if not self._deferred_init: return init, ctx, default_init, data = self._deferred_init self._deferred_init = () assert self.shape is not None and np.prod(self.shape) > 0, \ "Cannot in...

def _init_impl(self, data, ctx_list): """Sets data and grad.""" self._ctx_list = list(ctx_list) self._ctx_map = [[], []] for i, ctx in enumerate(self._ctx_list): dev_list = self._ctx_map[ctx.device_typeid&1] while len(dev_list) <= ctx.device_id: de...

def _init_grad(self): """Initialize grad buffers.""" if self.grad_req == 'null': self._grad = None return self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context, stype=self._grad_stype) for i in self._data] au...

def _reduce(self): """Reduce data from multiple context to cpu.""" ctx = context.cpu() if self._stype == 'default': block = self.list_data() data = ndarray.add_n(*(w.copyto(ctx) for w in block)) / len(block) else: # fetch all rows for 'row_sparse' para...

def initialize(self, init=None, ctx=None, default_init=initializer.Uniform(), force_reinit=False): """Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API. Parameters ---------- init : Initializer The initializer to use. Overrid...

def reset_ctx(self, ctx): """Re-assign Parameter to other contexts. Parameters ---------- ctx : Context or list of Context, default ``context.current_context()``. Assign Parameter to given context. If ctx is a list of Context, a copy will be made for each context...

def set_data(self, data): """Sets this parameter's value on all contexts.""" self.shape = data.shape if self._data is None: assert self._deferred_init, \ "Parameter '%s' has not been initialized"%self.name self._deferred_init = self._deferred_init[:3] + (...

def row_sparse_data(self, row_id): """Returns a copy of the 'row_sparse' parameter on the same context as row_id's. The copy only retains rows whose ids occur in provided row ids. The parameter must have been initialized on this context before. Parameters ---------- row_...

def list_row_sparse_data(self, row_id): """Returns copies of the 'row_sparse' parameter on all contexts, in the same order as creation. The copy only retains rows whose ids occur in provided row ids. The parameter must have been initialized before. Parameters ---------- ...

def data(self, ctx=None): """Returns a copy of this parameter on one context. Must have been initialized on this context before. For sparse parameters, use :py:meth:`Parameter.row_sparse_data` instead. Parameters ---------- ctx : Context Desired context. ...

def list_data(self): """Returns copies of this parameter on all contexts, in the same order as creation. For sparse parameters, use :py:meth:`Parameter.list_row_sparse_data` instead. Returns ------- list of NDArrays """ if self._stype != 'default': ...

def grad(self, ctx=None): """Returns a gradient buffer for this parameter on one context. Parameters ---------- ctx : Context Desired context. """ if self._data is not None and self._grad is None: raise RuntimeError( "Cannot get gr...

def list_grad(self): """Returns gradient buffers on all contexts, in the same order as :py:meth:`values`.""" if self._data is not None and self._grad is None: raise RuntimeError( "Cannot get gradient array for Parameter '%s' " \ "because grad_req='null...

def list_ctx(self): """Returns a list of contexts this parameter is initialized on.""" if self._data is None: if self._deferred_init: return self._deferred_init[1] raise RuntimeError("Parameter '%s' has not been initialized"%self.name) return self._ctx_lis...

def zero_grad(self): """Sets gradient buffer on all contexts to 0. No action is taken if parameter is uninitialized or doesn't require gradient.""" if self._grad is None: return for i in self._grad: ndarray.zeros_like(i, out=i)

def var(self): """Returns a symbol representing this parameter.""" if self._var is None: self._var = symbol.var(self.name, shape=self.shape, dtype=self.dtype, lr_mult=self.lr_mult, wd_mult=self.wd_mult, init=self.init, sty...

def cast(self, dtype): """Cast data and gradient of this Parameter to a new data type. Parameters ---------- dtype : str or numpy.dtype The new data type. """ self.dtype = dtype if self._data is None: return with autograd.pause(): ...

def get(self, name, **kwargs): """Retrieves a :py:class:`Parameter` with name ``self.prefix+name``. If not found, :py:func:`get` will first try to retrieve it from "shared" dict. If still not found, :py:func:`get` will create a new :py:class:`Parameter` with key-word arguments and insert...

def get_constant(self, name, value=None): """Retrieves a :py:class:`.Constant` with name ``self.prefix+name``. If not found, :py:func:`get` will first try to retrieve it from "shared" dict. If still not found, :py:func:`get` will create a new :py:class:`.Constant` with key-word arguments...

def update(self, other): """Copies all Parameters in ``other`` to self.""" for k, v in other.items(): if k in self._params: assert self._params[k] is v, \ "Cannot update self with other because they have different " \ "Parameters with t...

def initialize(self, init=initializer.Uniform(), ctx=None, verbose=False, force_reinit=False): """Initializes all Parameters managed by this dictionary to be used for :py:class:`NDArray` API. It has no effect when using :py:class:`Symbol` API. Parameters ---------- ...

def setattr(self, name, value): """Set an attribute to a new value for all Parameters. For example, set grad_req to null if you don't need gradient w.r.t a model's Parameters:: model.collect_params().setattr('grad_req', 'null') or change the learning rate multiplier:: ...

def save(self, filename, strip_prefix=''): """Save parameters to file. Parameters ---------- filename : str Path to parameter file. strip_prefix : str, default '' Strip prefix from parameter names before saving. """ arg_dict = {} f...

def load(self, filename, ctx=None, allow_missing=False, ignore_extra=False, restore_prefix=''): """Load parameters from file. Parameters ---------- filename : str Path to parameter file. ctx : Context or list of Context Context(s) initialize ...

def _make_torch_function(handle): """Create a Torch function from the FunctionHandle.""" # Get the property of function n_used_vars = mx_uint() n_scalars = mx_uint() n_mutate_vars = mx_uint() type_mask = ctypes.c_int() check_call(_LIB.MXFuncDescribe( handle, ctypes.byref(n_us...

def _init_torch_module(): """List and add all the torch backed ndarray functions to current module.""" plist = ctypes.POINTER(FunctionHandle)() size = ctypes.c_uint() check_call(_LIB.MXListFunctions(ctypes.byref(size), ctypes.byref(plist))) module_obj = sys.modul...

def inception_v3(pretrained=False, ctx=cpu(), root=os.path.join(base.data_dir(), 'models'), **kwargs): r"""Inception v3 model from `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_ paper. Parameters ---------- pretrained : bool, de...

def pack(header, s): """Pack a string into MXImageRecord. Parameters ---------- header : IRHeader Header of the image record. ``header.label`` can be a number or an array. See more detail in ``IRHeader``. s : str Raw image string to be packed. Returns ------- s ...

def unpack(s): """Unpack a MXImageRecord to string. Parameters ---------- s : str String buffer from ``MXRecordIO.read``. Returns ------- header : IRHeader Header of the image record. s : str Unpacked string. Examples -------- >>> record = mx.record...

def unpack_img(s, iscolor=-1): """Unpack a MXImageRecord to image. Parameters ---------- s : str String buffer from ``MXRecordIO.read``. iscolor : int Image format option for ``cv2.imdecode``. Returns ------- header : IRHeader Header of the image record. img...

def pack_img(header, img, quality=95, img_fmt='.jpg'): """Pack an image into ``MXImageRecord``. Parameters ---------- header : IRHeader Header of the image record. ``header.label`` can be a number or an array. See more detail in ``IRHeader``. img : numpy.ndarray Image to be ...

def open(self): """Opens the record file.""" if self.flag == "w": check_call(_LIB.MXRecordIOWriterCreate(self.uri, ctypes.byref(self.handle))) self.writable = True elif self.flag == "r": check_call(_LIB.MXRecordIOReaderCreate(self.uri, ctypes.byref(self.handle...

def _check_pid(self, allow_reset=False): """Check process id to ensure integrity, reset if in new process.""" if not self.pid == current_process().pid: if allow_reset: self.reset() else: raise RuntimeError("Forbidden operation in multiple processes...

def close(self): """Closes the record file.""" if not self.is_open: return if self.writable: check_call(_LIB.MXRecordIOWriterFree(self.handle)) else: check_call(_LIB.MXRecordIOReaderFree(self.handle)) self.is_open = False self.pid = Non...

def write(self, buf): """Inserts a string buffer as a record. Examples --------- >>> record = mx.recordio.MXRecordIO('tmp.rec', 'w') >>> for i in range(5): ... record.write('record_%d'%i) >>> record.close() Parameters ---------- buf : ...

def read(self): """Returns record as a string. Examples --------- >>> record = mx.recordio.MXRecordIO('tmp.rec', 'r') >>> for i in range(5): ... item = record.read() ... print(item) record_0 record_1 record_2 record_3 ...

def close(self): """Closes the record file.""" if not self.is_open: return super(MXIndexedRecordIO, self).close() self.fidx.close()

def seek(self, idx): """Sets the current read pointer position. This function is internally called by `read_idx(idx)` to find the current reader pointer position. It doesn't return anything.""" assert not self.writable self._check_pid(allow_reset=True) pos = ctypes.c_siz...

def tell(self): """Returns the current position of write head. Examples --------- >>> record = mx.recordio.MXIndexedRecordIO('tmp.idx', 'tmp.rec', 'w') >>> print(record.tell()) 0 >>> for i in range(5): ... record.write_idx(i, 'record_%d'%i) .....

def write_idx(self, idx, buf): """Inserts input record at given index. Examples --------- >>> for i in range(5): ... record.write_idx(i, 'record_%d'%i) >>> record.close() Parameters ---------- idx : int Index of a file. bu...

def _add_new_columns(dataframe, metrics): """Add new metrics as new columns to selected pandas dataframe. Parameters ---------- dataframe : pandas.DataFrame Selected dataframe needs to be modified. metrics : metric.EvalMetric New metrics to be added. """ #TODO(leodirac): we ...

def args_wrapper(*args): """Generates callback arguments for model.fit() for a set of callback objects. Callback objects like PandasLogger(), LiveLearningCurve() get passed in. This assembles all their callback arguments. """ out = defaultdict(list) for callback in args: callback_ar...

def append_metrics(self, metrics, df_name): """Append new metrics to selected dataframes. Parameters ---------- metrics : metric.EvalMetric New metrics to be added. df_name : str Name of the dataframe to be modified. """ dataframe = self._...

def train_cb(self, param): """Callback funtion for training. """ if param.nbatch % self.frequent == 0: self._process_batch(param, 'train')

def _process_batch(self, param, dataframe): """Update parameters for selected dataframe after a completed batch Parameters ---------- dataframe : pandas.DataFrame Selected dataframe needs to be modified. """ now = time.time() if param.eval_metric is no...

def epoch_cb(self): """Callback function after each epoch. Now it records each epoch time and append it to epoch dataframe. """ metrics = {} metrics['elapsed'] = self.elapsed() now = datetime.datetime.now() metrics['epoch_time'] = now - self.last_epoch_time ...

def _push_render(self): """Render the plot with bokeh.io and push to notebook. """ bokeh.io.push_notebook(handle=self.handle) self.last_update = time.time()

def _process_batch(self, param, df_name): """Update selected dataframe after a completed batch Parameters ---------- df_name : str Selected dataframe name needs to be modified. """ if param.eval_metric is not None: metrics = dict(param.eval_metric....

def build_vocab(nested_list): """ :param nested_list: list of list of string :return: dictionary mapping from string to int, inverse of that dictionary """ # Build vocabulary word_counts = Counter(itertools.chain(*nested_list)) # Mapping from index to label vocabulary_inv = [x[0] for x ...

def build_iters(data_dir, max_records, train_fraction, batch_size, buckets=None): """ Reads a csv of sentences/tag sequences into a pandas dataframe. Converts into X = array(list(int)) & Y = array(list(int)) Splits into training and test sets Builds dictionaries mapping from index labels to labels/ ...

def sym_gen(seq_len): """ Build NN symbol depending on the length of the input sequence """ sentence_shape = train_iter.provide_data[0][1] char_sentence_shape = train_iter.provide_data[1][1] entities_shape = train_iter.provide_label[0][1] X_sent = mx.symbol.Variable(train_iter.provide_data[...

def rand_zipfian(true_classes, num_sampled, range_max): """Draw random samples from an approximately log-uniform or Zipfian distribution. This operation randomly samples *num_sampled* candidates the range of integers [0, range_max). The elements of sampled_candidates are drawn with replacement from the bas...

def foreach(body, data, init_states, name="foreach"): """Run a for loop with user-defined computation over Symbols on dimension 0. This operator simulates a for loop and body has the computation for an iteration of the for loop. It runs the computation in body on each slice from the input NDArrays. ...

def while_loop(cond, func, loop_vars, max_iterations=None, name="while_loop"): """Run a while loop with user-defined computation and loop condition. This operator simulates a while loop which iterately does customized computation as long as the condition is satisfied. `loop_vars` is a Symbol or nested...

def cond(pred, then_func, else_func, name="cond"): """Run an if-then-else using user-defined condition and computation This operator simulates a if-like branch which chooses to do one of the two customized computations according to the specified condition. `pred` is a scalar MXNet Symbol, indicati...

def _index_unknown_and_reserved_tokens(self, unknown_token, reserved_tokens): """Indexes unknown and reserved tokens.""" self._unknown_token = unknown_token # Thus, constants.UNKNOWN_IDX must be 0. self._idx_to_token = [unknown_token] if reserved_tokens is None: sel...

def _index_counter_keys(self, counter, unknown_token, reserved_tokens, most_freq_count, min_freq): """Indexes keys of `counter`. Indexes keys of `counter` according to frequency thresholds such as `most_freq_count` and `min_freq`. """ assert isinsta...

def to_indices(self, tokens): """Converts tokens to indices according to the vocabulary. Parameters ---------- tokens : str or list of strs A source token or tokens to be converted. Returns ------- int or list of ints A token index or a...

def to_tokens(self, indices): """Converts token indices to tokens according to the vocabulary. Parameters ---------- indices : int or list of ints A source token index or token indices to be converted. Returns ------- str or list of strs ...

def _make_io_iterator(handle): """Create an io iterator by handle.""" name = ctypes.c_char_p() desc = ctypes.c_char_p() num_args = mx_uint() arg_names = ctypes.POINTER(ctypes.c_char_p)() arg_types = ctypes.POINTER(ctypes.c_char_p)() arg_descs = ctypes.POINTER(ctypes.c_char_p)() check_ca...

def _init_io_module(): """List and add all the data iterators to current module.""" plist = ctypes.POINTER(ctypes.c_void_p)() size = ctypes.c_uint() check_call(_LIB.MXListDataIters(ctypes.byref(size), ctypes.byref(plist))) module_obj = sys.modules[__name__] for i in range(size.value): hd...

def get_list(shapes, types): """Get DataDesc list from attribute lists. Parameters ---------- shapes : a tuple of (name_, shape_) types : a tuple of (name_, np.dtype) """ if types is not None: type_dict = dict(types) return [DataDesc(x[0]...

def next(self): """Get next data batch from iterator. Returns ------- DataBatch The data of next batch. Raises ------ StopIteration If the end of the data is reached. """ if self.iter_next(): return DataBatch(d...