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train | discriminator1 | First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
the layer loss | example/vae-gan/vaegan_mxnet.py | def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
the layer loss'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
d1 ... | def discriminator1(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''First part of the discriminator which takes a 32x32 image as input
and output a convolutional feature map, this is required to calculate
the layer loss'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
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train | discriminator2 | Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one | example/vae-gan/vaegan_mxnet.py | def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
... | def discriminator2(ndf, no_bias=True, fix_gamma=True, eps=1e-5 + 1e-12):
'''Second part of the discriminator which takes a 256x8x8 feature map as input
and generates the loss based on whether the input image was a real one or fake one'''
BatchNorm = mx.sym.BatchNorm
data = mx.sym.Variable('data')
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train | GaussianLogDensity | GaussianLogDensity loss calculation for layer wise loss | example/vae-gan/vaegan_mxnet.py | def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6):
'''GaussianLogDensity loss calculation for layer wise loss
'''
c = mx.sym.ones_like(log_var)*2.0 * 3.1416
c = mx.symbol.log(c)
var = mx.sym.exp(log_var)
x_mu2 = mx.symbol.square(x - mu) # [Issue] not sure the di... | def GaussianLogDensity(x, mu, log_var, name='GaussianLogDensity', EPSILON = 1e-6):
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c = mx.sym.ones_like(log_var)*2.0 * 3.1416
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train | DiscriminatorLayerLoss | Calculate the discriminator layer loss | example/vae-gan/vaegan_mxnet.py | def DiscriminatorLayerLoss():
'''Calculate the discriminator layer loss
'''
data = mx.sym.Variable('data')
label = mx.sym.Variable('label')
data = mx.sym.Flatten(data)
label = mx.sym.Flatten(label)
label = mx.sym.BlockGrad(label)
zeros = mx.sym.zeros_like(data)
output = -Gaussi... | def DiscriminatorLayerLoss():
'''Calculate the discriminator layer loss
'''
data = mx.sym.Variable('data')
label = mx.sym.Variable('label')
data = mx.sym.Flatten(data)
label = mx.sym.Flatten(label)
label = mx.sym.BlockGrad(label)
zeros = mx.sym.zeros_like(data)
output = -Gaussi... | [
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train | KLDivergenceLoss | KLDivergenceLoss loss | example/vae-gan/vaegan_mxnet.py | def KLDivergenceLoss():
'''KLDivergenceLoss loss
'''
data = mx.sym.Variable('data')
mu1, lv1 = mx.sym.split(data, num_outputs=2, axis=0)
mu2 = mx.sym.zeros_like(mu1)
lv2 = mx.sym.zeros_like(lv1)
v1 = mx.sym.exp(lv1)
v2 = mx.sym.exp(lv2)
mu_diff_sq = mx.sym.square(mu1 - mu2)
di... | def KLDivergenceLoss():
'''KLDivergenceLoss loss
'''
data = mx.sym.Variable('data')
mu1, lv1 = mx.sym.split(data, num_outputs=2, axis=0)
mu2 = mx.sym.zeros_like(mu1)
lv2 = mx.sym.zeros_like(lv1)
v1 = mx.sym.exp(lv1)
v2 = mx.sym.exp(lv2)
mu_diff_sq = mx.sym.square(mu1 - mu2)
di... | [
"KLDivergenceLoss",
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] | apache/incubator-mxnet | python | https://github.com/apache/incubator-mxnet/blob/1af29e9c060a4c7d60eeaacba32afdb9a7775ba7/example/vae-gan/vaegan_mxnet.py#L194-L211 | [
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train | get_data | Get the dataset | example/vae-gan/vaegan_mxnet.py | def get_data(path, activation):
'''Get the dataset
'''
data = []
image_names = []
for filename in os.listdir(path):
img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
image_names.append(filename)
if img is not None:
data.append(img)
data = np... | def get_data(path, activation):
'''Get the dataset
'''
data = []
image_names = []
for filename in os.listdir(path):
img = cv2.imread(os.path.join(path,filename), cv2.IMREAD_GRAYSCALE)
image_names.append(filename)
if img is not None:
data.append(img)
data = np... | [
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train | fill_buf | fill the ith grid of the buffer matrix with the values from the img
buf : buffer matrix
i : serial of the image in the 2D grid
img : image data
shape : ( height width depth ) of image | example/vae-gan/vaegan_mxnet.py | def fill_buf(buf, i, img, shape):
'''fill the ith grid of the buffer matrix with the values from the img
buf : buffer matrix
i : serial of the image in the 2D grid
img : image data
shape : ( height width depth ) of image'''
# grid height is a multiple of individual image height
m = buf.shap... | def fill_buf(buf, i, img, shape):
'''fill the ith grid of the buffer matrix with the values from the img
buf : buffer matrix
i : serial of the image in the 2D grid
img : image data
shape : ( height width depth ) of image'''
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train | visual | create a grid of images and save it as a final image
title : grid image name
X : array of images | example/vae-gan/vaegan_mxnet.py | def visual(title, X, activation):
'''create a grid of images and save it as a final image
title : grid image name
X : array of images
'''
assert len(X.shape) == 4
X = X.transpose((0, 2, 3, 1))
if activation == 'sigmoid':
X = np.clip((X)*(255.0), 0, 255).astype(np.uint8)
elif act... | def visual(title, X, activation):
'''create a grid of images and save it as a final image
title : grid image name
X : array of images
'''
assert len(X.shape) == 4
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X = np.clip((X)*(255.0), 0, 255).astype(np.uint8)
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train | train | adversarial training of the VAE | example/vae-gan/vaegan_mxnet.py | def train(dataset, nef, ndf, ngf, nc, batch_size, Z, lr, beta1, epsilon, ctx, check_point, g_dl_weight, output_path, checkpoint_path, data_path, activation,num_epoch, save_after_every, visualize_after_every, show_after_every):
'''adversarial training of the VAE
'''
#encoder
z_mu, z_lv, z = encoder(nef,... | def train(dataset, nef, ndf, ngf, nc, batch_size, Z, lr, beta1, epsilon, ctx, check_point, g_dl_weight, output_path, checkpoint_path, data_path, activation,num_epoch, save_after_every, visualize_after_every, show_after_every):
'''adversarial training of the VAE
'''
#encoder
z_mu, z_lv, z = encoder(nef,... | [
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train | create_and_validate_dir | Creates/Validates dir | example/vae-gan/vaegan_mxnet.py | def create_and_validate_dir(data_dir):
'''Creates/Validates dir
'''
if data_dir != "":
if not os.path.exists(data_dir):
try:
logging.info('create directory %s', data_dir)
os.makedirs(data_dir)
except OSError as exc:
if exc.errno... | def create_and_validate_dir(data_dir):
'''Creates/Validates dir
'''
if data_dir != "":
if not os.path.exists(data_dir):
try:
logging.info('create directory %s', data_dir)
os.makedirs(data_dir)
except OSError as exc:
if exc.errno... | [
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train | parse_args | Parse args | example/vae-gan/vaegan_mxnet.py | def parse_args():
'''Parse args
'''
parser = argparse.ArgumentParser(description='Train and Test an Adversarial Variatiional Encoder')
parser.add_argument('--train', help='train the network', action='store_true')
parser.add_argument('--test', help='test the network', action='store_true')
parser... | def parse_args():
'''Parse args
'''
parser = argparse.ArgumentParser(description='Train and Test an Adversarial Variatiional Encoder')
parser.add_argument('--train', help='train the network', action='store_true')
parser.add_argument('--test', help='test the network', action='store_true')
parser... | [
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train | get_rmse_log | Gets root mse between the logarithms of the prediction and the truth. | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | def get_rmse_log(net, X_train, y_train):
"""Gets root mse between the logarithms of the prediction and the truth."""
num_train = X_train.shape[0]
clipped_preds = nd.clip(net(X_train), 1, float('inf'))
return np.sqrt(2 * nd.sum(square_loss(
nd.log(clipped_preds), nd.log(y_train))).asscalar() / nu... | def get_rmse_log(net, X_train, y_train):
"""Gets root mse between the logarithms of the prediction and the truth."""
num_train = X_train.shape[0]
clipped_preds = nd.clip(net(X_train), 1, float('inf'))
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train | get_net | Gets a neural network. Better results are obtained with modifications. | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | def get_net():
"""Gets a neural network. Better results are obtained with modifications."""
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Dense(50, activation="relu"))
net.add(gluon.nn.Dense(1))
net.initialize()
return net | def get_net():
"""Gets a neural network. Better results are obtained with modifications."""
net = gluon.nn.Sequential()
with net.name_scope():
net.add(gluon.nn.Dense(50, activation="relu"))
net.add(gluon.nn.Dense(1))
net.initialize()
return net | [
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train | train | Trains the model. | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size):
"""Trains the model."""
dataset_train = gluon.data.ArrayDataset(X_train, y_train)
data_iter_train = gluon.data.DataLoader(dataset_train, batch_size,
shuffle... | def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
weight_decay, batch_size):
"""Trains the model."""
dataset_train = gluon.data.ArrayDataset(X_train, y_train)
data_iter_train = gluon.data.DataLoader(dataset_train, batch_size,
shuffle... | [
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train | k_fold_cross_valid | Conducts k-fold cross validation for the model. | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | def k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train,
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train | learn | Trains the model and predicts on the test data set. | example/gluon/house_prices/kaggle_k_fold_cross_validation.py | def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
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"""Trains the model and predicts on the test data set."""
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"""Trains the model and predicts on the test data set."""
net = get_net()
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train | capsnet | Create CapsNet | example/capsnet/capsulenet.py | def capsnet(batch_size, n_class, num_routing, recon_loss_weight):
"""Create CapsNet"""
# data.shape = [batch_size, 1, 28, 28]
data = mx.sym.Variable('data')
input_shape = (1, 28, 28)
# Conv2D layer
# net.shape = [batch_size, 256, 20, 20]
conv1 = mx.sym.Convolution(data=data,
... | def capsnet(batch_size, n_class, num_routing, recon_loss_weight):
"""Create CapsNet"""
# data.shape = [batch_size, 1, 28, 28]
data = mx.sym.Variable('data')
input_shape = (1, 28, 28)
# Conv2D layer
# net.shape = [batch_size, 256, 20, 20]
conv1 = mx.sym.Convolution(data=data,
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train | do_training | Perform CapsNet training | example/capsnet/capsulenet.py | def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
"""Perform CapsNet training"""
summary_writer = SummaryWriter(args.tblog_dir)
lr_scheduler = SimpleLRScheduler(learning_rate)
optimizer_params = {'lr_scheduler': lr_scheduler}
module.init_params()
module.init_opt... | def do_training(num_epoch, optimizer, kvstore, learning_rate, model_prefix, decay):
"""Perform CapsNet training"""
summary_writer = SummaryWriter(args.tblog_dir)
lr_scheduler = SimpleLRScheduler(learning_rate)
optimizer_params = {'lr_scheduler': lr_scheduler}
module.init_params()
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train | _shuffle | Shuffle the data. | example/capsnet/capsulenet.py | def _shuffle(data, idx):
"""Shuffle the data."""
shuffle_data = []
for idx_k, idx_v in data:
shuffle_data.append((idx_k, mx.ndarray.array(idx_v.asnumpy()[idx], idx_v.context)))
return shuffle_data | def _shuffle(data, idx):
"""Shuffle the data."""
shuffle_data = []
for idx_k, idx_v in data:
shuffle_data.append((idx_k, mx.ndarray.array(idx_v.asnumpy()[idx], idx_v.context)))
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train | LossMetric.update | Update the hyper-parameters and loss of CapsNet | example/capsnet/capsulenet.py | def update(self, labels, preds):
"""Update the hyper-parameters and loss of CapsNet"""
batch_sum_metric = 0
batch_num_inst = 0
for label, pred_outcaps in zip(labels[0], preds[0]):
label_np = int(label.asnumpy())
pred_label = int(np.argmax(pred_outcaps.asnumpy()))
... | def update(self, labels, preds):
"""Update the hyper-parameters and loss of CapsNet"""
batch_sum_metric = 0
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for label, pred_outcaps in zip(labels[0], preds[0]):
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train | MNISTCustomIter.reset | Reset class MNISTCustomIter(mx.io.NDArrayIter): | example/capsnet/capsulenet.py | def reset(self):
"""Reset class MNISTCustomIter(mx.io.NDArrayIter):"""
# shuffle data
if self.is_train:
np.random.shuffle(self.idx)
self.data = _shuffle(self.data, self.idx)
self.label = _shuffle(self.label, self.idx)
if self.last_batch_handle == 'rol... | def reset(self):
"""Reset class MNISTCustomIter(mx.io.NDArrayIter):"""
# shuffle data
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train | MNISTCustomIter.next | Generate next of iterator | example/capsnet/capsulenet.py | def next(self):
"""Generate next of iterator"""
if self.iter_next():
if self.is_train:
data_raw_list = self.getdata()
data_shifted = []
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train | AttrScope.get | Get the attribute dict given the attribute set by the symbol.
Parameters
----------
attr : dict of string to string
The attribute passed in by user during symbol creation.
Returns
-------
attr : dict of string to string
Updated attributes to add ... | python/mxnet/attribute.py | def get(self, attr):
"""
Get the attribute dict given the attribute set by the symbol.
Parameters
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attr : dict of string to string
The attribute passed in by user during symbol creation.
Returns
-------
attr : dict of string to stri... | def get(self, attr):
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Get the attribute dict given the attribute set by the symbol.
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attr : dict of string to string
The attribute passed in by user during symbol creation.
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train | _create_sparse_kvstore | Create kvstore assuming some parameters' storage types are row_sparse.
Parameters
----------
kvstore : KVStore or str
The kvstore.
Returns
-------
kvstore : KVStore
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kvstore : KVStore or str
The kvstore.
Returns
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kvstore : KVStore
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kvstore : KVStore or str
The kvstore.
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kvstore : KVStore
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train | _create_kvstore | Create kvstore
This function select and create a proper kvstore if given the kvstore type.
Parameters
----------
kvstore : KVStore or str
The kvstore.
num_device : int
The number of devices
arg_params : dict of str to `NDArray`.
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This function select and create a proper kvstore if given the kvstore type.
Parameters
----------
kvstore : KVStore or str
The kvstore.
num_device : int
The number of devices
arg_params : dict of str to ... | def _create_kvstore(kvstore, num_device, arg_params):
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kvstore : KVStore or str
The kvstore.
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train | _initialize_kvstore | Initialize kvstore | python/mxnet/model.py | def _initialize_kvstore(kvstore, param_arrays, arg_params, param_names, update_on_kvstore):
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name = param_names[idx]
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train | _update_params_on_kvstore_nccl | Perform update of param_arrays from grad_arrays on NCCL kvstore. | python/mxnet/model.py | def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names):
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valid_indices = [index for index, grad_list in
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valid_grad_arrays = [grad_arrays... | def _update_params_on_kvstore_nccl(param_arrays, grad_arrays, kvstore, param_names):
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train | _update_params_on_kvstore | Perform update of param_arrays from grad_arrays on kvstore. | python/mxnet/model.py | def _update_params_on_kvstore(param_arrays, grad_arrays, kvstore, param_names):
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train | _update_params | Perform update of param_arrays from grad_arrays not on kvstore. | python/mxnet/model.py | def _update_params(param_arrays, grad_arrays, updater, num_device,
kvstore=None, param_names=None):
"""Perform update of param_arrays from grad_arrays not on kvstore."""
updates = [[] for _ in range(num_device)]
for i, pair in enumerate(zip(param_arrays, grad_arrays)):
arg_list, g... | def _update_params(param_arrays, grad_arrays, updater, num_device,
kvstore=None, param_names=None):
"""Perform update of param_arrays from grad_arrays not on kvstore."""
updates = [[] for _ in range(num_device)]
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train | _multiple_callbacks | Sends args and kwargs to any configured callbacks.
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train | _train_multi_device | Internal training function on multiple devices.
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ctx : list of Context
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train | save_checkpoint | Checkpoint the model data into file.
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epoch : int
The epoch number of the model.
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prefix : str
Prefix of model name.
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The epoch number of the model.
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"""Checkpoint the model data into file.
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prefix : str
Prefix of model name.
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train | load_checkpoint | Load model checkpoint from file.
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Epoch number of model we would like to load.
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Prefix of model name.
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Epoch number of model we would like to load.
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Prefix of model name.
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Epoch number of model we would like to load.
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train | FeedForward._check_arguments | verify the argument of the default symbol and user provided parameters | python/mxnet/model.py | def _check_arguments(self):
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train | FeedForward._init_params | Initialize weight parameters and auxiliary states. | python/mxnet/model.py | def _init_params(self, inputs, overwrite=False):
"""Initialize weight parameters and auxiliary states."""
inputs = [x if isinstance(x, DataDesc) else DataDesc(*x) for x in inputs]
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train | FeedForward._init_predictor | Initialize the predictor module for running prediction. | python/mxnet/model.py | def _init_predictor(self, input_shapes, type_dict=None):
"""Initialize the predictor module for running prediction."""
shapes = {name: self.arg_params[name].shape for name in self.arg_params}
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train | FeedForward._init_iter | Initialize the iterator given input. | python/mxnet/model.py | def _init_iter(self, X, y, is_train):
"""Initialize the iterator given input."""
if isinstance(X, (np.ndarray, nd.NDArray)):
if y is None:
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train | FeedForward._init_eval_iter | Initialize the iterator given eval_data. | python/mxnet/model.py | def _init_eval_iter(self, eval_data):
"""Initialize the iterator given eval_data."""
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train | FeedForward.predict | Run the prediction, always only use one device.
Parameters
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X : mxnet.DataIter
num_batch : int or None
The number of batch to run. Go though all batches if ``None``.
Returns
-------
y : numpy.ndarray or a list of numpy.ndarray if the network... | python/mxnet/model.py | def predict(self, X, num_batch=None, return_data=False, reset=True):
"""Run the prediction, always only use one device.
Parameters
----------
X : mxnet.DataIter
num_batch : int or None
The number of batch to run. Go though all batches if ``None``.
Returns
... | def predict(self, X, num_batch=None, return_data=False, reset=True):
"""Run the prediction, always only use one device.
Parameters
----------
X : mxnet.DataIter
num_batch : int or None
The number of batch to run. Go though all batches if ``None``.
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train | FeedForward.score | Run the model given an input and calculate the score
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Parameters
----------
X : mxnet.DataIter
eval_metric : metric.metric
The metric for calculating score.
num_batch : int or None
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"""Run the model given an input and calculate the score
as assessed by an evaluation metric.
Parameters
----------
X : mxnet.DataIter
eval_metric : metric.metric
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train | FeedForward.fit | Fit the model.
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X : DataIter, or numpy.ndarray/NDArray
Training data. If `X` is a `DataIter`, the name or (if name not available)
the position of its outputs should match the corresponding variable
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work_load_list=None, monitor=None, eval_end_callback=LogValidationMetricsCallback(),
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train | FeedForward.save | Checkpoint the model checkpoint into file.
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The advantage of `load` and `save` (as compared to `pickle`) is that
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You can also use `pickle` to do the job if you only work on Python.
The advantage of `load` and `save` (as compared to `pickle`) is that
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You can also use `pickle` to do the job if you only work on Python.
The advantage of `load` and `save` (as compared to `pickle`) is that
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train | FeedForward.load | Load model checkpoint from file.
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prefix : str
Prefix of model name.
epoch : int
epoch number of model we would like to load.
ctx : Context or list of Context, optional
The device context of training and prediction.
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prefix : str
Prefix of model name.
epoch : int
epoch number of model we would like to load.
ctx : Context or list of Context, optional
... | def load(prefix, epoch, ctx=None, **kwargs):
"""Load model checkpoint from file.
Parameters
----------
prefix : str
Prefix of model name.
epoch : int
epoch number of model we would like to load.
ctx : Context or list of Context, optional
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train | FeedForward.create | Functional style to create a model.
This function is more consistent with functional
languages such as R, where mutation is not allowed.
Parameters
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symbol : Symbol
The symbol configuration of a computation network.
X : DataIter
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epoch_end_callback=None, batch_end_callback=None,
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train | build_save_containers | Entry point to build and upload all built dockerimages in parallel
:param platforms: List of platforms
:param registry: Docker registry name
:param load_cache: Load cache before building
:return: 1 if error occurred, 0 otherwise | ci/docker_cache.py | def build_save_containers(platforms, registry, load_cache) -> int:
"""
Entry point to build and upload all built dockerimages in parallel
:param platforms: List of platforms
:param registry: Docker registry name
:param load_cache: Load cache before building
:return: 1 if error occurred, 0 otherw... | def build_save_containers(platforms, registry, load_cache) -> int:
"""
Entry point to build and upload all built dockerimages in parallel
:param platforms: List of platforms
:param registry: Docker registry name
:param load_cache: Load cache before building
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train | _build_save_container | Build image for passed platform and upload the cache to the specified S3 bucket
:param platform: Platform
:param registry: Docker registry name
:param load_cache: Load cache before building
:return: Platform if failed, None otherwise | ci/docker_cache.py | def _build_save_container(platform, registry, load_cache) -> Optional[str]:
"""
Build image for passed platform and upload the cache to the specified S3 bucket
:param platform: Platform
:param registry: Docker registry name
:param load_cache: Load cache before building
:return: Platform if faile... | def _build_save_container(platform, registry, load_cache) -> Optional[str]:
"""
Build image for passed platform and upload the cache to the specified S3 bucket
:param platform: Platform
:param registry: Docker registry name
:param load_cache: Load cache before building
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train | _upload_image | Upload the passed image by id, tag it with docker tag and upload to S3 bucket
:param registry: Docker registry name
:param docker_tag: Docker tag
:param image_id: Image id
:return: None | ci/docker_cache.py | def _upload_image(registry, docker_tag, image_id) -> None:
"""
Upload the passed image by id, tag it with docker tag and upload to S3 bucket
:param registry: Docker registry name
:param docker_tag: Docker tag
:param image_id: Image id
:return: None
"""
# We don't have to retag the image ... | def _upload_image(registry, docker_tag, image_id) -> None:
"""
Upload the passed image by id, tag it with docker tag and upload to S3 bucket
:param registry: Docker registry name
:param docker_tag: Docker tag
:param image_id: Image id
:return: None
"""
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train | _login_dockerhub | Login to the Docker Hub account
:return: None | ci/docker_cache.py | def _login_dockerhub():
"""
Login to the Docker Hub account
:return: None
"""
dockerhub_credentials = _get_dockerhub_credentials()
logging.info('Logging in to DockerHub')
# We use password-stdin instead of --password to avoid leaking passwords in case of an error.
# This method will pro... | def _login_dockerhub():
"""
Login to the Docker Hub account
:return: None
"""
dockerhub_credentials = _get_dockerhub_credentials()
logging.info('Logging in to DockerHub')
# We use password-stdin instead of --password to avoid leaking passwords in case of an error.
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train | load_docker_cache | Load the precompiled docker cache from the registry
:param registry: Docker registry name
:param docker_tag: Docker tag to load
:return: None | ci/docker_cache.py | def load_docker_cache(registry, docker_tag) -> None:
"""
Load the precompiled docker cache from the registry
:param registry: Docker registry name
:param docker_tag: Docker tag to load
:return: None
"""
# We don't have to retag the image since it's already in the right format
if not regi... | def load_docker_cache(registry, docker_tag) -> None:
"""
Load the precompiled docker cache from the registry
:param registry: Docker registry name
:param docker_tag: Docker tag to load
:return: None
"""
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train | delete_local_docker_cache | Delete the local docker cache for the entire docker image chain
:param docker_tag: Docker tag
:return: None | ci/docker_cache.py | def delete_local_docker_cache(docker_tag):
"""
Delete the local docker cache for the entire docker image chain
:param docker_tag: Docker tag
:return: None
"""
history_cmd = ['docker', 'history', '-q', docker_tag]
try:
image_ids_b = subprocess.check_output(history_cmd)
image_... | def delete_local_docker_cache(docker_tag):
"""
Delete the local docker cache for the entire docker image chain
:param docker_tag: Docker tag
:return: None
"""
history_cmd = ['docker', 'history', '-q', docker_tag]
try:
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train | main | Utility to create and publish the Docker cache to Docker Hub
:return: | ci/docker_cache.py | def main() -> int:
"""
Utility to create and publish the Docker cache to Docker Hub
:return:
"""
# We need to be in the same directory than the script so the commands in the dockerfiles work as
# expected. But the script can be invoked from a different path
base = os.path.split(os.path.realp... | def main() -> int:
"""
Utility to create and publish the Docker cache to Docker Hub
:return:
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train | get_chinese_text | Download the chinese_text dataset and unzip it | example/cnn_chinese_text_classification/data_helpers.py | def get_chinese_text():
"""Download the chinese_text dataset and unzip it"""
if not os.path.isdir("data/"):
os.system("mkdir data/")
if (not os.path.exists('data/pos.txt')) or \
(not os.path.exists('data/neg')):
os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master... | def get_chinese_text():
"""Download the chinese_text dataset and unzip it"""
if not os.path.isdir("data/"):
os.system("mkdir data/")
if (not os.path.exists('data/pos.txt')) or \
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os.system("wget -q https://raw.githubusercontent.com/dmlc/web-data/master... | [
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train | load_data_and_labels | Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels. | example/cnn_chinese_text_classification/data_helpers.py | def load_data_and_labels():
"""Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# download dataset
get_chinese_text()
# Load data from files
positive_examples = list(codecs.open("./data/pos.txt", "r", "utf-8").readli... | def load_data_and_labels():
"""Loads MR polarity data from files, splits the data into words and generates labels.
Returns split sentences and labels.
"""
# download dataset
get_chinese_text()
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positive_examples = list(codecs.open("./data/pos.txt", "r", "utf-8").readli... | [
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train | MultiBoxMetric.reset | override reset behavior | example/ssd/train/metric.py | def reset(self):
"""
override reset behavior
"""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
else:
self.num_inst = [0] * self.num
self.sum_metric = [0.0] * self.num | def reset(self):
"""
override reset behavior
"""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
else:
self.num_inst = [0] * self.num
self.sum_metric = [0.0] * self.num | [
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train | MultiBoxMetric.reset_local | override reset behavior | example/ssd/train/metric.py | def reset_local(self):
"""
override reset behavior
"""
if getattr(self, 'num', None) is None:
self.num_inst = 0
self.sum_metric = 0.0
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self.sum_metric = [0.0] * self.num | def reset_local(self):
"""
override reset behavior
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self.sum_metric = [0.0] * self.num | [
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train | MultiBoxMetric.update | Implementation of updating metrics | example/ssd/train/metric.py | def update(self, labels, preds):
"""
Implementation of updating metrics
"""
# get generated multi label from network
cls_prob = preds[0].asnumpy()
loc_loss = preds[1].asnumpy()
cls_label = preds[2].asnumpy()
valid_count = np.sum(cls_label >= 0)
# o... | def update(self, labels, preds):
"""
Implementation of updating metrics
"""
# get generated multi label from network
cls_prob = preds[0].asnumpy()
loc_loss = preds[1].asnumpy()
cls_label = preds[2].asnumpy()
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train | MultiBoxMetric.get | Get the current evaluation result.
Override the default behavior
Returns
-------
name : str
Name of the metric.
value : float
Value of the evaluation. | example/ssd/train/metric.py | def get(self):
"""Get the current evaluation result.
Override the default behavior
Returns
-------
name : str
Name of the metric.
value : float
Value of the evaluation.
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Override the default behavior
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name : str
Name of the metric.
value : float
Value of the evaluation.
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action_num : int
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name : str, optional | example/reinforcement-learning/dqn/operators.py | def dqn_sym_nips(action_num, data=None, name='dqn'):
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Playing Atari with Deep Reinforcement Learning (https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)
Parameters
----------
action_num : int
data : mxnet.sym.Symbol, optional
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train | _monitor_callback_wrapper | A wrapper for the user-defined handle. | python/mxnet/executor.py | def _monitor_callback_wrapper(callback):
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""" ctypes function """
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return callback_handle | def _monitor_callback_wrapper(callback):
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train | Executor._get_dict | Get the dictionary given name and ndarray pairs. | python/mxnet/executor.py | def _get_dict(names, ndarrays):
"""Get the dictionary given name and ndarray pairs."""
nset = set()
for nm in names:
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train | Executor._get_outputs | List all the output NDArray.
Returns
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"""List all the output NDArray.
Returns
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A list of ndarray bound to the heads of executor.
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train | Executor.forward | Calculate the outputs specified by the bound symbol.
Parameters
----------
is_train: bool, optional
Whether this forward is for evaluation purpose. If True,
a backward call is expected to follow.
**kwargs
Additional specification of input arguments.
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"""Calculate the outputs specified by the bound symbol.
Parameters
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is_train: bool, optional
Whether this forward is for evaluation purpose. If True,
a backward call is expected to follow.
**kwargs
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"""Calculate the outputs specified by the bound symbol.
Parameters
----------
is_train: bool, optional
Whether this forward is for evaluation purpose. If True,
a backward call is expected to follow.
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train | Executor.backward | Do backward pass to get the gradient of arguments.
Parameters
----------
out_grads : NDArray or list of NDArray or dict of str to NDArray, optional
Gradient on the outputs to be propagated back.
This parameter is only needed when bind is called
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"""Do backward pass to get the gradient of arguments.
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----------
out_grads : NDArray or list of NDArray or dict of str to NDArray, optional
Gradient on the outputs to be propagated back.
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Gradient on the outputs to be propagated back.
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train | Executor.set_monitor_callback | Install callback for monitor.
Parameters
----------
callback : function
Takes a string and an NDArrayHandle.
monitor_all : bool, default False
If true, monitor both input and output, otherwise monitor output only.
Examples
--------
>>> de... | python/mxnet/executor.py | def set_monitor_callback(self, callback, monitor_all=False):
"""Install callback for monitor.
Parameters
----------
callback : function
Takes a string and an NDArrayHandle.
monitor_all : bool, default False
If true, monitor both input and output, otherwis... | def set_monitor_callback(self, callback, monitor_all=False):
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Takes a string and an NDArrayHandle.
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train | Executor.arg_dict | Get dictionary representation of argument arrrays.
Returns
-------
arg_dict : dict of str to NDArray
The dictionary that maps the names of arguments to NDArrays.
Raises
------
ValueError : if there are duplicated names in the arguments. | python/mxnet/executor.py | def arg_dict(self):
"""Get dictionary representation of argument arrrays.
Returns
-------
arg_dict : dict of str to NDArray
The dictionary that maps the names of arguments to NDArrays.
Raises
------
ValueError : if there are duplicated names in the a... | def arg_dict(self):
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arg_dict : dict of str to NDArray
The dictionary that maps the names of arguments to NDArrays.
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train | Executor.grad_dict | Get dictionary representation of gradient arrays.
Returns
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grad_dict : dict of str to NDArray
The dictionary that maps name of arguments to gradient arrays. | python/mxnet/executor.py | def grad_dict(self):
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-------
grad_dict : dict of str to NDArray
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train | Executor.aux_dict | Get dictionary representation of auxiliary states arrays.
Returns
-------
aux_dict : dict of str to NDArray
The dictionary that maps name of auxiliary states to NDArrays.
Raises
------
ValueError : if there are duplicated names in the auxiliary states. | python/mxnet/executor.py | def aux_dict(self):
"""Get dictionary representation of auxiliary states arrays.
Returns
-------
aux_dict : dict of str to NDArray
The dictionary that maps name of auxiliary states to NDArrays.
Raises
------
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"""Get dictionary representation of auxiliary states arrays.
Returns
-------
aux_dict : dict of str to NDArray
The dictionary that maps name of auxiliary states to NDArrays.
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------
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train | Executor.output_dict | Get dictionary representation of output arrays.
Returns
-------
output_dict : dict of str to NDArray
The dictionary that maps name of output names to NDArrays.
Raises
------
ValueError : if there are duplicated names in the outputs. | python/mxnet/executor.py | def output_dict(self):
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output_dict : dict of str to NDArray
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train | Executor.copy_params_from | Copy parameters from arg_params, aux_params into executor's internal array.
Parameters
----------
arg_params : dict of str to NDArray
Parameters, dict of name to NDArray of arguments.
aux_params : dict of str to NDArray, optional
Parameters, dict of name to NDAr... | python/mxnet/executor.py | def copy_params_from(self, arg_params, aux_params=None, allow_extra_params=False):
"""Copy parameters from arg_params, aux_params into executor's internal array.
Parameters
----------
arg_params : dict of str to NDArray
Parameters, dict of name to NDArray of arguments.
... | def copy_params_from(self, arg_params, aux_params=None, allow_extra_params=False):
"""Copy parameters from arg_params, aux_params into executor's internal array.
Parameters
----------
arg_params : dict of str to NDArray
Parameters, dict of name to NDArray of arguments.
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train | Executor.reshape | Return a new executor with the same symbol and shared memory,
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For runtime reshaping, variable length sequences, etc.
The returned executor shares state with the current one,
and cannot be used in parallel with it.
Parameters
----------
... | python/mxnet/executor.py | def reshape(self, partial_shaping=False, allow_up_sizing=False, **kwargs):
"""Return a new executor with the same symbol and shared memory,
but different input/output shapes.
For runtime reshaping, variable length sequences, etc.
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... | def reshape(self, partial_shaping=False, allow_up_sizing=False, **kwargs):
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For runtime reshaping, variable length sequences, etc.
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train | Executor.debug_str | Get a debug string about internal execution plan.
Returns
-------
debug_str : string
Debug string of the executor.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
>>> texec = c.bind(mx.cpu(), {'a... | python/mxnet/executor.py | def debug_str(self):
"""Get a debug string about internal execution plan.
Returns
-------
debug_str : string
Debug string of the executor.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
... | def debug_str(self):
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Returns
-------
debug_str : string
Debug string of the executor.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
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train | parse_voc_rec | parse pascal voc record into a dictionary
:param filename: xml file path
:return: list of dict | example/ssd/evaluate/eval_voc.py | def parse_voc_rec(filename):
"""
parse pascal voc record into a dictionary
:param filename: xml file path
:return: list of dict
"""
import xml.etree.ElementTree as ET
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_dict = dict()
obj_dict[... | def parse_voc_rec(filename):
"""
parse pascal voc record into a dictionary
:param filename: xml file path
:return: list of dict
"""
import xml.etree.ElementTree as ET
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_dict = dict()
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train | voc_eval | pascal voc evaluation
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:param annopath: annotations annopath.format(classname)
:param imageset_file: text file containing list of images
:param classname: category name
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pascal voc evaluation
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train | MXNetGraph.register | Register operators | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | def register(op_name):
"""Register operators"""
def wrapper(func):
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try:
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except ImportError:
pass
return func
... | def register(op_name):
"""Register operators"""
def wrapper(func):
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try:
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except ImportError:
pass
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train | MXNetGraph.convert_layer | Convert MXNet layer to ONNX | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | def convert_layer(node, **kwargs):
"""Convert MXNet layer to ONNX"""
op = str(node["op"])
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raise AttributeError("No conversion function registered for op type %s yet." % op)
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train | MXNetGraph.split_params | Helper function to split params dictionary into args and aux params
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sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
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Parameters
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sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
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MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
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train | MXNetGraph.get_outputs | Infer output shapes and return dictionary of output name to shape
:param :class:`~mxnet.symbol.Symbol` sym: symbol to perform infer shape on
:param dic of (str, nd.NDArray) params:
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:param in_label: name of label typicall... | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | def get_outputs(sym, params, in_shape, in_label):
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:param :class:`~mxnet.symbol.Symbol` sym: symbol to perform infer shape on
:param dic of (str, nd.NDArray) params:
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""" Infer output shapes and return dictionary of output name to shape
:param :class:`~mxnet.symbol.Symbol` sym: symbol to perform infer shape on
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train | MXNetGraph.convert_weights_to_numpy | Convert weights to numpy | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | def convert_weights_to_numpy(weights_dict):
"""Convert weights to numpy"""
return dict([(k.replace("arg:", "").replace("aux:", ""), v.asnumpy())
for k, v in weights_dict.items()]) | def convert_weights_to_numpy(weights_dict):
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train | MXNetGraph.create_onnx_graph_proto | Convert MXNet graph to ONNX graph
Parameters
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sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
Dict of converted parameters stored in ``mxnet.ndarray.NDArray`` format
in_shape : ... | python/mxnet/contrib/onnx/mx2onnx/export_onnx.py | def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False):
"""Convert MXNet graph to ONNX graph
Parameters
----------
sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
... | def create_onnx_graph_proto(self, sym, params, in_shape, in_type, verbose=False):
"""Convert MXNet graph to ONNX graph
Parameters
----------
sym : :class:`~mxnet.symbol.Symbol`
MXNet symbol object
params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
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train | get_lr_scheduler | Compute learning rate and refactor scheduler
Parameters:
---------
learning_rate : float
original learning rate
lr_refactor_step : comma separated str
epochs to change learning rate
lr_refactor_ratio : float
lr *= ratio at certain steps
num_example : int
number o... | example/ssd/train/train_net.py | def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio,
num_example, batch_size, begin_epoch):
"""
Compute learning rate and refactor scheduler
Parameters:
---------
learning_rate : float
original learning rate
lr_refactor_step : comma separated str... | def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio,
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"""
Compute learning rate and refactor scheduler
Parameters:
---------
learning_rate : float
original learning rate
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train | train_net | Wrapper for training phase.
Parameters:
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net : str
symbol name for the network structure
train_path : str
record file path for training
num_classes : int
number of object classes, not including background
batch_size : int
training batch-size
data_sh... | example/ssd/train/train_net.py | def train_net(net, train_path, num_classes, batch_size,
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train | imagenet50 | This is a set of 50 images representative of ImageNet images.
This dataset was collected by randomly finding a working ImageNet link and then pasting the
original ImageNet image into Google image search restricted to images licensed for reuse. A
similar image (now with rights to reuse) was downloaded as a ... | shap/datasets.py | def imagenet50(display=False, resolution=224):
""" This is a set of 50 images representative of ImageNet images.
This dataset was collected by randomly finding a working ImageNet link and then pasting the
original ImageNet image into Google image search restricted to images licensed for reuse. A
simila... | def imagenet50(display=False, resolution=224):
""" This is a set of 50 images representative of ImageNet images.
This dataset was collected by randomly finding a working ImageNet link and then pasting the
original ImageNet image into Google image search restricted to images licensed for reuse. A
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train | boston | Return the boston housing data in a nice package. | shap/datasets.py | def boston(display=False):
""" Return the boston housing data in a nice package. """
d = sklearn.datasets.load_boston()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
return df, d.target | def boston(display=False):
""" Return the boston housing data in a nice package. """
d = sklearn.datasets.load_boston()
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train | imdb | Return the clssic IMDB sentiment analysis training data in a nice package.
Full data is at: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Paper to cite when using the data is: http://www.aclweb.org/anthology/P11-1015 | shap/datasets.py | def imdb(display=False):
""" Return the clssic IMDB sentiment analysis training data in a nice package.
Full data is at: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Paper to cite when using the data is: http://www.aclweb.org/anthology/P11-1015
"""
with open(cache(github_data_url... | def imdb(display=False):
""" Return the clssic IMDB sentiment analysis training data in a nice package.
Full data is at: http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
Paper to cite when using the data is: http://www.aclweb.org/anthology/P11-1015
"""
with open(cache(github_data_url... | [
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train | communitiesandcrime | Predict total number of non-violent crimes per 100K popuation.
This dataset is from the classic UCI Machine Learning repository:
https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime+Unnormalized | shap/datasets.py | def communitiesandcrime(display=False):
""" Predict total number of non-violent crimes per 100K popuation.
This dataset is from the classic UCI Machine Learning repository:
https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime+Unnormalized
"""
raw_data = pd.read_csv(
cache(github_d... | def communitiesandcrime(display=False):
""" Predict total number of non-violent crimes per 100K popuation.
This dataset is from the classic UCI Machine Learning repository:
https://archive.ics.uci.edu/ml/datasets/Communities+and+Crime+Unnormalized
"""
raw_data = pd.read_csv(
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train | diabetes | Return the diabetes data in a nice package. | shap/datasets.py | def diabetes(display=False):
""" Return the diabetes data in a nice package. """
d = sklearn.datasets.load_diabetes()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
return df, d.target | def diabetes(display=False):
""" Return the diabetes data in a nice package. """
d = sklearn.datasets.load_diabetes()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
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train | iris | Return the classic iris data in a nice package. | shap/datasets.py | def iris(display=False):
""" Return the classic iris data in a nice package. """
d = sklearn.datasets.load_iris()
df = pd.DataFrame(data=d.data, columns=d.feature_names) # pylint: disable=E1101
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""" Return the classic iris data in a nice package. """
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train | adult | Return the Adult census data in a nice package. | shap/datasets.py | def adult(display=False):
""" Return the Adult census data in a nice package. """
dtypes = [
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""" Return the Adult census data in a nice package. """
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train | nhanesi | A nicely packaged version of NHANES I data with surivival times as labels. | shap/datasets.py | def nhanesi(display=False):
""" A nicely packaged version of NHANES I data with surivival times as labels.
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""" A nicely packaged version of NHANES I data with surivival times as labels.
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train | cric | A nicely packaged version of CRIC data with progression to ESRD within 4 years as the label. | shap/datasets.py | def cric(display=False):
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train | corrgroups60 | Correlated Groups 60
A simulated dataset with tight correlations among distinct groups of features. | shap/datasets.py | def corrgroups60(display=False):
""" Correlated Groups 60
A simulated dataset with tight correlations among distinct groups of features.
"""
# set a constant seed
old_seed = np.random.seed()
np.random.seed(0)
# generate dataset with known correlation
N = 1000
M = 60
# set... | def corrgroups60(display=False):
""" Correlated Groups 60
A simulated dataset with tight correlations among distinct groups of features.
"""
# set a constant seed
old_seed = np.random.seed()
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train | independentlinear60 | A simulated dataset with tight correlations among distinct groups of features. | shap/datasets.py | def independentlinear60(display=False):
""" A simulated dataset with tight correlations among distinct groups of features.
"""
# set a constant seed
old_seed = np.random.seed()
np.random.seed(0)
# generate dataset with known correlation
N = 1000
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train | rank | Ranking datasets from lightgbm repository. | shap/datasets.py | def rank():
""" Ranking datasets from lightgbm repository.
"""
rank_data_url = 'https://raw.githubusercontent.com/Microsoft/LightGBM/master/examples/lambdarank/'
x_train, y_train = sklearn.datasets.load_svmlight_file(cache(rank_data_url + 'rank.train'))
x_test, y_test = sklearn.datasets.load_svmligh... | def rank():
""" Ranking datasets from lightgbm repository.
"""
rank_data_url = 'https://raw.githubusercontent.com/Microsoft/LightGBM/master/examples/lambdarank/'
x_train, y_train = sklearn.datasets.load_svmlight_file(cache(rank_data_url + 'rank.train'))
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train | batch_remove_retrain | An approximation of holdout that only retraines the model once.
This is alse called ROAR (RemOve And Retrain) in work by Google. It is much more computationally
efficient that the holdout method because it masks the most important features in every sample
and then retrains the model once, instead of retrai... | shap/benchmark/measures.py | def batch_remove_retrain(nmask_train, nmask_test, X_train, y_train, X_test, y_test, attr_train, attr_test, model_generator, metric):
""" An approximation of holdout that only retraines the model once.
This is alse called ROAR (RemOve And Retrain) in work by Google. It is much more computationally
efficient... | def batch_remove_retrain(nmask_train, nmask_test, X_train, y_train, X_test, y_test, attr_train, attr_test, model_generator, metric):
""" An approximation of holdout that only retraines the model once.
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train | keep_retrain | The model is retrained for each test sample with the non-important features set to a constant.
If you want to know how important a set of features is you can ask how the model would be
different if only those features had existed. To determine this we can mask the other features
across the entire training ... | shap/benchmark/measures.py | def keep_retrain(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
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train | keep_mask | The model is revaluated for each test sample with the non-important features set to their mean. | shap/benchmark/measures.py | def keep_mask(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
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train | keep_impute | The model is revaluated for each test sample with the non-important features set to an imputed value.
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train | keep_resample | The model is revaluated for each test sample with the non-important features set to resample background values. | shap/benchmark/measures.py | def keep_resample(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
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train | local_accuracy | The how well do the features plus a constant base rate sum up to the model output. | shap/benchmark/measures.py | def local_accuracy(X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model):
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"""
X_train, X_test = to_array(X_train, X_test)
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assert X_train.shape[1] == X_t... | def local_accuracy(X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model):
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train | const_rand | Generate a random array with a fixed seed. | shap/benchmark/measures.py | def const_rand(size, seed=23980):
""" Generate a random array with a fixed seed.
"""
old_seed = np.random.seed()
np.random.seed(seed)
out = np.random.rand(size)
np.random.seed(old_seed)
return out | def const_rand(size, seed=23980):
""" Generate a random array with a fixed seed.
"""
old_seed = np.random.seed()
np.random.seed(seed)
out = np.random.rand(size)
np.random.seed(old_seed)
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"... | b280cb81d498b9d98565cad8dd16fc88ae52649f |
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