def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Conv2D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=getters.get_activation(activation)
if activation else activation,
use_bias=use_bias,
kernel_initializer=getters.get_initializer(kernel_initializer),
bias_initializer=getters.get_initializer(bias_initializer),
kernel_regularizer=getters.get_regularizer(kernel_regularizer),
bias_regularizer=getters.get_regularizer(bias_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
kernel_constraint=getters.get_constraint(kernel_constraint),
bias_constraint=getters.get_constraint(bias_constraint),
**kwargs)
def __init__(self,
units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(Dense, self).__init__(
units,
activation=getters.get_activation(activation)
if activation else activation,
use_bias=use_bias,
kernel_initializer=getters.get_initializer(kernel_initializer),
bias_initializer=getters.get_initializer(bias_initializer),
kernel_regularizer=getters.get_regularizer(kernel_regularizer),
bias_regularizer=getters.get_regularizer(bias_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
kernel_constraint=getters.get_constraint(kernel_constraint),
bias_constraint=getters.get_constraint(bias_constraint),
**kwargs)
def __init__(self,
units,
activation='tanh',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(SimpleRNN, self).__init__(
units=units,
activation=getters.get_activation(activation),
use_bias=use_bias,
kernel_initializer=getters.get_initializer(kernel_initializer),
recurrent_initializer=getters.get_initializer(recurrent_initializer),
bias_initializer=getters.get_initializer(bias_initializer),
kernel_regularizer=getters.get_regularizer(kernel_regularizer),
recurrent_regularizer=getters.get_regularizer(recurrent_regularizer),
bias_regularizer=getters.get_regularizer(bias_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
kernel_constraint=getters.get_constraint(kernel_constraint),
recurrent_constraint=getters.get_constraint(recurrent_constraint),
bias_constraint=getters.get_constraint(bias_constraint),
dropout=dropout,
recurrent_dropout=recurrent_dropout,
**kwargs)
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
depth_multiplier=1,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
pointwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
pointwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
pointwise_constraint=None,
bias_constraint=None,
**kwargs):
super(SeparableConv2D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
depth_multiplier=depth_multiplier,
activation=getters.get_activation(activation),
use_bias=use_bias,
depthwise_initializer=getters.get_initializer(
depthwise_initializer),
pointwise_initializer=getters.get_initializer(
pointwise_initializer),
bias_initializer=getters.get_initializer(bias_initializer),
depthwise_regularizer=getters.get_regularizer(
depthwise_regularizer),
pointwise_regularizer=getters.get_regularizer(
pointwise_regularizer),
bias_regularizer=getters.get_regularizer(bias_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
depthwise_constraint=getters.get_constraint(depthwise_constraint),
pointwise_constraint=getters.get_constraint(pointwise_constraint),
bias_constraint=getters.get_constraint(bias_constraint),
**kwargs)
def __init__(self,
input_dim,
output_dim,
embeddings_initializer='uniform',
embeddings_regularizer=None,
activity_regularizer=None,
embeddings_constraint=None,
mask_zero=False,
input_length=None,
**kwargs):
super(Embedding, self).__init__(
input_dim=input_dim,
output_dim=output_dim,
embeddings_initializer=getters.get_initializer(
embeddings_initializer),
embeddings_regularizer=getters.get_regularizer(
embeddings_regularizer),
activity_regularizer=getters.get_regularizer(activity_regularizer),
embeddings_constraint=getters.get_constraint(
embeddings_constraint),
mask_zero=mask_zero,
input_length=input_length,
**kwargs)
def variable(name,
shape=None,
dtype=tf.float32,
initializer=None,
regularizer=None,
trainable=True,
collections=None,
device='',
restore=True):
"""Instantiate a new variable.
Args:
name: `str`. A name for this variable.
shape: list of `int`. The variable shape (optional).
dtype: `type`. The variable data type.
initializer: `str` or `Tensor`. The variable initialization.
regularizer: `str` or `Tensor`. The variable regularizer.
trainable: `bool`. If True, this variable weights will be trained.
collections: `str`. A collection to add the new variable to (optional).
device: `str`. Device ID to store the variable. Default: '/cpu:0'.
restore: `bool`. Restore or not this variable when loading a pre-trained model.
Returns:
A Variable.
"""
if isinstance(initializer, six.string_types):
initializer = getters.get_initializer(initializer)
# Remove shape param if initializer is a Tensor
if not callable(initializer) and isinstance(initializer, tf.Tensor):
shape = None
if isinstance(regularizer, six.string_types):
regularizer = getters.get_regularizer(regularizer)
with tf.device(device_name_or_function=device):
var = tf.get_variable(name=name,
shape=shape,
dtype=dtype,
initializer=initializer,
regularizer=regularizer,
trainable=trainable,
collections=collections)
if not restore:
# TODO adapt restoring saver
tf.add_to_collection(name=tf.GraphKeys.EXCL_RESTORE_VARIABLES,
value=var)
return var
