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)