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):
if 'input_shape' not in kwargs:
if input_length:
kwargs['input_shape'] = (input_length,)
else:
kwargs['input_shape'] = (None,)
dtype = kwargs.pop('dtype', K.floatx())
super(Embedding, self).__init__(dtype=dtype, **kwargs)
self.input_dim = input_dim
self.output_dim = output_dim
self.embeddings_initializer = initializers.get(embeddings_initializer)
self.embeddings_regularizer = regularizers.get(embeddings_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.embeddings_constraint = constraints.get(embeddings_constraint)
self.mask_zero = mask_zero
self.supports_masking = mask_zero
self.input_length = input_length
def __init__(self,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None,
trainable=True,
virtual_batch_size=None,
adjustment=None,
name=None,
**kwargs):
super(BatchNormalization, self).__init__(
name=name, trainable=trainable, **kwargs)
if isinstance(axis, list):
self.axis = axis[:]
else:
self.axis = axis
self.momentum = momentum
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.moving_mean_initializer = initializers.get(moving_mean_initializer)
self.moving_variance_initializer = initializers.get(
moving_variance_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
self.renorm = renorm
self.virtual_batch_size = virtual_batch_size
self.adjustment = adjustment
if fused is None:
fused = True
self.supports_masking = True
self.fused = fused
self._bessels_correction_test_only = True
if renorm:
renorm_clipping = renorm_clipping or {}
keys = ['rmax', 'rmin', 'dmax']
if set(renorm_clipping) - set(keys):
raise ValueError('renorm_clipping %s contains keys not in %s' %
(renorm_clipping, keys))
self.renorm_clipping = renorm_clipping
self.renorm_momentum = renorm_momentum
def __init__(self,
axis=-1,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
trainable=True,
name=None,
**kwargs):
super(LayerNormalization, self).__init__(
name=name, trainable=trainable, **kwargs)
if isinstance(axis, (list, tuple)):
self.axis = axis[:]
elif isinstance(axis, int):
self.axis = axis
else:
raise ValueError('Expected an int or a list/tuple of ints for the '
'argument \'axis\', but received instead: %s' % axis)
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
self.supports_masking = True
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):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(Dense, self).__init__(
activity_regularizer=regularizers.get(activity_regularizer), **kwargs)
self.units = int(units)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.supports_masking = True
self.input_spec = InputSpec(min_ndim=2)
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
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(LocallyConnected1D, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
self.padding = conv_utils.normalize_padding(padding)
if self.padding != 'valid':
raise ValueError('Invalid border mode for LocallyConnected1D '
'(only "valid" is supported): ' + padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=3)
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
time_major=False,
**kwargs):
super(RNN, self).__init__(**kwargs) # pylint: disable=bad-super-call
self.units = units
cell_spec = collections.namedtuple('cell', ['state_size', 'output_size'])
self.cell = cell_spec(
state_size=(self.units, self.units), output_size=self.units)
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.return_sequences = return_sequences
self.return_state = return_state
self.go_backwards = go_backwards
self.stateful = stateful
self.time_major = time_major
self._num_constants = None
self._num_inputs = None
self._states = None
self.input_spec = [InputSpec(ndim=3)]
self.state_spec = [
InputSpec(shape=(None, dim)) for dim in (self.units, self.units)
]
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(ConvLSTM2DCell, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2,
'dilation_rate')
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.state_size = (self.filters, self.filters)
self._dropout_mask = None
self._recurrent_dropout_mask = None
def __init__(self,
norm_axis=None,
params_axis=-1,
epsilon=1e-12,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
trainable=True,
name=None,
**kwargs):
super(LayerNormalization, self).__init__(
name=name, trainable=trainable, **kwargs)
if isinstance(norm_axis, list):
self.norm_axis = norm_axis[:]
elif isinstance(norm_axis, int):
self.norm_axis = norm_axis
elif norm_axis is None:
self.norm_axis = None
else:
raise TypeError('norm_axis must be int or list or None, type given: %s'
% type(norm_axis))
if isinstance(params_axis, list):
self.params_axis = params_axis[:]
elif isinstance(params_axis, int):
self.params_axis = params_axis
else:
raise TypeError('params_axis must be int or list, type given: %s'
% type(params_axis))
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
self.supports_masking = True
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
go_backwards=False,
stateful=False,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
cell = ConvLSTM2DCell(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
recurrent_activation=recurrent_activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
bias_initializer=bias_initializer,
unit_forget_bias=unit_forget_bias,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout)
super(ConvLSTM2D, self).__init__(cell,
return_sequences=return_sequences,
go_backwards=go_backwards,
stateful=stateful,
**kwargs)
self.activity_regularizer = regularizers.get(activity_regularizer)
def __init__(self,
units,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
**kwargs):
self.units = units
cell_spec = collections.namedtuple('cell', 'state_size')
self._cell = cell_spec(state_size=(self.units, self.units))
super(CuDNNLSTM, self).__init__(
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
def __init__(self,
rank,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=True,
use_wscale=True,
lr_mul=1.0,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(ScaledConv, self).__init__(
rank,
filters,
kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
self.lr_mul = lr_mul
self.use_wscale = use_wscale
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,
num_bits=8,
mul_map_file='',
**kwargs):
self.num_bits = num_bits
self.mul_map_file = mul_map_file
super(FakeApproxConv2D, self).__init__(
rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
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,
spectral_normalization=True,
bias_constraint=None,
**kwargs):
if data_format is None:
data_format = K.image_data_format()
super(Conv2D, self).__init__(
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
self.u = K.random_normal_variable([1, filters], 0, 1, dtype=self.dtype, name="sn_estimate") # [1, out_channels]
self.spectral_normalization = spectral_normalization
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):
if 'input_shape' not in kwargs:
if input_length:
kwargs['input_shape'] = (input_length,)
else:
kwargs['input_shape'] = (None,)
if input_dim <= 0 or output_dim <= 0:
raise ValueError('Both `input_dim` and `output_dim` should be positive, '
'found input_dim {} and output_dim {}'.format(
input_dim, output_dim))
if (not base_layer_utils.v2_dtype_behavior_enabled() and
'dtype' not in kwargs):
# In TF1, the dtype defaults to the input dtype which is typically int32,
# so explicitly set it to floatx
kwargs['dtype'] = backend.floatx()
# We set autocast to False, as we do not want to cast floating- point inputs
# to self.dtype. In call(), we cast to int32, and casting to self.dtype
# before casting to int32 might cause the int32 values to be different due
# to a loss of precision.
kwargs['autocast'] = False
super(Embedding, self).__init__(**kwargs)
self.input_dim = input_dim
self.output_dim = output_dim
self.embeddings_initializer = initializers.get(embeddings_initializer)
self.embeddings_regularizer = regularizers.get(embeddings_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.embeddings_constraint = constraints.get(embeddings_constraint)
self.mask_zero = mask_zero
self.supports_masking = mask_zero
self.input_length = input_length
def __init__(self,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True,
return_attention=False,
**kwargs):
"""
Keras Layer that implements an Attention mechanism for temporal data.
Supports Masking.
Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
# Input shape
3D tensor with shape: `(samples, steps, features)`.
# Output shape
2D tensor with shape: `(samples, features)`.
:param kwargs:
Just put it on top of an RNN Layer (GRU/LSTM/SimpleRNN) with return_sequences=True.
The dimensions are inferred based on the output shape of the RNN.
Note: The layer has been tested with Keras 1.x
Example:
# 1
model.add(LSTM(64, return_sequences=True))
model.add(Attention())
# next add a Dense layer (for classification/regression) or whatever...
# 2 - Get the attention scores
hidden = LSTM(64, return_sequences=True)(words)
sentence, word_scores = Attention(return_attention=True)(hidden)
"""
self.supports_masking = True
self.return_attention = return_attention
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
super(Attention, self).__init__(**kwargs)
def __init__(self,
units,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
**kwargs):
self.units = units
cell_spec = collections.namedtuple('cell', 'state_size')
self._cell = cell_spec(state_size=(self.units, self.units))
super(CuDNNLSTM, self).__init__(return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
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,
implementation=1,
**kwargs):
super(LocallyConnected1D, self).__init__(**kwargs)
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, 1,
'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
self.padding = conv_utils.normalize_padding(padding)
if self.padding != 'valid' and implementation == 1:
raise ValueError(
'Invalid border mode for LocallyConnected1D '
'(only "valid" is supported if implementation is 1): ' +
padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.implementation = implementation
self.input_spec = InputSpec(ndim=3)
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):
if 'input_shape' not in kwargs:
if input_length:
kwargs['input_shape'] = (input_length,)
else:
kwargs['input_shape'] = (None,)
if input_dim <= 0 or output_dim <= 0:
raise ValueError('Both `input_dim` and `output_dim` should be positive, '
'found input_dim {} and output_dim {}'.format(
input_dim, output_dim))
dtype = kwargs.pop('dtype', K.floatx())
# We set autocast to False, as we do not want to cast floating- point inputs
# to self.dtype. In call(), we cast to int32, and casting to self.dtype
# before casting to int32 might cause the int32 values to be different due
# to a loss of precision.
kwargs['autocast'] = False
super(Embedding, self).__init__(dtype=dtype, **kwargs)
self.input_dim = input_dim
if self.input_dim <= 0:
raise ValueError('The argument `input_dim` should be greater than zero. '
'Received: %s' % input_dim)
self.output_dim = output_dim
self.embeddings_initializer = initializers.get(embeddings_initializer)
self.embeddings_regularizer = regularizers.get(embeddings_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.embeddings_constraint = constraints.get(embeddings_constraint)
self.mask_zero = mask_zero
self.supports_masking = mask_zero
self.input_length = input_length
self._supports_ragged_inputs = True
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=conv_init_relu,
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
binary=True,
**kwargs):
super(SparseConv2D, self).__init__(
activity_regularizer=regularizers.get(activity_regularizer),
**kwargs)
rank = 2
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank,
'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.dilation_rate = conv_utils.normalize_tuple(
dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.binary = binary
def __init__(self,
output_mul=1,
context_size=5,
stride=1,
group_size=1,
dilation=1,
grouped=False,
padding='same',
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,
input_dim=None,
**kwargs):
super(DepthGroupwiseTimeDelayLayer1D, self).__init__(
activation=activations.get(activation),
use_bias=use_bias,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
self.context_size = context_size
self.stride = stride
self.dilation = dilation
self.output_mul = output_mul
self.group_size = group_size
self.grouped = grouped
self.padding = padding
self.input_dim = input_dim
if input_dim is not None:
self.init_layers()
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,
use_spectral_norm=False,
is_training=False,
bias_constraint=None,
**kwargs):
super(MyKerasConv2D, self).__init__(
rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activations.get(activation),
use_bias=use_bias,
use_spectral_norm=use_spectral_norm,
is_training=is_training,
kernel_initializer=initializers.get(kernel_initializer),
bias_initializer=initializers.get(bias_initializer),
kernel_regularizer=regularizers.get(kernel_regularizer),
bias_regularizer=regularizers.get(bias_regularizer),
activity_regularizer=regularizers.get(activity_regularizer),
kernel_constraint=constraints.get(kernel_constraint),
bias_constraint=constraints.get(bias_constraint),
**kwargs)
def __init__(self,
units,
halfbandwidth=0,
param_reduction=0.5,
form='diagonal',
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(SzDense,
self).__init__(activity_regularizer=activity_regularizer,
**kwargs)
self.units = int(units) if not isinstance(units, int) else units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
self.halfbandwidth = halfbandwidth
self.form = form
self.reduction_sv = param_reduction
self.num_ones = 0
self.reduced_ratio = 0
self.num_weights = 0
self.reduced_ratio = 0
def __init__(self,
alpha_initializer='zeros',
alpha_regularizer=None,
alpha_constraint=None,
beta_initializer='ones',
beta_regularizer=None,
beta_constraint=None,
threshold_initializer='zeros',
threshold_regularizer=None,
threshold_constraint=None,
bias_initializer='zeros',
bias_regularizer=None,
bias_constraint=None,
shared_axes=None, **kwargs):
super(DPReLU, self).__init__(**kwargs)
self.supports_masking = True
self.alpha_initializer = initializers.get(alpha_initializer)
self.alpha_regularizer = regularizers.get(alpha_regularizer)
self.alpha_constraint = constraints.get(alpha_constraint)
self.beta_initializer = initializers.get(beta_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.threshold_initializer = initializers.get(threshold_initializer)
self.threshold_regularizer = regularizers.get(threshold_regularizer)
self.threshold_constraint = constraints.get(threshold_constraint)
self.bias_initializer = initializers.get(bias_initializer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.bias_constraint = constraints.get(bias_constraint)
if shared_axes is None:
self.shared_axes = None
elif not isinstance(shared_axes, (list, tuple)):
self.shared_axes = [shared_axes]
else:
self.shared_axes = list(shared_axes)
def __init__(self,
gamma=1.0,
trainable_gamma=False,
initializer='zeros',
regularizer=None,
constraint=None,
**kwargs):
super(GaussianKernel, self).__init__(**kwargs)
self.gamma = gamma
self.trainable_gamma = trainable_gamma
self.initializer = initializers.get(initializer)
self.regularizer = regularizers.get(regularizer)
self.constraint = constraints.get(constraint)
def testSaveTimeDistributedLayer(self):
model = keras.Sequential([
keras.layers.TimeDistributed(
keras.layers.Dense(1, kernel_regularizer=regularizers.get('l2')),
input_shape=(None, 1))])
predictions = model.predict_on_batch(array_ops.ones((3, 2, 1)))
saved_model_dir = self._save_model_dir()
model.save(saved_model_dir, save_format='tf')
loaded = keras_load.load(saved_model_dir)
self.assertAllClose(loaded.predict_on_batch(array_ops.ones((3, 2, 1))),
predictions)
def __init__(self,
axis=-1,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
trainable=True,
name=None,
**kwargs):
super(LayerNormalization, self).__init__(
name=name, trainable=trainable, **kwargs)
if isinstance(axis, (list, tuple)):
self.axis = axis[:]
elif isinstance(axis, int):
self.axis = axis
else:
raise TypeError('Expected an int or a list/tuple of ints for the '
'argument \'axis\', but received: %r' % axis)
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
self.supports_masking = True
# Indicates whether a faster fused implementation can be used. This will be
# set to True or False in build()"
self._fused = None
def __init__(
self,
feature_units,
attn_heads=1,
attn_heads_reduction="concat", # {"concat", "average"}
dropout_rate=0.5,
activation="relu",
attn_kernel_initializer="glorot_uniform",
attn_kernel_regularizer=None,
attn_kernel_constraint=None,
attention=True,
return_attention=False,
**kwargs):
if attn_heads_reduction not in {"concat", "average"}:
raise ValueError("Possbile reduction methods: concat, average")
super(GraphAttentionLayer, self).__init__(units=feature_units,
activation=activation,
**kwargs)
# Number of attention heads (K in the paper)
self.attn_heads = attn_heads
# Eq. 5 and 6 in the paper
self.attn_heads_reduction = attn_heads_reduction
# Internal dropout rate
self.dropout_rate = dropout_rate
self.attn_kernel_initializer \
= initializers.get(attn_kernel_initializer)
self.attn_kernel_regularizer \
= regularizers.get(attn_kernel_regularizer)
self.attn_kernel_constraint = constraints.get(attn_kernel_constraint)
self.attention = attention
self.return_attention = return_attention
self.input_spec = [InputSpec(ndim=3), InputSpec(ndim=3)]
self.supports_masking = False
# Populated by build()
self.kernels = []
self.biases = []
self.neighbor_kernels = []
self.attn_kernels = []
self.attention_biases = []
if attn_heads_reduction == "concat":
# Output will have shape (..., K * F")
self.output_dim = self.units * self.attn_heads
else:
# Output will have shape (..., F")
self.output_dim = self.units
def __init__(self,
units,
tau=1.,
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.,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
**kwargs):
if 'implementation' in kwargs:
kwargs.pop('implementation')
logging.warning('The `implementation` argument '
'in `SimpleCTRNN` has been deprecated. '
'Please remove it from your layer call.')
cell = SimpleCTRNNCell(units,
tau=tau,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout)
super(SimpleCTRNN, self).__init__(cell,
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
unroll=unroll,
**kwargs)
self.activity_regularizer = regularizers.get(activity_regularizer)
def __init__(self, upsample_ratio, filters, kernel_size, data_format = 'channels_first', activation = tf.keras.activations.linear, use_bias = True, kernel_initializer = None, bias_initializer = None, kernel_regularizer = None, bias_regularizer = None, activity_regularizer = None, kernel_constraint = None, bias_constraint = None, dimensions = None):
super().__init__()
if dimensions is None:
try:
self.dimensions = len(upsample_ratio)
except:
self.dimensions = len(kernel_size)
else:
self.dimensions = dimensions
self.rank = self.dimensions
self.filters = filters
if isinstance(kernel_size, int):
self.kernel_size = tuple([kernel_size for _ in range(self.dimensions)])
else:
self.kernel_size = tuple(kernel_size)
if isinstance(upsample_ratio, int):
self.upsample_ratio = tuple([upsample_ratio for _ in range(self.dimensions)])
else:
self.upsample_ratio = tuple(upsample_ratio)
self.data_format = data_format
self.activation = activation
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self._tf_data_format = convert_keras_dataformat_to_tf(self.data_format, self.dimensions)
self.deconv_method = eval('tf.nn.conv' + str(self.dimensions) + 'd_transpose')
def __init__(self,
center=True,
scale=True,
epsilon=None,
gamma_initializer='ones',
beta_initializer='zeros',
gamma_regularizer=None,
beta_regularizer=None,
gamma_constraint=None,
beta_constraint=None,
**kwargs):
"""Layer normalization layer
See: [Layer Normalization](https://arxiv.org/pdf/1607.06450.pdf)
:param center: Add an offset parameter if it is True.
:param scale: Add a scale parameter if it is True.
:param epsilon: Epsilon for calculating variance.
:param gamma_initializer: Initializer for the gamma weight.
:param beta_initializer: Initializer for the beta weight.
:param gamma_regularizer: Optional regularizer for the gamma weight.
:param beta_regularizer: Optional regularizer for the beta weight.
:param gamma_constraint: Optional constraint for the gamma weight.
:param beta_constraint: Optional constraint for the beta weight.
:param kwargs:
"""
super(LayerNormalization, self).__init__(**kwargs)
self.supports_masking = True
self.center = center
self.scale = scale
if epsilon is None:
epsilon = K.epsilon() * K.epsilon()
self.epsilon = epsilon
self.gamma_initializer = initializers.get(gamma_initializer)
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_constraint = constraints.get(gamma_constraint)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma, self.beta = None, None
def __init__(self, rank,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=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,
spectral_normalization=True,
**kwargs):
super(_ConvSN, self).__init__(**kwargs)
self.rank = rank
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
self.spectral_normalization = spectral_normalization
self.u = None
def __init__(self, filters,
kernel_size,
kernel_function,
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(KernelConv2D, self).__init__(**kwargs)
self.rank = 2
self.filters = filters
self.kernel_function = kernel_function
self.kernel_size = conv_utils.normalize_tuple(kernel_size, self.rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, self.rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, self.rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = engine.base_layer.InputSpec(ndim=self.rank + 2)
self.kernel = self.bias = None
def test_model_save_and_load(self):
input_arr = np.random.random((1, 3)).astype(np.float32)
target_arr = np.random.random((1, 4)).astype(np.float32)
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.layers[-1].activity_regularizer = regularizers.get('l2')
model.activity_regularizer = regularizers.get('l2')
model.compile(
loss='mse',
optimizer='rmsprop')
model.train_on_batch(input_arr, target_arr)
def callable_loss():
return math_ops.reduce_sum(model.weights[0])
model.add_loss(callable_loss)
saved_model_dir = self._save_model_dir()
tf_save.save(model, saved_model_dir)
loaded = keras_saved_model.load_from_saved_model_v2(saved_model_dir)
self.evaluate(variables.variables_initializer(loaded.variables))
self.assertAllClose(self.evaluate(model.weights),
self.evaluate(loaded.weights))
input_arr = constant_op.constant(
np.random.random((1, 3)).astype(np.float32))
self.assertAllClose(self.evaluate(model(input_arr)),
self.evaluate(loaded(input_arr)))
# Validate losses. The order of conditional losses may change between the
# model and loaded model, so sort the losses first.
if context.executing_eagerly():
self.assertAllClose(sorted(self.evaluate(model.losses)),
sorted(self.evaluate(loaded.losses)))
else:
self.assertAllClose(self.evaluate(model.get_losses_for(None)),
self.evaluate(loaded.get_losses_for(None)))
self.assertAllClose(
sorted(self.evaluate(model.get_losses_for(input_arr))),
sorted(self.evaluate(loaded.get_losses_for(input_arr))))
def __init__(self,
input_dim,
output_dim,
adj,
num_features_nonzero,
dropout_rate=0.0,
is_sparse_inputs=False,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer='l2',
bias_regularizer='l2',
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(GraphConvolution, self).__init__()
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.kernels = list()
self.bias = None
self.input_dim = input_dim
self.output_dim = output_dim
self.is_sparse_inputs = is_sparse_inputs
self.num_features_nonzero = num_features_nonzero
self.adjs = [
tf.SparseTensor(indices=am[0], values=am[1], dense_shape=am[2])
for am in adj
]
self.dropout_rate = dropout_rate
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,
scaling=True,
config=None,
bjorck_beta=0.5,
bjorck_iter=5,
bjorck_order=2,
**kwargs):
super(BjorckDense,
self).__init__(activity_regularizer=activity_regularizer,
**kwargs)
self.units = int(units) if not isinstance(units, int) else units
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
self.scaling = scaling
self.config = config
self.bjorck_beta = bjorck_beta
self.bjorck_iter = bjorck_iter
self.bjorck_order = bjorck_order
def __init__(self,
W_regularizer=None,
u_regularizer=None,
b_regularizer=None,
W_constraint=None,
u_constraint=None,
b_constraint=None,
bias=True,
**kwargs):
self.supports_masking = True
self.init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.u_regularizer = regularizers.get(u_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.u_constraint = constraints.get(u_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
super(AttentionWithContext, self).__init__(**kwargs)
def __init__(self,
units,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
kernel_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'), )
super(AngularLinear, self).__init__(**kwargs)
self.units = int(units)
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
def __init__(self,
kernel_initializer = 'glorot_uniform',
kernel_regularizer=None,
kernel_constraint=None,
table_shape=(1,4,4,1),
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(TableInterpolation, self).__init__(**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.table_shape = table_shape
def __init__(self,
use_bias=False,
activation=None,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
**kwargs):
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
super(FieldWiseBiInteraction, self).__init__(**kwargs)
def __init__(self,
relations,
activation=None,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super().__init__(**kwargs)
self.relations = relations
self.activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.kernel_1 = None
self.kernel_2 = None
self.kernel_g = None
self.bias = None
def __init__(self,
alpha_initializer='zeros',
alpha_regularizer=None,
alpha_constraint=None,
shared_axes=None,
**kwargs):
super(PReLU, self).__init__(**kwargs)
self.supports_masking = True
self.alpha_initializer = initializers.get(alpha_initializer)
self.alpha_regularizer = regularizers.get(alpha_regularizer)
self.alpha_constraint = constraints.get(alpha_constraint)
if shared_axes is None:
self.shared_axes = None
elif not isinstance(shared_axes, (list, tuple)):
self.shared_axes = [shared_axes]
else:
self.shared_axes = list(shared_axes)
def __init__(self,
axis=-1,
momentum=0.99,
epsilon=1e-3,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones',
beta_regularizer=None,
gamma_regularizer=None,
beta_constraint=None,
gamma_constraint=None,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None,
trainable=True,
virtual_batch_size=None,
adjustment=None,
name=None,
**kwargs):
super(BatchNormalizationBase, self).__init__(
name=name, trainable=trainable, **kwargs)
if isinstance(axis, list):
self.axis = axis[:]
elif isinstance(axis, int):
self.axis = axis
else:
raise TypeError('axis must be int or list, type given: %s'
% type(self.axis))
self.momentum = momentum
self.epsilon = epsilon
self.center = center
self.scale = scale
self.beta_initializer = initializers.get(beta_initializer)
self.gamma_initializer = initializers.get(gamma_initializer)
self.moving_mean_initializer = initializers.get(moving_mean_initializer)
self.moving_variance_initializer = initializers.get(
moving_variance_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
self.gamma_constraint = constraints.get(gamma_constraint)
self.renorm = renorm
self.virtual_batch_size = virtual_batch_size
self.adjustment = adjustment
if self._USE_V2_BEHAVIOR:
if fused:
self._raise_if_fused_cannot_be_used()
# We leave fused as None if self._fused_can_be_used()==True, since we
# still may set it to False in self.build() if the input rank is not 4.
elif fused is None and not self._fused_can_be_used():
fused = False
elif fused is None:
fused = True
self.supports_masking = True
self.fused = fused
self._bessels_correction_test_only = True
if renorm:
renorm_clipping = renorm_clipping or {}
keys = ['rmax', 'rmin', 'dmax']
if set(renorm_clipping) - set(keys):
raise ValueError('renorm_clipping %s contains keys not in %s' %
(renorm_clipping, keys))
self.renorm_clipping = renorm_clipping
self.renorm_momentum = renorm_momentum
