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 = initializations.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)
python类get()的实例源码
def __init__(self, output_dim, init='glorot_uniform', activation='relu',weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None, input_dim=None, **kwargs):
self.W_initializer = initializers.get(init)
self.b_initializer = initializers.get('zeros')
self.activation = activations.get(activation)
self.output_dim = output_dim
self.input_dim = input_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.initial_weights = weights
self.input_spec = InputSpec(ndim=2)
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(SparseFullyConnectedLayer, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
U_regularizer=None, b_start_regularizer=None, b_end_regularizer=None,
U_constraint=None, b_start_constraint=None, b_end_constraint=None,
weights=None,
**kwargs):
self.supports_masking = True
self.uses_learning_phase = True
self.input_spec = [InputSpec(ndim=3)]
self.init = initializations.get(init)
self.U_regularizer = regularizers.get(U_regularizer)
self.b_start_regularizer = regularizers.get(b_start_regularizer)
self.b_end_regularizer = regularizers.get(b_end_regularizer)
self.U_constraint = constraints.get(U_constraint)
self.b_start_constraint = constraints.get(b_start_constraint)
self.b_end_constraint = constraints.get(b_end_constraint)
self.initial_weights = weights
super(ChainCRF, self).__init__(**kwargs)
def __init__(self, alpha_initializer=0.2,
beta_initializer=5.0,
alpha_regularizer=None,
alpha_constraint=None,
beta_regularizer=None,
beta_constraint=None,
shared_axes=None,
**kwargs):
super(ParametricSoftplus, 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)
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, units,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
kernel_constraint=constraints.NonNeg(),
k_initializer='zeros',
k_regularizer=None,
k_constraint=None,
tied_k=False,
activity_regularizer=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(SoftMinMax, self).__init__(**kwargs)
self.units = units
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.k_initializer = initializers.get(k_initializer)
self.k_regularizer = regularizers.get(k_regularizer)
self.k_constraint = constraints.get(k_constraint)
self.tied_k = tied_k
self.activity_regularizer = regularizers.get(activity_regularizer)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
def __init__(self, units,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
kernel_constraint=constraints.NonNeg(),
activity_regularizer=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(WeightedMean, self).__init__(**kwargs)
self.units = units
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
def __init__(self, output_dim, init='glorot_uniform',
activation=None, weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.output_dim = output_dim
self.input_dim = input_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
self.input_spec = [InputSpec(ndim='2+')]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(DenseNonNeg, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
activation=None, weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
self.input_spec = [InputSpec(ndim='2+')]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(Feedback, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
activation=None, weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
self.input_spec = [InputSpec(ndim='2+')]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(DivisiveNormalization, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
U_regularizer=None, b_start_regularizer=None, b_end_regularizer=None,
U_constraint=None, b_start_constraint=None, b_end_constraint=None,
weights=None,
**kwargs):
self.supports_masking = True
self.uses_learning_phase = True
self.input_spec = [InputSpec(ndim=3)]
self.init = initializations.get(init)
self.U_regularizer = regularizers.get(U_regularizer)
self.b_start_regularizer = regularizers.get(b_start_regularizer)
self.b_end_regularizer = regularizers.get(b_end_regularizer)
self.U_constraint = constraints.get(U_constraint)
self.b_start_constraint = constraints.get(b_start_constraint)
self.b_end_constraint = constraints.get(b_end_constraint)
self.initial_weights = weights
super(ChainCRF, self).__init__(**kwargs)
discrimination.py 文件源码
项目：Keras-GAN-Animeface-Character
作者: forcecore
项目源码
文件源码
阅读 23
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def __init__(self, nb_kernels, kernel_dim, init='glorot_uniform', weights=None,
W_regularizer=None, activity_regularizer=None,
W_constraint=None, input_dim=None, **kwargs):
self.init = initializers.get(init)
self.nb_kernels = nb_kernels
self.kernel_dim = kernel_dim
self.input_dim = input_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=2)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(MinibatchDiscrimination, self).__init__(**kwargs)
def __init__(self, input_dim, output_dim,
init='uniform', input_length=None,
W_regularizer=None, activity_regularizer=None,
W_constraint=None,
mask_zero=False,
weights=None, **kwargs):
self.input_dim = input_dim
self.output_dim = output_dim
self.init = initializations.get(init)
self.input_length = input_length
self.mask_zero = mask_zero
self.W_constraint = constraints.get(W_constraint)
self.constraints = [self.W_constraint]
self.W_regularizer = regularizers.get(W_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.initial_weights = weights
kwargs['input_shape'] = (self.input_dim,)
super(Embedding2D, self).__init__(**kwargs)
def __init__(self, input_dim, output_dim,
init='uniform', input_length=None,
W_regularizer=None, activity_regularizer=None,
W_constraint=None,
mask_zero=False,
weights=None, **kwargs):
self.input_dim = input_dim
self.output_dim = output_dim
self.init = initializations.get(init)
self.input_length = input_length
self.mask_zero = mask_zero
self.W_constraint = constraints.get(W_constraint)
self.constraints = [self.W_constraint]
self.W_regularizer = regularizers.get(W_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.initial_weights = weights
kwargs['input_shape'] = (self.input_dim,)
super(Embedding, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
U_regularizer=None,
b_start_regularizer=None,
b_end_regularizer=None,
U_constraint=None,
b_start_constraint=None,
b_end_constraint=None,
weights=None,
**kwargs):
super(ChainCRF, self).__init__(**kwargs)
self.init = initializers.get(init)
self.U_regularizer = regularizers.get(U_regularizer)
self.b_start_regularizer = regularizers.get(b_start_regularizer)
self.b_end_regularizer = regularizers.get(b_end_regularizer)
self.U_constraint = constraints.get(U_constraint)
self.b_start_constraint = constraints.get(b_start_constraint)
self.b_end_constraint = constraints.get(b_end_constraint)
self.initial_weights = weights
self.supports_masking = True
self.uses_learning_phase = True
self.input_spec = [InputSpec(ndim=3)]
def __init__(self, downsampling_factor=10, init='glorot_uniform', activation='linear',
weights=None, W_regularizer=None, activity_regularizer=None,
W_constraint=None, input_dim=None, **kwargs):
self.downsampling_factor = downsampling_factor
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.W_regularizer = regularizers.get(W_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.initial_weights = weights
self.input_dim = input_dim
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
self.input_spec = [InputSpec(ndim=4)]
super(EltWiseProduct, self).__init__(**kwargs)
def __init__(self, nb_classes, frequency_table=None, mode=0, init='glorot_uniform', weights=None, W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, verbose=False, **kwargs):
'''
# Arguments:
nb_classes: Number of classes.
frequency_table: list. Frequency of each class. More frequent classes will have shorter huffman codes.
mode: integer. One of [0, 1]
verbose: boolean. Set to true to see the progress of building huffman tree.
'''
self.nb_classes = nb_classes
if frequency_table is None:
frequency_table = [1] * nb_classes
self.frequency_table = frequency_table
self.mode = mode
self.init = initializations.get(init)
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.initial_weights = weights
self.verbose = verbose
super(Huffmax, self).__init__(**kwargs)
def __init__(self,
W_regularizer=None, u_regularizer=None, b_regularizer=None,
W_constraint=None, u_constraint=None, b_constraint=None,
W_dropout=0., u_dropout=0., bias=True, **kwargs):
self.supports_masking = True
self.W_init = initializers.get('orthogonal')
self.u_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.W_dropout = min(1., max(0., W_dropout))
self.u_dropout = min(1., max(0., u_dropout))
self.bias = bias
super(AttentionWithContext, self).__init__(**kwargs)
def __init__(self,
W_regularizer=None, b_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **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.
Example:
model.add(LSTM(64, return_sequences=True))
model.add(Attention())
"""
self.supports_masking = True
self.init = initializations.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, output_dim, window_size=3, stride=1,
kernel_initializer='uniform', bias_initializer='zero',
activation='linear', activity_regularizer=None,
kernel_regularizer=None, bias_regularizer=None,
kernel_constraint=None, bias_constraint=None,
use_bias=True, input_dim=None, input_length=None, **kwargs):
self.output_dim = output_dim
self.window_size = window_size
self.strides = (stride, 1)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.activation = activations.get(activation)
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)]
self.input_dim = input_dim
self.input_length = input_length
if self.input_dim:
kwargs['input_shape'] = (self.input_length, self.input_dim)
super(GCNN, self).__init__(**kwargs)
def __init__(self, units, window_size=2, stride=1,
return_sequences=False, go_backwards=False,
stateful=False, unroll=False, activation='tanh',
kernel_initializer='uniform', bias_initializer='zero',
kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None, bias_constraint=None,
dropout=0, use_bias=True, input_dim=None, input_length=None,
**kwargs):
self.return_sequences = return_sequences
self.go_backwards = go_backwards
self.stateful = stateful
self.unroll = unroll
self.units = units
self.window_size = window_size
self.strides = (stride, 1)
self.use_bias = use_bias
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.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = dropout
self.supports_masking = True
self.input_spec = [InputSpec(ndim=3)]
self.input_dim = input_dim
self.input_length = input_length
if self.input_dim:
kwargs['input_shape'] = (self.input_length, self.input_dim)
super(QRNN, self).__init__(**kwargs)
def __init__(self,
ratio,
data_format=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(SE, self).__init__(**kwargs)
self.ratio = ratio
self.data_format= conv_utils.normalize_data_format(data_format)
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.supports_masking = True
def __init__(self,
kernel_size,
strides=(1, 1),
padding='valid',
depth_multiplier=1,
data_format=None,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs):
super(DepthwiseConv2D, self).__init__(
filters=None,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.bias_initializer = initializers.get(bias_initializer)
def __init__(self, filters, kernel_size,
kernel_initializer='glorot_uniform', activation=None, weights=None,
padding='valid', strides=(1, 1), data_format=None,
kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None, bias_constraint=None,
use_bias=True, **kwargs):
if data_format is None:
data_format = K.image_data_format()
if padding not in {'valid', 'same', 'full'}:
raise ValueError('Invalid border mode for CosineConvolution2D:', padding)
self.filters = filters
self.kernel_size = kernel_size
self.nb_row, self.nb_col = self.kernel_size
self.kernel_initializer = initializers.get(kernel_initializer)
self.activation = activations.get(activation)
self.padding = padding
self.strides = tuple(strides)
self.data_format = normalize_data_format(data_format)
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.use_bias = use_bias
self.input_spec = [InputSpec(ndim=4)]
self.initial_weights = weights
super(CosineConvolution2D, self).__init__(**kwargs)
def __init__(self,
kernel_size,
strides=(1, 1),
padding='valid',
depth_multiplier=1,
data_format=None,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs):
super(DepthwiseConv2D, self).__init__(
filters=None,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.bias_initializer = initializers.get(bias_initializer)
self._padding = _preprocess_padding(self.padding)
self._strides = (1,) + self.strides + (1,)
self._data_format = "NHWC"
def __init__(self, units,
activation='linear',
weights=None,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
kernel_constraint=None,
bias_initializer='uniform',
bias_regularizer=None,
bias_constraint=None,
activity_regularizer=None,
bias=True,
input_dim=None,
factorization=simple_tensor_factorization(),
**kwargs):
self.activation = activations.get(activation)
self.units = units
self.input_dim = input_dim
self.factorization = factorization
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_initializer = get_initializer(kernel_initializer)
self.bias_initializer = get_initializer(bias_initializer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.bias = bias
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=2)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(DenseTensor, self).__init__(**kwargs)
def __init__(self, init='one', power_init=1, weights=None, axis=-1, fit=True, **kwargs):
self.supports_masking = True
self.init = initializations.get(init)
self.initial_weights = weights
self.axis = axis
self.power_init = power_init
self.fit = fit
super(PowerReLU, self).__init__(**kwargs)
def __init__(self, degree=2, init='zero', init1='one', weights=None, **kwargs):
self.supports_masking = True
self.init1 = initializations.get(init1)
self.init = initializations.get(init)
self.initial_weights = weights
self.degree = degree
super(Polynomial, self).__init__(**kwargs)
def __init__(self, filters,
sum_axes,
filter_axes,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_activation=None,
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(FilterDims, self).__init__(**kwargs)
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.activation = activations.get(activation)
self.kernel_activation = activations.get(kernel_activation)
self.filters = filters
self.sum_axes = list(sum_axes)
self.sum_axes.sort()
self.filter_axes = list(filter_axes)
self.filter_axes.sort()
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.use_bias = use_bias
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
def __init__(self, output_dim,
init='glorot_uniform', activation='linear', weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
input_dim=None, input_length1=None, input_length2=None, **kwargs):
self.output_dim = output_dim
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.constraints = [self.W_constraint, self.b_constraint]
self.initial_weights = weights
self.input_dim = input_dim
self.input_length1 = input_length1
self.input_length2 = input_length2
if self.input_dim:
kwargs['input_shape'] = (self.input_length1, self.input_length2, self.input_dim)
self.input = K.placeholder(ndim=4)
super(HigherOrderTimeDistributedDense, self).__init__(**kwargs)
def get_initial_state(self, inputs):
print('inputs shape:', inputs.get_shape())
# apply the matrix on the first time step to get the initial s0.
s0 = activations.tanh(K.dot(inputs[:, 0], self.W_s))
# from keras.layers.recurrent to initialize a vector of (batchsize,
# output_dim)
y0 = K.zeros_like(inputs) # (samples, timesteps, input_dims)
y0 = K.sum(y0, axis=(1, 2)) # (samples, )
y0 = K.expand_dims(y0) # (samples, 1)
y0 = K.tile(y0, [1, self.output_dim])
return [y0, s0]
