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 build(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_shape = tensor_shape.TensorShape(input_shape).as_list()
batch_size = input_shape[0] if self.stateful else None
self.input_dim = input_shape[2]
self.input_spec[0] = InputSpec(shape=(batch_size, None, self.input_dim))
self.states = [None]
if self.stateful:
self.reset_states()
self.kernel = self.add_weight(
shape=(self.input_dim, self.units),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
if self.use_bias:
self.bias = self.add_weight(
shape=(self.units,),
name='bias',
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.built = True
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
input_dim = input_shape[2]
if input_dim is None:
raise ValueError('Axis 2 of input should be fully-defined. '
'Found shape:', input_shape)
output_length = conv_utils.conv_output_length(
input_shape[1], self.kernel_size[0], self.padding, self.strides[0])
self.kernel_shape = (output_length, self.kernel_size[0] * input_dim,
self.filters)
self.kernel = self.add_weight(
shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(
shape=(output_length, self.filters),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.input_spec = InputSpec(ndim=3, axes={2: input_dim})
self.built = True
def __init__(self, pool_size=2, strides=None, padding='valid', **kwargs):
super(_Pooling1D, self).__init__(**kwargs)
if strides is None:
strides = pool_size
self.pool_size = conv_utils.normalize_tuple(pool_size, 1, 'pool_size')
self.strides = conv_utils.normalize_tuple(strides, 1, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.input_spec = InputSpec(ndim=3)
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
self.conv_layers = {c: [] for c in ['i', 'f', 'c', 'o', 'a', 'ahat']}
for l in range(self.nb_layers):
for c in ['i', 'f', 'c', 'o']:
act = self.LSTM_activation if c == 'c' else self.LSTM_inner_activation
self.conv_layers[c].append(
Convolution2D(self.R_stack_sizes[l],
self.R_filt_sizes[l],
padding='same',
data_format="channels_last",
activation=act))
act = 'relu' if l == 0 else self.A_activation
self.conv_layers['ahat'].append(
Convolution2D(self.stack_sizes[l],
self.Ahat_filt_sizes[l],
padding='same',
data_format="channels_last",
activation=act))
if l < self.nb_layers - 1:
self.conv_layers['a'].append(
Convolution2D(self.stack_sizes[l + 1],
self.A_filt_sizes[l],
padding='same',
data_format="channels_last",
activation=self.A_activation))
self.upsample = UpSampling2D(data_format="channels_last") # upsampling
self.pool = MaxPooling2D(data_format="channels_last") # downsampling
self._trainable_weights = []
nb_row, nb_col = (input_shape[-3], input_shape[-2])
# Super model
for c in sorted(self.conv_layers.keys()):
for l in range(len(self.conv_layers[c])):
ds_factor = 2**l
if c == 'ahat':
nb_channels = self.R_stack_sizes[l]
elif c == 'a':
nb_channels = 2 * self.stack_sizes[l]
else: # i, c, o, f
nb_channels = self.stack_sizes[l] * 2 + self.R_stack_sizes[
l]
if l < self.nb_layers - 1:
nb_channels += self.R_stack_sizes[l + 1]
in_shape = (input_shape[0], nb_row // ds_factor,
nb_col // ds_factor, nb_channels
) # up -> downsampling
self.conv_layers[c][l].build(in_shape)
self._trainable_weights += self.conv_layers[c][
l].trainable_weights
self.states = [None] * self.nb_layers * 3 # ['r', 'c', 'e']
if self.extrap_start_time is not None:
self.t_extrap = K.variable(np.array(self.extrap_start_time),
'int32')
self.states += [None] * 2
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
if self.data_format == 'channels_last':
input_row, input_col = input_shape[1:-1]
input_filter = input_shape[3]
else:
input_row, input_col = input_shape[2:]
input_filter = input_shape[1]
if input_row is None or input_col is None:
raise ValueError('The spatial dimensions of the inputs to '
' a LocallyConnected2D layer '
'should be fully-defined, but layer received '
'the inputs shape ' + str(input_shape))
output_row = conv_utils.conv_output_length(input_row,
self.kernel_size[0],
self.padding,
self.strides[0])
output_col = conv_utils.conv_output_length(input_col,
self.kernel_size[1],
self.padding,
self.strides[1])
self.output_row = output_row
self.output_col = output_col
self.kernel_shape = (output_row * output_col, self.kernel_size[0] *
self.kernel_size[1] * input_filter, self.filters)
self.kernel = self.add_weight(shape=self.kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(output_row, output_col,
self.filters),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
if self.data_format == 'channels_first':
self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
else:
self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
self.built = True
def __init__(self,
units,
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,
dropout=0.,
recurrent_dropout=0.,
**kwargs):
super(LSTM, self).__init__(**kwargs)
self.units = units
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.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.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.state_spec = [
InputSpec(shape=(None, self.units)),
InputSpec(shape=(None, self.units))
]
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
assert len(input_shape) >= 3
self.input_spec = InputSpec(shape=input_shape)
child_input_shape = [input_shape[0]] + input_shape[2:]
if not self.layer.built:
self.layer.build(child_input_shape)
self.layer.built = True
super(TimeDistributed, self).build()
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
super(Dense, self).build(input_shape)
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
if self.kernel_constraint:
self.constraints[self.kernel] = self.kernel_constraint
if self.use_bias and self.bias_constraint:
self.constraints[self.bias] = self.bias_constraint
def __init__(self, rate, data_format=None, **kwargs):
super(SpatialDropout3D, self).__init__(rate, **kwargs)
if data_format is None:
data_format = K.image_data_format()
if data_format not in {'channels_last', 'channels_first'}:
raise ValueError('data_format must be in '
'{"channels_last", "channels_first"}')
self.data_format = data_format
self.input_spec = InputSpec(ndim=5)
def __init__(self,
pool_size=(2, 2, 2),
strides=None,
padding='valid',
data_format=None,
**kwargs):
super(_Pooling3D, self).__init__(**kwargs)
if strides is None:
strides = pool_size
self.pool_size = conv_utils.normalize_tuple(pool_size, 3, 'pool_size')
self.strides = conv_utils.normalize_tuple(strides, 3, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.input_spec = InputSpec(ndim=5)
def build(self, input_shape):
assert len(input_shape) >= 2
input_dim = input_shape[-1]
self.kernel = self.add_weight((input_dim, self.units),
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
if self.use_bias:
self.bias = self.add_weight((self.units, ),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True
def __init__(self,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
implementation=0,
**kwargs):
super(Recurrent, self).__init__(**kwargs)
self.return_sequences = return_sequences
self.return_state = return_state
self.go_backwards = go_backwards
self.stateful = stateful
self.unroll = unroll
self.implementation = implementation
self.supports_masking = True
self.input_spec = [InputSpec(ndim=3)]
self.state_spec = None
self.dropout = 0
self.recurrent_dropout = 0
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
param_shape = input_shape[1:]
self.param_broadcast = [False] * len(param_shape)
if self.shared_axes is not None:
for i in self.shared_axes:
param_shape[i - 1] = 1
self.param_broadcast[i - 1] = True
self.alpha = self.add_weight(shape=param_shape,
name='alpha',
initializer=self.alpha_initializer,
regularizer=self.alpha_regularizer,
constraint=self.alpha_constraint)
# Set input spec
axes = {}
if self.shared_axes:
for i in range(1, len(input_shape)):
if i not in self.shared_axes:
axes[i] = input_shape[i]
self.input_spec = InputSpec(ndim=len(input_shape), axes=axes)
self.built = True
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
dim = input_shape[self.axis]
if dim is None:
raise ValueError('Axis ' + str(self.axis) + ' of '
'input tensor should have a defined dimension '
'but the layer received an input with shape ' +
str(input_shape) + '.')
self.input_spec = InputSpec(ndim=len(input_shape), axes={self.axis: dim})
shape = (dim,)
if self.scale:
self.gamma = self.add_weight(
shape,
name='gamma',
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
else:
self.gamma = None
if self.center:
self.beta = self.add_weight(
shape,
name='beta',
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint)
else:
self.beta = None
self.moving_mean = self.add_weight(
shape,
name='moving_mean',
initializer=self.moving_mean_initializer,
trainable=False)
self.moving_variance = self.add_weight(
shape,
name='moving_variance',
initializer=self.moving_variance_initializer,
trainable=False)
self.built = True
def __init__(self,
filters,
kernel_size,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
return_sequences=False,
go_backwards=False,
stateful=False,
**kwargs):
super(ConvRecurrent2D, 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.return_sequences = return_sequences
self.go_backwards = go_backwards
self.stateful = stateful
self.input_spec = InputSpec(ndim=5)
def build(self, input_shape):
if len(input_shape) < 4:
raise ValueError(
'Inputs to `DepthwiseConv2D` should have rank 4. '
'Received input shape:', str(input_shape))
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = 3
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs to '
'`DepthwiseConv2D` '
'should be defined. Found `None`.')
input_dim = int(input_shape[channel_axis])
depthwise_kernel_shape = (self.kernel_size[0], self.kernel_size[1],
input_dim, self.depth_multiplier)
self.depthwise_kernel = self.add_weight(
shape=depthwise_kernel_shape,
initializer=self.depthwise_initializer,
name='depthwise_kernel',
regularizer=self.depthwise_regularizer,
constraint=self.depthwise_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(input_dim *
self.depth_multiplier, ),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
# Set input spec.
self.input_spec = InputSpec(ndim=4, axes={channel_axis: input_dim})
self.built = True
def __init__(self, **kwargs): super(Flatten, self).__init__(**kwargs) self.input_spec = InputSpec(min_ndim=3)
def __init__(self, dims, **kwargs): super(Permute, self).__init__(**kwargs) self.dims = tuple(dims) self.input_spec = InputSpec(ndim=len(self.dims) + 1)
def __init__(self, size=2, data_format=None, **kwargs):
super(DepthToSpace, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
self.size = size
self.input_spec = InputSpec(ndim=4)
def __init__(self, rate, **kwargs): super(SpatialDropout1D, self).__init__(rate, **kwargs) self.input_spec = InputSpec(ndim=3)
def build(self, input_shape):
input_shape = tensor_shape.TensorShape(input_shape).as_list()
# TODO(fchollet): better handling of input spec
self.input_spec = InputSpec(shape=input_shape)
if self.stateful:
self.reset_states()
else:
# initial states: 2 all-zero tensor of shape (filters)
self.states = [None, None]
if self.data_format == 'channels_first':
channel_axis = 1
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
kernel_shape = self.kernel_size + (input_dim, self.filters * 4)
self.kernel_shape = kernel_shape
recurrent_kernel_shape = self.kernel_size + (self.filters, self.filters * 4)
self.kernel = self.add_weight(
kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
recurrent_kernel_shape,
initializer=self.recurrent_initializer,
name='recurrent_kernel',
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
if self.use_bias:
self.bias = self.add_weight(
(self.filters * 4,),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
if self.unit_forget_bias:
bias_value = np.zeros((self.filters * 4,))
bias_value[self.filters:self.filters * 2] = 1.
K.set_value(self.bias, bias_value)
else:
self.bias = None
self.kernel_i = self.kernel[:, :, :, :self.filters]
self.recurrent_kernel_i = self.recurrent_kernel[:, :, :, :self.filters]
self.kernel_f = self.kernel[:, :, :, self.filters:self.filters * 2]
self.recurrent_kernel_f = self.recurrent_kernel[:, :, :, self.filters:
self.filters * 2]
self.kernel_c = self.kernel[:, :, :, self.filters * 2:self.filters * 3]
self.recurrent_kernel_c = self.recurrent_kernel[:, :, :, self.filters * 2:
self.filters * 3]
self.kernel_o = self.kernel[:, :, :, self.filters * 3:]
self.recurrent_kernel_o = self.recurrent_kernel[:, :, :, self.filters * 3:]
if self.use_bias:
self.bias_i = self.bias[:self.filters]
self.bias_f = self.bias[self.filters:self.filters * 2]
self.bias_c = self.bias[self.filters * 2:self.filters * 3]
self.bias_o = self.bias[self.filters * 3:]
else:
self.bias_i = None
self.bias_f = None
self.bias_c = None
self.bias_o = None
self.built = True
def build(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_shape = tensor_shape.TensorShape(input_shape).as_list()
batch_size = input_shape[0] if self.stateful else None
self.input_dim = input_shape[2]
self.input_spec[0] = InputSpec(shape=(batch_size, None, self.input_dim))
self.states = [None, None]
if self.stateful:
self.reset_states()
self.kernel = self.add_weight(
shape=(self.input_dim, self.units * 4),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 4),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
if self.use_bias:
if self.unit_forget_bias:
def bias_initializer(_, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.units,), *args, **kwargs),
initializers.Ones()((self.units,), *args, **kwargs),
self.bias_initializer((self.units * 2,), *args, **kwargs),
])
else:
bias_initializer = self.bias_initializer
self.bias = self.add_weight(
shape=(self.units * 4,),
name='bias',
initializer=bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.kernel_i = self.kernel[:, :self.units]
self.kernel_f = self.kernel[:, self.units:self.units * 2]
self.kernel_c = self.kernel[:, self.units * 2:self.units * 3]
self.kernel_o = self.kernel[:, self.units * 3:]
self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
self.recurrent_kernel_f = self.recurrent_kernel[:, self.units:
self.units * 2]
self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2:
self.units * 3]
self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]
if self.use_bias:
self.bias_i = self.bias[:self.units]
self.bias_f = self.bias[self.units:self.units * 2]
self.bias_c = self.bias[self.units * 2:self.units * 3]
self.bias_o = self.bias[self.units * 3:]
else:
self.bias_i = None
self.bias_f = None
self.bias_c = None
self.bias_o = None
self.built = True
def __init__(self, data_format=None, **kwargs): super(_GlobalPooling3D, self).__init__(**kwargs) self.data_format = conv_utils.normalize_data_format(data_format) self.input_spec = InputSpec(ndim=5)
def __init__(self, **kwargs): super(_GlobalPooling1D, self).__init__(**kwargs) self.input_spec = InputSpec(ndim=3)
def __init__(self,
stack_sizes,
R_stack_sizes,
A_filt_sizes,
Ahat_filt_sizes,
R_filt_sizes,
pixel_max=1.,
error_activation='relu',
A_activation='relu',
LSTM_activation='tanh',
LSTM_inner_activation='hard_sigmoid',
output_mode='error',
extrap_start_time=None,
use_roi_loss=False,
threshold=None,
**kwargs):
self.stack_sizes = stack_sizes # output_dim for each layer
self.nb_layers = len(stack_sizes) # layer num
assert len(
R_stack_sizes
) == self.nb_layers, 'len(R_stack_sizes) must equal len(stack_sizes)'
self.R_stack_sizes = R_stack_sizes # R state dim
assert len(A_filt_sizes) == (
self.nb_layers -
1), 'len(A_filt_sizes) must equal len(stack_sizes) - 1'
self.A_filt_sizes = A_filt_sizes # A2E filter
assert len(
Ahat_filt_sizes
) == self.nb_layers, 'len(Ahat_filt_sizes) must equal len(stack_sizes)'
self.Ahat_filt_sizes = Ahat_filt_sizes # Ahat2E filter
assert len(R_filt_sizes) == (
self.nb_layers), 'len(R_filt_sizes) must equal len(stack_sizes)'
self.R_filt_sizes = R_filt_sizes # R_l+1, El 2 R_l filter
self.extrap_start_time = extrap_start_time
self.use_roi_loss = use_roi_loss
self.threshold = threshold
self.pixel_max = pixel_max
self.error_activation = activations.get(error_activation)
self.A_activation = activations.get(A_activation)
self.LSTM_activation = activations.get(LSTM_activation)
self.LSTM_inner_activation = activations.get(LSTM_inner_activation)
default_output_modes = ['prediction', 'error', 'all']
layer_output_modes = [
layer + str(n) for n in range(self.nb_layers)
for layer in ['R', 'E', 'A', 'Ahat']
]
assert output_mode in default_output_modes + layer_output_modes, 'Invalid output_mode: ' + str(
output_mode)
self.output_mode = output_mode
if self.output_mode in layer_output_modes:
self.output_layer_type = self.output_mode[:-1]
self.output_layer_num = int(self.output_mode[-1])
else:
self.output_layer_type = None
self.output_layer_num = None
# self.dim_ordering = 'tf'
self.channel_axis = -1
self.row_axis = -3
self.column_axis = -2
super(PredNet, self).__init__(**kwargs)
self.input_spec = [InputSpec(ndim=5)
] # (batch_num, time_step, row, column, channel)
def __init__(self, n, **kwargs): super(RepeatVector, self).__init__(**kwargs) self.n = n self.input_spec = InputSpec(ndim=2)
def __init__(self, size=size_mult, data_format=None, **kwargs):
super(BilinearUpSampling2D, self).__init__(**kwargs)
self.data_format = conv_utils.normalize_data_format(data_format)
self.size = conv_utils.normalize_tuple(size, 2, 'size')
self.input_spec = InputSpec(ndim=4)
def build(self, input_shape):
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_shape = tuple(tensor_shape.TensorShape(input_shape).as_list())
batch_size = input_shape[0] if self.stateful else None
self.input_spec[0] = InputSpec(shape=(batch_size, None) +
input_shape[2:])
if self.stateful:
self.reset_states()
else:
# initial states: 2 all-zero tensor of shape (filters)
self.states = [None, None]
if self.data_format == 'channels_first':
channel_axis = 2
else:
channel_axis = -1
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_dim = input_shape[channel_axis]
state_shape = [None] * 4
state_shape[channel_axis] = input_dim
state_shape = tuple(state_shape)
self.state_spec = [
InputSpec(shape=state_shape),
InputSpec(shape=state_shape)
]
kernel_shape = self.kernel_size + (input_dim, self.filters * 4)
self.kernel_shape = kernel_shape
recurrent_kernel_shape = self.kernel_size + (self.filters,
self.filters * 4)
self.kernel = self.add_weight(shape=kernel_shape,
initializer=self.kernel_initializer,
name='kernel',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=recurrent_kernel_shape,
initializer=self.recurrent_initializer,
name='recurrent_kernel',
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
if self.use_bias:
self.bias = self.add_weight(shape=(self.filters * 4, ),
initializer=self.bias_initializer,
name='bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
if self.unit_forget_bias:
bias_value = np.zeros((self.filters * 4, ))
bias_value[self.filters:self.filters * 2] = 1.
K.set_value(self.bias, bias_value)
else:
self.bias = None
self.kernel_i = self.kernel[:, :, :, :self.filters]
self.recurrent_kernel_i = self.recurrent_kernel[:, :, :, :self.filters]
self.kernel_f = self.kernel[:, :, :, self.filters:self.filters * 2]
self.recurrent_kernel_f = self.recurrent_kernel[:, :, :, self.
filters:self.filters *
2]
self.kernel_c = self.kernel[:, :, :, self.filters * 2:self.filters * 3]
self.recurrent_kernel_c = self.recurrent_kernel[:, :, :, self.filters *
2:self.filters * 3]
self.kernel_o = self.kernel[:, :, :, self.filters * 3:]
self.recurrent_kernel_o = self.recurrent_kernel[:, :, :,
self.filters * 3:]
if self.use_bias:
self.bias_i = self.bias[:self.filters]
self.bias_f = self.bias[self.filters:self.filters * 2]
self.bias_c = self.bias[self.filters * 2:self.filters * 3]
self.bias_o = self.bias[self.filters * 3:]
else:
self.bias_i = None
self.bias_f = None
self.bias_c = None
self.bias_o = None
self.built = True
