def call(self, inputs):
if not isinstance(inputs, list):
raise ValueError('A merge layer should be called ' 'on a list of inputs.')
if self._reshape_required:
reshaped_inputs = []
input_ndims = list(map(K.ndim, inputs))
if None not in input_ndims:
# If ranks of all inputs are available,
# we simply expand each of them at axis=1
# until all of them have the same rank.
max_ndim = max(input_ndims)
for x in inputs:
x_ndim = K.ndim(x)
for _ in range(max_ndim - x_ndim):
x = K.expand_dims(x, 1)
reshaped_inputs.append(x)
return self._merge_function(reshaped_inputs)
else:
# Transpose all inputs so that batch size is the last dimension.
# (batch_size, dim1, dim2, ... ) -> (dim1, dim2, ... , batch_size)
transposed = False
for x in inputs:
x_ndim = K.ndim(x)
if x_ndim is None:
x_shape = K.shape(x)
batch_size = x_shape[0]
new_shape = K.concatenate([x_shape[1:], K.expand_dims(batch_size)])
x_transposed = K.reshape(x,
K.stack([batch_size,
K.prod(x_shape[1:])]))
x_transposed = K.permute_dimensions(x_transposed, (1, 0))
x_transposed = K.reshape(x_transposed, new_shape)
reshaped_inputs.append(x_transposed)
transposed = True
elif x_ndim > 1:
dims = list(range(1, x_ndim)) + [0]
reshaped_inputs.append(K.permute_dimensions(x, dims))
transposed = True
else:
# We don't transpose inputs if they are 1D vectors or scalars.
reshaped_inputs.append(x)
y = self._merge_function(reshaped_inputs)
y_ndim = K.ndim(y)
if transposed:
# If inputs have been transposed, we have to transpose the output too.
if y_ndim is None:
y_shape = K.shape(y)
y_ndim = K.shape(y_shape)[0]
batch_size = y_shape[y_ndim - 1]
new_shape = K.concatenate(
[K.expand_dims(batch_size), y_shape[:y_ndim - 1]])
y = K.reshape(y, (-1, batch_size))
y = K.permute_dimensions(y, (1, 0))
y = K.reshape(y, new_shape)
elif y_ndim > 1:
dims = [y_ndim - 1] + list(range(y_ndim - 1))
y = K.permute_dimensions(y, dims)
return y
else:
return self._merge_function(inputs)
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
return None
# Make a list of masks while making sure
# the dimensionality of each mask
# is the same as the corresponding input.
masks = []
for input_i, mask_i in zip(inputs, mask):
if mask_i is None:
# Input is unmasked. Append all 1s to masks,
masks.append(K.ones_like(input_i, dtype='bool'))
elif K.ndim(mask_i) < K.ndim(input_i):
# Mask is smaller than the input, expand it
masks.append(K.expand_dims(mask_i))
else:
masks.append(mask_i)
concatenated = K.concatenate(masks, axis=self.axis)
return K.all(concatenated, axis=-1, keepdims=False)
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
return None
# Make a list of masks while making sure
# the dimensionality of each mask
# is the same as the corresponding input.
masks = []
for input_i, mask_i in zip(inputs, mask):
if mask_i is None:
# Input is unmasked. Append all 1s to masks,
# but cast it to bool first
masks.append(K.cast(K.ones_like(input_i), 'bool'))
elif K.ndim(mask_i) < K.ndim(input_i):
# Mask is smaller than the input, expand it
masks.append(K.expand_dims(mask_i))
else:
masks.append(mask_i)
concatenated = K.concatenate(masks, axis=self.axis)
return K.all(concatenated, axis=-1, keepdims=False)
def call(self, inputs, training=None, mask=None):
kwargs = {}
if has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if 0 < self.layer.dropout + self.layer.recurrent_dropout:
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
return output
def call(self, inputs, training=None, mask=None):
kwargs = {}
if has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if (getattr(y, '_uses_learning_phase', False)
or getattr(y_rev, '_uses_learning_phase', False)):
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
return output
def bias_initializer(_, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.filters, ), *args,
**kwargs),
initializers.Ones()((self.filters, ), *args, **kwargs),
self.bias_initializer((self.filters * 2, ), *args,
**kwargs),
])
def call(self, inputs, training=None, mask=None, initial_state=None):
kwargs = {}
if has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
if initial_state is not None and has_arg(self.layer.call,
'initial_state'):
if not isinstance(initial_state, list):
raise ValueError(
'When passing `initial_state` to a Bidirectional RNN, the state '
'should be a list containing the states of the underlying RNNs. '
'Found: ' + str(initial_state))
forward_state = initial_state[:len(initial_state) // 2]
backward_state = initial_state[len(initial_state) // 2:]
y = self.forward_layer.call(inputs,
initial_state=forward_state,
**kwargs)
y_rev = self.backward_layer.call(inputs,
initial_state=backward_state,
**kwargs)
else:
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_state:
states = y[1:] + y_rev[1:]
y = y[0]
y_rev = y_rev[0]
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if (getattr(y, '_uses_learning_phase', False)
or getattr(y_rev, '_uses_learning_phase', False)):
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
if self.return_state:
if self.merge_mode is None:
return output + states
return [output] + states
return output
def call(self, inputs,
training=None,
mask=None,
initial_state=None,
constants=None):
"""`Bidirectional.call` implements the same API as the wrapped `RNN`."""
kwargs = {}
if generic_utils.has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if generic_utils.has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
if generic_utils.has_arg(self.layer.call, 'constants'):
kwargs['constants'] = constants
if initial_state is not None and generic_utils.has_arg(
self.layer.call, 'initial_state'):
forward_state = initial_state[:len(initial_state) // 2]
backward_state = initial_state[len(initial_state) // 2:]
y = self.forward_layer.call(inputs, initial_state=forward_state, **kwargs)
y_rev = self.backward_layer.call(
inputs, initial_state=backward_state, **kwargs)
else:
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_state:
states = y[1:] + y_rev[1:]
y = y[0]
y_rev = y_rev[0]
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if (getattr(y, '_uses_learning_phase', False) or
getattr(y_rev, '_uses_learning_phase', False)):
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
if self.return_state:
if self.merge_mode is None:
return output + states
return [output] + states
return output
def call(self, inputs, training=None, mask=None, initial_state=None):
kwargs = {}
if has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
if initial_state is not None and has_arg(self.layer.call, 'initial_state'):
if not isinstance(initial_state, list):
raise ValueError(
'When passing `initial_state` to a Bidirectional RNN, the state '
'should be a list containing the states of the underlying RNNs. '
'Found: ' + str(initial_state))
forward_state = initial_state[:len(initial_state) // 2]
backward_state = initial_state[len(initial_state) // 2:]
y = self.forward_layer.call(inputs, initial_state=forward_state, **kwargs)
y_rev = self.backward_layer.call(
inputs, initial_state=backward_state, **kwargs)
else:
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_state:
states = y[1:] + y_rev[1:]
y = y[0]
y_rev = y_rev[0]
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if (getattr(y, '_uses_learning_phase', False) or
getattr(y_rev, '_uses_learning_phase', False)):
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
if self.return_state:
if self.merge_mode is None:
return output + states
return [output] + states
return output
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
return None
masks = [K.expand_dims(m, 0) for m in mask if m is not None]
return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)
def compute_mask(self, inputs, mask=None):
if mask is None:
return None
if not isinstance(mask, list):
raise ValueError('`mask` should be a list.')
if not isinstance(inputs, list):
raise ValueError('`inputs` should be a list.')
if len(mask) != len(inputs):
raise ValueError('The lists `inputs` and `mask` '
'should have the same length.')
if all([m is None for m in mask]):
return None
masks = [K.expand_dims(m, 0) for m in mask if m is not None]
return K.all(K.concatenate(masks, axis=0), axis=0, keepdims=False)
def call(self, inputs, training=None, mask=None, initial_state=None):
kwargs = {}
if generic_utils.has_arg(self.layer.call, 'training'):
kwargs['training'] = training
if generic_utils.has_arg(self.layer.call, 'mask'):
kwargs['mask'] = mask
if initial_state is not None and generic_utils.has_arg(
self.layer.call, 'initial_state'):
forward_state = initial_state[:len(initial_state) // 2]
backward_state = initial_state[len(initial_state) // 2:]
y = self.forward_layer.call(inputs, initial_state=forward_state, **kwargs)
y_rev = self.backward_layer.call(
inputs, initial_state=backward_state, **kwargs)
else:
y = self.forward_layer.call(inputs, **kwargs)
y_rev = self.backward_layer.call(inputs, **kwargs)
if self.return_state:
states = y[1:] + y_rev[1:]
y = y[0]
y_rev = y_rev[0]
if self.return_sequences:
y_rev = K.reverse(y_rev, 1)
if self.merge_mode == 'concat':
output = K.concatenate([y, y_rev])
elif self.merge_mode == 'sum':
output = y + y_rev
elif self.merge_mode == 'ave':
output = (y + y_rev) / 2
elif self.merge_mode == 'mul':
output = y * y_rev
elif self.merge_mode is None:
output = [y, y_rev]
# Properly set learning phase
if (getattr(y, '_uses_learning_phase', False) or
getattr(y_rev, '_uses_learning_phase', False)):
if self.merge_mode is None:
for out in output:
out._uses_learning_phase = True
else:
output._uses_learning_phase = True
if self.return_state:
if self.merge_mode is None:
return output + states
return [output] + states
return output
def preprocess_input(self, inputs, training=None):
if self.implementation == 0:
input_shape = inputs.get_shape().as_list()
input_dim = input_shape[2]
timesteps = input_shape[1]
x_i = _time_distributed_dense(
inputs,
self.kernel_i,
self.bias_i,
self.dropout,
input_dim,
self.units,
timesteps,
training=training)
x_f = _time_distributed_dense(
inputs,
self.kernel_f,
self.bias_f,
self.dropout,
input_dim,
self.units,
timesteps,
training=training)
x_c = _time_distributed_dense(
inputs,
self.kernel_c,
self.bias_c,
self.dropout,
input_dim,
self.units,
timesteps,
training=training)
x_o = _time_distributed_dense(
inputs,
self.kernel_o,
self.bias_o,
self.dropout,
input_dim,
self.units,
timesteps,
training=training)
return K.concatenate([x_i, x_f, x_c, x_o], axis=2)
else:
return inputs
def call(self, inputs, memory, training=None):
batch_size = int(inputs.shape[0])
memory = K.reshape(memory, (batch_size, self.mem_slots, self.mem_size))
inputs = self._linear(inputs, self.kernel_in, self.bias_in)
inputs_reshape = K.expand_dims(inputs, axis=1)
memory_plus_input = K.concatenate([memory, inputs_reshape], axis=1)
next_memory = self._attend_over_memory(memory_plus_input)
n = inputs_reshape.get_shape().as_list()[1]
next_memory = next_memory[:, :-n, :]
input_gate, forget_gate = self._create_gates(inputs_reshape, memory)
next_memory = input_gate * K.tanh(next_memory)
next_memory += forget_gate * memory
next_memory = K.batch_flatten(next_memory)
return next_memory, (next_memory, )
def preprocess_input(self, inputs, training=None):
if self.implementation == 0:
input_shape = inputs.get_shape().as_list()
input_dim = input_shape[2]
timesteps = input_shape[1]
x_z = _time_distributed_dense(
inputs,
self.kernel_z,
self.bias_z,
self.dropout,
input_dim,
self.units,
timesteps,
training=training)
x_r = _time_distributed_dense(
inputs,
self.kernel_r,
self.bias_r,
self.dropout,
input_dim,
self.units,
timesteps,
training=training)
x_h = _time_distributed_dense(
inputs,
self.kernel_h,
self.bias_h,
self.dropout,
input_dim,
self.units,
timesteps,
training=training)
return K.concatenate([x_z, x_r, x_h], axis=2)
else:
return inputs
def bias_initializer(shape, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.units * 5, ), *args, **kwargs),
initializers.Ones()((self.units, ), *args, **kwargs),
self.bias_initializer((self.units * 2, ), *args, **kwargs),
])
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),
])
def call(self, inputs):
if not isinstance(inputs, list):
raise ValueError('A `Concatenate` layer should be called '
'on a list of inputs.')
return K.concatenate(inputs, axis=self.axis)
def _merge_function(self, inputs): return K.concatenate(inputs, axis=self.axis)
def _merge_function(self, inputs):
return K.concatenate(inputs, axis=self.axis)
def call(self, inputs):
if not isinstance(inputs, list):
raise ValueError('A `Concatenate` layer should be called '
'on a list of inputs.')
return K.concatenate(inputs, axis=self.axis)
def call(self, inputs):
if not isinstance(inputs, list):
raise ValueError('A merge layer should be called '
'on a list of inputs.')
if self._reshape_required:
reshaped_inputs = []
input_ndims = list(map(K.ndim, inputs))
if None not in input_ndims:
# If ranks of all inputs are available,
# we simply expand each of them at axis=1
# until all of them have the same rank.
max_ndim = max(input_ndims)
for x in inputs:
x_ndim = K.ndim(x)
for _ in range(max_ndim - x_ndim):
x = K.expand_dims(x, 1)
reshaped_inputs.append(x)
return self._merge_function(reshaped_inputs)
else:
# Transpose all inputs so that batch size is the last dimension.
# (batch_size, dim1, dim2, ... ) -> (dim1, dim2, ... , batch_size)
transposed = False
for x in inputs:
x_ndim = K.ndim(x)
if x_ndim is None:
x_shape = K.shape(x)
batch_size = x_shape[0]
new_shape = K.concatenate(
[x_shape[1:],
K.expand_dims(batch_size)])
x_transposed = K.reshape(
x, K.stack([batch_size,
K.prod(x_shape[1:])]))
x_transposed = K.permute_dimensions(
x_transposed, (1, 0))
x_transposed = K.reshape(x_transposed, new_shape)
reshaped_inputs.append(x_transposed)
transposed = True
elif x_ndim > 1:
dims = list(range(1, x_ndim)) + [0]
reshaped_inputs.append(K.permute_dimensions(x, dims))
transposed = True
else:
# We don't transpose inputs if they are 1D vectors or scalars.
reshaped_inputs.append(x)
y = self._merge_function(reshaped_inputs)
y_ndim = K.ndim(y)
if transposed:
# If inputs have been transposed, we have to transpose the output too.
if y_ndim is None:
y_shape = K.shape(y)
y_ndim = K.shape(y_shape)[0]
batch_size = y_shape[y_ndim - 1]
new_shape = K.concatenate(
[K.expand_dims(batch_size), y_shape[:y_ndim - 1]])
y = K.reshape(y, (-1, batch_size))
y = K.permute_dimensions(y, (1, 0))
y = K.reshape(y, new_shape)
elif y_ndim > 1:
dims = [y_ndim - 1] + list(range(y_ndim - 1))
y = K.permute_dimensions(y, dims)
return y
else:
return self._merge_function(inputs)
style_reference_images.append(K.variable(preprocess_image(style_path)))
# this will contain our generated image
combination_image = K.placeholder((1, img_width, img_height, 3))
image_tensors = [base_image]
for style_image_tensor in style_reference_images:
image_tensors.append(style_image_tensor)
image_tensors.append(combination_image)
nb_tensors = len(image_tensors)
print("nb_tensors", nb_tensors)
nb_style_images = nb_tensors - 2 # Content and Output image not considered
# combine the various image into a single Keras tensor
input_tensor = K.concatenate(image_tensors, axis=0)
shape = (nb_tensors, img_width, img_height, 3)
inp_shape = (img_width, img_height, 3)
ip = Input(tensor=input_tensor, batch_shape=shape)
model = VGG16(include_top=False, weights='Imagenet', input_tensor=input_tensor,
input_shape=inp_shape, pooling='max')
print("Model loaded")
# get the symbolic outputs of each "key" layer (we gave them unique names)
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
shape_dict = dict([(layer.name, layer.output_shape) for layer in model.layers])
