def set_weights(self, weights):
"""Sets the weights of the optimizer, from Numpy arrays.
Should only be called after computing the gradients
(otherwise the optimizer has no weights).
Arguments:
weights: a list of Numpy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the optimizer (i.e. it should match the
output of `get_weights`).
Raises:
ValueError: in case of incompatible weight shapes.
"""
params = self.weights
if len(params) != len(weights):
raise ValueError('Length of the specified weight list (' +
str(len(weights)) +
') does not match the number of weights '
'of the optimizer (' + str(len(params)) + ')')
weight_value_tuples = []
param_values = K.batch_get_value(params)
for pv, p, w in zip(param_values, params, weights):
if pv.shape != w.shape:
raise ValueError('Optimizer weight shape ' + str(pv.shape) +
' not compatible with '
'provided weight shape ' + str(w.shape))
weight_value_tuples.append((p, w))
K.batch_set_value(weight_value_tuples)
def set_weights(self, weights):
"""Sets the weights of the layer, from Numpy arrays.
Arguments:
weights: a list of Numpy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the layer (i.e. it should match the
output of `get_weights`).
Raises:
ValueError: If the provided weights list does not match the
layer's specifications.
"""
params = self.weights
if len(params) != len(weights):
raise ValueError('You called `set_weights(weights)` on layer "' +
self.name + '" with a weight list of length ' +
str(len(weights)) +
', but the layer was expecting ' +
str(len(params)) +
' weights. Provided weights: ' +
str(weights)[:50] + '...')
if not params:
return
weight_value_tuples = []
param_values = K.batch_get_value(params)
for pv, p, w in zip(param_values, params, weights):
if pv.shape != w.shape:
raise ValueError('Layer weight shape ' + str(pv.shape) +
' not compatible with '
'provided weight shape ' + str(w.shape))
weight_value_tuples.append((p, w))
K.batch_set_value(weight_value_tuples)
def set_weights(self, weights):
"""Sets the weights of the layer, from Numpy arrays.
Arguments:
weights: a list of Numpy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the layer (i.e. it should match the
output of `get_weights`).
Raises:
ValueError: If the provided weights list does not match the
layer's specifications.
"""
params = self.weights
if len(params) != len(weights):
raise ValueError('You called `set_weights(weights)` on layer "' +
self.name + '" with a weight list of length ' +
str(len(weights)) + ', but the layer was expecting ' +
str(len(params)) + ' weights. Provided weights: ' +
str(weights)[:50] + '...')
if not params:
return
weight_value_tuples = []
param_values = K.batch_get_value(params)
for pv, p, w in zip(param_values, params, weights):
if pv.shape != w.shape:
raise ValueError('Layer weight shape ' + str(pv.shape) +
' not compatible with '
'provided weight shape ' + str(w.shape))
weight_value_tuples.append((p, w))
K.batch_set_value(weight_value_tuples)
def set_weights(self, weights):
"""Sets the weights of the optimizer, from Numpy arrays.
Should only be called after computing the gradients
(otherwise the optimizer has no weights).
Arguments:
weights: a list of Numpy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the optimizer (i.e. it should match the
output of `get_weights`).
Raises:
ValueError: in case of incompatible weight shapes.
"""
params = self.weights
if len(params) != len(weights):
raise ValueError(
'Length of the specified weight list (' + str(len(weights)) +
') does not match the number of weights '
'of the optimizer (' + str(len(params)) + ')')
weight_value_tuples = []
param_values = K.batch_get_value(params)
for pv, p, w in zip(param_values, params, weights):
if pv.shape != w.shape:
raise ValueError(
'Optimizer weight shape ' + str(pv.shape) + ' not compatible with '
'provided weight shape ' + str(w.shape))
weight_value_tuples.append((p, w))
K.batch_set_value(weight_value_tuples)
def load_weights_from_hdf5_group_by_name(f, layers):
"""Implements name-based weight loading.
(instead of topological weight loading).
Layers that have no matching name are skipped.
Arguments:
f: A pointer to a HDF5 group.
layers: a list of target layers.
Raises:
ValueError: in case of mismatch between provided layers
and weights file.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version'].decode('utf8')
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend'].decode('utf8')
else:
original_backend = None
# New file format.
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
# Reverse index of layer name to list of layers with name.
index = {}
for layer in layers:
if layer.name:
index.setdefault(layer.name, []).append(layer)
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [g[weight_name] for weight_name in weight_names]
for layer in index.get(name, []):
symbolic_weights = layer.weights
weight_values = preprocess_weights_for_loading(
layer, weight_values, original_keras_version, original_backend)
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) + ' (named "' +
layer.name + '") expects ' +
str(len(symbolic_weights)) +
' weight(s), but the saved weights' +
' have ' + str(len(weight_values)) +
' element(s).')
# Set values.
for i in range(len(weight_values)):
weight_value_tuples.append(
(symbolic_weights[i], weight_values[i]))
K.batch_set_value(weight_value_tuples)
def load_weights_from_hdf5_group_by_name(f, layers):
"""Implements name-based weight loading.
(instead of topological weight loading).
Layers that have no matching name are skipped.
Arguments:
f: A pointer to a HDF5 group.
layers: a list of target layers.
Raises:
ValueError: in case of mismatch between provided layers
and weights file.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version'].decode('utf8')
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend'].decode('utf8')
else:
original_backend = None
# New file format.
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
# Reverse index of layer name to list of layers with name.
index = {}
for layer in layers:
if layer.name:
index.setdefault(layer.name, []).append(layer)
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [g[weight_name] for weight_name in weight_names]
for layer in index.get(name, []):
symbolic_weights = layer.weights
weight_values = preprocess_weights_for_loading(
layer, weight_values, original_keras_version, original_backend)
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
'") expects ' + str(len(symbolic_weights)) +
' weight(s), but the saved weights' + ' have ' +
str(len(weight_values)) + ' element(s).')
# Set values.
for i in range(len(weight_values)):
weight_value_tuples.append((symbolic_weights[i], weight_values[i]))
K.batch_set_value(weight_value_tuples)
def convert_all_kernels_in_model(model):
"""Converts all convolution kernels in a model from Theano to TensorFlow.
Also works from TensorFlow to Theano.
Arguments:
model: target model for the conversion.
"""
# Note: SeparableConvolution not included
# since only supported by TF.
conv_classes = {
'Conv1D',
'Conv2D',
'Conv3D',
'Conv2DTranspose',
}
to_assign = []
for layer in model.layers:
if layer.__class__.__name__ in conv_classes:
original_kernel = K.get_value(layer.kernel)
converted_kernel = convert_kernel(original_kernel)
to_assign.append((layer.kernel, converted_kernel))
K.batch_set_value(to_assign)
def convert_all_kernels_in_model(model):
"""Converts all convolution kernels in a model from Theano to TensorFlow.
Also works from TensorFlow to Theano.
Arguments:
model: target model for the conversion.
"""
# Note: SeparableConvolution not included
# since only supported by TF.
conv_classes = {
'Conv1D',
'Conv2D',
'Conv3D',
'Conv2DTranspose',
}
to_assign = []
for layer in model.layers:
if layer.__class__.__name__ in conv_classes:
original_kernel = K.get_value(layer.kernel)
converted_kernel = convert_kernel(original_kernel)
to_assign.append((layer.kernel, converted_kernel))
K.batch_set_value(to_assign)
def load_weights_from_hdf5_group(f, layers):
"""Implements topological (order-based) weight loading.
Arguments:
f: A pointer to a HDF5 group.
layers: a list of target layers.
Raises:
ValueError: in case of mismatch between provided layers
and weights file.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version'].decode('utf8')
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend'].decode('utf8')
else:
original_backend = None
filtered_layers = []
for layer in layers:
weights = layer.weights
if weights:
filtered_layers.append(layer)
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
filtered_layer_names = []
for name in layer_names:
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
if weight_names:
filtered_layer_names.append(name)
layer_names = filtered_layer_names
if len(layer_names) != len(filtered_layers):
raise ValueError('You are trying to load a weight file '
'containing ' + str(len(layer_names)) +
' layers into a model with ' + str(len(filtered_layers)) +
' layers.')
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [g[weight_name] for weight_name in weight_names]
layer = filtered_layers[k]
symbolic_weights = layer.weights
weight_values = preprocess_weights_for_loading(
layer, weight_values, original_keras_version, original_backend)
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
'" in the current model) was found to '
'correspond to layer ' + name + ' in the save file. '
'However the new layer ' + layer.name + ' expects ' +
str(len(symbolic_weights)) +
' weights, but the saved weights have ' +
str(len(weight_values)) + ' elements.')
weight_value_tuples += zip(symbolic_weights, weight_values)
K.batch_set_value(weight_value_tuples)
def load_weights_from_hdf5_group(f, layers):
"""Implements topological (order-based) weight loading.
Arguments:
f: A pointer to a HDF5 group.
layers: a list of target layers.
Raises:
ValueError: in case of mismatch between provided layers
and weights file.
"""
if 'keras_version' in f.attrs:
original_keras_version = f.attrs['keras_version'].decode('utf8')
else:
original_keras_version = '1'
if 'backend' in f.attrs:
original_backend = f.attrs['backend'].decode('utf8')
else:
original_backend = None
filtered_layers = []
for layer in layers:
weights = layer.weights
if weights:
filtered_layers.append(layer)
layer_names = load_attributes_from_hdf5_group(f, 'layer_names')
filtered_layer_names = []
for name in layer_names:
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
if weight_names:
filtered_layer_names.append(name)
layer_names = filtered_layer_names
if len(layer_names) != len(filtered_layers):
raise ValueError('You are trying to load a weight file '
'containing ' + str(len(layer_names)) +
' layers into a model with ' +
str(len(filtered_layers)) + ' layers.')
# We batch weight value assignments in a single backend call
# which provides a speedup in TensorFlow.
weight_value_tuples = []
for k, name in enumerate(layer_names):
g = f[name]
weight_names = load_attributes_from_hdf5_group(g, 'weight_names')
weight_values = [g[weight_name] for weight_name in weight_names]
layer = filtered_layers[k]
symbolic_weights = layer.weights
weight_values = preprocess_weights_for_loading(layer, weight_values,
original_keras_version,
original_backend)
if len(weight_values) != len(symbolic_weights):
raise ValueError('Layer #' + str(k) + ' (named "' + layer.name +
'" in the current model) was found to '
'correspond to layer ' + name +
' in the save file. '
'However the new layer ' + layer.name +
' expects ' + str(len(symbolic_weights)) +
' weights, but the saved weights have ' +
str(len(weight_values)) + ' elements.')
weight_value_tuples += zip(symbolic_weights, weight_values)
K.batch_set_value(weight_value_tuples)
