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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=protected-access
"""Model saving utilities.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import numpy as np
from six.moves import zip # pylint: disable=redefined-builtin
from tensorflow.python.keras._impl.keras import backend as K
from tensorflow.python.keras._impl.keras import optimizers
from tensorflow.python.keras._impl.keras.utils import conv_utils
from tensorflow.python.keras._impl.keras.utils.io_utils import ask_to_proceed_with_overwrite
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util.tf_export import tf_export
# pylint: disable=g-import-not-at-top
try:
import h5py
HDF5_OBJECT_HEADER_LIMIT = 64512
except ImportError:
h5py = None
try:
import yaml
except ImportError:
yaml = None
# pylint: enable=g-import-not-at-top
@tf_export('keras.models.save_model')
def save_model(model, filepath, overwrite=True, include_optimizer=True):
"""Saves a model to a HDF5 file.
The saved model contains:
- the model's configuration (topology)
- the model's weights
- the model's optimizer's state (if any)
Thus the saved model can be reinstantiated in
the exact same state, without any of the code
used for model definition or training.
Arguments:
model: Keras model instance to be saved.
filepath: String, path where to save the model.
overwrite: Whether we should overwrite any existing
model at the target location, or instead
ask the user with a manual prompt.
include_optimizer: If True, save optimizer's state together.
Raises:
ImportError: if h5py is not available.
"""
if h5py is None:
raise ImportError('`save_model` requires h5py.')
def get_json_type(obj):
"""Serializes any object to a JSON-serializable structure.
Arguments:
obj: the object to serialize
Returns:
JSON-serializable structure representing `obj`.
Raises:
TypeError: if `obj` cannot be serialized.
"""
# if obj is a serializable Keras class instance
# e.g. optimizer, layer
if hasattr(obj, 'get_config'):
return {'class_name': obj.__class__.__name__, 'config': obj.get_config()}
# if obj is any numpy type
if type(obj).__module__ == np.__name__:
if isinstance(obj, np.ndarray):
return {'type': type(obj), 'value': obj.tolist()}
else:
return obj.item()
# misc functions (e.g. loss function)
if callable(obj):
return obj.__name__
# if obj is a python 'type'
if type(obj).__name__ == type.__name__:
return obj.__name__
raise TypeError('Not JSON Serializable:', obj)
from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
# If file exists and should not be overwritten.
if not overwrite and os.path.isfile(filepath):
proceed = ask_to_proceed_with_overwrite(filepath)
if not proceed:
return
with h5py.File(filepath, mode='w') as f:
f.attrs['keras_version'] = str(keras_version).encode('utf8')
f.attrs['backend'] = K.backend().encode('utf8')
f.attrs['model_config'] = json.dumps(
{
'class_name': model.__class__.__name__,
'config': model.get_config()
},
default=get_json_type).encode('utf8')
model_weights_group = f.create_group('model_weights')
model_layers = model.layers
save_weights_to_hdf5_group(model_weights_group, model_layers)
if include_optimizer and hasattr(model, 'optimizer'):
if isinstance(model.optimizer, optimizers.TFOptimizer):
logging.warning(
'TensorFlow optimizers do not '
'make it possible to access '
'optimizer attributes or optimizer state '
'after instantiation. '
'As a result, we cannot save the optimizer '
'as part of the model save file.'
'You will have to compile your model again after loading it. '
'Prefer using a Keras optimizer instead '
'(see keras.io/optimizers).')
else:
f.attrs['training_config'] = json.dumps(
{
'optimizer_config': {
'class_name': model.optimizer.__class__.__name__,
'config': model.optimizer.get_config()
},
'loss': model.loss,
'metrics': model.metrics,
'sample_weight_mode': model.sample_weight_mode,
'loss_weights': model.loss_weights,
},
default=get_json_type).encode('utf8')
# Save optimizer weights.
symbolic_weights = getattr(model.optimizer, 'weights')
if symbolic_weights:
optimizer_weights_group = f.create_group('optimizer_weights')
weight_values = K.batch_get_value(symbolic_weights)
weight_names = []
for w, val in zip(symbolic_weights, weight_values):
name = str(w.name)
weight_names.append(name.encode('utf8'))
optimizer_weights_group.attrs['weight_names'] = weight_names
for name, val in zip(weight_names, weight_values):
param_dset = optimizer_weights_group.create_dataset(
name, val.shape, dtype=val.dtype)
if not val.shape:
# scalar
param_dset[()] = val
else:
param_dset[:] = val
f.flush()
@tf_export('keras.models.load_model')
def load_model(filepath, custom_objects=None, compile=True): # pylint: disable=redefined-builtin
"""Loads a model saved via `save_model`.
Arguments:
filepath: String, path to the saved model.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
compile: Boolean, whether to compile the model
after loading.
Returns:
A Keras model instance. If an optimizer was found
as part of the saved model, the model is already
compiled. Otherwise, the model is uncompiled and
a warning will be displayed. When `compile` is set
to False, the compilation is omitted without any
warning.
Raises:
ImportError: if h5py is not available.
ValueError: In case of an invalid savefile.
"""
if h5py is None:
raise ImportError('`load_model` requires h5py.')
if not custom_objects:
custom_objects = {}
def convert_custom_objects(obj):
"""Handles custom object lookup.
Arguments:
obj: object, dict, or list.
Returns:
The same structure, where occurrences
of a custom object name have been replaced
with the custom object.
"""
if isinstance(obj, list):
deserialized = []
for value in obj:
deserialized.append(convert_custom_objects(value))
return deserialized
if isinstance(obj, dict):
deserialized = {}
for key, value in obj.items():
deserialized[key] = convert_custom_objects(value)
return deserialized
if obj in custom_objects:
return custom_objects[obj]
return obj
with h5py.File(filepath, mode='r') as f:
# instantiate model
model_config = f.attrs.get('model_config')
if model_config is None:
raise ValueError('No model found in config file.')
model_config = json.loads(model_config.decode('utf-8'))
model = model_from_config(model_config, custom_objects=custom_objects)
# set weights
load_weights_from_hdf5_group(f['model_weights'], model.layers)
# Early return if compilation is not required.
if not compile:
return model
# instantiate optimizer
training_config = f.attrs.get('training_config')
if training_config is None:
logging.warning('No training configuration found in save file: '
'the model was *not* compiled. Compile it manually.')
return model
training_config = json.loads(training_config.decode('utf-8'))
optimizer_config = training_config['optimizer_config']
optimizer = optimizers.deserialize(
optimizer_config, custom_objects=custom_objects)
# Recover loss functions and metrics.
loss = convert_custom_objects(training_config['loss'])
metrics = convert_custom_objects(training_config['metrics'])
sample_weight_mode = training_config['sample_weight_mode']
loss_weights = training_config['loss_weights']
# Compile model.
model.compile(
optimizer=optimizer,
loss=loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=sample_weight_mode)
# Set optimizer weights.
if 'optimizer_weights' in f:
# Build train function (to get weight updates).
model._make_train_function()
optimizer_weights_group = f['optimizer_weights']
optimizer_weight_names = [
n.decode('utf8')
for n in optimizer_weights_group.attrs['weight_names']
]
optimizer_weight_values = [
optimizer_weights_group[n] for n in optimizer_weight_names
]
try:
model.optimizer.set_weights(optimizer_weight_values)
except ValueError:
logging.warning('Error in loading the saved optimizer '
'state. As a result, your model is '
'starting with a freshly initialized '
'optimizer.')
return model
@tf_export('keras.models.model_from_config')
def model_from_config(config, custom_objects=None):
"""Instantiates a Keras model from its config.
Arguments:
config: Configuration dictionary.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance (uncompiled).
Raises:
TypeError: if `config` is not a dictionary.
"""
if isinstance(config, list):
raise TypeError('`model_from_config` expects a dictionary, not a list. '
'Maybe you meant to use '
'`Sequential.from_config(config)`?')
from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top
return deserialize(config, custom_objects=custom_objects)
@tf_export('keras.models.model_from_yaml')
def model_from_yaml(yaml_string, custom_objects=None):
"""Parses a yaml model configuration file and returns a model instance.
Arguments:
yaml_string: YAML string encoding a model configuration.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance (uncompiled).
Raises:
ImportError: if yaml module is not found.
"""
if yaml is None:
raise ImportError('Requires yaml module installed.')
config = yaml.load(yaml_string)
from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top
return deserialize(config, custom_objects=custom_objects)
@tf_export('keras.models.model_from_json')
def model_from_json(json_string, custom_objects=None):
"""Parses a JSON model configuration file and returns a model instance.
Arguments:
json_string: JSON string encoding a model configuration.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance (uncompiled).
"""
config = json.loads(json_string)
from tensorflow.python.keras._impl.keras.layers import deserialize # pylint: disable=g-import-not-at-top
return deserialize(config, custom_objects=custom_objects)
def preprocess_weights_for_loading(layer,
weights,
original_keras_version=None,
original_backend=None):
"""Converts layers weights from Keras 1 format to Keras 2.
Arguments:
layer: Layer instance.
weights: List of weights values (Numpy arrays).
original_keras_version: Keras version for the weights, as a string.
original_backend: Keras backend the weights were trained with,
as a string.
Returns:
A list of weights values (Numpy arrays).
"""
if layer.__class__.__name__ == 'Bidirectional':
num_weights_per_layer = len(weights) // 2
forward_weights = preprocess_weights_for_loading(
layer.forward_layer, weights[:num_weights_per_layer],
original_keras_version, original_backend)
backward_weights = preprocess_weights_for_loading(
layer.backward_layer, weights[num_weights_per_layer:],
original_keras_version, original_backend)
weights = forward_weights + backward_weights
if original_keras_version == '1':
if layer.__class__.__name__ == 'TimeDistributed':
weights = preprocess_weights_for_loading(
layer.layer, weights, original_keras_version, original_backend)
if layer.__class__.__name__ == 'Conv1D':
shape = weights[0].shape
# Handle Keras 1.1 format
if shape[:2] != (layer.kernel_size[0], 1) or shape[3] != layer.filters:
# Legacy shape:
# (filters, input_dim, filter_length, 1)
assert shape[0] == layer.filters and shape[2:] == (layer.kernel_size[0],
1)
weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
weights[0] = weights[0][:, 0, :, :]
if layer.__class__.__name__ == 'Conv2D':
if layer.data_format == 'channels_first':
# old: (filters, stack_size, kernel_rows, kernel_cols)
# new: (kernel_rows, kernel_cols, stack_size, filters)
weights[0] = np.transpose(weights[0], (2, 3, 1, 0))
if layer.__class__.__name__ == 'Conv2DTranspose':
if layer.data_format == 'channels_last':
# old: (kernel_rows, kernel_cols, stack_size, filters)
# new: (kernel_rows, kernel_cols, filters, stack_size)
weights[0] = np.transpose(weights[0], (0, 1, 3, 2))
if layer.data_format == 'channels_first':
# old: (filters, stack_size, kernel_rows, kernel_cols)
# new: (kernel_rows, kernel_cols, filters, stack_size)
weights[0] = np.transpose(weights[0], (2, 3, 0, 1))
if layer.__class__.__name__ == 'Conv3D':
if layer.data_format == 'channels_first':
# old: (filters, stack_size, ...)
# new: (..., stack_size, filters)
weights[0] = np.transpose(weights[0], (2, 3, 4, 1, 0))
if layer.__class__.__name__ == 'GRU':
if len(weights) == 9:
kernel = np.concatenate([weights[0], weights[3], weights[6]], axis=-1)
recurrent_kernel = np.concatenate(
[weights[1], weights[4], weights[7]], axis=-1)
bias = np.concatenate([weights[2], weights[5], weights[8]], axis=-1)
weights = [kernel, recurrent_kernel, bias]
if layer.__class__.__name__ == 'LSTM':
if len(weights) == 12:
# old: i, c, f, o
# new: i, f, c, o
kernel = np.concatenate(
[weights[0], weights[6], weights[3], weights[9]], axis=-1)
recurrent_kernel = np.concatenate(
[weights[1], weights[7], weights[4], weights[10]], axis=-1)
bias = np.concatenate(
[weights[2], weights[8], weights[5], weights[11]], axis=-1)
weights = [kernel, recurrent_kernel, bias]
if layer.__class__.__name__ == 'ConvLSTM2D':
if len(weights) == 12:
kernel = np.concatenate(
[weights[0], weights[6], weights[3], weights[9]], axis=-1)
recurrent_kernel = np.concatenate(
[weights[1], weights[7], weights[4], weights[10]], axis=-1)
bias = np.concatenate(
[weights[2], weights[8], weights[5], weights[11]], axis=-1)
if layer.data_format == 'channels_first':
# old: (filters, stack_size, kernel_rows, kernel_cols)
# new: (kernel_rows, kernel_cols, stack_size, filters)
kernel = np.transpose(kernel, (2, 3, 1, 0))
recurrent_kernel = np.transpose(recurrent_kernel, (2, 3, 1, 0))
weights = [kernel, recurrent_kernel, bias]
if layer.__class__.__name__ in ['Model', 'Sequential']:
new_weights = []
# trainable weights
for sublayer in layer.layers:
num_weights = len(sublayer.trainable_weights)
if num_weights > 0:
new_weights.extend(
preprocess_weights_for_loading(
layer=sublayer,
weights=weights[:num_weights],
original_keras_version=original_keras_version,
original_backend=original_backend))
weights = weights[num_weights:]
# non-trainable weights
for sublayer in layer.layers:
num_weights = len([
l for l in sublayer.weights if l not in sublayer.trainable_weights
])
if num_weights > 0:
new_weights.extend(
preprocess_weights_for_loading(
layer=sublayer,
weights=weights[:num_weights],
original_keras_version=original_keras_version,
original_backend=original_backend))
weights = weights[num_weights:]
weights = new_weights
conv_layers = ['Conv1D', 'Conv2D', 'Conv3D', 'Conv2DTranspose', 'ConvLSTM2D']
if layer.__class__.__name__ in conv_layers:
if original_backend == 'theano':
weights[0] = conv_utils.convert_kernel(weights[0])
if layer.__class__.__name__ == 'ConvLSTM2D':
weights[1] = conv_utils.convert_kernel(weights[1])
if K.int_shape(layer.weights[0]) != weights[0].shape:
weights[0] = np.transpose(weights[0], (3, 2, 0, 1))
if layer.__class__.__name__ == 'ConvLSTM2D':
weights[1] = np.transpose(weights[1], (3, 2, 0, 1))
# Convert the weights of CuDNNLSTM so that they could be loaded into LSTM
if layer.__class__.__name__ == 'LSTM' and len(weights) == 3:
# Determine if loading a CuDNNLSTM layer from the number of bias weights:
# CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4)
# if there's no bias weight in the file, skip this conversion
units = weights[1].shape[0]
bias = weights[2]
if len(bias) == units * 8:
# reshape the kernels
kernels = np.split(weights[0], 4, axis=1)
kernels = [
kernel.reshape(-1).reshape(kernel.shape, order='F')
for kernel in kernels
]
weights[0] = np.concatenate(kernels, axis=1)
# transpose the recurrent kernels
recurrent_kernels = np.split(weights[1], 4, axis=1)
recurrent_kernels = [kernel.T for kernel in recurrent_kernels]
weights[1] = np.concatenate(recurrent_kernels, axis=1)
# split the bias into half and merge
weights[2] = bias[:units * 4] + bias[units * 4:]
return convert_rnn_weights(layer, weights)
def convert_rnn_weights(layer, weights):
"""Converts weights for RNN layers between native and CuDNN format.
Input kernels for each gate are transposed and converted between Fortran
and C layout, recurrent kernels are transposed. For LSTM biases are summed/
split in half, for GRU biases are reshaped.
Weights can be converted in both directions between `LSTM` and`CuDNNSLTM`
and between `CuDNNGRU` and `GRU(reset_after=True)`. Default `GRU` is not
compatible with `CuDNNGRU`.
For missing biases in `LSTM`/`GRU` (`use_bias=False`) no conversion is made.
Arguments:
layer: Target layer instance.
weights: List of source weights values (input kernels, recurrent
kernels, [biases]) (Numpy arrays).
Returns:
A list of converted weights values (Numpy arrays).
Raises:
ValueError: for incompatible GRU layer/weights or incompatible biases
"""
def transform_kernels(kernels, func, n_gates):
"""Transforms kernel for each gate separately using given function.
Arguments:
kernels: Stacked array of kernels for individual gates.
func: Function applied to kernel of each gate.
n_gates: Number of gates (4 for LSTM, 3 for GRU).
Returns:
Stacked array of transformed kernels.
"""
return np.hstack([func(k) for k in np.hsplit(kernels, n_gates)])
def transpose_input(from_cudnn):
"""Makes a function that transforms input kernels from/to CuDNN format.
It keeps the shape, but changes between the layout (Fortran/C). Eg.:
```
Keras CuDNN
[[0, 1, 2], <---> [[0, 2, 4],
[3, 4, 5]] [1, 3, 5]]
```
It can be passed to `transform_kernels()`.
Arguments:
from_cudnn: `True` if source weights are in CuDNN format, `False`
if they're in plain Keras format.
Returns:
Function that converts input kernel to the other format.
"""
order = 'F' if from_cudnn else 'C'
def transform(kernel):
return kernel.T.reshape(kernel.shape, order=order)
return transform
target_class = layer.__class__.__name__
# convert the weights between CuDNNLSTM and LSTM
if target_class in ['LSTM', 'CuDNNLSTM'] and len(weights) == 3:
# determine if we're loading a CuDNNLSTM layer
# from the number of bias weights:
# CuDNNLSTM has (units * 8) weights; while LSTM has (units * 4)
# if there's no bias weight in the file, skip this conversion
units = weights[1].shape[0]
bias_shape = weights[2].shape
n_gates = 4
if bias_shape == (2 * units * n_gates,):
source = 'CuDNNLSTM'
elif bias_shape == (units * n_gates,):
source = 'LSTM'
else:
raise ValueError('Invalid bias shape: ' + str(bias_shape))
def convert_lstm_weights(weights, from_cudnn=True):
# Transpose (and reshape) input and recurrent kernels.
kernels = transform_kernels(weights[0], transpose_input(from_cudnn),
n_gates)
recurrent_kernels = transform_kernels(weights[1], lambda k: k.T, n_gates)
if from_cudnn: # Merge input and recurrent biases into a single set.
biases = np.sum(np.split(weights[2], 2, axis=0), axis=0)
else:
# Split single set of biases evenly to two sets.
biases = np.tile(0.5 * weights[2], 2)
return [kernels, recurrent_kernels, biases]
if source != target_class:
weights = convert_lstm_weights(weights, from_cudnn=source == 'CuDNNLSTM')
# TODO(fchollet): add feature after GRU is refactored:
# convert the weights between `CuDNNGRU` and `GRU(reset_after=True)`
return weights
def save_weights_to_hdf5_group(f, layers):
from tensorflow.python.keras._impl.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
save_attributes_to_hdf5_group(
f, 'layer_names', [layer.name.encode('utf8') for layer in layers])
f.attrs['backend'] = K.backend().encode('utf8')
f.attrs['keras_version'] = str(keras_version).encode('utf8')
for layer in layers:
g = f.create_group(layer.name)
symbolic_weights = layer.weights
weight_values = K.batch_get_value(symbolic_weights)
weight_names = []
for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
if hasattr(w, 'name') and w.name:
name = str(w.name)
else:
name = 'param_' + str(i)
weight_names.append(name.encode('utf8'))
save_attributes_to_hdf5_group(g, 'weight_names', weight_names)
for name, val in zip(weight_names, weight_values):
param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
if not val.shape:
# scalar
param_dset[()] = val
else:
param_dset[:] = val
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_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 save_attributes_to_hdf5_group(group, name, data):
"""Saves attributes (data) of the specified name into the HDF5 group.
This method deals with an inherent problem of HDF5 file which is not
able to store data larger than HDF5_OBJECT_HEADER_LIMIT bytes.
Arguments:
group: A pointer to a HDF5 group.
name: A name of the attributes to save.
data: Attributes data to store.
Raises:
RuntimeError: If any single attribute is too large to be saved.
"""
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
bad_attributes = [x for x in data if len(x) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError('The following attributes cannot be saved to HDF5 '
'file because they are larger than %d bytes: %s' %
(HDF5_OBJECT_HEADER_LIMIT,
', '.join([x for x in bad_attributes])))
data_npy = np.asarray(data)
num_chunks = 1
chunked_data = np.array_split(data_npy, num_chunks)
# This will never loop forever thanks to the test above.
while any([x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data]):
num_chunks += 1
chunked_data = np.array_split(data_npy, num_chunks)
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(chunked_data):
group.attrs['%s%d' % (name, chunk_id)] = chunk_data
else:
group.attrs[name] = data
def load_attributes_from_hdf5_group(group, name):
"""Loads attributes of the specified name from the HDF5 group.
This method deals with an inherent problem
of HDF5 file which is not able to store
data larger than HDF5_OBJECT_HEADER_LIMIT bytes.
Arguments:
group: A pointer to a HDF5 group.
name: A name of the attributes to load.
Returns:
data: Attributes data.
"""
if name in group.attrs:
data = [n.decode('utf8') for n in group.attrs[name]]
else:
data = []
chunk_id = 0
while '%s%d' % (name, chunk_id) in group.attrs:
data.extend(
[n.decode('utf8') for n in group.attrs['%s%d' % (name, chunk_id)]])
chunk_id += 1
return data