mirror of
https://github.com/wassname/keras-contrib.git
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BenandMichael Oliver
4f619fa686
* save_all_weights and load_all_weights * doc updates, fix test, remove .gitkeep
109 lines
4.9 KiB
Python
109 lines
4.9 KiB
Python
import warnings
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import h5py
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import keras.backend as K
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from keras import optimizers
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from keras.engine import topology
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from keras.legacy import models as legacy_models
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def save_all_weights(model, filepath, include_optimizer=True):
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"""
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Save model weights and optimizer weights but not configuration to a HDF5 file.
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Functionally between `save` and `save_weights`.
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The HDF5 file contains:
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- the model's weights
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- the model's optimizer's state (if any)
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If you have a complicated model or set of models that do not serialize to JSON correctly, use this method.
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# Arguments
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model: Keras model instance to be saved.
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filepath: String, path where to save the model.
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include_optimizer: If True, save optimizer's state together.
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# Raises
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ImportError: if h5py is not available.
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"""
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if h5py is None:
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raise ImportError('`save_all_weights` requires h5py.')
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with h5py.File(filepath, 'w') as f:
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model_weights_group = f.create_group('model_weights')
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if legacy_models.needs_legacy_support(model):
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model_layers = legacy_models.legacy_sequential_layers(model)
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else:
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model_layers = model.layers
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topology.save_weights_to_hdf5_group(model_weights_group, model_layers)
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if include_optimizer and hasattr(model, 'optimizer') and model.optimizer:
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if isinstance(model.optimizer, optimizers.TFOptimizer):
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warnings.warn(
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'TensorFlow optimizers do not '
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'make it possible to access '
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'optimizer attributes or optimizer state '
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'after instantiation. '
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'As a result, we cannot save the optimizer '
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'as part of the model save file.'
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'You will have to compile your model again after loading it. '
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'Prefer using a Keras optimizer instead '
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'(see keras.io/optimizers).')
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else:
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# Save optimizer weights.
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symbolic_weights = getattr(model.optimizer, 'weights')
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if symbolic_weights:
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optimizer_weights_group = f.create_group('optimizer_weights')
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weight_values = K.batch_get_value(symbolic_weights)
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weight_names = []
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for i, (w, val) in enumerate(zip(symbolic_weights, weight_values)):
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# Default values of symbolic_weights is /variable for theano
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if K.backend() == 'theano':
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if hasattr(w, 'name') and w.name != "/variable":
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name = str(w.name)
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else:
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name = 'param_' + str(i)
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else:
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if hasattr(w, 'name') and w.name:
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name = str(w.name)
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else:
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name = 'param_' + str(i)
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weight_names.append(name.encode('utf8'))
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optimizer_weights_group.attrs['weight_names'] = weight_names
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for name, val in zip(weight_names, weight_values):
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param_dset = optimizer_weights_group.create_dataset(
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name,
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val.shape,
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dtype=val.dtype)
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if not val.shape:
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# scalar
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param_dset[()] = val
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else:
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param_dset[:] = val
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def load_all_weights(model, filepath, include_optimizer=True):
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"""Loads the weights of a model saved via `save_all_weights`.
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If model has been compiled, optionally load its optimizer's weights.
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# Arguments
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model: instantiated model with architecture matching the saved model.
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Compile the model beforehand if you want to load optimizer weights.
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filepath: String, path to the saved model.
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# Returns
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None. The model will have its weights updated.
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# Raises
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ImportError: if h5py is not available.
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ValueError: In case of an invalid savefile.
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"""
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if h5py is None:
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raise ImportError('`load_all_weights` requires h5py.')
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with h5py.File(filepath, mode='r') as f:
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# set weights
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topology.load_weights_from_hdf5_group(f['model_weights'], model.layers)
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# Set optimizer weights.
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if include_optimizer and 'optimizer_weights' in f and hasattr(model, 'optimizer') and model.optimizer:
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optimizer_weights_group = f['optimizer_weights']
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optimizer_weight_names = [n.decode('utf8') for n in
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optimizer_weights_group.attrs['weight_names']]
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optimizer_weight_values = [optimizer_weights_group[n] for n in
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optimizer_weight_names]
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model.optimizer.set_weights(optimizer_weight_values)
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