Python backend.batch_set_value方法代码示例

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import batch_set_value [as 别名]
def load_weights(model, weights_path):
    """Load weights from Caffe models."""
    print("Loading weights...")
    if h5py is None:
        raise ImportError('`load_weights` requires h5py.')
    f = h5py.File(weights_path, mode='r')

    # New file format.
    layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]

    # Reverse index of layer name to list of layers with name.
    index = {}
    for layer in model.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 = [n.decode('utf8') for n in g.attrs['weight_names']]
        weight_values = [g[weight_name] for weight_name in weight_names]

        for layer in index.get(name, []):
            symbolic_weights = layer.weights
            # 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)

    return layer_names 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import batch_set_value [as 别名]
def load_weights(model, filepath):
    """Load all weights possible into model from filepath.

    This is a modified version of keras load_weights that loads as much as it can
    if there is a mismatch between file and model. It returns the weights
    of the first layer in which the mismatch has happened
    """
    print('Loading', filepath, 'to', model.name)
    with h5py.File(filepath, mode='r') as f:
        # new file format
        layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]

        # we batch weight value assignments in a single backend call
        # which provides a speedup in TensorFlow.
        weight_value_tuples = []
        for name in layer_names:
            print(name)
            g = f[name]
            weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
            if len(weight_names):
                weight_values = [g[weight_name] for weight_name in weight_names]
                try:
                    layer = model.get_layer(name=name)
                except:
                    layer = None
                if not layer:
                    print('failed to find layer', name, 'in model')
                    print('weights', ' '.join(str_shape(w) for w in weight_values))
                    print('stopping to load all other layers')
                    weight_values = [np.array(w) for w in weight_values]
                    break
                symbolic_weights = layer.trainable_weights + layer.non_trainable_weights
                weight_value_tuples += zip(symbolic_weights, weight_values)
                weight_values = None
        K.batch_set_value(weight_value_tuples)
    return weight_values 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import batch_set_value [as 别名]
def partial_load_weights(self, model, f):
        '''
        Keras does not seem to support partially loading weights from one
        model into another model. This function achieves the same purpose so
        we can serialise the final RNN hidden state to disk.

        TODO: find / engineer a more elegant and general approach
        '''

        flattened_layers = model.layers

        # new file format
        filtered_layers = []
        for layer in flattened_layers:
            weights = layer.weights
            if weights:
                filtered_layers.append(layer)
        flattened_layers = filtered_layers

        layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
        filtered_layer_names = []
        for name in layer_names[:-1]: # -1 so we clip out the output layer
            g = f[name]
            weight_names = [n.decode('utf8') for n in g.attrs['weight_names']]
            if len(weight_names):
                filtered_layer_names.append(name)
        layer_names = filtered_layer_names
        if len(layer_names) != len(flattened_layers):
            raise Exception('You are trying to load a weight file '
                            'containing ' + str(len(layer_names)) +
                            ' layers into a model with ' +
                            str(len(flattened_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 = [n.decode('utf8') for n in g.attrs['weight_names']]
            weight_values = [g[weight_name] for weight_name in weight_names]
            layer = flattened_layers[k]
            symbolic_weights = layer.weights
            if len(weight_values) != len(symbolic_weights):
                raise Exception('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) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import batch_set_value [as 别名]
def load_weights(model, weights_path):
    from keras import backend as K

    if not os.path.isfile(weights_path):
        raise Exception("File does not exist.")

    import h5py
    f = h5py.File(weights_path, mode='r')

    # new file format
    layer_names = [n.decode('utf8') for n in f.attrs['layer_names']]
    if len(layer_names) != len(model.layers):
        print("Warning: Layer count different")

    # 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 = [n.decode('utf8') for n in g.attrs['weight_names']]

        layer = model.get_layer(name=name)
        if layer and len(weight_names):
            weight_values = [g[weight_name] for weight_name in weight_names]
            if not hasattr(layer, 'trainable_weights'):
                print("Layer %s (%s) has no trainable weights, but we tried to load it." % (
                name, type(layer).__name__))
            else:
                symbolic_weights = layer.trainable_weights + layer.non_trainable_weights

                if len(weight_values) != len(symbolic_weights):
                    raise Exception('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 += list(zip(symbolic_weights, weight_values))
    K.batch_set_value(weight_value_tuples)

    f.close() 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import batch_set_value [as 别名]
def _load_weights_from_hdf5_group(f,
                                  layers):
    """
    Implements topological (order-based) weight loading.

    Parameters
    ----------
    f : File
        A pointer to a HDF5 group.
    layers : list of np.array
        List of target layers.
    """
    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.")

    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 = [np.asarray(g[weight_name]) for weight_name in weight_names]
        layer = filtered_layers[k]
        symbolic_weights = layer.weights
        weight_values = _preprocess_weights_for_loading(
            layer=layer,
            weights=weight_values)
        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) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import batch_set_value [as 别名]
def _load_weights_from_hdf5_group_by_name(f,
                                          layers):
    """
    Implements name-based weight loading.

    Parameters
    ----------
    f : File
        A pointer to a HDF5 group.
    layers : list of np.array
        List of target layers.
    """
    # 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)

    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 = [np.asarray(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=layer,
                weights=weight_values)
            if len(weight_values) != len(symbolic_weights):
                warnings.warn("Skipping loading of weights for layer {} due to mismatch in number of weights ({} vs"
                              " {}).".format(layer, len(symbolic_weights), len(weight_values)))
                continue
            # Set values.
            for i in range(len(weight_values)):
                symbolic_shape = K.int_shape(symbolic_weights[i])
                if symbolic_shape != weight_values[i].shape:
                    warnings.warn("Skipping loading of weights for layer {} due to mismatch in shape ({} vs"
                                  " {}).".format(layer, symbolic_weights[i].shape, weight_values[i].shape))
                    continue
                else:
                    weight_value_tuples.append((symbolic_weights[i],
                                                weight_values[i]))
    K.batch_set_value(weight_value_tuples) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import batch_set_value [as 别名]
def test_load_layers():
    from keras.layers import ConvLSTM2D, TimeDistributed, Bidirectional, Conv2D, Input
    from keras.models import Model

    if K.backend() == 'tensorflow' or K.backend() == 'cntk':
        inputs = Input(shape=(10, 20, 20, 1))
    else:
        inputs = Input(shape=(10, 1, 20, 20))
    td_conv = TimeDistributed(Conv2D(15, (5, 5)))(inputs)
    bi_convlstm2d = Bidirectional(ConvLSTM2D(10, (3, 3)), merge_mode='concat')(td_conv)
    model = Model(inputs=inputs, outputs=bi_convlstm2d)

    weight_value_tuples = []

    # TimeDistributed Conv2D layer
    # use 'channels_first' data format to check that the function is being called correctly for Conv2D
    # old: (filters, stack_size, kernel_rows, kernel_cols)
    # new: (kernel_rows, kernel_cols, stack_size, filters)
    weight_tensor_td_conv_old = list()
    weight_tensor_td_conv_old.append(np.zeros((15, 1, 5, 5)))
    weight_tensor_td_conv_old.append(np.zeros((15,)))
    td_conv_layer = model.layers[1]
    td_conv_layer.layer.data_format = 'channels_first'
    weight_tensor_td_conv_new = topology.preprocess_weights_for_loading(
        td_conv_layer,
        weight_tensor_td_conv_old,
        original_keras_version='1')
    symbolic_weights = td_conv_layer.weights
    assert (len(symbolic_weights) == len(weight_tensor_td_conv_new))
    weight_value_tuples += zip(symbolic_weights, weight_tensor_td_conv_new)

    # Bidirectional ConvLSTM2D layer
    # old ConvLSTM2D took a list of 12 weight tensors, returns a list of 3 concatenated larger tensors.
    weight_tensor_bi_convlstm_old = []
    for j in range(2):  # bidirectional
        for i in range(4):
            weight_tensor_bi_convlstm_old.append(np.zeros((3, 3, 15, 10)))  # kernel
            weight_tensor_bi_convlstm_old.append(np.zeros((3, 3, 10, 10)))  # recurrent kernel
            weight_tensor_bi_convlstm_old.append(np.zeros((10,)))  # bias

    bi_convlstm_layer = model.layers[2]
    weight_tensor_bi_convlstm_new = topology.preprocess_weights_for_loading(
        bi_convlstm_layer,
        weight_tensor_bi_convlstm_old,
        original_keras_version='1')

    symbolic_weights = bi_convlstm_layer.weights
    assert (len(symbolic_weights) == len(weight_tensor_bi_convlstm_new))
    weight_value_tuples += zip(symbolic_weights, weight_tensor_bi_convlstm_new)

    K.batch_set_value(weight_value_tuples)

    assert np.all(K.eval(model.layers[1].weights[0]) == weight_tensor_td_conv_new[0])
    assert np.all(K.eval(model.layers[1].weights[1]) == weight_tensor_td_conv_new[1])
    assert np.all(K.eval(model.layers[2].weights[0]) == weight_tensor_bi_convlstm_new[0])
    assert np.all(K.eval(model.layers[2].weights[1]) == weight_tensor_bi_convlstm_new[1])
    assert np.all(K.eval(model.layers[2].weights[2]) == weight_tensor_bi_convlstm_new[2])
    assert np.all(K.eval(model.layers[2].weights[3]) == weight_tensor_bi_convlstm_new[3])
    assert np.all(K.eval(model.layers[2].weights[4]) == weight_tensor_bi_convlstm_new[4])
    assert np.all(K.eval(model.layers[2].weights[5]) == weight_tensor_bi_convlstm_new[5])