自学教程：Python layers.deserialize方法代码示例

示例1: from_config

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import deserialize [as 别名]def from_config(layer, config_dic):    config_correct = {}    config_correct['class_name'] = str(type(layer))    config_correct['config'] = config_dic    return layer_from_config(config_correct, custom_objects={str(type(layer)): layer}) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import deserialize [as 别名]def assemble_narx(params, final_reshape=True):    """Construct a NARX model of the form: X-[H1-H2-...-HN]-Y.    All the H-layers are Dense and optional, i.e., depend on whether they are    specified in the params dictionary. Here, X is a sequence.    """    # Input layer    input_shape = params['input_shape']    inputs = layers.Input(shape=input_shape)    # Flatten the time dimension    target_shape = (np.prod(input_shape), )    previous = layers.Reshape(target_shape)(inputs)    # Hidden layers    for layer in params['hidden_layers']:        Layer = layers.deserialize(            {'class_name': layer['name'], 'config': layer['config']})        previous = Layer(previous)        if 'dropout' in layer and layer['dropout'] is not None:            previous = layers.Dropout(layer['dropout'])(previous)        if 'batch_norm' in layer and layer['batch_norm'] is not None:            previous = layers.BatchNormalization(**layer['batch_norm'])(previous)    # Output layer    output_shape = params['output_shape']    output_dim = np.prod(output_shape)    outputs = layers.Dense(output_dim)(previous)    if final_reshape:        outputs = layers.Reshape(output_shape)(outputs)    return KerasModel(inputs=inputs, outputs=outputs) 

示例3: assemble_rnn

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import deserialize [as 别名]def assemble_rnn(params, final_reshape=True):    """Construct an RNN/LSTM/GRU model of the form: X-[H1-H2-...-HN]-Y.    All the H-layers are optional recurrent layers and depend on whether they    are specified in the params dictionary.    """    # Input layer    input_shape = params['input_shape']    inputs = layers.Input(shape=input_shape)    # inputs = layers.Input(batch_shape=[20] + list(input_shape))    # Masking layer    previous = layers.Masking(mask_value=0.0)(inputs)    # Hidden layers    for layer in params['hidden_layers']:        Layer = layers.deserialize(            {'class_name': layer['name'], 'config': layer['config']})        previous = Layer(previous)        if 'dropout' in layer and layer['dropout'] is not None:            previous = layers.Dropout(layer['dropout'])(previous)        if 'batch_norm' in layer and layer['batch_norm'] is not None:            previous = layers.BatchNormalization(**layer['batch_norm'])(previous)    # Output layer    output_shape = params['output_shape']    output_dim = np.prod(output_shape)    outputs = layers.Dense(output_dim)(previous)    if final_reshape:        outputs = layers.Reshape(output_shape)(outputs)    return KerasModel(inputs=inputs, outputs=outputs) 

# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import deserialize [as 别名]def optimize_separableconv2d_batchnorm_block(m, initial_model, input_layers, conv, bn, verbose=False):    from keras import layers    from keras.models import Model    conv_config = conv.get_config()    conv_config['use_bias'] = True    bn_config = bn.get_config()    if conv_config['activation'] != 'linear':        print('Only linear activation supported for conv + bn optimization!')        exit()    layer_copy = layers.deserialize({'class_name': conv.__class__.__name__, 'config': conv_config})    # We use batch norm name here to find it later    layer_copy.name = bn.name    # Create new model to initialize layer. We need to store other output tensors as well    output_tensor, output_names = get_layers_without_output(m, verbose)    input_layer_name = initial_model.layers[input_layers[0]].name    prev_layer = m.get_layer(name=input_layer_name)    x = layer_copy(prev_layer.output)    output_tensor_to_use = [x]    for i in range(len(output_names)):        if output_names[i] != input_layer_name:            output_tensor_to_use.append(output_tensor[i])    if len(output_tensor_to_use) == 1:        output_tensor_to_use = output_tensor_to_use[0]    tmp_model = Model(inputs=m.input, outputs=output_tensor_to_use)    if conv.get_config()['use_bias']:        (conv_weights_3, conv_weights_1, conv_bias) = conv.get_weights()    else:        (conv_weights_3, conv_weights_1) = conv.get_weights()    if bn_config['scale']:        gamma, beta, run_mean, run_std = bn.get_weights()    else:        gamma = 1.0        beta, run_mean, run_std = bn.get_weights()    eps = bn_config['epsilon']    A = gamma / np.sqrt(run_std + eps)    if conv.get_config()['use_bias']:        B = beta + (gamma * (conv_bias - run_mean) / np.sqrt(run_std + eps))    else:        B = beta - ((gamma * run_mean) / np.sqrt(run_std + eps))    for i in range(conv_weights_1.shape[-1]):        conv_weights_1[:, :, :, i] *= A[i]    # print(conv_weights_3.shape, conv_weights_1.shape, A.shape)    tmp_model.get_layer(layer_copy.name).set_weights((conv_weights_3, conv_weights_1, B))    return tmp_model 

注：本文中的keras.layers.deserialize方法
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