在Keras中实现自定义卷积层-加载模型时出错

Question

我已经按照下面的步骤-https://github.com/basveeling/wavenet实施了Wavenet的最小示例。

问题在于，模型使用了自定义层，该层在训练过程中可以正常工作，但是一旦重新加载模型，即使我使用的是自定义对象，Keras也无法找到因果层。 < / p>

我正在使用 tensorflow 1.13 和 keras 2.2.4

这里是对象的前三个键/值对的示例。

objects = {'initial_causal_conv': <class 'wavenet_utils.CausalConv1D'>,
           'dilated_conv_1_tanh_s0': <class 'wavenet_utils.CausalConv1D'>,
           'dilated_conv_1_sigm_s0': <class 'wavenet_utils.CausalConv1D'>,
           '...': <class 'wavenet_utils.CausalConv1D'>,
           '...': <class 'wavenet_utils.CausalConv1D'>}

model.fit(x=[x_tr1, x_tr2],
             y=y_tr1,
             epochs=epochs,
             batch_size=batch_size,
             validation_data=([x_vl1, x_vl2], y_vl1),
             callbacks=[checkpoint, early_stopping],
             verbose=verbose,
             shuffle=True,
             class_weight=class_weight)
model = load_model('model.h5', custom_objects=objects)

然后返回此错误：

Traceback (most recent call last):
  File "/home/xxx/PycharmProjects/WAVE/DATA_NN.py", line 48, in <module>
    objects=objects)
  File "/home/xxx/PycharmProjects/WAVE/functions.py", line 572, in run_neural_net
    model = load_model('model_conv.h5', custom_objects=objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/saving.py", line 419, in load_model
    model = _deserialize_model(f, custom_objects, compile)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/saving.py", line 225, in _deserialize_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/saving.py", line 458, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/layers/__init__.py", line 55, in deserialize
    printable_module_name='layer')
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object
    list(custom_objects.items())))
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/network.py", line 1022, in from_config
    process_layer(layer_data)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/network.py", line 1008, in process_layer
    custom_objects=custom_objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/layers/__init__.py", line 55, in deserialize
    printable_module_name='layer')
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 138, in deserialize_keras_object
    ': ' + class_name)
ValueError: Unknown layer: CausalConv1D

构建模型时，必须从wavenet_utils.py导入CausalConv1D

下面是完整的 build_model 功能 这是 wavenet_utils，其中包含类CausalConv1D ：

from keras.layers import Conv1D
from keras.utils.conv_utils import conv_output_length
import tensorflow as tf


class CausalConv1D(Conv1D):
    def __init__(self, filters, kernel_size, init='glorot_uniform', activation=None,
                 padding='valid', strides=1, dilation_rate=1, bias_regularizer=None,
                 activity_regularizer=None, kernel_constraint=None, bias_constraint=None, use_bias=True, causal=False,
                 output_dim=1,
                 **kwargs):
        self.output_dim = output_dim

        super(CausalConv1D, self).__init__(filters,
                                           kernel_size=kernel_size,
                                           strides=strides,
                                           padding=padding,
                                           dilation_rate=dilation_rate,
                                           activation=activation,
                                           use_bias=use_bias,
                                           kernel_initializer=init,
                                           activity_regularizer=activity_regularizer,
                                           bias_regularizer=bias_regularizer,
                                           kernel_constraint=kernel_constraint,
                                           bias_constraint=bias_constraint,
                                           **kwargs)

        self.causal = causal
        if self.causal and padding != 'valid':
            raise ValueError("Causal mode dictates border_mode=valid.")

    def build(self, input_shape):
        super(CausalConv1D, self).build(input_shape)

    def call(self, x):
        if self.causal:
            def asymmetric_temporal_padding(x, left_pad=1, right_pad=1):
                pattern = [[0, 0], [left_pad, right_pad], [0, 0]]
                return tf.pad(x, pattern)

            x = asymmetric_temporal_padding(x, self.dilation_rate[0] * (self.kernel_size[0] - 1), 0)
        return super(CausalConv1D, self).call(x)

    def compute_output_shape(self, input_shape):
        input_length = input_shape[1]

        if self.causal:
            input_length += self.dilation_rate[0] * (self.kernel_size[0] - 1)

        length = conv_output_length(input_length,
                                    self.kernel_size[0],
                                    self.padding,
                                    self.strides[0],
                                    dilation=self.dilation_rate[0])

        shape = tf.TensorShape(input_shape).as_list()
        shape[-1] = self.output_dim
        return (input_shape[0], length, self.filters)

    def get_config(self):
        base_config = super(CausalConv1D, self).get_config()
        base_config['output_dim'] = self.output_dim
        return base_config

编辑：

我之前也尝试过这种方法。

objects = {'CausalConv1D': <class 'wavenet_utils.CausalConv1D'>}

model.fit(x=[x_tr1, x_tr2],
             y=y_tr1,
             epochs=epochs,
             batch_size=batch_size,
             validation_data=([x_vl1, x_vl2], y_vl1),
             callbacks=[checkpoint, early_stopping],
             verbose=verbose,
             shuffle=True,
             class_weight=class_weight)
model = load_model('model.h5', custom_objects=objects)

然后返回此错误：

Traceback (most recent call last):
  File "/home/xxx/PycharmProjects/WAVE/DATA_NN.py", line 47, in <module>
    objects=objects)
  File "/home/xxx/PycharmProjects/WAVE/functions.py", line 574, in run_neural_net
    model = load_model('model.h5', custom_objects=objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/saving.py", line 419, in load_model
    model = _deserialize_model(f, custom_objects, compile)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/saving.py", line 225, in _deserialize_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/saving.py", line 458, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/layers/__init__.py", line 55, in deserialize
    printable_module_name='layer')
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 145, in deserialize_keras_object
    list(custom_objects.items())))
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/network.py", line 1022, in from_config
    process_layer(layer_data)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/network.py", line 1008, in process_layer
    custom_objects=custom_objects)
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/layers/__init__.py", line 55, in deserialize
    printable_module_name='layer')
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
    return cls.from_config(config['config'])
  File "/home/xxx/PycharmProjects/WAVE/venv/lib/python3.6/site-packages/keras/engine/base_layer.py", line 1109, in from_config
    return cls(**config)
  File "/home/xxx/PycharmProjects/WAVE/wavenet_utils.py", line 26, in __init__
    **kwargs)
TypeError: __init__() got multiple values for keyword argument 'kernel_initializer'

这可能是这里https://github.com/keras-team/keras/issues/12316中提到的问题吗？

如果是的话，有什么办法解决吗？

Answer 1

只有一个自定义对象，即CausalConv1D。

objects = {'CausalConv1D': wavenet_utils.CausalConv1D}

现在，您必须确保get_config方法正确无误，并具有图层__init__方法中所需的所有内容。

它缺少causal属性，并且有一个kernel_initializer来自您的__init__方法不支持的基类。

让我们列出您需要的每个属性，然后检查基本配置中的属性：

• 过滤器：以基本
• 内核大小：以基本
• init：不在基础中，但是在基础中有kernel_initializer ！
  • kernel_initializer是您的__init__方法不支持的配置项
  • 将此init参数重命名为kernel_initializer
• 激活：基础
• 填充：在基础中
• 大步前进：在基础
• dilation_rate：基础
• bias_regularizer：在基础中
• activity_regularizer：在基础中
• kernel_constraint：在基础中
• bias_constraint：在基础中
• use_bias：在基础中
• 原因：不在基础上！
  • 必须将此添加到您的配置中！ （否则模型将始终使用默认值）
• output_dim：不在基础上！
• ** kwargs：在基地

图层的__init__：

def __init__(self, filters, kernel_size, 

             ############## here:
             kernel_initializer='glorot_uniform', 
             #############

             activation=None,
             padding='valid', strides=1, dilation_rate=1, bias_regularizer=None,
             activity_regularizer=None, kernel_constraint=None, bias_constraint=None, use_bias=True, causal=False,
             output_dim=1,
             **kwargs):

图层的get_config

它必须包含所有不在基类中的__init__参数：

def get_config(self):
    base_config = super(CausalConv1D, self).get_config()
    base_config['causal'] = self.causal
    base_config['output_dim'] = self.output_dim
    return base_config

Answer 2

以某种方式，到目前为止，我尝试过的任何方法都无法在使用load_model时正确地加载模型。以下是一个简单的工作，仅保存权重，然后删除现有模型，构建新模型并再次编译，然后 loads保存了权重，即使存在自定义图层，权重也可以正确保存。

model = build_model()

checkpoint = ModelCheckpoint('model.h5', monitor='val_acc',
                             verbose=1, save_best_only=True, save_weights_only=True, mode='max')

model.fit(x, y)

del model

model = build_model()

model.load_weights('model.h5')

model.predict(x_test)