自定义神经网络时的注意事项

问题描述

通过继承tf.keras.Model自定义神经网络模型时遇到的一系列问题。
代码如下，

c在这里插入代码片复制代码

class STFT_ConV2D(tf.keras.Model):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.pre_layer = tf.keras.Sequential([
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(768, activation='relu')
        ])

        self.add = tf.keras.layers.Add()
        self.output_dense = tf.keras.layers.Dense(1, activation='sigmoid')

    def call(self, inputs):
        x, y = inputs
        x = tf.keras.layers.Conv2D(filters=3, kernel_size=8, input_shape=Input_shape_x)(x)
        x = tf.keras.layers.Conv2D(filters=3, kernel_size=16, input_shape=Input_shape_x)(x)
        x = tf.keras.layers.Conv2D(filters=1, kernel_size=32, input_shape=Input_shape_x)(x)
        x = self.pre_layer(x)

        y = tf.keras.layers.Conv2D(filters=3, kernel_size=8, input_shape=Input_shape_y)(y)
        y = tf.keras.layers.Conv2D(filters=3, kernel_size=16, input_shape=Input_shape_y)(y)
        y = tf.keras.layers.Conv2D(filters=1, kernel_size=32, input_shape=Input_shape_y)(y)
        y = self.pre_layer(y)
        output = self.add([x, y])
        output = self.output_dense(output)
        return output

产生的bug为，

markup 复制代码

  ValueError: Exception encountered when calling layer 'sequential' (type Sequential).
        
  Input 0 of layer "dense" is incompatible with the layer: expected axis -1 of input shape to have value 11368, but received input with shape (None, 210680)

x输入和y输入都使用了成员变量pre_layer，共享了pre_layer层，也就共享了pre_layer层的参数矩阵和结构。
由于x先经过三层卷积层后shape由原来的shape=(360, 256, 109, 1)变成了shape=(360, 203, 56, 1)
再经过pre_layer层里的Flatten时，除" batchsize "轴(axis=0)外，其余轴被铺平，输出shape=(360,11368)。接着处理y输入，经过三层卷积层后，shape由原来的shape=(360, 511, 513, 1)变成了shape=(360,458, 460, 1),之后执行到y = self.pre_layer(y)时，如果执行成功，则输出shape=(360,21068)，此时与x的shape=(360,11368)维度冲突，从而产生异常。

要点归纳：

通过继承tf.keras.Model写神经网络模型时，每一个神经网络层只能被同一个输入占有。

所有tf.keras.layers下的层对象不能直接出现在call()方法中，必须以成员变量的形式在构造器中定义，然后在call()方法中通过self.成员变量的方式调用

卷积层tf.keras.layers.Conv2D()当神经网络第一层时，必须通过参数input_shape指定输入shape，该shape中不能包含" batchsize "轴，例如输入x的shape为(a, b, c, d)，其中a代表样本数，b代表行像素，c代表列像素，d代表通道数。则应该指定input_shape=x.shape[1:]，去除a所在轴，以免卷积层对该轴造成影响。

解决方案：

python 复制代码

class STFT_ConV2D(tf.keras.Model):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.conV2d_x1 = tf.keras.layers.Conv2D(filters=3, kernel_size=8, input_shape=Input_shape_x)
        self.conV2d_x2 = tf.keras.layers.Conv2D(filters=3, kernel_size=16, input_shape=Input_shape_x)
        self.conV2d_x3 = tf.keras.layers.Conv2D(filters=1, kernel_size=32, input_shape=Input_shape_x)

        self.conV2d_y1 = tf.keras.layers.Conv2D(filters=3, kernel_size=8, input_shape=Input_shape_y)
        self.conV2d_y2 = tf.keras.layers.Conv2D(filters=3, kernel_size=16, input_shape=Input_shape_y)
        self.conV2d_y3 = tf.keras.layers.Conv2D(filters=1, kernel_size=32, input_shape=Input_shape_y)

        self.flatten_x = tf.keras.layers.Flatten()
        self.flatten_y = tf.keras.layers.Flatten()

        self.dense_x = tf.keras.layers.Dense(768, activation='relu')
        self.dense_y = tf.keras.layers.Dense(768, activation='relu')

        self.add = tf.keras.layers.Add()
        self.output_dense = tf.keras.layers.Dense(1, activation='sigmoid')

    def call(self, inputs):
        # x.shape = (360, 256, 109, 1) , y.shape = (360, 511, 513, 1)
        # inputs = (x, y)
        x, y = inputs  
        x = self.conV2d_x1(x) # (360, 249, 102, 3)
        x = self.conV2d_x2(x) # (360, 234, 87, 3)
        x = self.conV2d_x3(x) # (360, 203, 56, 1)
        x = self.flatten_x(x) # (360, 11368)
        x = self.dense_x(x)  # (360, 768)

        y = self.conV2d_y1(y)
        y = self.conV2d_y2(y)
        y = self.conV2d_y3(y)
        y = self.flatten_y(y)
        y = self.dense_y(y)

        output = self.add([x, y]) # (360, 768)
        output = self.output_dense(output)
        return output