问题描述
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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，
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 = inputsx = 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，
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：
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
