Python TensorFlow 形状不匹配（WaveNet）

Posted 2023-02-16

【中文标题】Python TensorFlow 形状不匹配（WaveNet）

【英文标题】：Python Tensorflow Shape Mismatch (WaveNet)

【发布时间】：2021-12-29 04:06:37

【问题描述】：

我正在尝试运行 https://github.com/mjpyeon/wavenet-classifier/blob/master/WaveNetClassifier.py 中指定的 WaveNet。

我的部分代码如下：

def residual_block(self, x, i):
    tanh_out = Conv1D(self.n_filters, self.kernel_size, dilation_rate=self.kernel_size ** i,
                      padding='causal', name='dilated_conv_%d_tanh' % (self.kernel_size ** i),
                      activation='tanh')(x)

    sigm_out = Conv1D(self.n_filters, self.kernel_size, dilation_rate=self.kernel_size ** i,
                      padding='causal', name='dilated_conv_%d_sigm' % (self.kernel_size ** i),
                      activation='sigmoid')(x)

    # 'z' multiplies the 2 Conv1D layer (one with tanh activation function & the other with
    # sigmoid activation function)
    z = Multiply(name='gated_activation_%d' % (i))([tanh_out, sigm_out])

    # Skip Layer includes 'z' going through Conv1D layer
    skip = Conv1D(self.n_filters, 1, name='skip_%d' % (i))(z)

    # Residual Layer adds the output from the skip layer & the original input
    res = Add(name='residual_block_%d' % (i))([skip, x])

    return res, skip

def train_dataset(self, X_train, y_train, validation_data=None, epochs=100):
    with tf.device('/GPU:0'):
        # 1. Input Layer
        x = Input(shape=self.input_shape, name='original_input')

        
        # 2. Creating a Skip Connection using specified no. of residual blocks
        skip_connections = []
        out = Conv1D(self.n_filters, 2, dilation_rate=1, padding='causal',
                     name='dilated_conv_1')(x)
        for i in range(1, self.dilation_depth + 1):
            # The output from a residual block is fed back to the next residual block
            out, skip = self.residual_block(out, i)
            skip_connections.append(skip)

            
        # 3. ReLU Activation Function
        out = Add(name='skip_connections')(skip_connections)
        out = Activation('relu')(out)
        
        
        # 4. Series of Conv1D and AveragePooling1D Layer
        out = Conv1D(self.n_filters, 80, strides=1, padding='same', name='conv_5ms', 
                     activation='relu')(out)
        out = AveragePooling1D(80, padding='same', name='downsample_to_200Hz')(out)
        out = Conv1D(self.n_filters, 100, padding='same', activation='relu', 
                     name='conv_500ms')(out)
        out = Conv1D(self.output_shape[0], 100, padding='same', activation='relu', 
                     name='conv_500ms_target_shape')(out)
        out = AveragePooling1D(100, padding='same', name='downsample_to_2Hz')(out)
        out = Conv1D(self.output_shape[0], (int) (self.input_shape[0] / 8000), 
                     padding='same', name='final_conv')(out)
        out = AveragePooling1D((int) (self.input_shape[0] / 8000), name='final_pooling')(out)
        
        
        # 5. Reshaping into output dimension & Going through activation function
        out = Reshape(self.output_shape)(out)
        out = Activation('sigmoid')(out)
        print(out.shape)
        
        model = Model(x, out)
        model.summary()

        # Compiling the Model
        model.compile('adam', 'binary_crossentropy',
                           metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.7)])

        # Early Stopping
        callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=10)

        history = model.fit(X_train, y_train, shuffle=True, epochs=epochs, batch_size=32,
                                 validation_data=validation_data, callbacks=callback)

        return history

这里，self.input_shape=X_train.shape 和 self.output_shape=(11,)

它成功打印出模型的摘要，但输出以下错误：

ValueError: Input 0 is incompatible with layer model_1: expected shape=(None, 19296, 110250), found shape=(32, 110250)

但是，我的X_train 的形状为(19296, 110250)。 我试图弄清楚为什么 X_train 已从 (19296, 110250) 改造成 (32, 110250)，但找不到。

（19296 是歌曲的数量，110250 是使用 Python Librosa 库处理的采样率为 22050 的 5 秒长度的音频文件）

我的代码有什么问题？ 提前谢谢！

【问题讨论】：

x_train(19296, 110250)中的两个维度代表什么？什么是19296，什么是110250？

19296 表示批次数，110250 是使用 Python Librosa 库处理的采样率为 22050 的 5 秒长度的音频文件

【参考方案1】：

您的数据缺少维度。 Conv1D 层需要输入形状 (timesteps, features)。您似乎只有时间步长或功能。所以也许可以尝试这样的事情：

import tensorflow as tf

sample = 1
x_train = tf.random.normal((sample, 110250))
option1 = tf.expand_dims(x_train, axis=-1)
tf.print('expand_dims -->',option1.shape)

shape = tf.shape(x_train)
option2 = tf.reshape(x_train, (tf.shape(x_train)[0], 5, 22050)) 
tf.print('reshape -->',option2.shape)

expand_dims --> TensorShape([1, 110250, 1])
reshape --> TensorShape([1, 5, 22050])

请注意，我只使用了一个示例，但我想你明白了。

【讨论】：

我已经改变了形状，但我仍然得到同样的错误：ValueError: Input 0 is incompatible with layer model_1: expected shape=(None, 19296, 22050, 5), found shape=( 32, 22050, 5) 出于某种原因:(

你的 self.input_shape 是什么？必须是 (22050, 5)

我已将 self.input_shape 更改为 (19296, 22050, 5)

错了..它必须是 (22050, 5) ... 19296 是您的样本数..