残差块的卷积神经网络的python代码

以下是一个使用残差块的卷积神经网络的Python代码示例：

import tensorflow as tf

def residual_block(x, filters, kernel_size, dilation_rate=1, dropout_rate=0.0):
    # Convolutional layer 1
    x_skip = x
    x = tf.keras.layers.Conv1D(filters, kernel_size, padding='same', dilation_rate=dilation_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)

    # Convolutional layer 2
    x = tf.keras.layers.Conv1D(filters, kernel_size, padding='same', dilation_rate=dilation_rate)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.Dropout(dropout_rate)(x)

    # Skip connection
    if x_skip.shape[-1] != x.shape[-1]:
        x_skip = tf.keras.layers.Conv1D(filters, kernel_size=1, padding='same')(x_skip)
    x = tf.keras.layers.Add()([x, x_skip])
    x = tf.keras.layers.Activation('relu')(x)

    return x

def build_model(input_shape, num_classes):
    inputs = tf.keras.layers.Input(shape=input_shape)

    # Residual blocks
    x = residual_block(inputs, filters=32, kernel_size=3)
    x = residual_block(x, filters=32, kernel_size=3)
    x = residual_block(x, filters=64, kernel_size=3, dilation_rate=2)
    x = residual_block(x, filters=64, kernel_size=3, dilation_rate=2)
    x = residual_block(x, filters=128, kernel_size=3, dilation_rate=4)
    x = residual_block(x, filters=128, kernel_size=3, dilation_rate=4)

    # Global average pooling and output
    x = tf.keras.layers.GlobalAveragePooling1D()(x)
    outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(x)

    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    return model

这个例子中使用了一维卷积层，但是使用二维卷积层也是类似的。在这个例子中，我们定义了一个残差块函数`residual_block()`，并在模型中使用了多个这样的块。模型的输出是全局平均池化层和一个全连接层，用于分类。这个模型可以接受输入形状为`(batch_size, input_length, input_dim)`的张量，并输出形状为`(batch_size, num_classes)`的张量。