resnet的代码实现

以下是一个简单的ResNet实现示例代码：

import tensorflow as tf

def conv_block(inputs, filters, kernel_size, strides, name):
    """
    Convolutional block with batch normalization and ReLU activation.
    """
    conv_layer = tf.keras.layers.Conv2D(
        filters=filters, kernel_size=kernel_size, strides=strides,
        padding='same', use_bias=False, kernel_initializer='he_normal',
        name=name + '_conv'
    )(inputs)
    bn_layer = tf.keras.layers.BatchNormalization(name=name + '_bn')(conv_layer)
    relu_layer = tf.keras.layers.Activation('relu', name=name + '_relu')(bn_layer)
    return relu_layer

def identity_block(inputs, filters, kernel_size, name):
    """
    Identity block with batch normalization and ReLU activation.
    """
    conv_layer1 = tf.keras.layers.Conv2D(
        filters=filters, kernel_size=kernel_size, strides=(1, 1),
        padding='same', use_bias=False, kernel_initializer='he_normal',
        name=name + '_conv1'
    )(inputs)
    bn_layer1 = tf.keras.layers.BatchNormalization(name=name + '_bn1')(conv_layer1)
    relu_layer1 = tf.keras.layers.Activation('relu', name=name + '_relu1')(bn_layer1)
    conv_layer2 = tf.keras.layers.Conv2D(
        filters=filters, kernel_size=kernel_size, strides=(1, 1),
        padding='same', use_bias=False, kernel_initializer='he_normal',
        name=name + '_conv2'
    )(relu_layer1)
    bn_layer2 = tf.keras.layers.BatchNormalization(name=name + '_bn2')(conv_layer2)
    residual_layer = tf.keras.layers.Add(name=name + '_add')([bn_layer2, inputs])
    relu_layer2 = tf.keras.layers.Activation('relu', name=name + '_relu2')(residual_layer)
    return relu_layer2

def resnet(input_shape, num_classes):
    """
    ResNet model with 50 layers.
    """
    inputs = tf.keras.layers.Input(shape=input_shape)
    conv_layer1 = conv_block(inputs, filters=64, kernel_size=(7, 7), strides=(2, 2), name='conv1')
    maxpool_layer1 = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='maxpool1')(conv_layer1)
    identity_block1a = identity_block(maxpool_layer1, filters=64, kernel_size=(3, 3), name='id1a')
    identity_block1b = identity_block(identity_block1a, filters=64, kernel_size=(3, 3), name='id1b')
    identity_block1c = identity_block(identity_block1b, filters=64, kernel_size=(3, 3), name='id1c')
    identity_block2a = identity_block(identity_block1c, filters=128, kernel_size=(3, 3), name='id2a')
    identity_block2b = identity_block(identity_block2a, filters=128, kernel_size=(3, 3), name='id2b')
    identity_block2c = identity_block(identity_block2b, filters=128, kernel_size=(3, 3), name='id2c')
    identity_block2d = identity_block(identity_block2c, filters=128, kernel_size=(3, 3), name='id2d')
    identity_block3a = identity_block(identity_block2d, filters=256, kernel_size=(3, 3), name='id3a')
    identity_block3b = identity_block(identity_block3a, filters=256, kernel_size=(3, 3), name='id3b')
    identity_block3c = identity_block(identity_block3b, filters=256, kernel_size=(3, 3), name='id3c')
    identity_block3d = identity_block(identity_block3c, filters=256, kernel_size=(3, 3), name='id3d')
    identity_block3e = identity_block(identity_block3d, filters=256, kernel_size=(3, 3), name='id3e')
    identity_block3f = identity_block(identity_block3e, filters=256, kernel_size=(3, 3), name='id3f')
    identity_block4a = identity_block(identity_block3f, filters=512, kernel_size=(3, 3), name='id4a')
    identity_block4b = identity_block(identity_block4a, filters=512, kernel_size=(3, 3), name='id4b')
    identity_block4c = identity_block(identity_block4b, filters=512, kernel_size=(3, 3), name='id4c')
    avgpool_layer1 = tf.keras.layers.GlobalAveragePooling2D(name='avgpool1')(identity_block4c)
    outputs = tf.keras.layers.Dense(num_classes, activation='softmax', kernel_initializer='he_normal', name='fc')(avgpool_layer1)
    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
    return model

在这个实现中，我们定义了conv_block和identity_block两个函数分别实现卷积块和恒等块。然后我们定义了一个resnet函数，该函数将这些块连接起来以构建一个ResNet模型。这个实现中使用的是ResNet-50，共50层。最后，我们通过调用tf.keras.models.Model函数来定义整个模型。