写一个ResNet50的结构Python

代码是什么？

好的，下面是ResNet50的结构Python代码：

import tensorflow as tf

def identity_block(X, f, filters, stage, block):
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    F1, F2, F3 = filters

    X_shortcut = X

    X = tf.keras.layers.Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base+'2a')(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+'2a')(X)
    X = tf.keras.layers.Activation('relu')(X)

    X = tf.keras.layers.Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base+'2b')(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+'2b')(X)
    X = tf.keras.layers.Activation('relu')(X)

    X = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base+'2c')(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+'2c')(X)

    X = tf.keras.layers.Add()([X, X_shortcut])
    X = tf.keras.layers.Activation('relu')(X)

    return X

def convolutional_block(X, f, filters, stage, block, s=2):
    conv_name_base = 'res' + str(stage) + block + '_branch'
    bn_name_base = 'bn' + str(stage) + block + '_branch'

    F1, F2, F3 = filters

    X_shortcut = X

    X = tf.keras.layers.Conv2D(filters=F1, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base+'2a')(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+'2a')(X)
    X = tf.keras.layers.Activation('relu')(X)

    X = tf.keras.layers.Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same', name=conv_name_base+'2b')(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+'2b')(X)
    X = tf.keras.layers.Activation('relu')(X)

    X = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid', name=conv_name_base+'2c')(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+'2c')(X)

    X_shortcut = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid', name=conv_name_base+'1')(X_shortcut)
    X_shortcut = tf.keras.layers.BatchNormalization(axis=3, name=bn_name_base+'1')(X_shortcut)

    X = tf.keras.layers.Add()([X, X_shortcut])
    X = tf.keras.layers.Activation('relu')(X)

    return X

def ResNet50(input_shape=(64, 64, 3), classes=6):
    X_input = tf.keras.layers.Input(input_shape)

    X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input)

    X = tf.keras.layers.Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(X)
    X = tf.keras.layers.BatchNormalization(axis=3, name='bn_conv1')(X)
    X = tf.keras.layers.Activation('relu')(X)
    X = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(X)

    X = convolutional_block(X, f=3, filters=[64, 64, 256], stage=2, block='a', s=1)
    X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')
    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')

    X = convolutional_block(X, f=3, filters=[128, 128, 512], stage=3, block='a', s=2)
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')
    X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')

    X = convolutional_block(X, f=3, filters=[256, 256, 1024], stage=4, block='a', s=2)
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')
    X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')

    X = convolutional_block(X, f=3, filters=[512, 512, 2048], stage=5, block='a', s=2)
    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')
    X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')

    X = tf.keras.layers.AveragePooling2D((2, 2), name='avg_pool')(X)

    X = tf.keras.layers.Flatten()(X)
    X = tf.keras.layers.Dense(classes, activation='softmax', name='fc'+str(classes))(X)

    model = tf.keras.Model(inputs=X_input, outputs=X, name='ResNet50')

    return model

这是一个基于tensorflow框架实现的ResNet50网络结构的Python代码。如果您对该代码还有任何疑问，请随时联系我。