python实现resnet50网络结构的代码

以下是 Python 实现 ResNet50 网络结构的代码：

import tensorflow as tf

def conv_layer(inputs, filters, kernel_size, strides):
    return tf.layers.conv2d(inputs=inputs,
                            filters=filters,
                            kernel_size=kernel_size,
                            strides=strides,
                            padding='same',
                            kernel_initializer=tf.contrib.layers.xavier_initializer())

def identity_block(inputs, filters):
    F1, F2, F3 = filters
    
    X_shortcut = inputs
    
    X = conv_layer(inputs, filters=F1, kernel_size=1, strides=1)
    X = tf.layers.batch_normalization(X)
    X = tf.nn.relu(X)
    
    X = conv_layer(X, filters=F2, kernel_size=3, strides=1)
    X = tf.layers.batch_normalization(X)
    X = tf.nn.relu(X)
    
    X = conv_layer(X, filters=F3, kernel_size=1, strides=1)
    X = tf.layers.batch_normalization(X)
    
    X = tf.add(X, X_shortcut)
    X = tf.nn.relu(X)
    
    return X

def convolutional_block(inputs, filters, strides):
    F1, F2, F3 = filters
    
    X_shortcut = inputs
    
    X = conv_layer(inputs, filters=F1, kernel_size=1, strides=strides)
    X = tf.layers.batch_normalization(X)
    X = tf.nn.relu(X)
    
    X = conv_layer(X, filters=F2, kernel_size=3, strides=1)
    X = tf.layers.batch_normalization(X)
    X = tf.nn.relu(X)
    
    X = conv_layer(X, filters=F3, kernel_size=1, strides=1)
    X = tf.layers.batch_normalization(X)
    
    X_shortcut = conv_layer(X_shortcut, filters=F3, kernel_size=1, strides=strides)
    X_shortcut = tf.layers.batch_normalization(X_shortcut)
    
    X = tf.add(X, X_shortcut)
    X = tf.nn.relu(X)
    
    return X

def ResNet50(inputs):
    X = conv_layer(inputs, filters=64, kernel_size=7, strides=2)
    X = tf.layers.batch_normalization(X)
    X = tf.nn.relu(X)
    X = tf.layers.max_pooling2d(X, pool_size=3, strides=2, padding='same')
    
    X = convolutional_block(X, filters=[64, 64, 256], strides=1)
    X = identity_block(X, filters=[64, 64, 256])
    X = identity_block(X, filters=[64, 64, 256])
    
    X = convolutional_block(X, filters=[128, 128, 512], strides=2)
    X = identity_block(X, filters=[128, 128, 512])
    X = identity_block(X, filters=[128, 128, 512])
    X = identity_block(X, filters=[128, 128, 512])
    
    X = convolutional_block(X, filters=[256, 256, 1024], strides=2)
    X = identity_block(X, filters=[256, 256, 1024])
    X = identity_block(X, filters=[256, 256, 1024])
    X = identity_block(X, filters=[256, 256, 1024])
    X = identity_block(X, filters=[256, 256, 1024])
    X = identity_block(X, filters=[256, 256, 1024])
    
    X = convolutional_block(X, filters=[512, 512, 2048], strides=2)
    X = identity_block(X, filters=[512, 512, 2048])
    X = identity_block(X, filters=[512, 512, 2048])
    
    X = tf.layers.average_pooling2d(X, pool_size=7, strides=1, padding='valid')
    
    X = tf.layers.flatten(X)
    X = tf.layers.dense(X, units=1000, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer())
    
    return X

inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
logits = ResNet50(inputs)
predictions = tf.nn.softmax(logits)