写一个TensorFlow的resnet网络构造代码优化器用adam学习率为001

import tensorflow as tf

#定义残差块函数 def residual_block(input_layer, output_channels, stride=1): input_channels = input_layer.get_shape().as_list()[-1]

# 定义残差块中第一层卷积层
conv1 = tf.layers.conv2d(input_layer, 
                         filters=output_channels, 
                         kernel_size=[3, 3], 
                         strides=[stride, stride], 
                         padding="SAME", 
                         activation=tf.nn.relu)

# 定义残差块中第二层卷积层
conv2 = tf.layers.conv2d(conv1, 
                         filters=output_channels, 
                         kernel_size=[3, 3], 
                         strides=[1, 1], 
                         padding="SAME", 
                         activation=None)

# 如果输入输出大小有变化，需要对输入进行调整
if input_channels != output_channels or stride != 1:
    input_layer = tf.layers.conv2d(input_layer, 
                                   filters=output_channels, 
                                   kernel_size=[1, 1], 
                                   strides=[stride, stride], 
                                   padding="SAME", 
                                   activation=None)

# 将卷积结果与调整后的输入相加，并通过ReLU激活函数输出
residual_output = tf.nn.relu(conv2 + input_layer)

return residual_output

#定义ResNet网络 def resnet(input_layer, num_classes): #定义第一个卷积层 conv1 = tf.layers.conv2d(input_layer, filters=64, kernel_size=[7, 7], strides=[2, 2], padding="SAME", activation=tf.nn.relu)

#定义池化层
pool1 = tf.layers.max_pooling2d(conv1, pool_size=[3, 3], strides=[2, 2], padding="SAME")

#定义残差块部分
block1_output = residual_block(pool1, output_channels=64, stride=1)
block2_output = residual_block(block1_output, output_channels=64, stride=1)
block3_output = residual_block(block2_output, output_channels=64, stride=1)

block4_output = residual_block(block3_output, output_channels=128, stride=2)
block5_output = residual_block(block4_output, output_channels=128, stride=1)
block6_output = residual_block(block5_output, output_channels=128, stride=1)
block7_output = residual_block(block6_output, output_channels=128, stride=1)

block8_output = residual_block(block7_output, output_channels=256, stride=2)
block9_output = residual_block(block8_output, output_channels=256, stride=1)
block10_output = residual_block(block9_output, output_channels=256, stride=1)
block11_output = residual_block(block10_output, output_channels=256, stride=1)
block12_output = residual_block(block11_output, output_channels=256, stride=1)
block13_output = residual_block(block12_output, output_channels=256, stride=1)

block14_output = residual_block(block13_output, output_channels=512, stride=2)
block15_output = residual_block(block14_output, output_channels=512, stride=1)
block16_output = residual_block(block15_output, output_channels=512, stride=1)

#定义全局平均池化层
pool2 = tf.reduce_mean(block16_output, axis=[1, 2])

#定义输出层
logits = tf.layers.dense(pool2, units=num_classes)

return logits

#定义输入输出占位符 input_layer = tf.placeholder(tf.float32, [None, 224, 224, 3]) label_layer = tf.placeholder(tf.int32, [None])

#构建ResNet网络 logits = resnet(input_layer, num_classes=1000)

#定义损失函数 loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label_layer) total_loss = tf.reduce_mean(loss)

#定义优化器 optimizer = tf.train.AdamOptimizer(learning_rate=0.01) train_op = optimizer.minimize(total_loss