Alexnet代码实现（学习）

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#coding=utf-8
from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

# 定义网络超参数
learning_rate = 0.001
training_iters = 10000 #迭代次数
batch_size = 64 #每个batch的大小
display_step = 20 #每20步展示一下结果

# 定义网络参数
n_input = 784 # 输入的维度
n_classes = 10 # 标签的维度


# 占位符输入
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32)

# 卷积操作，统一卷积格式（stride\padding），且加上relu
def conv2d(name, l_input, w, b):
return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b), name=name)

# 最大下采样操作
def max_pool(name, l_input, k):
return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)

# 归一化操作
def norm(name, l_input, lsize=4):
return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)

# 定义整个网络
def alex_net(_X, _weights, _biases, _dropout):
# 向量转为矩阵
_X = tf.reshape(_X, shape=[-1, 28, 28, 1]) #自动分好batch数

# 卷积层
conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
# 下采样层
pool1 = max_pool('pool1', conv1, k=2)
# 归一化层
norm1 = norm('norm1', pool1, lsize=4)
# Dropout
norm1 = tf.nn.dropout(norm1, _dropout)

# 卷积
conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
# 下采样
pool2 = max_pool('pool2', conv2, k=2)
# 归一化
norm2 = norm('norm2', pool2, lsize=4)
# Dropout
norm2 = tf.nn.dropout(norm2, _dropout)

# 卷积
conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
# 下采样
pool3 = max_pool('pool3', conv3, k=2)
# 归一化
norm3 = norm('norm3', pool3, lsize=4)
# Dropout
norm3 = tf.nn.dropout(norm3, _dropout)

# 全连接层，先把特征图转为向量
dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]])
dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')
# 全连接层
dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation

# 网络输出层
out = tf.matmul(dense2, _weights['out']) + _biases['out']
return out

# 存储所有的网络参数
weights = {
'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),
'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),
'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),
'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),
'wd2': tf.Variable(tf.random_normal([1024, 1024])),
'out': tf.Variable(tf.random_normal([1024, 10]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([64])),
'bc2': tf.Variable(tf.random_normal([128])),
'bc3': tf.Variable(tf.random_normal([256])),
'bd1': tf.Variable(tf.random_normal([1024])),
'bd2': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_classes]))
}

# 构建模型
pred = alex_net(x, weights, biases, keep_prob) #pred是计算完的值，此时还没归一化
a=tf.nn.softmax(pred) #a是归一化后的值。
# 定义损失函数和学习步骤
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred, labels = y))#这个是损失loss

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) #最小化loss

# 测试网络
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# 初始化所有的共享变量
init = tf.initialize_all_variables()

# 开启一个训练
with tf.Session() as sess:
sess.run(init)
step = 1
# Keep training until reach max iterations
while step * batch_size < training_iters: #直到达到最大迭代次数，没考虑梯度！！！
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# 获取批数据
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
if step % display_step == 0: #每一步里有64batch，64*20=1280
# 计算精度
acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
# 计算损失值
loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy = " + "{:.5f}".format(acc))
step += 1
print ("Optimization Finished!")
# 计算测试精度
print ("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})) #拿前256个来测试
print ("Testing Result:", sess.run(a, feed_dict={x: mnist.test.images[63:64], y: mnist.test.labels[63:64], keep_prob: 1.})) #数组范围，从0开始，含左不含右

引用：https://blog.csdn.net/qq_28123095/article/details/79787782?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522161812577816780274183212%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fall.%2522%257D&request_id=161812577816780274183212&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~first_rank_v2~rank_v29-2-79787782.pc_search_result_no_baidu_js&utm_term=alexnet%E7%94%A8mnist%E4%BB%A3%E7%A0%81%E5%AE%9E%E7%8E%B0