MNIST的AlexNet实现_mnist alexnet画图

import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

#获取mnist数据集的新方式，旧方法已不能用
old_v = tf.logging.get_verbosity()
tf.logging.set_verbosity(tf.logging.ERROR)
mnist = input_data.read_data_sets(“MNIST_data/”, one_hot=True)
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
tf.logging.set_verbosity(old_v)

#定义网络的超参数
learning_rate = 0.001
training_iters = 200000
batch_size = 128
display_step = 10

#定义网络的参数
n_input = 784 # 输入的维度（img_shape: 28*28）
n_classes = 10 # 标记的维度（0-9 digits）
dropout = 0.75 # Dropout的概率，输出的可能性

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

#构建网络模型
#定义卷积操作
def conv2d(name, x, W, b, strides=1):
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding=‘SAME’)
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x, name=name) # 使用relu激活函数

#定义池化层操作
def maxpool2d(name, x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding=‘SAME’, name=name)

#规范化操作
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)

#定义所有的网络参数
weights = {
‘wc1’: tf.Variable(tf.random_normal([11, 11, 1, 96])),
‘wc2’: tf.Variable(tf.random_normal([5, 5, 96, 256])),
‘wc3’: tf.Variable(tf.random_normal([3, 3, 256, 384])),
‘wc4’: tf.Variable(tf.random_normal([3, 3, 384, 384])),
‘wc5’: tf.Variable(tf.random_normal([3, 3, 384, 256])),
‘wd1’: tf.Variable(tf.random_normal([44256, 4096])),
‘wd2’: tf.Variable(tf.random_normal([4096, 4096])),
‘out’: tf.Variable(tf.random_normal([4096, 10]))
}

biases = {
‘bc1’: tf.Variable(tf.random_normal([96])),
‘bc2’: tf.Variable(tf.random_normal([256])),
‘bc3’: tf.Variable(tf.random_normal([384])),
‘bc4’: tf.Variable(tf.random_normal([384])),
‘bc5’: tf.Variable(tf.random_normal([256])),
‘bd1’: tf.Variable(tf.random_normal([4096])),
‘bd2’: tf.Variable(tf.random_normal([4096])),
‘out’: tf.Variable(tf.random_normal([n_classes])),
}

#定义AlexNet的网络模型
#定义整个网络
def alex_net(x, weights, biases, dropout):
# 改造输入图像的形状
x = tf.reshape(x, shape=[-1, 28, 28, 1])

# 第一层卷积
# 卷积
conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1'])
# 下采样
pool1 = maxpool2d('pool1', conv1, k=2)
# 规范化
norm1 = norm('norm1', pool1, lsize=4)

# 第二层卷积
# 卷积
conv2 = conv2d('conv2', conv1, weights['wc2'], biases['bc2'])
# 下采样
pool2 = maxpool2d('pool2', conv2, k=2)
# 规范化
norm2 = norm('norm2', pool2, lsize=4)

# 第三层卷积
# 卷积
conv3 = conv2d('conv3', norm2, weights['wc3'], biases['bc3'])
# 下采样
pool3 = maxpool2d('pool3', conv3, k=2)
# 规范化
norm3 = norm('norm3', pool3, lsize=4)

# 第四层卷积
# 卷积
conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4'])
# 第五层卷积
conv5 = conv2d('conv5', norm3, weights['wc5'], biases['bc5'])
# 下采样
pool5 = maxpool2d('pool5', conv5, k=2)
# 规范化
norm5 = norm('norm5', pool5, lsize=4)

# 全连接层1
fc1 = tf.reshape(norm5, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# dropout
fc1 = tf.nn.dropout(fc1, dropout)

# 全连接层2
fc2 = tf.reshape(fc1, [-1, weights['wd1'].get_shape().as_list()[0]])
fc2 = tf.add(tf.matmul(fc2, weights['wd1']), biases['bd1'])
fc2 = tf.nn.relu(fc2)
# dropout
fc2 = tf.nn.dropout(fc2, dropout)

# 输出层
out = tf.add(tf.matmul(fc2, weights['out']), biases['out'])
return out
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#构建模型，定义损失函数和优化器，并构建评估函数
#构建模型
pred = alex_net(x, weights, biases, keep_prob)

#定义损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=pred, logits=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

#评估函数
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

#训练与评估模型
#初始化变量
init = tf.global_variables_initializer()

with tf.Session() as sess:
sess.run(init)
step = 1
# 开始训练，直到达到training_iters,即200000
while step * batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
if step % display_step == 0:
# 计算损失之和准确度， 输出
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + “{:.6f}”.format(loss) +
", Training Accuracy= " + “{:.5f}”.format(acc))
step += 1
print(“Optimization Finished!”)
# 计算测试集的准确度
print(“Testing Accuracy:”, sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
y: mnist.test.labels[:256],
keep_prob: 1.}))