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AlexNet_main.py
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AlexNet_main.py
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# !usr/bin/python
# coding=utf-8
"""
应用TensorFlow实现AlexNet训练MNIST数据集
"""
import tensorflow as tf
import os
# 学习测试测试学习
# 进行冲突学习
# 定义权值
def weight_init(shape, name):
return tf.Variable(tf.random_normal(shape=shape, name=name))
# 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)
# 定义池化操作
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)
# local response normalization
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AlexNet网络模型
def alex_net(x, weights, biases, dropout):
x = tf.reshape(x, shape=[-1, 28, 28, 1])
with tf.name_scope('one'):
conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1'])
pool1 = maxpool2d('pool1', conv1, k=2)
norm1 = norm('norm1', pool1, lsize=4) # 14
with tf.name_scope('two'):
conv2 = conv2d('conv2', norm1, weights['wc2'], biases['bc2'])
pool2 = maxpool2d('pool2', conv2, k=2)
norm2 = norm('norm2', pool2, lsize=4) # 7
with tf.name_scope('three'):
conv3 = conv2d('conv2', norm2, weights['wc3'], biases['bc3'])
pool3 = maxpool2d('pool3', conv3, k=2)
norm3 = norm('norm3', pool3, lsize=4)
with tf.name_scope('four'):
conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4'])
conv5 = conv2d('conv5', conv4, weights['wc5'], biases['bc5'])
pool5 = maxpool2d('pool5', conv5, k=2)
norm5 = norm('norm5', pool5, lsize=4)
with tf.name_scope('fc_one'):
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)
fc1 = tf.nn.dropout(fc1, dropout)
with tf.name_scope('fc_two'):
# fc2 = tf.reshape(fc1, [-1, weights['wd2'].get_shape().as_list()[0]])
fc2 = tf.add(tf.matmul(fc1, weights['wd2']), biases['bd2'])
fc2 = tf.nn.relu(fc2)
fc2 = tf.nn.dropout(fc2, dropout)
out = tf.add(tf.matmul(fc2, weights['out']), biases['out'])
return out
def run_main():
# 定义网络超参数
learning_rate = 0.001
training_iters = 200000
batch_size = 128
display_step = 10
# 定义网络参数
n_input = 784
n_classes = 10
dropout = 0.75
# 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)
# 定义所有网络参数
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([2*2*256, 4096])),
'wd2': tf.Variable(tf.random_normal([4096, 4096])),
'out': tf.Variable(tf.random_normal([4096, n_classes]))
}
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]))
}
# 构建模型
pred = alex_net(x, weights, biases, keep_prob)
# 损失函数和优化器
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=pred))
tf.summary.scalar('cost', cost)
optimizer = tf.train.AdadeltaOptimizer(learning_rate=learning_rate).minimize(cost)
# 衡量矩阵
correct_pred = tf.equal(tf.arg_max(pred, 1), tf.arg_max(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar('accuracy', accuracy)
merged_summaries = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('./logs/train', tf.get_default_graph())
# train_writer.close()
# ================== 开始训练模型 ===========================
# 设置GPU按需增长
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# 初始化变量
init = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(init)
step = 1
# 第一步载入数据
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("../../MNIST_data/", one_hot=True)
while step*batch_size < training_iters:
batch_x, batch_y = mnist.train.next_batch(batch_size)
_, s= sess.run([optimizer, merged_summaries], feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
train_writer.add_summary(s, step)
if step % display_step == 0:
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
# print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
# y: mnist.test.labels[:256],
# keep_prob: 1}))
if __name__ == '__main__':
run_main()
# 进行冲突学习