ALX2.py

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
import ANI
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'

mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
learning_rate = 0.001
training_iters = 200000
display_step = 20
n_classes = 10
batch_size = 128
_dropout1 = 0.8
_dropout2 = 0.6

n_input = 784
n_classes = 10

keep_prob = tf.placeholder('float')
x = tf.placeholder('float', [None, 784])
y = tf.placeholder('float', [None, n_classes])

def conv2d(x, weights, b):
    return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, weights, strides=[1, 1, 1, 1], padding='SAME'), b))

def maxpool2d(x, k):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')

def lrn(x):
    return tf.nn.lrn(x, 4, bias=1, alpha=1e-3/9, beta=0.75, name="lrn")

def convolutional_neural_network(x):
    weights = {
        'W_conv1': tf.Variable(tf.random_normal([7, 7, 1, 64])),
        'W_conv2': tf.Variable(tf.random_normal([5, 5, 64, 192])),
        'W_conv3': tf.Variable(tf.random_normal([3, 3, 192, 384])),
        'W_conv4': tf.Variable(tf.random_normal([3, 3, 384, 128])),
        'W_conv5': tf.Variable(tf.random_normal([3, 3, 128, 256])),

        'W_fc1': tf.Variable(tf.random_normal([4*4*256, 4096])),
        'W_fc2': tf.Variable(tf.random_normal([4096, 4096])),
        'W_fc3': tf.Variable(tf.random_normal([4096, 1000])),
        'out': tf.Variable(tf.random_normal([1000, n_classes]))}

    biases = {
        'b_conv1': tf.Variable(tf.random_normal([64])),
        'b_conv2': tf.Variable(tf.random_normal([192])),
        'b_conv3': tf.Variable(tf.random_normal([384])),
        'b_conv4': tf.Variable(tf.random_normal([128])),
        'b_conv5': tf.Variable(tf.random_normal([256])),
        'b_fc1': tf.Variable(tf.random_normal([4096])),
        'b_fc2': tf.Variable(tf.random_normal([4096])),
        'b_fc3': tf.Variable(tf.random_normal([1000])),
        'out': tf.Variable(tf.random_normal([n_classes]))}

    x = tf.reshape(x, shape=[-1, 28, 28, 1])

#CL
    conv1 = conv2d(x, weights['W_conv1'], biases['b_conv1'])
    conv1 = maxpool2d(conv1, k=2)
    conv1 = lrn(conv1)
    conv1 = tf.nn.dropout(conv1, _dropout1)

    conv2 = conv2d(conv1, weights['W_conv2'], biases['b_conv2'])
    conv2 = maxpool2d(conv2, k=2)
    conv2 = lrn(conv2)
    conv2 = tf.nn.dropout(conv2, _dropout2)

    conv3 = conv2d(conv2, weights['W_conv3'], biases['b_conv3'])
    conv3 = maxpool2d(conv3, k=2)
    conv3 = lrn(conv3)
    conv3 = tf.nn.dropout(conv3, _dropout2)


    conv4 = conv2d(conv3, weights['W_conv4'], biases['b_conv4'])
    conv4 = maxpool2d(conv4, k=1)

    conv5 = conv2d(conv4, weights['W_conv5'], biases['b_conv5'])
    conv5 = maxpool2d(conv5, k=1)

#FCL
    fc1 = tf.reshape(conv5,[-1, weights['W_fc1'].get_shape().as_list()[0]])

    fc1 = tf.nn.relu(tf.matmul(fc1, weights['W_fc1'])+biases['b_fc1'])

    fc2 = tf.nn.relu(tf.matmul(fc1, weights['W_fc2'])+biases['b_fc2'])

    fc3 = tf.nn.relu(tf.matmul(fc2, weights['W_fc3'])+biases['b_fc3'])

    output = tf.matmul(fc3, weights['out'])+biases['out']
    return output



def train_neural_network(x):
    pred = convolutional_neural_network(x)

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

    # 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))

    init = tf.initialize_all_variables()
    with tf.Session() as sess:
        sess.run(init)
        step = 1
        # Keep training until reach max iterations
        while step * batch_size < training_iters:
            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: _dropout1})
            if step % display_step == 0:
                # 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!")
        print("Testing Accuracy:", sess.run(accuracy,
                feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})


train_neural_network(x)