tensorflow学习笔记六----------神经网络

使用mnist数据集进行神经网络的构建

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('data/', one_hot=True)

这个神经网络共有三层。输入层有n个1*784的矩阵，第一层有256个神经元，第二层有128个神经元，输出层是一个十分类的结果。对w1、b1、w2、b2以及输出层的参数进行随机初始化

# NETWORK TOPOLOGIES

n_input    = 784

n_hidden_1 = 256

n_hidden_2 = 128

n_classes  = 10  

# INPUTS AND OUTPUTS

x = tf.placeholder("float", [None, n_input])

y = tf.placeholder("float", [None, n_classes])

# NETWORK PARAMETERS

stddev = 0.1

weights = {

    'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)),

    'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)),

    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev))

}

biases = {

    'b1': tf.Variable(tf.random_normal([n_hidden_1])),

    'b2': tf.Variable(tf.random_normal([n_hidden_2])),

    'out': tf.Variable(tf.random_normal([n_classes]))

}

print ("NETWORK READY")

开始进行前向传播

def multilayer_perceptron(_X, _weights, _biases):

    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['w1']), _biases['b1']))

    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['w2']), _biases['b2']))

    return (tf.matmul(layer_2, _weights['out']) + _biases['out'])

用前向传播函数算出预测值；算出损失值（此处使用交叉熵）；构造梯度下降最优构造器；算出精度；

# PREDICTION

pred = multilayer_perceptron(x, weights, biases)

# LOSS AND OPTIMIZER

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))

optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost)

corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

accr = tf.reduce_mean(tf.cast(corr, "float"))

# INITIALIZER

init = tf.global_variables_initializer()

print ("FUNCTIONS READY")

定义迭代次数；使用以上定义好的神经网络函数

training_epochs = 20

batch_size      = 100

display_step    = 4

# LAUNCH THE GRAPH

sess = tf.Session()

sess.run(init)

# OPTIMIZE

for epoch in range(training_epochs):

    avg_cost = 0.

    total_batch = int(mnist.train.num_examples/batch_size)

    # ITERATION

    for i in range(total_batch):

        batch_xs, batch_ys = mnist.train.next_batch(batch_size)

        feeds = {x: batch_xs, y: batch_ys}

        sess.run(optm, feed_dict=feeds)

        avg_cost += sess.run(cost, feed_dict=feeds)

    avg_cost = avg_cost / total_batch

    # DISPLAY

    if (epoch+1) % display_step == 0:

        print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))

        feeds = {x: batch_xs, y: batch_ys}

        train_acc = sess.run(accr, feed_dict=feeds)

        print ("TRAIN ACCURACY: %.3f" % (train_acc))

        feeds = {x: mnist.test.images, y: mnist.test.labels}

        test_acc = sess.run(accr, feed_dict=feeds)

        print ("TEST ACCURACY: %.3f" % (test_acc))

print ("OPTIMIZATION FINISHED")

巴特西

tensorflow学习笔记六----------神经网络

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