BP神经网络基于TensorFlow的mnist数据集分类

最新推荐文章于 2025-02-26 21:32:17 发布

原创最新推荐文章于 2025-02-26 21:32:17 发布 · 1k 阅读

5 ·

CC 4.0 BY-SA版权

文章标签：

#tensorflow #深度学习 #dropout

算法同时被 3 个专栏收录

33 篇文章

订阅专栏

机器学习

21 篇文章

订阅专栏

数学

14 篇文章

订阅专栏

本文介绍了一种使用TensorFlow实现的手写数字识别模型。该模型通过多层神经网络对MNIST数据集进行训练，利用Adam优化器最小化交叉熵损失，最终在训练集、测试集和验证集上达到了较高的准确率。

部署运行你感兴趣的模型镜像

# -*- coding: utf-8 -*-

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

mnist = input_data.read_data_sets("mnist/", one_hot=True)

# 定义回归模型
X = tf.placeholder(tf.float32, [None, 784]) # 输入的X的值
Y = tf.placeholder(tf.float32, [None, 10])  # 输出真实值


def create_model():
    w1 = tf.Variable(tf.random_uniform([784, 1024], -1, 1))
    b1 = tf.Variable(tf.random_uniform([1024], -1, 1))
    y1 = tf.sigmoid(tf.matmul(X, w1) + b1)
    y1 = tf.nn.dropout(y1, keep_prob=1)

    w2 = tf.Variable(tf.truncated_normal([1024, 512]))
    b2 = tf.Variable(tf.truncated_normal([512]))
    y2 = tf.sigmoid(tf.matmul(y1, w2) + b2)
    y2 = tf.nn.dropout(y2, keep_prob=1)

    w3 = tf.Variable(tf.random_uniform([512, 128], -1, 1))
    b3 = tf.Variable(tf.random_uniform([128], -1, 1))
    y3 = tf.sigmoid(tf.matmul(y2, w3) + b3)
    y3 = tf.nn.dropout(y3, keep_prob=1)

    w4 = tf.Variable(tf.truncated_normal([128, 10]))
    b4 = tf.Variable(tf.truncated_normal([10]))
    outputs = tf.matmul(y3, w4) + b4

    # softmax loss
    cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=outputs, labels=Y))
    #train_op = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy_loss)
    train_op = tf.train.AdamOptimizer(0.001).minimize(cross_entropy_loss)

    #预测、准确率
    pred = tf.equal(tf.argmax(outputs, 1), tf.argmax(Y, 1))
    accuracy = tf.reduce_mean(tf.cast(pred, tf.float32))

    return train_op, cross_entropy_loss, accuracy


with tf.Session() as sess:
    train_op1, cross_entropy_loss1, accuracy1 = create_model()

    sess.run(tf.global_variables_initializer())

    for i in range(10000000):
        xs, ys = mnist.train.next_batch(100)
        _, loss_, acc_ = sess.run([train_op1, cross_entropy_loss1, accuracy1], feed_dict={X: xs, Y: ys})
        if i % 100 != 0:
            continue
        # 测试当前模型在训练数据、测试数据、验证数据中的准确率，
        # 数据较少，验证所有的数据
        print("step:%s, loss: %s, train_acc:%s, test_acc:%s, valid_acc:%s" % (i, loss_,
                sess.run(accuracy1, feed_dict={X: mnist.train.images, Y: mnist.train.labels}),
                sess.run(accuracy1, feed_dict={X: mnist.test.images, Y: mnist.test.labels}),
                sess.run(accuracy1, feed_dict={X: mnist.validation.images, Y: mnist.validation.labels})))