tensorflow使用简单两层神经网络训练测试minst

浅浅的花香味﹌ 2023-07-11 11:17 49阅读 0赞

import numpy as np
    import tensorflow as tf
    import matplotlib.pyplot as plt
    import input_data

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

# NETWORK TOPOLOGIES
    n_hidden_1 = 256 
    n_hidden_2 = 128 
    n_input    = 784 
    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")

![在这里插入图片描述][2020030511534252.png]

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'])

# PREDICTION
    pred = multilayer_perceptron(x, weights, biases)
    
    # LOSS AND OPTIMIZER
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=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")

![在这里插入图片描述][20200305115523348.png]

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")

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70]

[2020030511534252.png]: https://img-blog.csdnimg.cn/2020030511534252.png
[20200305115523348.png]: https://img-blog.csdnimg.cn/20200305115523348.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200305115555686.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg==,size_16,color_FFFFFF,t_70