tensorflow使用两层CNN训练测试minst

桃扇骨 2023-07-11 11:24 104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)
    trainimg   = mnist.train.images
    trainlabel = mnist.train.labels
    testimg    = mnist.test.images
    testlabel  = mnist.test.labels
    print ("MNIST ready")

n_input  = 784
    n_output = 10
    weights  = { 
            'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.1)),
            'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.1)),
            'wd1': tf.Variable(tf.random_normal([7*7*128, 1024], stddev=0.1)),
            'wd2': tf.Variable(tf.random_normal([1024, n_output], stddev=0.1))
        }
    biases   = { 
            'bc1': tf.Variable(tf.random_normal([64], stddev=0.1)),
            'bc2': tf.Variable(tf.random_normal([128], stddev=0.1)),
            'bd1': tf.Variable(tf.random_normal([1024], stddev=0.1)),
            'bd2': tf.Variable(tf.random_normal([n_output], stddev=0.1))
        }

def conv_basic(_input, _w, _b, _keepratio):
            # INPUT
            _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])
            # CONV LAYER 1
            _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')
            #_mean, _var = tf.nn.moments(_conv1, [0, 1, 2])
            #_conv1 = tf.nn.batch_normalization(_conv1, _mean, _var, 0, 1, 0.0001)
            _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1']))
            _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
            _pool_dr1 = tf.nn.dropout(_pool1, _keepratio)
            # CONV LAYER 2
            _conv2 = tf.nn.conv2d(_pool_dr1, _w['wc2'], strides=[1, 1, 1, 1], padding='SAME')
            #_mean, _var = tf.nn.moments(_conv2, [0, 1, 2])
            #_conv2 = tf.nn.batch_normalization(_conv2, _mean, _var, 0, 1, 0.0001)
            _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2, _b['bc2']))
            _pool2 = tf.nn.max_pool(_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
            _pool_dr2 = tf.nn.dropout(_pool2, _keepratio)
            # VECTORIZE
            _dense1 = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]])
            # FULLY CONNECTED LAYER 1
            _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w['wd1']), _b['bd1']))
            _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)
            # FULLY CONNECTED LAYER 2
            _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2'])
            # RETURN
            out = {  'input_r': _input_r, 'conv1': _conv1, 'pool1': _pool1, 'pool1_dr1': _pool_dr1,
                'conv2': _conv2, 'pool2': _pool2, 'pool_dr2': _pool_dr2, 'dense1': _dense1,
                'fc1': _fc1, 'fc_dr1': _fc_dr1, 'out': _out
            }
            return out

x = tf.placeholder(tf.float32, [None, n_input])
    y = tf.placeholder(tf.float32, [None, n_output])
    keepratio = tf.placeholder(tf.float32)
    
    # FUNCTIONS
    
    _pred = conv_basic(x, weights, biases, keepratio)['out']
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=_pred, labels=y))
    optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
    _corr = tf.equal(tf.argmax(_pred,1), tf.argmax(y,1)) 
    accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) 
    init = tf.global_variables_initializer()
        
    # SAVER
    print ("GRAPH READY")

sess = tf.Session()
    sess.run(init)
    
    training_epochs = 15
    batch_size      = 16
    display_step    = 1
    for epoch in range(training_epochs):
        avg_cost = 0.
        #total_batch = int(mnist.train.num_examples/batch_size)
        total_batch = 10
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Fit training using batch data
            sess.run(optm, feed_dict={ x: batch_xs, y: batch_ys, keepratio:0.7})
            # Compute average loss
            avg_cost += sess.run(cost, feed_dict={ x: batch_xs, y: batch_ys, keepratio:1.})/total_batch
    
        # Display logs per epoch step
        if epoch % display_step == 0: 
            print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
            train_acc = sess.run(accr, feed_dict={ x: batch_xs, y: batch_ys, keepratio:1.})
            print (" Training accuracy: %.3f" % (train_acc))
            #test_acc = sess.run(accr, feed_dict={x: testimg, y: testlabel, keepratio:1.})
            #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]

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