python,tensorflow,CNN实现mnist数据集的训练与验证正确率，,1.工程目录2.导入

python,tensorflow,CNN实现mnist数据集的训练与验证正确率，,1.工程目录2.导入

1.工程目录

2.导入data和input_data.py

链接：https://pan.baidu.com/s/1EBNyNurBXWeJVyhNeVnmnA
提取码：4nnl

3.CNN.py

import tensorflow as tfimport matplotlib.pyplot as pltimport input_datamnist = input_data.read_data_sets(‘data/‘, one_hot=True)trainimg = mnist.train.imagestrainlabel = mnist.train.labelstestimg = mnist.test.imagestestlabel = mnist.test.labelsprint(‘MNIST ready‘)n_input = 784n_output = 10weights = {    ‘wc1‘: tf.Variable(tf.truncated_normal([3, 3, 1, 64], stddev=0.1)),    ‘wc2‘: tf.Variable(tf.truncated_normal([3, 3, 64, 128], stddev=0.1)),    ‘wd1‘: tf.Variable(tf.truncated_normal([7*7*128, 1024], stddev=0.1)),    ‘wd2‘: tf.Variable(tf.truncated_normal([1024, n_outpot], 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_outpot], stddev=0.1)),}def conv_basic(_input, _w, _b, _keepratio):    _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])    _conv1 = tf.nn.conv2d(_input_r, _w[‘wc1‘], strides=[1, 1, 1, 1], padding=‘SAME‘)    _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)    _conv2 = tf.nn.conv2d(_pool_dr1, _w[‘wc2‘], strides=[1, 1, 1, 1], padding=‘SAME‘)    _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)    _densel = tf.reshape(_pool_dr2, [-1, _w[‘wd1‘].get_shape().as_list()[0]])    _fc1 = tf.nn.relu(tf.add(tf.matmul(_densel, _w[‘wd1‘]), _b[‘bd1‘]))    _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)    _out = tf.add(tf.matmul(_fc_dr1, _w[‘wd2‘]), _b[‘bd2‘])    out = {        ‘input_r‘: _input_r, ‘conv1‘: _conv1, ‘pool1‘: _pool1, ‘pool_dr1‘: _pool_dr1,        ‘conv2‘: _conv2, ‘pool2‘: _pool2, ‘pool_dr2‘: _pool_dr2, ‘densel‘: _densel,        ‘fc1‘: _fc1, ‘fc_dr1‘: _fc_dr1, ‘out‘: _out    }    return outprint(‘CNN READY‘)x = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_output])keepratio = tf.placeholder(tf.float32)_pred = conv_basic(x, weights, biases, keepratio)[‘out‘]cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))optm = tf.train.AdamOptimizer(learning_rate=0.01).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()print(‘GRAPH READY‘)sess = tf.Session()sess.run(init)training_epochs = 15batch_size = 16display_step = 1for epoch in range(training_epochs):    avg_cost = 0.    total_batch = 10    for i in range(total_batch):        batch_xs, batch_ys = mnist.train.next_batch(batch_size)        sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keepratio: 0.7})        avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keepratio: 1.0})/total_batch    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))res_dict = {‘weight‘: sess.run(weights), ‘biases‘: sess.run(biases)}import picklewith open(‘res_dict.pkl‘, ‘wb‘) as f:    pickle.dump(res_dict, f, pickle.HIGHEST_PROTOCOL)

4.test.py

import pickleimport numpy as npdef load_file(path, name):    with open(path+‘‘+name+‘.pkl‘, ‘rb‘) as f:        return pickle.load(f)res_dict = load_file(‘‘, ‘res_dict‘)print(res_dict[‘weight‘][‘wc1‘])index = 0import input_datamnist = input_data.read_data_sets(‘data/‘, one_hot=True)test_image = mnist.test.imagestest_label = mnist.test.labelsimport tensorflow as tfdef conv_basic(_input, _w, _b, _keepratio):    _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])    _conv1 = tf.nn.conv2d(_input_r, _w[‘wc1‘], strides=[1, 1, 1, 1], padding=‘SAME‘)    _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)    _conv2 = tf.nn.conv2d(_pool_dr1, _w[‘wc2‘], strides=[1, 1, 1, 1], padding=‘SAME‘)    _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)    _densel = tf.reshape(_pool_dr2, [-1, _w[‘wd1‘].shape[0]])    _fc1 = tf.nn.relu(tf.add(tf.matmul(_densel, _w[‘wd1‘]), _b[‘bd1‘]))    _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)    _out = tf.add(tf.matmul(_fc_dr1, _w[‘wd2‘]), _b[‘bd2‘])    out = {        ‘input_r‘: _input_r, ‘conv1‘: _conv1, ‘pool1‘: _pool1, ‘pool_dr1‘: _pool_dr1,        ‘conv2‘: _conv2, ‘pool2‘: _pool2, ‘pool_dr2‘: _pool_dr2, ‘densel‘: _densel,        ‘fc1‘: _fc1, ‘fc_dr1‘: _fc_dr1, ‘out‘: _out    }    return outn_input = 784n_output = 10x = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_output])keepratio = tf.placeholder(tf.float32)_pred = conv_basic(x, res_dict[‘weight‘], res_dict[‘biases‘], keepratio)[‘out‘]cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))_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()sess = tf.Session()sess.run(init)training_epochs = 1batch_size = 1display_step = 1for epoch in range(training_epochs):    avg_cost = 0.    total_batch = 10    for i in range(total_batch):        batch_xs, batch_ys = mnist.train.next_batch(batch_size)    if epoch % display_step == 0:        print(‘_pre:‘, np.argmax(sess.run(_pred, feed_dict={x: batch_xs, keepratio: 1. })))        print(‘answer:‘, np.argmax(batch_ys))

python,tensorflow,CNN实现mnist数据集的训练与验证正确率