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data和input_data.py

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

3.CNN.py

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.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 out

print('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 = 15
batch_size = 16
display_step = 1

for 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 pickle
with open('res_dict.pkl', 'wb') as f:
    pickle.dump(res_dict, f, pickle.HIGHEST_PROTOCOL)

4.test.py

import pickle
import numpy as np

def 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 = 0

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

test_image = mnist.test.images
test_label = mnist.test.labels

import tensorflow as tf

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'].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 out

n_input = 784
n_output = 10

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, 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 = 1
batch_size = 1
display_step = 1

for 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))