#代码可运行,准确率高达97% #tensorflow实现的用cnn处理mnist数据集,2个卷积层,2个全连接层,注意tf.reshape(...)函数的用法 |
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""" | |
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly. | |
""" | |
from __future__ import print_function | |
import tensorflow as tf | |
from tensorflow.examples.tutorials.mnist import input_data | |
# number 1 to 10 data | |
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) | |
def compute_accuracy(v_xs, v_ys): | |
global prediction | |
y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1}) | |
correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1)) | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1}) | |
return result | |
def weight_variable(shape): | |
initial = tf.truncated_normal(shape, stddev=0.1) | |
return tf.Variable(initial) | |
def bias_variable(shape): | |
initial = tf.constant(0.1, shape=shape) | |
return tf.Variable(initial) | |
def conv2d(x, W): | |
# stride [1, x_movement, y_movement, 1] | |
# Must have strides[0] = strides[3] = 1 | |
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') | |
def max_pool_2x2(x): | |
# stride [1, x_movement, y_movement, 1] | |
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') | |
# define placeholder for inputs to network | |
xs = tf.placeholder(tf.float32, [None, 784])/255. # 28x28 | |
ys = tf.placeholder(tf.float32, [None, 10]) | |
keep_prob = tf.placeholder(tf.float32) | |
x_image = tf.reshape(xs, [-1, 28, 28, 1]) | |
# print(x_image.shape) # [n_samples, 28,28,1] | |
## conv1 layer ## | |
W_conv1 = weight_variable([5,5, 1,32]) # patch 5x5, in size 1, out size 32 | |
b_conv1 = bias_variable([32]) | |
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) # output size 28x28x32 | |
h_pool1 = max_pool_2x2(h_conv1) # output size 14x14x32 | |
## conv2 layer ## | |
W_conv2 = weight_variable([5,5, 32, 64]) # patch 5x5, in size 32, out size 64 | |
b_conv2 = bias_variable([64]) | |
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 14x14x64 | |
h_pool2 = max_pool_2x2(h_conv2) # output size 7x7x64 | |
## fc1 layer ## | |
W_fc1 = weight_variable([7*7*64, 1024]) | |
b_fc1 = bias_variable([1024]) | |
# [n_samples, 7, 7, 64] ->> [n_samples, 7*7*64] | |
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) | |
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) | |
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) | |
## fc2 layer ## | |
W_fc2 = weight_variable([1024, 10]) | |
b_fc2 = bias_variable([10]) | |
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) | |
# the error between prediction and real data | |
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), | |
reduction_indices=[1])) # loss | |
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) | |
sess = tf.Session() | |
# important step | |
# tf.initialize_all_variables() no long valid from | |
# 2017-03-02 if using tensorflow >= 0.12 | |
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1: | |
init = tf.initialize_all_variables() | |
else: | |
init = tf.global_variables_initializer() | |
sess.run(init) | |
for i in range(1000): | |
batch_xs, batch_ys = mnist.train.next_batch(100) | |
sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5}) | |
if i % 50 == 0: | |
print(compute_accuracy( | |
mnist.test.images[:1000], mnist.test.labels[:1000])) |
cnn for mnist
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转载自blog.csdn.net/tianguiyuyu/article/details/80200227
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