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July 13, 2016 15:58
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#!/usr/bin/env python | |
# def load_data(): | |
# test_image = [] | |
# filenames = [] | |
# for i in range(1, len(sys.argv)): | |
# img = cv2.imread(sys.argv[i]) | |
# img = cv2.resize(img, (28, 28)) | |
# test_image.append(img.flatten().astype(np.float32)/255.0) | |
# filenames.append(sys.argv[i]) | |
# test_image = np.asarray(test_image) | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) | |
import tensorflow as tf | |
sess = tf.InteractiveSession() | |
x = tf.placeholder(tf.float32, shape=[None, 784]) | |
y_ = tf.placeholder(tf.float32, shape=[None, 10]) | |
W = tf.Variable(tf.zeros([784,10])) | |
b = tf.Variable(tf.zeros([10])) | |
sess.run(tf.initialize_all_variables()) | |
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): | |
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') | |
def max_pool_2x2(x): | |
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], | |
strides=[1, 2, 2, 1], padding='SAME') | |
W_conv1 = weight_variable([5, 5, 1, 32]) | |
b_conv1 = bias_variable([32]) | |
x_image = tf.reshape(x, [-1,28,28,1]) | |
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) | |
h_pool1 = max_pool_2x2(h_conv1) | |
W_conv2 = weight_variable([5, 5, 32, 64]) | |
b_conv2 = bias_variable([64]) | |
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) | |
h_pool2 = max_pool_2x2(h_conv2) | |
W_fc1 = weight_variable([7 * 7 * 64, 1024]) | |
b_fc1 = bias_variable([1024]) | |
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) | |
keep_prob = tf.placeholder(tf.float32) | |
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) | |
W_fc2 = weight_variable([1024, 10]) | |
b_fc2 = bias_variable([10]) | |
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) | |
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1])) | |
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) | |
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) | |
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
sess.run(tf.initialize_all_variables()) | |
for i in range(20000): | |
batch = mnist.train.next_batch(50) | |
if i%100 == 0: | |
train_accuracy = accuracy.eval(feed_dict={ | |
x:batch[0], y_: batch[1], keep_prob: 1.0}) | |
print("step %d, training accuracy %g"%(i, train_accuracy)) | |
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) | |
print("test accuracy %g"%accuracy.eval(feed_dict={ | |
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) |
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