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import sys, os
sys.path.append(os.pardir)
from dataset.mnist import load_mnist
import tensorflow as tf
import numpy as np
(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)
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')
def main(_):
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, [None, 784])
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.matmul(h_fc1_drop, W_fc2) + b_fc2
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
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.global_variables_initializer())
saver = tf.train.Saver()
train_size = x_train.shape[0]
batch_size = 100
for i in range(20000):
batch_mask = np.random.choice(train_size, batch_size)
batch_xs = x_train[batch_mask]
batch_ys = t_train[batch_mask]
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch_xs, y_: batch_ys, keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
saver.save(sess, "exp2_model.ckpt")
print("test accuracy %g"%accuracy.eval(feed_dict={x: x_test, y_: t_test, keep_prob: 1.0}))
if __name__ == '__main__':
tf.app.run(main=main)
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