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nhwc_nchw.py
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import argparse
import sys
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import time
FLAGS = None
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 nhwc(_):
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')
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(tf.nn.bias_add(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(tf.nn.bias_add(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.nn.bias_add(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.bias_add(tf.matmul(h_fc1_drop, W_fc2), b_fc2)
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))
'''
config = tf.ConfigProto(
device_count={'GPU': 0}
)
'''
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
sess.run(tf.global_variables_initializer())
start_time = time.time()
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
end_time = time.time()
#
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g, cost time: %.2f" % (i, train_accuracy, end_time-start_time))
#
start_time = end_time
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}))
def nchw(_):
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', data_format='NCHW')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 1, 2, 2], strides=[1, 1, 2, 2], padding='SAME', data_format='NCHW')
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 1, 28, 28])
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x_image, W_conv1), b_conv1, data_format='NCHW'))
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(tf.nn.bias_add(conv2d(h_pool1, W_conv2), b_conv2, data_format='NCHW'))
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([64 * 7 * 7, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 64 * 7 * 7])
h_fc1 = tf.nn.relu(tf.nn.bias_add(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.bias_add(tf.matmul(h_fc1_drop, W_fc2), b_fc2)
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))
'''
config = tf.ConfigProto(
device_count={'GPU': 0}
)
'''
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
sess.run(tf.global_variables_initializer())
start_time = time.time()
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0:
end_time = time.time()
#
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g, cost time: %.2f" % (i, train_accuracy, end_time - start_time))
#
start_time = end_time
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}))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='./mnist',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=nhwc, argv=[sys.argv[0]] + unparsed)