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conv_mnist.py
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151 lines (117 loc) · 5.43 KB
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# load MNIST data
from tensorflow.examples.tutorials.mnist import input_data
import argparse
import sys
import tensorflow as tf
FLAGS = None
# start tensorflow interactiveSession
#整体网络
def conv():
mnist = input_data.read_data_sets("/tmp/data", one_hot=True)
sess = tf.InteractiveSession()
#summery
def variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var) #均值
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) #标准差
tf.summary.scalar('mean', mean)
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
# weight initialization
def weight_variable(shape):
weights = tf.truncated_normal(shape, stddev=0.1)
variable_summaries(weights)
return tf.Variable(weights)
def bias_variable(shape):
bias = tf.constant(0.1, shape = shape)
variable_summaries(bias)
return tf.Variable(bias)
# convolution
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', use_cudnn_on_gpu=False)
# pooling
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# Create the model
# placeholder
x = tf.placeholder("float", [None, 784])
y_ = tf.placeholder("float", [None, 10])
# variables
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
# first convolutinal layer
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)
# second convolutional layer
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)
# densely connected layer
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)
# dropout
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# readout layer
w_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
with tf.name_scope('softmax'):
softmax = y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)
tf.summary.histogram('softmax', softmax)
# train and evaluate the model
with tf.name_scope('cross_entropy'):
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
tf.summary.scalar('cross_entropy', cross_entropy)
# 梯度下降，可只有选择梯度下降的方法
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(learning_rate=FLAGS.learning_rate).minimize(cross_entropy)
# 预测&正确率
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
tf.global_variables_initializer().run()
for i in range(FLAGS.max_steps):
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})
summary, acc = sess.run([merged, accuracy], feed_dict={x: batch[0], y_: batch[1], keep_prob:1.0})
test_writer.add_summary(summary, i)
print("步数%d, 训练精度 %g" %(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
# print("测试精度 %g" %accuracy.eval(feed_dict={x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0}))
summary, _ = sess.run([merged, accuracy], feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob:1.0})
test_writer.add_summary(summary, i)
def main(_):
if tf.gfile.Exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.gfile.MakeDirs(FLAGS.log_dir)
conv()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--max_steps', type=int, default=1000,
help='迭代次数')
parser.add_argument('--learning_rate', type=float, default=0.001,
help='初始学习率')
parser.add_argument('--dropout', type=float, default=0.9,
help='dropout')
parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/conv_summery/input_data',
help='存input数据的文件路径')
parser.add_argument('--log_dir', type=str, default='/tmp/tensorflow/conv_summery/logs/summery',
help='日志文件')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)