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RNN.py
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# -*- coding: utf-8 -*-
# @author: Awesome_Tang
# @date: 2019-02-22
# @version: python2.7
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from datetime import datetime
import os
class Config(object):
"""
CNN 模型参数
"""
classes = 10 # 类别数
num_filters = 32 # 卷积核数
kernel_size = 3 # 卷积核大小
alpha = 1e-2 # 学习率
keep_prob = 0.5 # 保留比例
steps = 10000 # 迭代次数
batch_size = 128 # 每批次训练样本数
tensorboard_dir = 'tensorboard/RNN' # log输出路径
saver_folder = 'checkpoints_RNN' # 模型保存路径
print_per_batch = 100 # 每多少轮输出一次结果
save_per_batch = 10 # 每多少轮存入tensorboard
num_units = 128
num_layers = 2
decay_rate = 0.9 # 衰减率
decay_steps = 100 # 衰减次数
class RNN:
def __init__(self):
print('Loading data......')
self.mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
self.input_x = tf.placeholder(tf.float32, [None, 784], name='input_x')
self.input_y = tf.placeholder(tf.float32, [None, Config.classes], name='input_y')
self.keep_prob = tf.placeholder("float")
if not os.path.exists(Config.saver_folder):
os.mkdir(Config.saver_folder)
self.save_path = os.path.join(Config.saver_folder, 'best_validation')
self.run_model()
@staticmethod
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
@staticmethod
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def feed_data(self, x, y, keep_prob=1.):
feed_dict = {self.input_x: x,
self.input_y: y,
self.keep_prob: keep_prob}
return feed_dict
def build_cells(self):
"""
建立多层RNN网络
:return: cells
"""
cells = []
for _ in range(Config.num_layers):
rnn_cell = tf.nn.rnn_cell.GRUCell(num_units=Config.num_units, name='basic_lstm_cell')
rnn_cell = tf.contrib.rnn.DropoutWrapper(cell=rnn_cell,
output_keep_prob=self.keep_prob)
cells.append(rnn_cell)
return cells
def restore_model(self):
"""
加载本地模型文件
:return: session
"""
session = tf.Session()
session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=session, save_path=self.save_path)
return session
def rnn_model(self):
# 定义RNN（LSTM）结构
x_image = tf.reshape(self.input_x, [-1, 28, 28])
cells = self.build_cells()
rnn_cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True)
outputs, final_state = tf.nn.dynamic_rnn(cell=rnn_cell,
inputs=x_image,
initial_state=None,
dtype=tf.float32,
time_major=False)
output = tf.layers.dense(inputs=outputs[:, -1, :], units=Config.classes)
gloabl_steps = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(Config.alpha, gloabl_steps, Config.decay_steps,
Config.decay_rate, staircase=True)
loss = tf.losses.softmax_cross_entropy(onehot_labels=self.input_y, logits=output) # 计算loss
train_step = tf.train.AdamOptimizer(
learning_rate).minimize(loss, global_step=gloabl_steps)
correct_prediction = tf.equal(tf.argmax(self.input_y, axis=1), tf.argmax(output, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) # 计算正确率
tf.summary.scalar("loss", loss)
tf.summary.scalar("accuracy", accuracy)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(Config.tensorboard_dir)
return train_step, gloabl_steps, accuracy, loss, merged_summary, writer
def run_model(self):
train_step, gloabl_steps, accuracy, loss, merged_summary, writer = self.rnn_model()
saver = tf.train.Saver(max_to_keep=1)
start_time = datetime.now()
best_acc = 0
last_improved_step = 0
require_steps = 10000
print('Training & Evaluating......')
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(Config.steps):
data_x, data_y = self.mnist.train.next_batch(Config.batch_size)
feed_dict = self.feed_data(data_x, data_y, Config.keep_prob)
sess.run([train_step, gloabl_steps], feed_dict=feed_dict)
if i % Config.save_per_batch == 0:
feed_dict[self.keep_prob] = 1.
s = sess.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(s, i)
if i % Config.print_per_batch == 0:
train_acc, train_loss = sess.run([accuracy, loss],
feed_dict=feed_dict)
data_x, data_y = self.mnist.validation.images, self.mnist.validation.labels
feed_dict = self.feed_data(data_x, data_y)
val_acc, val_loss = sess.run([accuracy, loss],
feed_dict=feed_dict)
if val_acc > best_acc:
# 只在验证集准确率有提升的时候保存模型
best_acc = val_acc
last_improved_step = i
saver.save(sess=sess, save_path=self.save_path)
improved = '*'
else:
improved = ''
msg = 'Step {:5}, train_acc:{:8.2%}, train_loss:{:6.2f},' \
' val_acc:{:8.2%}, val_loss:{:6.2f}, improved:{:3}'
print(msg.format(i, train_acc, train_loss, val_acc, val_loss, improved))
if i - last_improved_step > require_steps:
# 超过require_steps验证集准确率提升，提前结束训练
print('No improvement for over %d steps, auto-stopping....' % require_steps)
break
end_time = datetime.now()
time_diff = (end_time - start_time).seconds
print 'Time Usage : {:.2f} hours'.format(time_diff / 3600.)
data_x, data_y = self.mnist.test.images, self.mnist.test.labels
feed_dict = self.feed_data(data_x, data_y)
sess = self.restore_model()
test_acc, test_loss = sess.run([accuracy, loss],
feed_dict=feed_dict)
print("Test accuracy :{:8.2%}, loss:{:6.2f}".format(test_acc, test_loss))
sess.close()
if __name__ == "__main__":
RNN()