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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import sru
import time
import os
import sys
import numpy as np
class Tools(object):
@staticmethod
def currect_time():
return time.strftime("%H:%M:%S", time.localtime()) + '.%03d' % (time.time() % 1 * 1000)
@staticmethod
def log_print(content):
print("[" + Tools.currect_time() + "] " + content)
class Setting(object):
@staticmethod
def checkpoint_dir():
return "model"
class Network(object):
def __init__(self):
self.num_input = 28
self.timesteps = 28
self.num_hidden = 128
self.num_classes = 10
self.lstm_layers = 2
self.using_sru = sys.argv[1] == "SRU"
print("Using SRU" if self.using_sru else "Using LSTM")
self.X = tf.placeholder("float", [None, self.timesteps, self.num_input])
self.Y = tf.placeholder("float", [None, self.num_classes])
weights = {
'out': tf.Variable(tf.random_normal([self.num_hidden, self.num_classes]))
}
biases = {
'out': tf.Variable(tf.random_normal([self.num_classes]))
}
x = tf.unstack(self.X, self.timesteps, 1)
if self.using_sru:
rnn_cell = lambda: sru.SRUCell(self.num_hidden, False)
else:
rnn_cell = lambda: tf.nn.rnn_cell.LSTMCell(self.num_hidden, forget_bias=1.0)
cell_stack = tf.nn.rnn_cell.MultiRNNCell([rnn_cell() for _ in range(self.lstm_layers)], state_is_tuple=True)
outputs, _ = tf.nn.static_rnn(cell_stack, x, dtype=tf.float32)
self.logits = tf.matmul(outputs[-1], weights['out']) + biases['out']
self.prediction = tf.nn.softmax(self.logits)
def get_input_ops(self):
return self.X, self.Y
def get_input_shape(self):
return self.timesteps, self.num_input
def get_output_ops(self):
return self.logits, self.prediction
def save_model(saver, sess, model_path):
Tools.log_print('save model to {0}.'.format(model_path))
saver.save(sess, model_path)
def load_model(saver, sess, model_path):
Tools.log_print('try to load model from {0}.'.format(model_path))
saver.restore(sess, model_path)
Tools.log_print('load model success')
return True
def train():
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
model_dir = 'model'
model_path = os.path.join(model_dir, 'mnist_nn')
network = Network()
X, Y = network.get_input_ops()
timesteps, num_input = network.get_input_shape()
logits, prediction = network.get_output_ops()
learning_rate = 1e-2
display_step = 100
train_epochs = 3
train_batchsize = 128
test_batchsize = 128
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits= logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
grads = optimizer.compute_gradients(loss_op)
max_grad_norm = 1.0
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(loss_op, tvars), max_grad_norm)
train_op = optimizer.apply_gradients(zip(grads, tvars))
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
saver = tf.train.Saver(tf.global_variables())
if not os.path.exists(model_dir):
os.makedirs(model_dir)
for epoch in range(1, train_epochs+1):
train_steps = len(mnist.train.labels) / train_batchsize
for step in range(1, train_steps+1):
batch_x, batch_y = mnist.train.next_batch(train_batchsize)
batch_x = batch_x.reshape((train_batchsize, timesteps, num_input))
sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
if step % display_step == 0 or step == 1:
loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y})
Tools.log_print("Epoch[%d/%d] Step[%d/%d] Train Minibatch Loss= %.4f, Training Accuracy= %.4f" % (epoch, train_epochs, step, train_steps, loss, acc))
Tools.log_print("Optimization Finished!")
save_model(saver, sess, model_path)
if load_model(saver, sess, model_path):
test_steps = len(mnist.test.labels) / test_batchsize
acc_list = []
for step in range(1, test_steps+1):
batch_x, batch_y = mnist.test.next_batch(test_batchsize)
batch_x = batch_x.reshape((test_batchsize, timesteps, num_input))
batch_acc = sess.run(accuracy, feed_dict={X: batch_x, Y: batch_y})
acc_list.append(batch_acc)
acc = np.mean(acc_list)
Tools.log_print("Testing Accuracy: {0}".format(acc))
if __name__ == '__main__':
train()
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