#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
from __future__ import print_function
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
from tensorflow.examples.tutorials.mnist import input_data
tf.reset_default_graph()
def initialize_weight_bias(in_size, out_size):
weight = tf.truncated_normal(shape=(in_size, out_size), stddev=0.01, mean=0.0)
bias = tf.constant(0.1, shape=[out_size])
return tf.Variable(weight), tf.Variable(bias)
def model(data, target, dropout, num_hidden=200, num_layers=3):
# establish RNN model
cells = list()
for _ in range(num_layers):
cell = tf.nn.rnn_cell.GRUCell(num_units=num_hidden)
cell = tf.nn.rnn_cell.DropoutWrapper(cell=cell, output_keep_prob=1.0-dropout)
cells.append(cell)
network = tf.nn.rnn_cell.MultiRNNCell(cells=cells)
outputs, last_state = tf.nn.dynamic_rnn(cell=network, inputs=data, dtype=tf.float32)
# get last output
outputs = tf.transpose(outputs, (1, 0, 2))
last_output = tf.gather(outputs, int(outputs.get_shape()[0])-1)
# add softmax layer
out_size = int(target.get_shape()[1])
weight, bias = initialize_weight_bias(in_size=num_hidden, out_size=out_size)
logits = tf.add(tf.matmul(last_output, weight), bias)
return logits
def main():
# define some parameters
default_epochs = 10
default_batch_size = 64
default_dropout = 0.5
test_freq = 150 # every 150 batches
logs_path = 'data/log'
# get train and test data
mnist_data = input_data.read_data_sets('data/mnist', one_hot=True)
total_steps = int(mnist_data.train.num_examples/default_batch_size)
total_test_steps = int(mnist_data.test.num_examples/default_batch_size)
print('number of training examples: %d' % mnist_data.train.num_examples) # 55000
print('number of test examples: %d' % mnist_data.test.num_examples) # 10000
# fit RNN model
input_x = tf.placeholder(tf.float32, shape=(None, 28, 28))
input_y = tf.placeholder(tf.float32, shape=(None, 10))
dropout = tf.placeholder(tf.float32)
input_logits = model(input_x, input_y, dropout)
# define loss and optimizer
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=input_logits, labels=input_y))
train_op = tf.train.RMSPropOptimizer(0.001).minimize(loss)
input_prob = tf.nn.softmax(input_logits)
error_count = tf.not_equal(tf.arg_max(input_prob, 1), tf.arg_max(input_y, 1))
error_rate_op = tf.reduce_mean(tf.cast(error_count, tf.float32))
# add summary
tf.summary.scalar('error_rate', error_rate_op)
tf.summary.scalar('loss', loss)
merge_summary_op = tf.summary.merge_all()
# train and test
with tf.Session() as session:
session.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(logdir=logs_path, graph=tf.get_default_graph())
# train
for epoch in range(default_epochs):
for step in range(total_steps):
train_x, train_y = mnist_data.train.next_batch(default_batch_size)
train_x = train_x.reshape(-1, 28, 28)
feed_dict = {input_x: train_x,
input_y: train_y,
dropout: default_dropout}
_, summary = session.run([train_op, merge_summary_op], feed_dict=feed_dict)
# write logs
summary_writer.add_summary(summary, global_step=epoch*total_steps+step)
# test
if step > 0 and (step % test_freq == 0):
avg_error = 0
for test_step in range(total_test_steps):
test_x, test_y = mnist_data.test.next_batch(default_batch_size)
test_x = test_x.reshape(-1, 28, 28)
feed_dict = {input_x: test_x,
input_y: test_y,
dropout: 0}
test_error = session.run(error_rate_op, feed_dict=feed_dict)
avg_error += test_error / total_test_steps
print('epoch: %d, steps: %d, avg_test_error: %.4f' % (epoch, step, avg_error))
if __name__ == '__main__':
main()
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