import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
tf.set_random_seed(1)
np.random.seed(1)
# Hyper Parameters
BATCH_SIZE = 64
TIME_STEP = 28 # rnn time step / image height
INPUT_SIZE = 28 # rnn input size / image width
LR = 0.01 # learning rate
# data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # they has been normalized to range (0,1)
test_x = mnist.test.images[:2000]
test_y = mnist.test.labels[:2000]
# plot one example
print(mnist.train.images.shape) # (55000, 28 * 28)
print(mnist.train.labels.shape) # (55000, 10)
# plt.imshow(mnist.train.images[0].reshape((28, 28)), cmap='gray')
# plt.title('%i' % np.argmax(mnist.train.labels[0]))
# plt.show()
# tensorflow placeholders
tf_x = tf.placeholder(tf.float32, [None, TIME_STEP * INPUT_SIZE]) # shape(batch, 784)
image = tf.reshape(tf_x, [-1, TIME_STEP, INPUT_SIZE]) # (batch, height, width, channel)
tf_y = tf.placeholder(tf.int32, [None, 10]) # input y
# RNN
rnn_cell = tf.contrib.rnn.BasicLSTMCell(num_units=64)
outputs, (h_c, h_n) = tf.nn.dynamic_rnn(
rnn_cell, # cell you have chosen
image, # input
initial_state=None, # the initial hidden state
dtype=tf.float32, # must given if set initial_state = None
time_major=False, # False: (batch, time step, input); True: (time step, batch, input)
)
output = tf.layers.dense(outputs[:, -1, :], 10) # output based on the last output step
loss = tf.losses.softmax_cross_entropy(onehot_labels=tf_y, logits=output) # compute cost
train_op = tf.train.AdamOptimizer(LR).minimize(loss)
accuracy = tf.metrics.accuracy( # return (acc, update_op), and create 2 local variables
labels=tf.argmax(tf_y, axis=1), predictions=tf.argmax(output, axis=1),)[1]
sess = tf.Session()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) # the local var is for accuracy_op
sess.run(init_op) # initialize var in graph
for step in range(1200): # training
b_x, b_y = mnist.train.next_batch(BATCH_SIZE)
_, loss_ = sess.run([train_op, loss], {tf_x: b_x, tf_y: b_y})
if step % 50 == 0: # testing
accuracy_ = sess.run(accuracy, {tf_x: test_x, tf_y: test_y})
print('train loss: %.4f' % loss_, '| test accuracy: %.2f' % accuracy_)
# print 10 predictions from test data
test_output = sess.run(output, {tf_x: test_x[:10]})
pred_y = np.argmax(test_output, 1)
print(pred_y, 'prediction number')
print(np.argmax(test_y[:10], 1), 'real number')
