"""
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https://morvanzhou.github.io/tutorials/
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"""
# please note, all tutorial code are running under python3.5.
# If you use the version like python2.7, please modify the code accordingly
# 8 - RNN Classifier example
# to try tensorflow, un-comment following two lines
# import os
# os.environ['KERAS_BACKEND']='tensorflow'
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import SimpleRNN, Activation, Dense
from keras.optimizers import Adam
TIME_STEPS = 28 # same as the height of the image
INPUT_SIZE = 28 # same as the width of the image
BATCH_SIZE = 50
BATCH_INDEX = 0
OUTPUT_SIZE = 10
CELL_SIZE = 50
LR = 0.001
# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# data pre-processing
X_train = X_train.reshape(-1, 28, 28) / 255. # normalize
X_test = X_test.reshape(-1, 28, 28) / 255. # normalize
y_train = np_utils.to_categorical(y_train, num_classes=10)
y_test = np_utils.to_categorical(y_test, num_classes=10)
# build RNN model
model = Sequential()
# RNN cell
model.add(SimpleRNN(
# for batch_input_shape, if using tensorflow as the backend, we have to put None for the batch_size.
# Otherwise, model.evaluate() will get error.
batch_input_shape=(None, TIME_STEPS, INPUT_SIZE), # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
output_dim=CELL_SIZE,
unroll=True,
))
# output layer
model.add(Dense(OUTPUT_SIZE))
model.add(Activation('softmax'))
# optimizer
adam = Adam(LR)
model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
# training
for step in range(4001):
# data shape = (batch_num, steps, inputs/outputs)
X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]
cost = model.train_on_batch(X_batch, Y_batch)
BATCH_INDEX += BATCH_SIZE
BATCH_INDEX = 0 if BATCH_INDEX >= X_train.shape[0] else BATCH_INDEX
if step % 500 == 0:
cost, accuracy = model.evaluate(X_test, y_test, batch_size=y_test.shape[0], verbose=False)
print('test cost: ', cost, 'test accuracy: ', accuracy)
