
# 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 os
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,Bidirectional,GRU
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
unit = 32


# 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,
#     # return_sequences=True#这里默认是false
# ))

#GRU
model.add(Bidirectional(
     # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,

    GRU(units=32, activation='tanh',
            # batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),       # Or: input_dim=INPUT_SIZE, input_length=TIME_STEPS,
            # output_dim=CELL_SIZE,
            # unroll=True,
            return_sequences=False),#因为这里只有一层GRU所以这里可以设置为只输出一个
            batch_input_shape=(None, TIME_STEPS, INPUT_SIZE),
            merge_mode='mul'))

# 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)

    print('X_train')
    # print(X_train)
    print(X_train.shape)
    print('y_train')
    # print(y_train)
    print(y_train.shape)
    # os.system('pause')

    X_batch = X_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :, :]
    Y_batch = y_train[BATCH_INDEX: BATCH_INDEX+BATCH_SIZE, :]

    print('X_batch')
    # print(X_batch)
    print(X_batch.shape)
    print('Y_batch')
    # print(Y_batch)
    print(Y_batch.shape)
    os.system('pause')

    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)
