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# Source: https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py

from __future__ import absolute_import
from __future__ import print_function
import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.preprocessing import sequence
from keras.optimizers import SGD, RMSprop, Adagrad
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM, GRU
from keras.datasets import imdb

'''
    Train a LSTM on the IMDB sentiment classification task.
    The dataset is actually too small for LSTM to be of any advantage
    compared to simpler, much faster methods such as TF-IDF+LogReg.
    Notes:
    - RNNs are tricky. Choice of batch size is important,
    choice of loss and optimizer is critical, etc.
    Some configurations won't converge.
    - LSTM loss decrease patterns during training can be quite different
    from what you see with CNNs/MLPs/etc.
    GPU command:
        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_lstm.py
        
'''

max_features = 20000
maxlen = 100  # cut texts after this number of words (among top max_features most common words)
batch_size = 32

print("Loading data...")
(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features, test_split=0.2)
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')

print("Pad sequences (samples x time)")
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)

print('Build model...')
model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(LSTM(128))  # try using a GRU instead, for fun
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

# try using different optimizers and different optimizer configs
model.compile(loss='binary_crossentropy', optimizer='adam', class_mode="binary")

print("Train...")
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=4, validation_data=(X_test, y_test), show_accuracy=True)
score, acc = model.evaluate(X_test, y_test, batch_size=batch_size, show_accuracy=True)
print('Test score:', score)
print('Test accuracy:', acc)