same as 2c, but save model to file

seq2d.py

from keras.models import Sequential
from keras.layers import LSTM, TimeDistributed, Dense, Activation
from keras.models import load_model
import numpy as np


# same as 2c except it saves model to file.

def train_model():
    train_input = [np.random.randint(0,2) for r in xrange(1000)]
    train_output = [0]

    for i in range(1, len(train_input)):
        if(train_input[i]==train_input[i-1]):
            train_output.append(1)
        else:
            train_output.append(0)

    train_input = np.reshape(train_input, (200, 5, 1))
    train_output = np.reshape(train_output, (200,5,1))


    print "test and training data loaded"

    model = Sequential()
    model.add(LSTM(10,  return_sequences=True, batch_input_shape=(1, 5, 1), stateful=True))
    model.add(TimeDistributed(Dense(1)))
    model.add(Activation('sigmoid'))

    model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])

    model.fit(np.asarray(train_input), np.asarray(train_output), epochs=400, batch_size=1, shuffle=False)
    model.save('seq_2d.h5')


#train_model()
model = load_model('seq_2d.h5')

print model.predict(np.asarray([1,0,0,1,1]).reshape(1,5,1))
print model.predict(np.asarray([1,1,0,0,1]).reshape(1,5,1))
print model.predict(np.asarray([0,0,0,0,1]).reshape(1,5,1))