predict whether two numbers in sequence are same or not using stateful LSTM with steps = 1 batch size =1

seq2a

from keras.models import Sequential
from keras.layers import LSTM, Dense, Activation

import numpy as np

# predict whether two numbers in sequence are same or not.
# use a stateful LSTM
# this requires that i train with a batch size of 1 and a sequence size of 1


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][0][0]==train_input[i-1][0][0]):
        train_output.append([1])
    else:
        train_output.append([0])

#train has to be processed as a whole



print "test and training data loaded"

model = Sequential()
model.add(LSTM(10,  return_sequences=False, batch_input_shape=(1, 1,1), stateful=True))
model.add(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=100, batch_size=1, shuffle=False)


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