Pasties 2016-12-18 17:10:36 +0000

import numpy as np
np.random.seed(1337)

import keras
from keras.models import Sequential
from keras.layers import Dense, LSTM, TimeDistributed
from keras.utils import np_utils
from tqdm import tqdm

import random
random.seed(1)


def generate():
    X = np.array([[random.choice([0, 1]) for _ in range(n)]
                  for _ in range(length)])
    Y = np.concatenate(([[0] * n], X[:-1]))
    X = X.reshape(length, 1, n)
    Y = Y.reshape(length, 1, n)
    return X, Y

n = 1

model = Sequential()
model.add(LSTM(n + max(n, 5), batch_input_shape=(1, 1, n),
               return_sequences=True, stateful=True))
model.add(TimeDistributed(Dense(n, activation='sigmoid')))
model.compile(loss='binary_crossentropy', optimizer='adam',
              metrics=['binary_accuracy'])


for i in tqdm(range(10)):
    length = 30
    X, Y = generate()
    for _ in tqdm(range(15)):
        model.fit(X, Y, batch_size=1, nb_epoch=1, shuffle=False, verbose=0)
        model.reset_states()

print()

for i in range(5):
    length = 20
    X, Y = generate()

    print(model.evaluate(X, Y, batch_size=1, verbose=0)[1])
    model.reset_states()