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I'm trying to implement a model of a recurrent neural network to solve a temporal version of the XOR problem, but I am not still able to do that. Any hints?

Cecilia
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2 Answers2

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I think following this link helps you, I have gone trough those tutorials previously. Also take a look at this tutorial .

Green Falcon
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Sampath Madala
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Use the following code:

import keras
import numpy as np



a = np.array([[1, 1], [0, 1], [1, 0], [0, 0]])
b = np.array([[0], [1], [1], [0]])

model = keras.Sequential()
model.add(keras.layers.Dense(2, activation = 'sigmoid', input_shape = (2, )))
model.add(keras.layers.Dense(1, activation = 'sigmoid'))

model.compile(optimizer = keras.optimizers.rmsprop(), metrics = ['accuracy'], loss = 'binary_crossentropy')
model.fit(x = a, y = b, epochs = 750)

If you don't get 100% accuracy increase the number of epochs. Also take a look at here which may help you how increasing the size of each layer affects the learning process.

Green Falcon
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