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- import numpy as np
- from keras.utils import np_utils
- import tensorflow as tf
- tf.python.control_flow_ops = tf
- # Set random seed
- np.random.seed(42)
- # Our data
- X = np.array([[0,0],[0,1],[1,0],[1,1]]).astype('float32')
- y = np.array([[0],[1],[1],[0]]).astype('float32')
- # Initial Setup for Keras
- from keras.models import Sequential
- from keras.layers.core import Dense, Activation
- # Building the model
- xor = Sequential()
- # Add required layers
- xor.add(Dense(32, input_dim=2))
- xor.add(Activation("tanh"))
- xor.add(Dense(1))
- xor.add(Activation("sigmoid"))
- # Specify loss as "binary_crossentropy", optimizer as "adam",
- # and add the accuracy metric
- xor.compile(loss="binary_crossentropy", optimizer="adam", metrics = ["accuracy"])
- # Uncomment this line to print the model architecture
- xor.summary()
- # Fitting the model
- history = xor.fit(X, y, nb_epoch=200, verbose=0)
- # Scoring the model
- score = xor.evaluate(X, y)
- print("\nAccuracy: ", score[-1])
- # Checking the predictions
- print("\nPredictions:")
- print(xor.predict_proba(X))
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