03_01_logistic_regressioni_or

import numpy as np

from keras.models import Sequential

from keras.layers import Dense, Activation

from keras.optimizers import SGD

np.random.seed(0)

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])

Y = np.array([[0], [1], [1], [1]])

model = Sequential([

# Dense(input_dim=2, output_dim=1), # Keras 1

Dense(input_dim=2, units=1), # Keras 2

Activation('sigmoid')

])

model.compile(loss='binary_crossentropy', optimizer=SGD(lr=0.1))

# model.fit(X, Y, nb_epoch=200, batch_size=1) # Keras 1

model.fit(X, Y, epochs=200, batch_size=1) # Keras 2

classes = model.predict_classes(X, batch_size=1)

prob = model.predict_proba(X, batch_size=1)

print('classified:')

print(Y == classes)

print()

print('output probability:')

print(prob)

classified:
[[ True]
 [ True]
 [ True]
 [ True]]


output probability:
[[0.20687966]
 [0.92517704]
 [0.91522515]
 [0.99804986]]