text_plain_output_2.png application_vnd.jupyter.stderr_output_1.png
from keras.layers.core import Dense

from keras.models import Sequential

import numpy as np

import numpy as np # linear algebra

import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense
training_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], 'float32')
target_data = np.array([[0], [1], [1], [0]], 'float32')
model = Sequential()
model.add(Dense(16, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])
model.fit(training_data, target_data, epochs=10)
scores = model.evaluate(training_data, target_data)
print('\n%s: %.2f%%' % (model.metrics_names[1], scores[1] * 100))
print(model.predict(training_data).round())