# coding: utf-8
import numpy as np
from materials import double_moon
from connections.models import Model
from connections.layers import DenseLayer
if __name__ == '__main__':
m = Model()
m.add_layer(DenseLayer(2, 5, activation='sigmoid'))
m.add_layer(DenseLayer(5, 1, activation='sigmoid'))
m.compile(lr=0.1)
double_moon = double_moon(50, 80, 1)
train_x = double_moon[['x', 'y']].as_matrix()
train_y = double_moon['class'].as_matrix()
train_y = train_y.reshape((train_y.shape[0], 1))
m.train(train_x, train_y)
# coding: utf-8
import numpy as np
from connections.models import Model
from connections.layers import DenseLayer, RecurrentLayer
if __name__ == '__main__':
m = Model()
m.add_layer(RecurrentLayer(2, 5, activation='tanh', return_sequences=True))
m.add_layer(DenseLayer(5, 1, activation='sigmoid'))
m.compile(lr=0.1)
print m.predict(np.arange(6).reshape(3, 2))