import numpy as np
from neural_network import NeuralNetwork
from train import prepare_train_and_show
if __name__ == "__main__":
train_data = np.array([[i] for i in np.linspace(-50, 50, 26)])
train_labels = np.array(np.square(train_data))
test_data = np.array([[i] for i in np.linspace(-50, 50, 101)])
test_labels = np.array(np.square(test_data))
neural_network = NeuralNetwork.init_from_scratch(0.25, [1, 25, 1],
['sigmoid', 'sigmoid'])
prepare_train_and_show(neural_network, train_data, train_labels, test_data,
test_labels)
from keras import models, layers, optimizers
import numpy as np
from train import prepare_train_and_show
if __name__ == "__main__":
train_data = np.array([[i] for i in np.linspace(0, 6, 61)])
train_labels = np.array(np.sin(train_data * 3 * np.pi / 2))
test_data = np.array([[i] for i in np.linspace(0, 6, 481)])
test_labels = np.array(np.sin(test_data * 3 * np.pi / 2))
model = models.Sequential()
model.add(layers.Dense(50, activation='tanh', input_shape=(1, )))
model.add(layers.Dense(75, activation='sigmoid'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=optimizers.RMSprop(lr=0.005),
loss="mse",
metrics=['accuracy'])
prepare_train_and_show(model, train_data, train_labels, test_data,
test_labels)