else:
labels.append(0)
if verbose:
# print(data)
# print(files)
print(labels)
print()
return data, labels
if __name__ == '__main__':
train_data, train_target = fetch_data('downgesture_train.list')
neuralnet = NeuralNetwork()
neuralnet.add_layer(size=1, input_size=len(train_data[0]), type='input')
neuralnet.add_layer(size=100)
neuralnet.add_layer(size=1, type='output')
neuralnet.fit(data=train_data,
target=train_target,
eta=0.1,
verbose=verbose)
# if needed clean data
# fit a model
# train a model
test_data, test_target = fetch_data('downgesture_test.list')
predicted_target = neuralnet.predict(test_data)
accuracy = neuralnet.accuracy(test_target, predicted_target)
print("Accuracy:", accuracy)
print("nn topo : {}".format(topo))
## Momentum
learning_rate = MomentumLearningRate(learning_rate=alpha, beta=0.9)
network = NeuralNetwork(topo=topo,
alpha=alpha,
learning_rate=learning_rate,
lambdaa=lambdaa,
regularization=L2Regularization).initialize()
for epoch in range(300):
network.forward(train_X)
network.backward(train_Y)
if epoch % 10 == 0:
train_loss = np.mean(network.loss(train_X, train_Y))
test_loss = np.mean(network.loss(test_X, test_Y))
test_acc = network.accuracy(test_X, test_Y)
print("Epoch:{} Training Loss:{} Test Loss:{} Test Acc:{}".format(
epoch, train_loss, test_loss, test_acc))
## final
train_loss = np.mean(network.loss(train_X, train_Y))
test_loss = np.mean(network.loss(test_X, test_Y))
test_acc = network.accuracy(test_X, test_Y)
print("Epoch:{} Training Loss:{} Test Loss:{} Test Acc:{}".format(
"Final", train_loss, test_loss, test_acc))
# pre = (network.predict(test_X)>0.5).astype(float)
pre = network.predict(test_X)
print("Predict : {}".format(pre))
