import numpy as np from sklearn.datasets import make_classification, make_regression from neural_network_theano.layer_classes import fully_connected_layer, convolutional_layer from neural_network_theano.neural_network import NeuralNetworkClassifier, NeuralNetworkRegressor np.random.seed(0) # Classification X, y = make_classification(1000, 64, n_informative=10, n_classes=5) layers = [convolutional_layer()] nn = NeuralNetworkClassifier(layers=layers, batch_size=1000, max_epochs=300, learning_rate=1e-4, verbose=True) nn.fit(X, y) print "Classification Score %.8f" % nn.score(X, y) # Regression X, y = make_regression(1000, 50) layers = [fully_connected_layer(n_hidden=30)] nn = NeuralNetworkRegressor(layers=layers, batch_size=10, max_epochs=20, learning_rate=1e-4, verbose=True) nn.fit(X, y) print "Regression Score %.8f" % nn.score(X, y)
import numpy as np from sklearn.datasets import load_digits from neural_network_theano.layer_classes import fully_connected_layer, convolutional_layer from neural_network_theano.neural_network import NeuralNetworkClassifier, NeuralNetworkRegressor np.random.seed(0) # Classification data = load_digits() X, y = data.data, data.target X /= 255. layers = [convolutional_layer(), fully_connected_layer()] nn = NeuralNetworkClassifier(layers=layers, batch_size=200, max_epochs=200, learning_rate=5e-6, verbose=True) nn.fit(X, y) print "Classification Score %.8f" % nn.score(X, y)
