def test_fit_and_predict(self):
ann = NeuralNetwork([4, 2], alpha=1e-5)
ann.fit(self.X, self.y)
T = self.X[[10, 60, 110]]
predictions = ann.predict(T)
print(predictions)
np.testing.assert_array_equal(predictions, np.array([0, 1, 2]))
def test_predict_probabilities(self):
ann = NeuralNetwork([4, 2], alpha=1e-5)
ann.fit(self.X, self.y)
T = self.X[[15, 65, 115, 117]]
ps = ann.predict_proba(T)
margin = np.min(np.max(ps, axis=1))
self.assertGreater(margin, 0.90)
def test_on_digits(self):
data_full = datasets.load_digits()
data, resp = utils.shuffle(data_full.data, data_full.target)
m = data.shape[0]
X, y = data[:m // 2], resp[:m // 2]
X_test, y_test = data[m // 2:], resp[m // 2:]
ann = NeuralNetwork([20, 5], alpha=1e-5)
ann.fit(X, y)
y_hat = ann.predict(X_test)
acc = metrics.accuracy_score(y_test, y_hat)
self.assertGreater(acc, 0.85)
parser.add_argument("--dropout", help="Percent Dropout",
default=0.25, type=float)
parser.add_argument("--train", help="Train dataset", default='dataset/train', type=str)
parser.add_argument("--test", help="Test dataset", default='dataset/test', type=str)
if __name__ == "__main__":
args = parser.parse_args()
ann = NeuralNetwork(
tsSize=args.timeseries,
lstmSize=args.lstmSize,
dropout=args.dropout,
)
if os.path.isfile(args.weights):
ann.model.load_weights(args.weights)
if args.action == "train":
ann.fit(args.weights, args.train, args.test,
epochs=args.epochs, batch_size=args.batchSize)
if args.action == "serve":
serve(ann, args.test)
if args.action == "cli":
while 1:
raw = input('Sent > ')
print(ann.predict(raw))