def _train_impl(self, X, y):
if self.spec.is_convolution:
X = X.reshape(X.shape[:3])
self.iterations = 0
data = zip(X, y)
self.dataset = SequentialDataset(data)
minibatches = MiniBatches(self.dataset, batch_size=20)
self.trainer.run(minibatches, controllers=self.controllers)
return self
char_vectors.append(
np.eye(1, 26, char_code - ord("a"), dtype=FLOATX)[0])
if len(char_vectors) >= 20:
continue
word_matrix = np.vstack(char_vectors)
data.append((word_matrix, label))
# Shuffle the data
random.Random(3).shuffle(data)
# Separate data
valid_size = int(len(data) * 0.15)
train_set = data[valid_size:]
valid_set = data[:valid_size]
dataset = SequentialDataset(train_set, valid=valid_set)
dataset.pad_left(20)
dataset.report()
batch_set = MiniBatches(dataset)
if __name__ == '__main__':
model = NeuralClassifier(input_dim=26, input_tensor=3)
model.stack(
RNN(hidden_size=30,
input_type="sequence",
output_type="sequence",
vector_core=0.1),
RNN(hidden_size=30,
input_type="sequence",
output_type="sequence",
for i in range(SEQUENCE_LEN):
a = rand.uniform(0, 1)
b = 1 if i in selected_items else 0
if b == 1:
sum += a
sequence.append(np.array([a, b], dtype=FLOATX))
sequence = np.vstack(sequence)
sum = np.array([sum], dtype=FLOATX)
data.append((sequence, sum))
# Separate data
valid_size = int(1000)
train_set = data[valid_size:]
valid_set = data[:valid_size]
dataset = SequentialDataset(train_set, valid=valid_set)
dataset.report()
batch_set = MiniBatches(dataset, batch_size=32)
if __name__ == '__main__':
ap = ArgumentParser()
ap.add_argument("--model", default=os.path.join(os.path.dirname(__file__), "models", "sequence_adding_100_2.gz"))
args = ap.parse_args()
model = NeuralRegressor(input_dim=2, input_tensor=3)
model.stack(IRNN(hidden_size=100, input_type="sequence",
output_type="one"),
Dense(1))
# Create one-hot vector
char_vectors.append(np.eye(1, 26, char_code - ord("a"), dtype=FLOATX)[0])
if len(char_vectors) >= 20:
continue
word_matrix = np.vstack(char_vectors)
data.append((word_matrix, label))
# Shuffle the data
random.Random(3).shuffle(data)
# Separate data
valid_size = int(len(data) * 0.15)
train_set = data[valid_size:]
valid_set = data[:valid_size]
dataset = SequentialDataset(train_set, valid=valid_set)
dataset.pad_left(20)
dataset.report()
batch_set = MiniBatches(dataset)
if __name__ == '__main__':
model = NeuralClassifier(input_dim=26, input_tensor=3)
model.stack(RNN(hidden_size=30, input_type="sequence", output_type="sequence", vector_core=0.1),
RNN(hidden_size=30, input_type="sequence", output_type="sequence", vector_core=0.3),
RNN(hidden_size=30, input_type="sequence", output_type="sequence", vector_core=0.6),
RNN(hidden_size=30, input_type="sequence", output_type="one", vector_core=0.9),
Dense(4),
Softmax())
trainer = SGDTrainer(model)
