def train_model(dataset):
X = T.tensor4()
S = T.tensor3()
layers = []
layers.append(ConvPoolLayer(
input=X,
input_shape=(3, 160, 80),
filter_shape=(32, 3, 5, 5),
pool_shape=(2, 2),
active_func=actfuncs.tanh,
flatten=False
))
layers.append(ConvPoolLayer(
input=layers[-1].output,
input_shape=layers[-1].output_shape,
filter_shape=(64, 32, 5, 5),
pool_shape=(2, 2),
active_func=actfuncs.tanh,
flatten=False
))
layers.append(FullConnLayer(
input=layers[-1].output.flatten(2),
input_shape=np.prod(layers[-1].output_shape),
output_shape=1024,
dropout_ratio=0.1,
active_func=actfuncs.tanh
))
layers.append(FullConnLayer(
input=layers[-1].output,
input_shape=layers[-1].output_shape,
output_shape=37 * 17,
dropout_input=layers[-1].dropout_output,
active_func=actfuncs.sigmoid
))
model = NeuralNet(layers, X, layers[-1].output)
model.target = S
'''
model.cost = costfuncs.binxent(layers[-1].dropout_output, S.flatten(2)) + \
1e-3 * model.get_norm(2)
model.error = costfuncs.binerr(layers[-1].output, S.flatten(2))
'''
model.cost = costfuncs.weighted_norm2(
layers[-1].dropout_output, S.flatten(2), 1.0) + \
1e-3 * model.get_norm(2)
model.error = costfuncs.weighted_norm2(
layers[-1].output, S.flatten(2), 1.0)
sgd.train(model, dataset, lr=1e-2, momentum=0.9,
batch_size=100, n_epochs=300,
epoch_waiting=10)
return model
def train_model(dataset):
X = T.matrix()
S = T.tensor3()
layers = []
layers.append(FullConnLayer(
input=X,
input_shape=2784,
output_shape=1024,
dropout_ratio=0.1,
active_func=actfuncs.tanh
))
"""
layers.append(FullConnLayer(
input=layers[-1].output,
input_shape=layers[-1].output_shape,
output_shape=1024,
dropout_ratio=0.1,
dropout_input=layers[-1].dropout_output,
active_func=actfuncs.tanh
))
"""
layers.append(FullConnLayer(
input=layers[-1].output,
input_shape=layers[-1].output_shape,
output_shape=37 * 17,
dropout_input=layers[-1].dropout_output,
active_func=actfuncs.sigmoid
))
model = NeuralNet(layers, X, layers[-1].output)
model.target = S
'''
model.cost = costfuncs.binxent(layers[-1].dropout_output, S.flatten(2)) + \
1e-3 * model.get_norm(2)
model.error = costfuncs.binerr(layers[-1].output, S.flatten(2))
'''
model.cost = costfuncs.weighted_norm2(
layers[-1].dropout_output, S.flatten(2), 1.0) + \
1e-3 * model.get_norm(2)
model.error = costfuncs.weighted_norm2(
layers[-1].output, S.flatten(2), 1.0)
sgd.train(model, dataset, lr=1e-2, momentum=0.9,
batch_size=100, n_epochs=300,
epoch_waiting=10, never_stop=True)
return model