def __call__(self, nn, train_history):
if self.ls is None:
self.ls = np.linspace(self.start, self.stop, nn.max_epochs)
epoch = train_history[-1]['epoch']
new_value = float32(self.ls[epoch - 1])
getattr(nn, self.name).set_value(new_value)
pool1_pool_size=(2, 2),
dropout1_p=0.1,
conv2_num_filters=64,
conv2_filter_size=(2, 2),
pool2_pool_size=(2, 2),
dropout2_p=0.2,
conv3_num_filters=128,
conv3_filter_size=(2, 2),
pool3_pool_size=(2, 2),
dropout3_p=0.3,
hidden4_num_units=500,
dropout4_p=0.5,
hidden5_num_units=500,
output_num_units=30,
output_nonlinearity=None,
update_learning_rate=theano.shared(float32(0.03)),
update_momentum=theano.shared(float32(0.9)),
regression=True,
batch_iterator_train=FlipBatchIterator(batch_size=128),
on_epoch_finished=[
AdjustVariable('update_learning_rate',
start=0.03,
stop=0.0001),
AdjustVariable('update_momentum', start=0.9, stop=0.999)
],
max_epochs=3000,
verbose=1)
X, y = load2d() # load 2D data
net6.fit(X, y)