def init_cnn():
net = NeuralNet(
layers=[
# input layer
(layers.InputLayer, {
'shape': (None, bastdm5.classification.settings.CHANNELS,
bastdm5.classification.settings.MINI_SEGMENT_LENGTH,
bastdm5.classification.settings.MEL_DATA_POINTS)
}),
# convolution layers 1
(layers.Conv2DLayer, {
'num_filters': 32,
'filter_size': (8, 1)
}),
(layers.MaxPool2DLayer, {
'pool_size': (4, 1),
'stride': (2, 1)
}),
# convolution layers 2
(layers.Conv2DLayer, {
'num_filters': 32,
'filter_size': (8, 1)
}),
(layers.MaxPool2DLayer, {
'pool_size': (4, 1),
'stride': (2, 1)
}),
# dense layer
(layers.DenseLayer, {
'num_units': 100
}),
(layers.DropoutLayer, {}),
(layers.DenseLayer, {
'num_units': 50
}),
# output layer
(layers.DenseLayer, {
'num_units': 6,
'nonlinearity': nonlinearities.softmax
})
],
# learning rate parameters
update_learning_rate=0.001,
update_momentum=0.9,
regression=False,
max_epochs=999,
verbose=1,
)
net.batch_iterator_test = TestSegmentBatchIterator(
batch_size=bastdm5.classification.settings.MINI_BATCH_SIZE)
y_mapping = utils.load_from_pickle(CNN_Y_MAPPING_PATH)
net.load_params_from(CNN_WEIGHTS_PATH)
return net, y_mapping
def create_clf(cls, data=None):
"""Create neural network."""
try:
bits = data.shape[1]
except AttributeError:
bits = cls.default_bits
net_params = {
"layers": [("input", InputLayer), ("inputdrop", DropoutLayer),
("hidden", DenseLayer), ("hiddendrop", DropoutLayer),
("output", DenseLayer)],
"input_shape": (None, bits),
"inputdrop_p":
cls.input_dropout_rate,
"hidden_num_units":
cls.hidden_num_units,
"hidden_nonlinearity":
cls.hidden_nonlinearity,
"hiddendrop_p":
cls.hidden_dropout_rate,
"output_num_units":
2,
"output_nonlinearity":
cls.output_nonlinearity,
"update_learning_rate":
cls.leakiness,
"max_epochs":
cls.max_epochs,
"on_epoch_finished":
EarlyStopping(patience=cls.patience)
}
clf = NeuralNet(**net_params)
if data is not None:
batch_size = min(
cls.max_batch_size,
int(cls.min_percent_data_in_batch * data.shape[0]))
clf.batch_iterator_train = BalancedClassIterator(
batch_size=batch_size)
clf.batch_iterator_test = BalancedClassIterator(
batch_size=batch_size)
return clf
X_new[i, j] = np.rot90(Xb[i, j, sx, sy] + noise[j], k=nrotate)
return X_new, yb
count = 0
print("Starting model combination...")
for startx in range(4):
for starty in range(4):
for rotate in range(4):
net.batch_iterator_test = AugmentedBatchIterator(batch_size=128,
crop_size=4,
testing=True,
startx=startx,
starty=starty,
rotate=rotate)
y_pred_valid[:, count] = net.predict_proba(X_valid)[:, 1]
count += 1
print("Iteration: {} / 64".format(count))
combine = bmc.BMC()
combine.fit(y_pred_valid, y_valid)
print("Validation set done.")
X_test = np.load("../data/sdss_test_images.npy")
y_test = np.load("../data/sdss_test_labels.npy")
X_new[i, j] = np.rot90(Xb[i, j, sx, sy] + noise[j], k=nrotate)
return X_new, yb
count = 0
print("Starting model combination...")
for startx in range(4):
for starty in range(4):
for rotate in range(4):
net.batch_iterator_test=AugmentedBatchIterator(
batch_size=128,
crop_size=4,
testing=True,
startx=startx,
starty=starty,
rotate=rotate
)
y_pred_valid[:, count] = net.predict_proba(X_valid)[:, 1]
count += 1
print("Iteration: {} / 64".format(count))
combine = bmc.BMC()
combine.fit(y_pred_valid, y_valid)
print("Validation set done.")
X_test = np.load("../data/sdss_test_images.npy")
