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
print(('train_labels_file = %s' % (train_labels_file)))
print(('trained_net_file = %s' % (trained_net_file)))
if retrain:
print('retraining net...')
data, labels = load2d(train_data_file, train_labels_file)
train_net(data, labels, net13, trained_net_file)
plot_loss(net13, outfile=join(root, 'plots', 'net13_loss.png'))
elif test:
print('loading net from disk...')
with open(trained_net_file, 'rb') as ifile:
net13 = pickle.load(ifile)
print('loading test data...')
data, _ = load2d(test_data_file)
names = np.load(test_names_file)
print('generating kaggle submission...')
generate_submission(data, names, net13, submission_file, header_file, N=50)
elif finetune:
print('loading pre-trained net from disk...')
with open(trained_net_file, 'rb') as ifile:
net13 = pickle.load(ifile)
print('loading training data...')
data, labels = load2d(train_data_file, train_labels_file)
net13.batch_iterator_train = BatchIterator(batch_size=128)
net13.update_learning_rate = 0.00001
net13.max_epochs = 1
#net13.on_training_finished = [early_stopping.load_best_weights]
train_net(data, labels, net13, finetuned_net_file)
input_shape=(None, 1, PIXELS, PIXELS),
conv1_num_filters=32, conv1_filter_size=(3, 3), pool1_ds=(2, 2),
conv2_num_filters=64, conv2_filter_size=(2, 2), pool2_ds=(2, 2),
hidden4_num_units=500,
output_num_units=2, output_nonlinearity=nonlinearities.softmax,
update_learning_rate=0.01,
update_momentum=0.9,
regression=False,
max_epochs=1000,
verbose=1,
)
class SimpleBatchIterator(BatchIterator):
def transform(self, Xb, yb):
Xb, yb = super(SimpleBatchIterator, self).transform(Xb, yb)
# The 'incomming' and outcomming shape is (batchsize, 1, 28, 28)
return Xb[:,:,::-1,:], yb #<--- Here we do the flipping
net1.batch_iterator_train = SimpleBatchIterator(batch_size=128)
net1.fit(X, y)
