def define_net():
define_net_specific_parameters()
io = ImageIO()
# Read pandas csv labels
y = util.load_labels()
if params.SUBSET is not 0:
y = y[:params.SUBSET]
X = np.arange(y.shape[0])
mean, std = io.load_mean_std(circularized=params.CIRCULARIZED_MEAN_STD)
keys = y.index.values
if params.AUGMENT:
train_iterator = AugmentingParallelBatchIterator(keys,
params.BATCH_SIZE,
std,
mean,
y_all=y)
else:
train_iterator = ParallelBatchIterator(keys,
params.BATCH_SIZE,
std,
mean,
y_all=y)
test_iterator = ParallelBatchIterator(keys,
params.BATCH_SIZE,
std,
mean,
y_all=y)
if params.REGRESSION:
y = util.float32(y)
y = y[:, np.newaxis]
if 'gpu' in theano.config.device:
# Half of coma does not support cuDNN, check whether we can use it on this node
# If not, use cuda_convnet bindings
from theano.sandbox.cuda.dnn import dnn_available
if dnn_available() and not params.DISABLE_CUDNN:
from lasagne.layers import dnn
Conv2DLayer = dnn.Conv2DDNNLayer
MaxPool2DLayer = dnn.MaxPool2DDNNLayer
else:
from lasagne.layers import cuda_convnet
Conv2DLayer = cuda_convnet.Conv2DCCLayer
MaxPool2DLayer = cuda_convnet.MaxPool2DCCLayer
else:
Conv2DLayer = layers.Conv2DLayer
MaxPool2DLayer = layers.MaxPool2DLayer
Maxout = layers.pool.FeaturePoolLayer
net = NeuralNet(
layers=[
('input', layers.InputLayer),
('conv0', Conv2DLayer),
('pool0', MaxPool2DLayer),
('conv1', Conv2DLayer),
('pool1', MaxPool2DLayer),
('conv2', Conv2DLayer),
('pool2', MaxPool2DLayer),
('conv3', Conv2DLayer),
('pool3', MaxPool2DLayer),
('conv4', Conv2DLayer),
('pool4', MaxPool2DLayer),
('dropouthidden1', layers.DropoutLayer),
('hidden1', layers.DenseLayer),
('maxout1', Maxout),
('dropouthidden2', layers.DropoutLayer),
('hidden2', layers.DenseLayer),
('maxout2', Maxout),
('dropouthidden3', layers.DropoutLayer),
('output', layers.DenseLayer),
],
input_shape=(None, params.CHANNELS, params.PIXELS, params.PIXELS),
conv0_num_filters=32,
conv0_filter_size=(5, 5),
conv0_stride=(2, 2),
pool0_pool_size=(2, 2),
pool0_stride=(2, 2),
conv1_num_filters=64,
conv1_filter_size=(3, 3),
conv1_border_mode='same',
pool1_pool_size=(2, 2),
pool1_stride=(2, 2),
conv2_num_filters=128,
conv2_filter_size=(3, 3),
conv2_border_mode='same',
pool2_pool_size=(2, 2),
pool2_stride=(2, 2),
conv3_num_filters=192,
conv3_filter_size=(3, 3),
conv3_border_mode='same',
pool3_pool_size=(2, 2),
pool3_stride=(2, 2),
conv4_num_filters=256,
conv4_filter_size=(3, 3),
conv4_border_mode='same',
pool4_pool_size=(2, 2),
pool4_stride=(2, 2),
hidden1_num_units=1024,
hidden2_num_units=1024,
dropouthidden1_p=0.5,
dropouthidden2_p=0.5,
dropouthidden3_p=0.5,
maxout1_pool_size=2,
maxout2_pool_size=2,
output_num_units=1 if params.REGRESSION else 5,
output_nonlinearity=None
if params.REGRESSION else nonlinearities.softmax,
update_learning_rate=theano.shared(
util.float32(params.START_LEARNING_RATE)),
update_momentum=theano.shared(util.float32(params.MOMENTUM)),
custom_score=('kappa', quadratic_kappa),
regression=params.REGRESSION,
batch_iterator_train=train_iterator,
batch_iterator_test=test_iterator,
on_epoch_finished=[
AdjustVariable('update_learning_rate',
start=params.START_LEARNING_RATE),
stats.Stat(),
ModelSaver()
],
max_epochs=500,
verbose=1,
# Only relevant when create_validation_split = True
eval_size=0.1,
# Need to specify splits manually like indicated below!
create_validation_split=params.SUBSET > 0,
)
# It is recommended to use the same training/validation split every model for ensembling and threshold optimization
#
# To set specific training/validation split:
net.X_train = np.load(params.IMAGE_SOURCE + "/X_train.npy")
net.X_valid = np.load(params.IMAGE_SOURCE + "/X_valid.npy")
net.y_train = np.load(params.IMAGE_SOURCE + "/y_train.npy")
net.y_valid = np.load(params.IMAGE_SOURCE + "/y_valid.npy")
return net, X, y
print(' Creating new modifier *1.2 ...')
test = stats.Modifier(source, 1.2, dice.MOD_OPERATORS['*'])
print(' stats.Modifier(5) __str__(): {}\n'.format(test.__str__()))
print(' Creating new modifier /1.1 ...')
test = stats.Modifier(source, 1.1, dice.MOD_OPERATORS['/'])
print(' stats.Modifier(5) __str__(): {}\n'.format(test.__str__()))
print('Testing ModifierSets...')
print(' Creating new set with 10 modifiers...')
test = stats.ModifierSet([
stats.Modifier(source, 5),
stats.Modifier(source, 1.2, dice.MOD_OPERATORS['/']),
stats.Modifier(source, 10, dice.MOD_OPERATORS['-']),
stats.Modifier(source, 5, stats.add_percent),
stats.Modifier(source, 15, stats.add_percent),
stats.Modifier(source, 1.05, dice.MOD_OPERATORS['*']),
stats.Modifier(source, 2, dice.MOD_OPERATORS['-']),
stats.Modifier(source, 12, dice.MOD_OPERATORS['+']),
stats.Modifier(source, 1.1, dice.MOD_OPERATORS['*']),
stats.Modifier(source, 15, stats.sub_percent)
])
print(' Sorted Descriptor of ModifierSet: {}'.format(test.__str__()))
print('\nTesting Stats...')
print(" Creating new stat using previous modifiers...")
stat_test = stats.Stat("STR", 55, "Strength", test)
print(' Stat Abbreviation: {}'.format(stat_test.abbreviation))
print(' Stat Name: {}'.format(stat_test.name))
print(' Stat Base Score: {}'.format(stat_test.base_score))
print(' Stat Modified Score: {}'.format(stat_test.score))
print(' Stat Modifiers: {}'.format(stat_test.modifiers.__str__()))
def produceStats(year, name, playerteam, playerid):
stat = stats.Stat()
return stat.outputP(year, name, playerteam, playerid)
