def MultiLayerPerceptron(*args, **kwargs):
'''
positional arguments:
the number of hidden units of each layer
keyword arguments(optional):
activation: ReLU by default
affine_monitor: bool
activation_monitor: bool
storage: dictionary
'''
assert all(isinstance(arg, int) for arg in args)
try:
activation = kwargs.pop('activation', 'ReLU')
activation = getattr(builder, activation)()
except:
raise Exception('unsupported activation function')
affine_monitor = kwargs.pop('affine_monitor', False)
activation_monitor = kwargs.pop('activation_monitor', False)
if affine_monitor or activation_monitor:
try:
storage = kwargs['storage']
except:
raise Exception('storage required to monitor intermediate result')
network = builder.Sequential()
for i, arg in enumerate(args[:-1]):
network.append(builder.Affine(arg))
if affine_monitor:
network.append(builder.Export('affine%d' % i, storage))
network.append(activation)
if activation_monitor:
network.append(builder.Export('activation%d' % i, storage))
network.append(builder.Affine(args[-1]))
return network
from solver_primitives import *
sys.path.append('../')
from utilities.data_utility import load_cifar10
data = load_cifar10(path='../utilities/cifar/', center=True, rescale=True)
X = data[0][:16]
hidden_layers = 4
shapes = (1024, ) * hidden_layers + (10, )
activation = builder.ReLU
storage = {}
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Affine(shapes[-1]))
model = builder.Model(mlp, 'softmax', (3072, ))
'''
for key, value in model.param_configs.items():
if 'weight' in key:
value['init_rule'] = 'gaussian'
value['init_config'] = {'stdvar' : 1}
'''
initialize(model)
for key, value in model.params.items():
if 'weight' in key:
print np.std(value)
from custom_layers import *
from solver_primitives import *
sys.path.append('../')
from utilities.data_utility import load_cifar10
data = load_cifar10(path='../utilities/cifar/', center=True, rescale=True)
hidden_layers = 4
shapes = (1024, ) * hidden_layers + (10, )
activation = builder.ReLU
storage = {}
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Affine(shapes[-1]))
mlp.append(builder.Export('affine%d' % (len(shapes) - 1), storage))
model = builder.Model(mlp, 'softmax', (3072, ))
batch_size = 128
batches = len(data[0]) // batch_size
batch_index = 0
# raise Exception()
iterations = 10000
interval = 10
# settings = {}
settings = {'learning_rate': 0.01}
'''
ACTIVATION = 'ReLU'
DEVICE = 0
DR_INTERVAL = 10
shapes = (1024, ) * 4 + (10, )
activation = getattr(builder, ACTIVATION)
set_context(gpu(DEVICE))
storage = {}
chd_list = []
mlp = builder.Sequential()
for i, shape in enumerate(shapes[:-1]):
mlp.append(builder.Affine(shape))
mlp.append(builder.Export('affine%d' % i, storage))
mlp.append(activation())
mlp.append(builder.Export('activation%d' % i, storage))
mlp.append(ChannelDivision(np.ones(shape)))
chd_list.append(mlp[-1])
mlp.append(builder.Export('chd%d' % i, storage))
mlp.append(builder.Affine(shapes[-1]))
model = builder.Model(mlp, 'softmax', (3072, ))
batch_size = 100
batches = len(data[0]) // batch_size
batch_index = 0
iterations = 25000
interval = 10
