outputs = layers.softmax(outputs)
return outputs, self.initializer
activation = sys.argv[1]
filter_count = int(sys.argv[2])
model = PooledCNN(activation, filter_count, Initializer.DReLUInitializer(2.0))
optimizer_settings = {
'lr' : 0.01,
'lr_decay_interval' : 1,
'lr_decay_factor' : 1.0,
'optimizer' : 'SGD',
'weight_decay' : 0
}
data = data_utility.load_cifar10(path='../utilities/cifar/', reshape=True, center=True, rescale=True)
solver_configuration = {
'batch_size' : 128,
'data' : data,
'devices' : GPU_utility.GPU_availability()[:3],
'epoch' : 60,
'optimizer_settings' : optimizer_settings,
}
label = 'pooled-CNN-%s-%d-filters-lr-%f' % (activation, filter_count, optimizer_settings['lr'])
solver = Solver.MXSolver(model, **solver_configuration)
history = solver.train()
pickle.dump(history, open('../../models/%s-history' % label, 'wb'))
pickle.dump((solver.model.arg_params, solver.model.aux_params), open('../../models/%s-parameters' % label, 'wb'))
import minpy.numpy as np
import minpy.nn.model_builder as builder
from minpy.context import set_context, cpu, gpu
# set_context(gpu(0))
set_context(cpu())
import sys
sys.path.append('../../nn')
from custom_layers import *
from facility 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)
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, ))
import cPickle as pickle
import marshal
import mxnet as mx
import numpy as np
import sys
sys.path.append('..')
from utilities.data_utility import load_cifar10
data = load_cifar10('../utilities/cifar/', reshape=True)
sys.path.append('../../nn')
from MXInitializer import DReLUInitializer
initializer = DReLUInitializer(1.0)
from MXModels.ResidualNetwork import ResidualNetwork
# activation = 'ReLU'
activation = sys.argv[1]
symbol, _ = ResidualNetwork(3, activation, initializer)(data[0].shape)
args = symbol.list_arguments()
shapes, _, _ = symbol.infer_shape(data=data[0].shape)
parameters = {}
for arg, shape in zip(args, shapes):
print arg
parameters[arg] = mx.nd.zeros(shape, mx.cpu())
initializer(arg, parameters[arg])
