from ift6266h15.code.pylearn2.datasets.variable_image_dataset import DogsVsCats, RandomCrop
from pylearn2.models.mlp import MLP, ConvRectifiedLinear, Softmax, Tanh
from pylearn2.space import Conv2DSpace
from pylearn2.training_algorithms.sgd import SGD
from pylearn2.costs.cost import MethodCost
from pylearn2.training_algorithms.learning_rule import Momentum, MomentumAdjustor
from pylearn2.termination_criteria import EpochCounter, MonitorBased
from pylearn2.train import Train
from pylearn2.train_extensions.best_params import MonitorBasedSaveBest
batchSize = 20
cropSize = 200
#Create datasets
train = DogsVsCats(RandomCrop(256, cropSize), start=0, stop=19999)
valid = DogsVsCats(RandomCrop(256, cropSize), start=20000, stop=22500)
#Instantiate layers
h0 = ConvRectifiedLinear(output_channels=40,
kernel_shape=[7, 7],
pool_shape=[2, 2],
pool_stride=[2, 2],
layer_name="h0",
irange=0.1,
border_mode="full")
h1 = ConvRectifiedLinear(output_channels=40,
kernel_shape=[7, 7],
pool_shape=[2, 2],
pool_stride=[2, 2],
layer_name="h1",
'''
Created on Feb 2, 2015
@author: Alexandre
'''
from ift6266h15.code.pylearn2.datasets.variable_image_dataset import DogsVsCats, RandomCrop
dataset = DogsVsCats(RandomCrop(256, 221), start=0, stop=19999)
iterator = dataset.iterator(mode='batchwise_shuffled_sequential',
batch_size=100)
for X, y in iterator:
print X.shape, y.shape