import os

import argparse

import numpy

import theano

from theano.tensor.shared_randomstreams import RandomStreams

from nose.tools import (assert_true,

assert_is_instance,

assert_greater,

assert_greater_equal,

assert_less_equal,

assert_equal)

from simplelearn.nodes import (Node,

AffineLayer,

CastNode,

Dropout,

CrossEntropy,

Misclassification,

SoftmaxLayer)

from simplelearn.utils import safe_izip

from simplelearn.asserts import (assert_all_greater,

assert_all_less_equal,

assert_all_integer)

from simplelearn.io import SerializableModel

from simplelearn.data.dataset import Dataset

from simplelearn.data.mnist import load_mnist

from simplelearn.formats import DenseFormat

from simplelearn.training import (SgdParameterUpdater,

Sgd,

# LogsToLists,

SavesAtMinimum,

MeanOverEpoch,

LimitsNumEpochs,

LinearlyInterpolatesOverEpochs,

# PicklesOnEpoch,

ValidationCallback,

StopsOnStagnation,

EpochLogger)

import pdb

mnist_training, mnist_testing = load_mnist()

# split training set into training and validation sets

tensors = mnist_training.tensors

training_tensors = [t[:-args.validation_size, ...] for t in tensors]

validation_tensors = [t[-args.validation_size:, ...] for t in tensors]

if args.no_shuffle_dataset == False:

def shuffle_in_unison_inplace(a, b):

assert len(a) == len(b)

p = numpy.random.permutation(len(a))

return a[p], b[p]

[training_tensors[0],training_tensors[1]] = shuffle_in_unison_inplace(training_tensors[0],training_tensors[1])

[validation_tensors[0], validation_tensors[1]] = shuffle_in_unison_inplace(validation_tensors[0], validation_tensors[1])

mnist_training = Dataset(tensors=training_tensors,

names=mnist_training.names,

formats=mnist_training.formats)

mnist_validation = Dataset(tensors=validation_tensors,

names=mnist_training.names,

formats=mnist_training.formats)

mnist_validation_iterator = mnist_validation.iterator(

iterator_type='sequential',

batch_size=args.batch_size)

image_uint8_node, label_node = mnist_validation_iterator.make_input_nodes()

image_node = CastNode(image_uint8_node, 'floatX')

# image_node = RescaleImage(image_uint8_node)