"""
import numpy as np;
import theano
import theano.tensor as T;
import matplotlib.pyplot as plt;
import telauges.utils as utils;
from telauges.conv_ae import ConvAE;
n_epochs=10;
training_portion=1;
batch_size=50;
nkerns=49;
datasets=utils.load_mnist("data/mnist.pkl.gz");
rng=np.random.RandomState(23455);
### Loading and preparing dataset
train_set_x, train_set_y = datasets[0];
valid_set_x, valid_set_y = datasets[1];
test_set_x, test_set_y = datasets[2];
n_train_batches=int(train_set_x.get_value(borrow=True).shape[0]*training_portion);
n_valid_batches=valid_set_x.get_value(borrow=True).shape[0];
n_test_batches=test_set_x.get_value(borrow=True).shape[0];
n_train_batches /= batch_size; # number of train data batches
n_valid_batches /= batch_size; # number of valid data batches
n_test_batches /= batch_size; # number of test data batches
import cPickle as pickle
import numpy as np
import theano
import theano.tensor as T
import matplotlib.pyplot as plt
import telauges.utils as utils
from telauges.hidden_layer import AutoEncoder
n_epochs = 100
training_portion = 1
batch_size = 100
datasets = utils.load_mnist("data/mnist.pkl.gz")
rng = np.random.RandomState(23455)
### Loading and preparing dataset
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = int(
train_set_x.get_value(borrow=True).shape[0] * training_portion)
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
print n_train_batches
print n_valid_batches
print n_test_batches