def main():
########################################
# Initialization things with arguments #
########################################
logger.config_root_logger()
log.info("Creating a new DAE")
mnist = MNIST()
config = {"output_path": '../../../outputs/dae/mnist/'}
dae = DenoisingAutoencoder(config=config, dataset=mnist)
# # Load initial weights and biases from file
# params_to_load = 'dae_params.pkl'
# dae.load_params(params_to_load)
optimizer = AdaDelta(dae, mnist)
optimizer.train()
# Save some reconstruction output images
import opendeep.data.dataset as datasets
n_examples = 100
test_xs = mnist.getDataByIndices(indices=range(n_examples), subset=datasets.TEST)
dae.create_reconstruction_image(test_xs)
def run_dae():
########################################
# Initialization things with arguments #
########################################
config_root_logger()
log.info("Creating a new DAE")
mnist = MNIST()
config = {"output_path": '../../../../outputs/dae/mnist/'}
dae = DenoisingAutoencoder(config=config, dataset=mnist)
# # Load initial weights and biases from file
# params_to_load = 'dae_params.pkl'
# dae.load_params(params_to_load)
optimizer = AdaDelta(dae, mnist)
optimizer.train()
# Save some reconstruction output images
import opendeep.data.dataset as datasets
n_examples = 100
test_xs = mnist.getDataByIndices(indices=range(n_examples), subset=datasets.TEST)
dae.create_reconstruction_image(test_xs)
import logging
import opendeep.log.logger as logger
logger.config_root_logger()
log = logging.getLogger(__name__)
log.info("Creating softmax!")
# grab the MNIST dataset
mnist = MNIST()
# create the softmax classifier
s = SoftmaxLayer(input_size=28 * 28, output_size=10, out_as_probs=False)
# make an optimizer to train it (AdaDelta is a good default)
optimizer = AdaDelta(model=s, dataset=mnist, n_epoch=20)
# perform training!
optimizer.train()
# test it on some images!
test_data = mnist.getDataByIndices(indices=range(25), subset=TEST)
# use the predict function!
preds = s.predict(test_data)
print '-------'
print preds
print mnist.getLabelsByIndices(indices=range(25), subset=TEST)
print
print
del mnist
del s
del optimizer
log.info("Creating softmax with categorical cross-entropy!")
# grab the MNIST dataset
mnist = MNIST(one_hot=True)
from opendeep.data.standard_datasets.image.mnist import MNIST
from opendeep.optimization.adadelta import AdaDelta
# grab the MNIST dataset
mnist = MNIST()
# create your shiny new DAE
dae = DenoisingAutoencoder()
# make an optimizer to train it (AdaDelta is a good default)
optimizer = AdaDelta(model=dae, dataset=mnist)
# perform training!
optimizer.train()
# test it on some images!
test_data = mnist.getDataByIndices(indices=range(25), subset=TEST)
corrupted_test = salt_and_pepper(test_data, 0.4)
# use the predict function!
reconstructed_images = dae.predict(test_data)
# create an image from this reconstruction!
# imports for working with tiling outputs into one image
from opendeep.utils.image import tile_raster_images
import numpy
import PIL
# stack the image matrices together in three 5x5 grids next to each other using numpy
stacked = numpy.vstack([
numpy.vstack([
test_data[i * 5:(i + 1) * 5],
corrupted_test.eval()[i * 5:(i + 1) * 5],
reconstructed_images[i * 5:(i + 1) * 5]
