def run_dae():
########################################
# Initialization things with arguments #
########################################
config_root_logger()
log.info("Creating a new DAE")
mnist = MNIST()
config = {
"outdir": 'outputs/dae/mnist/',
"input_size": 28 * 28,
"tied_weights": True
}
dae = DenoisingAutoencoder(**config)
# # 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.getSubset(subset=datasets.TEST)
test_xs = test_xs[:n_examples].eval()
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 = {
"outdir": 'outputs/dae/mnist/',
"input_size": 28*28,
"tied_weights": True
}
dae = DenoisingAutoencoder(**config)
# # 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.getSubset(subset=datasets.TEST)
test_xs = test_xs[:n_examples].eval()
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 = {
"outdir": 'outputs/dae/mnist/',
"input_size": 28 * 28,
"tied_weights": True
}
dae = DenoisingAutoencoder(**config)
# # Load initial weights and biases from file
# params_to_load = 'dae_params.pkl'
# dae.load_params(params_to_load)
optimizer = AdaDelta(model=dae, dataset=mnist, epochs=100)
optimizer.train()
# Save some reconstruction output images
n_examples = 100
test_xs = mnist.test_inputs[:n_examples]
dae.create_reconstruction_image(test_xs)
del dae, mnist
def run_dae():
########################################
# Initialization things with arguments #
########################################
config_root_logger()
log.info("Creating a new DAE")
mnist = MNIST()
config = {
"outdir": 'outputs/dae/mnist/',
"input_size": 28*28,
"tied_weights": True
}
dae = DenoisingAutoencoder(**config)
# # Load initial weights and biases from file
# params_to_load = 'dae_params.pkl'
# dae.load_params(params_to_load)
optimizer = AdaDelta(model=dae, dataset=mnist, epochs=100)
optimizer.train()
# Save some reconstruction output images
n_examples = 100
test_xs = mnist.test_inputs[:n_examples]
dae.create_reconstruction_image(test_xs)
del dae, mnist
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)