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Conv GAN is working.
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Ragav Venkatesan committed Mar 10, 2017
1 parent 3a66aeb commit 845efe4
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163 changes: 64 additions & 99 deletions pantry/tutorials/gan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,61 +12,6 @@
from yann.special.gan import gan
from theano import tensor as T

def cook_mnist( verbose = 1, **kwargs):
"""
Wrapper to cook mnist dataset specifically for the gan. Will take as input,
Args:
save_directory: which directory to save the cooked dataset onto.
dataset_parms: default is the dictionary. Refer to :mod:`setup_dataset`
preprocess_params: default is the dictionary. Refer to :mod:`setup_dataset`
Notes:
By default, this will create a dataset that is not mean-subtracted.
"""
from yann.utils.dataset import setup_dataset

if not 'data_params' in kwargs.keys():

data_params = {
"source" : 'skdata',
"name" : 'mnist',
"location" : '',
"mini_batch_size" : 100,
"mini_batches_per_batch" : (500, 100, 100),
"batches2train" : 1,
"batches2test" : 1,
"batches2validate" : 1,
"height" : 28,
"width" : 28,
"channels" : 1 }

else:
data_params = kwargs['data_params']

if not 'preprocess_params' in kwargs.keys():

# parameters relating to preprocessing.
preprocess_params = {
"normalize" : True,
"ZCA" : False,
"grayscale" : False,
"zero_mean" : True,
}
else:
preprocess_params = kwargs['preprocess_params']

if not 'save_directory' in kwargs.keys():
save_directory = '_datasets'
else:
save_directory = kwargs ['save_directory']

dataset = setup_dataset(dataset_init_args = data_params,
save_directory = save_directory,
preprocess_init_args = preprocess_params,
verbose = 3)
return dataset

def shallow_gan ( dataset= None, verbose = 1 ):
"""
This function is a demo example of a generative adversarial network.
Expand Down Expand Up @@ -514,32 +459,36 @@ def deep_gan (dataset, verbose = 1 ):
early_terminate = True,
verbose = verbose)

def deep_deconvolutional_gan (dataset, verbose = 1 ):
def deep_deconvolutional_gan (dataset,
regularize = True,
batch_norm = False,
dropout_rate = None,
verbose = 1 ):
"""
This function is a demo example of a generative adversarial network.
This is an example code. You should study this code rather than merely run it.
This method uses a few deconvolutional layers as was used in the DCGAN paper.
Args:
Args:
dataset: Supply a dataset.
regularize: ``True`` (default) supplied to layer arguments
batch_norm: ``True`` (default) supplied to layer arguments
dropout_rate: ``None`` (default) supplied to layer arguments
verbose: Similar to the rest of the dataset.
Returns:
net: A Network object.
Notes:
This is not setup properly therefore does not learn at the moment. This network here mimics
Ian Goodfellow's original code and implementation for MNIST adapted from his source code:
https://github.com/goodfeli/adversarial/blob/master/mnist.yaml .It might not be a perfect
replicaiton, but I tried as best as I could.
This is not setup properly therefore does not learn at the moment.
"""
if verbose >=2:
print (".. Creating a GAN network")

optimizer_params = {
"momentum_type" : 'polyak',
"momentum_params" : (0.5, 0.7, 20),
"regularization" : (0.000, 0.000),
"momentum_type" : 'nesterov',
"momentum_params" : (0.65, 0.65, 20),
"regularization" : (0.0001, 0.0001),
"optimizer_type" : 'rmsprop',
"id" : "main"
}
Expand Down Expand Up @@ -577,7 +526,7 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
#z - latent space created by random layer
net.add_layer(type = 'random',
id = 'z',
num_neurons = (100,10),
num_neurons = (100,100),
distribution = 'normal',
mu = 0,
sigma = 1,
Expand All @@ -590,8 +539,8 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
id = "G1",
num_neurons = 1200,
activation = 'relu',
regularize = True,
# batch_norm = True,
regularize = regularize,
batch_norm = batch_norm,
verbose = verbose
)

Expand All @@ -600,15 +549,16 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
id = "G2",
num_neurons = 1440,
activation = 'relu',
regularize = True,
# batch_norm = True,
regularize = regularize,
batch_norm = batch_norm,
verbose = verbose
)

net.add_layer ( type = "unflatten",
origin = "G2",
id = "G2-unflatten",
shape = (12, 12, 10),
batch_norm = batch_norm,
verbose = verbose
)

Expand All @@ -617,21 +567,22 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
id = "G3",
num_neurons = 10,
filter_size = (3,3),
output_shape = (26,26,20),
output_shape = (26,26,32),
activation = 'relu',
regularize = True,
regularize = regularize,
batch_norm = batch_norm,
stride = (2,2),
verbose = verbose
)

net.add_layer ( type = "deconv",
origin = "G3",
id = "G(z)",
num_neurons = 20,
num_neurons = 32,
filter_size = (3,3),
output_shape = (28,28,1),
activation = 'tanh',
# regularize = True,
# regularize = regularize,
stride = (1,1),
verbose = verbose
)
Expand All @@ -655,7 +606,8 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
filter_size = (5,5),
pool_size = (2,2),
activation = 'relu',
regularize = True,
regularize = regularize,
batch_norm = batch_norm,
verbose = verbose
)

Expand All @@ -666,7 +618,8 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
filter_size = (5,5),
pool_size = (2,2),
activation = 'relu',
regularize = True,
regularize = regularize,
batch_norm = batch_norm,
input_params = net.dropout_layers["D1-x"].params,
verbose = verbose
)
Expand All @@ -679,7 +632,8 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
filter_size = (3,3),
pool_size = (2,2),
activation = 'relu',
regularize = True,
regularize = regularize,
batch_norm = batch_norm,
verbose = verbose
)

Expand All @@ -691,7 +645,8 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
filter_size = (3,3),
pool_size = (2,2),
activation = 'relu',
regularize = True,
regularize = regularize,
batch_norm = batch_norm,
input_params = net.dropout_layers["D2-x"].params,
verbose = verbose
)
Expand All @@ -701,9 +656,9 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
origin = "D2-x",
num_neurons = 1200,
activation = 'relu',
regularize = True,
# batch_norm = True,
dropout_rate = 0.5,
regularize = regularize,
batch_norm = batch_norm,
dropout_rate = dropout_rate,
verbose = verbose
)

Expand All @@ -713,9 +668,9 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
input_params = net.dropout_layers["D3-x"].params,
num_neurons = 1200,
activation = 'relu',
regularize = True,
# batch_norm = True,
dropout_rate = 0.5,
regularize = regularize,
batch_norm = batch_norm,
dropout_rate = dropout_rate,
verbose = verbose
)

Expand All @@ -724,9 +679,9 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
origin = "D3-x",
num_neurons = 1200,
activation = 'relu',
regularize = True,
# batch_norm = True,
dropout_rate = 0.5,
regularize = regularize,
batch_norm = batch_norm,
dropout_rate = dropout_rate,
verbose = verbose
)

Expand All @@ -736,9 +691,9 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
input_params = net.dropout_layers["D4-x"].params,
num_neurons = 1200,
activation = 'relu',
regularize = True,
dropout_rate = 0.5,
# batch_norm = True,
regularize = regularize,
dropout_rate = dropout_rate,
batch_norm = batch_norm,
verbose = verbose
)

Expand All @@ -748,6 +703,7 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
origin = "D4-x",
num_neurons = 1,
activation = 'sigmoid',
regularize = regularize,
verbose = verbose
)

Expand All @@ -757,6 +713,7 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
origin = "D4-z",
num_neurons = 1,
activation = 'sigmoid',
regularize = regularize,
input_params = net.dropout_layers["D(x)"].params,
verbose = verbose
)
Expand All @@ -767,6 +724,7 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
id = "softmax",
origin = "D4-x",
num_classes = 10,
regularize = regularize,
activation = 'softmax',
verbose = verbose
)
Expand Down Expand Up @@ -826,11 +784,11 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):
game_layers = ("D(x)", "D(G(z))"),
verbose = verbose )

learning_rates = (0.00004, 0.001 )
learning_rates = (0.00004, 0.01 )

net.train( epochs = (20),
k = 1,
pre_train_discriminator = 0,
k = 3,
pre_train_discriminator = 3,
validate_after_epochs = 1,
visualize_after_epochs = 1,
training_accuracy = True,
Expand All @@ -842,11 +800,14 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):


if __name__ == '__main__':

from yann.special.datasets import cook_mnist_normalized_zero_mean as cm
import sys

dataset = None
if len(sys.argv) > 1:
if sys.argv[1] == 'create_dataset':
data = cook_mnist (verbose = 2)
data = cm (verbose = 2)
dataset = data.dataset_location()
else:
dataset = sys.argv[1]
Expand All @@ -855,9 +816,13 @@ def deep_deconvolutional_gan (dataset, verbose = 1 ):

if dataset is None:
print " creating a new dataset to run through"
data = cook_mnist (verbose = 2)
dataset = data.dataset_location()

# net = shallow_gan ( dataset, verbose = 2 )
# net = deep_gan ( dataset, verbose = 2 )
net = deep_deconvolutional_gan ( dataset, verbose = 2 )
data = cm (verbose = 2)
dataset = data.dataset_location()

net = shallow_gan ( dataset, verbose = 2 )
net = deep_gan ( dataset, verbose = 2 )
net = deep_deconvolutional_gan ( batch_norm = True,
dropout_rate = 0.5,
regularize = True,
dataset = dataset,
verbose = 2 )

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