def main():
#input
parser = argparse.ArgumentParser()
parser.add_argument(
"--mnist-dir",
default='/tmp/mnist-data',
help="Directory where mnist downloaded dataset will be stored")
parser.add_argument("--output-dir",
default='output',
help="Directory where models will be saved")
parser.add_argument(
"--train-digits",
help=
"Comma separated list of digits to train generators for (e.g. '1,2,3')"
)
parser.add_argument(
"--train-mnist",
action='store_true',
help=
"If specified, train the mnist classifier based on generated digits from saved models"
)
global args
args = parser.parse_args()
mnist_data = tf.contrib.learn.datasets.mnist.read_data_sets(args.mnist_dir,
one_hot=True)
if args.train_digits:
gan = GAN()
for digit in map(int, args.train_digits.split(',')):
path = "%s/digit-%d/model" % (args.output_dir, digit)
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
gan.train_digit(mnist_data, digit, path)
elif args.train_mnist:
gan = GAN()
print("Loading generator models...")
sessions = [
gan.restore_session("%s/digit-%d" % (args.output_dir, digit))
for digit in range(10)
]
print("Done")
samples = [[], []]
mnist = MNIST()
for step in range(20000):
if len(samples[0]) < 50:
samples = gen_samples(gan, sessions)
xs = samples[0][:50]
ys = samples[1][:50]
samples[0] = samples[0][50:]
samples[1] = samples[1][50:]
mnist.train_batch(xs, ys, step)
test_accuracy = mnist.eval_batch(mnist_data.test.images,
mnist_data.test.labels)
print("Test accuracy %g" % test_accuracy)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--mnist-dir",
default='/tmp/mnist-data',
help="Directory where mnist downloaded dataset will be stored")
parser.add_argument("--output-dir",
default='output',
help="Directory where models will be saved")
parser.add_argument(
"--train-digits",
help=
"Comma separated list of digits to train generators for (e.g. '1,2,3')"
)
parser.add_argument(
"--train-mnist",
action='store_true',
help=
"If specified, train the mnist classifier based on generated digits from saved models"
)
global args
args = parser.parse_args() # used to store input arguments
mnist_data = tf.contrib.learn.datasets.mnist.read_data_sets(
args.mnist_dir,
one_hot=True) # loads mnist data from tensorflow datasets
if args.train_digits: # checks if the user has input digits to train on
gan = GAN()
for digit in map(
int, args.train_digits.split(',')
): # iterates through a list of user input digits ---- map() applies a function to a list and
path = "%s/digit-%d/model" % (
args.output_dir, digit
) # creates a variable to store the path for saving the models
if not os.path.exists(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
gan.train_digit(
mnist_data, digit, path
) # reads the mnist data for each digit in the training set provided by the user and saves the trained session to path
elif args.train_mnist: # if the user doesn't input any training data
gan = GAN()
print("Loading generator models...")
sessions = [
gan.restore_session("%s/digit-%d" % (args.output_dir, digit))
for digit in range(10)
] # restores saved generator sessions for each digits 0 through 9
print("Done")
samples = [[], []]
mnist = MNIST()
for step in range(20000):
if len(samples[0]) < 50:
samples = gen_samples(gan, sessions)
xs = samples[0][:50]
ys = samples[1][:50]
samples[0] = samples[0][50:]
samples[1] = samples[1][50:]
mnist.train_batch(xs, ys, step)
test_accuracy = mnist.eval_batch(mnist_data.test.images,
mnist_data.test.labels)
print("Test accuracy %g" % test_accuracy)
