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Changed typo in name

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This commit is contained in:
Jacob Gildenblat
2016-03-04 16:39:59 +02:00
parent 3049f6d9bd
commit 7dbad4832d
1 changed files with 16 additions and 19 deletions
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dcgan.py
+16 -19
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@@ -38,7 +38,7 @@ def generator_model():
model.add(Activation('tanh'))
return model
def descriminator_model():
def discriminator_model():
model = Sequential()
model.add(Convolution2D(128, 5, 5, subsample=(2, 2), input_shape=(3, 64, 64), border_mode = 'same'))
model.add(LeakyReLU(0.2))
@@ -57,11 +57,11 @@ def descriminator_model():
model.add(Activation('sigmoid'))
return model
def generator_containing_descriminator(generator, descriminator):
def generator_containing_discriminator(generator, discriminator):
model = Sequential()
model.add(generator)
descriminator.trainable = False
model.add(descriminator)
discriminator.trainable = False
model.add(discriminator)
return model
def load_image(path):
@@ -79,14 +79,14 @@ def train(path, BATCH_SIZE):
paths = glob.glob(os.path.join(path, "*.jpg"))
print "Got paths.."
descriminator = descriminator_model()
discriminator = discriminator_model()
generator = generator_model()
descriminator_on_generator = generator_containing_descriminator(generator, descriminator)
discriminator_on_generator = generator_containing_discriminator(generator, discriminator)
adam=Adam(lr=0.0002, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
generator.compile(loss='binary_crossentropy', optimizer=adam)
descriminator_on_generator.compile(loss='binary_crossentropy', optimizer=adam)
descriminator.trainable = True
descriminator.compile(loss='binary_crossentropy', optimizer=adam)
discriminator_on_generator.compile(loss='binary_crossentropy', optimizer=adam)
discriminator.trainable = True
discriminator.compile(loss='binary_crossentropy', optimizer=adam)
for epoch in range(5):
print "Epoch is", epoch
@@ -106,8 +106,8 @@ def train(path, BATCH_SIZE):
X = np.concatenate((image_batch, generated_images))
y = [1] * BATCH_SIZE + [0] * BATCH_SIZE
print "Batch", index, "Training descriminator.."
d_loss = descriminator.train_on_batch(X, y)
print "Batch", index, "Training discriminator.."
d_loss = discriminator.train_on_batch(X, y)
for j in range(1):
noise = np.zeros((BATCH_SIZE, 100))
@@ -116,17 +116,15 @@ def train(path, BATCH_SIZE):
print "Training generator.."
g_loss = descriminator_on_generator.train_on_batch(noise, [1] * BATCH_SIZE)
print "Generator loss", g_loss, "Descriminator loss", d_loss, "Total:", g_loss[0] + d_loss[0]
g_loss = discriminator_on_generator.train_on_batch(noise, [1] * BATCH_SIZE)
print "Generator loss", g_loss, "Discriminator loss", d_loss, "Total:", g_loss[0] + d_loss[0]
if index % 10 == 9:
print 'Saving weights..'
generator.save_weights('generator', True)
descriminator.save_weights('descriminator', True)
discriminator.save_weights('discriminator', True)
def generate():
BATCH_SIZE = 32
def generate(BATCH_SIZE):
generator = generator_model()
adam=Adam(lr=0.0002, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
generator.compile(loss='binary_crossentropy', optimizer=adam)
@@ -152,9 +150,8 @@ def get_args():
if __name__ == "__main__":
args = get_args()
path = args.path
if args.mode == "train":
train(path = args.path, BATCH_SIZE = args.batch_size)
elif args.mode == "generate":
generate()
generate(BATCH_SIZE = args.batch_size)