def train(self, epochs, batch_size=128, sample_interval=50):
# Load the dataset
loader = Loader('faces94/malestaff/voudcx', 'faces94/malestaff/tony')
img_a, img_b = loader.get_all()
assert img_a.shape[0] == batch_size
# Adversarial ground truths
valid = np.ones((batch_size, 1), dtype=np.float32)
fake = np.zeros((batch_size, 1), dtype=np.float32)
for epoch in range(epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Train the discriminator
img_fake, _ = self.adversarial_autoencoder.predict([img_a, img_b])
save_img('fake.jpg', img_fake[10])
history_real = self.discriminator.fit(img_b, valid, batch_size=batch_size)
history_fake = self.discriminator.fit(img_a, fake, batch_size=batch_size)
d_loss_real = history_real.history['loss']
d_loss_fake = history_fake.history['loss']
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
# Train the generator
history_g = self.adversarial_autoencoder.fit([img_a, img_b], [img_b, valid], batch_size=batch_size)
g_loss = history_g.history['loss']
print(history_g.history.keys())
# Plot the progress
#print ("%d [D loss: %f] [G loss: %f, mse: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss[0], g_loss[1]))
print ("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss[0], g_loss[0]))
from loader import Loader
loader = Loader('faces94/malestaff/voudcx', 'faces94/malestaff/tony')
a, b = loader.get_all()
print(a.shape)
print(b.shape)
print(e)
exit()
BATCH_SIZE = 256
BUFFER_SIZE = 60000
EPOCHES = 300
OUTPUT_DIR = "img" # The output directory where the images of the generator a stored during training
ROW = 200
COL = 180
CHANNEL = 3
directory = 'img/'
path_a = 'faces94/malestaff/tony/'
path_b = 'faces94/malestaff/voudcx/'
AB_loader = Loader(path_a, path_b)
train_images_a, train_images_b = AB_loader.get_all()
assert train_images_a[0].shape == (ROW, COL, CHANNEL)
train_dataset = tf.data.Dataset.from_tensor_slices(
(train_images_a, train_images_b)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
# ## Generator Network
class Generator(keras.Model):
def __init__(self):
super().__init__(name='generator')
#layers
self.input_layer_a = keras.layers.Conv2D(16,
3,
padding='same',
