def train(x_train, learning_rate, batch_size, num_epochs):
autoencoder = VAE(input_shape=(256, 64, 1),
conv_filters=(512, 256, 128, 64, 32),
conv_kernel=(3, 3, 3, 3, 3),
conv_strides=(2, 2, 2, 2, (2, 1)),
latent_dim=128)
autoencoder.summary()
autoencoder.compile(learning_rate)
autoencoder.train(x_train, batch_size, num_epochs)
return autoencoder
def vae_train():
# load train
x_train = load_dataset('adi')
# load test normal and anomaly
test_anomaly = load_dataset('vans')
test_normal = load_dataset('adi_test')
# drfine train and valid iamge for train
trains = x_train[10:]
valid = x_train[:10]
print(trains.shape, valid.shape, test_anomaly.shape, test_normal.shape)
# try to plot
plt.imshow(x_train[10].reshape(256, 256))
plt.gray()
plt.show()
# train
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
def step_decay(epoch):
initial_lrate = 0.0001
decay_rate = 0.5
decay_steps = 8.0
lrate = initial_lrate * math.pow(decay_rate,
math.floor((1 + epoch) / decay_steps))
return lrate
callback = []
callback.append(
HistoryCheckpoint(filepath='tb/LearningCurve_{history}.png',
verbose=1,
period=300))
callback.append(LearningRateScheduler(step_decay))
model = VAE()
model, loss = model.vae_net()
#model.load_weights("vae_model.h5")
model.add_loss(loss)
model.compile(optimizer=Adam(lr=0.0001))
model.summary()
try:
model.fit(trains,
batch_size=20,
epochs=300,
callbacks=callback,
validation_data=(valid, None))
finally:
model.save('weight/vae_model.h5')
### Build Model
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
### Model Parameters
latent_dim = 124
input_dim = len(int2labels_map) - 1
dropout = .1
maxnorm = None
vae_b1 , vae_b2 = .02 , .1
# Build Model
model = VAE(latent_dim, input_dim, measures, measure_len, dropout,
maxnorm, vae_b1 , vae_b2)
model.build(tf.TensorShape([None, measures, measure_len, input_dim]))
model.summary()
### Train Model
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Training Parameters
batch_size = 300
epochs = 10
# Cost Function
cost_function = model.vae_loss
# Optimizer and learning_rate schedule
lr_0 = .001
decay_rate = .998
lr_decay = lambda t: lr_0 * decay_rate**t
# Reference
[1] Kingma, Diederik P., and Max Welling.
"Auto-encoding variational bayes."
https://arxiv.org/abs/1312.6114
'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from VAE import VAE
from Encoder import Encoder
from Decoder import Decoder
from Parameters import x_train, x_test, y_test, latent_dim, input_shape, epochs, batch_size
from keras.utils import plot_model
from Util import plot_results
if __name__ == '__main__':
encoder = Encoder(input_data=input_shape)
decoder = Decoder(input_data=(latent_dim, ))
models = (encoder, decoder)
data = (x_test, y_test)
vae = VAE(input_data=input_shape, encoder=encoder, decoder=decoder)
vae.compile(optimizer='adam')
vae.summary()
plot_model(vae, to_file='vae_mlp.png', show_shapes=True)
vae.fit(x=x_train,
y=None,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, None))
vae.save_weights('vae_mlp_mnist.h5')
plot_results(models, data, batch_size=batch_size, model_name="vae_mlp")
