def main( dirs, z_size=32, batch_size=100, learning_rate=0.0001, kl_tolerance=0.5, epochs=100, save_model=False, verbose=True, optimizer="Adam" ):
if save_model:
model_save_path = "tf_vae"
if not os.path.exists(model_save_path):
os.makedirs(model_save_path)
gen = DriveDataGenerator(dirs, image_size=(64,64), batch_size=batch_size, shuffle=True, max_load=10000, images_only=True )
num_batches = len(gen)
reset_graph()
vae = ConvVAE(z_size=z_size,
batch_size=batch_size,
learning_rate=learning_rate,
kl_tolerance=kl_tolerance,
is_training=True,
reuse=False,
gpu_mode=True,
optimizer=optimizer)
early = EarlyStopping(monitor='loss', min_delta=0.1, patience=5, verbose=verbose, mode='auto')
early.set_model(vae)
early.on_train_begin()
best_loss = sys.maxsize
if verbose:
print("epoch\tstep\tloss\trecon_loss\tkl_loss")
for epoch in range(epochs):
for idx in range(num_batches):
batch = gen[idx]
obs = batch.astype(np.float)/255.0
feed = {vae.x: obs,}
(train_loss, r_loss, kl_loss, train_step, _) = vae.sess.run([
vae.loss, vae.r_loss, vae.kl_loss, vae.global_step, vae.train_op
], feed)
if train_loss < best_loss:
best_loss = train_loss
if save_model:
if ((train_step+1) % 5000 == 0):
vae.save_json("tf_vae/vae.json")
if verbose:
print("{} of {}\t{}\t{:.2f}\t{:.2f}\t{:.2f}".format( epoch, epochs, (train_step+1), train_loss, r_loss, kl_loss) )
gen.on_epoch_end()
early.on_epoch_end(epoch, logs={"loss": train_loss})
if vae.stop_training:
break
early.on_train_end()
# finished, final model:
if save_model:
vae.save_json("tf_vae/vae.json")
return best_loss
X_train = [np.absolute(X_train[i]) for i in cat
] + [X_train[num]] # + [X_train[env1]] + [X_train[env2]]
X_val = [np.absolute(X_val[i])
for i in cat] + [X_val[num]] # + [X_val[env1]] + [X_val[env2]]
model = model_NN()
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=[])
es = EarlyStopping(monitor='val_CRPS',
mode='min',
restore_best_weights=True,
verbose=2,
patience=5)
es.set_model(model)
metric = Metric(model, [es], [(X_train, y_train), (X_val, y_val)])
for i in range(1):
model.fit(X_train, y_train, verbose=False)
for i in range(1):
model.fit(X_train, y_train, batch_size=64, verbose=False)
for i in range(1):
model.fit(X_train, y_train, batch_size=128, verbose=False)
for i in range(1):
model.fit(X_train, y_train, batch_size=256, verbose=False)
model.fit(X_train,
y_train,
callbacks=[metric],
epochs=100,
batch_size=1024,
verbose=False)
def train_model(model, data, config, include_tensorboard):
model_history = History()
model_history.on_train_begin()
saver = ModelCheckpoint(full_path(config.model_file()), verbose=1, save_best_only=True, period=1)
saver.set_model(model)
early_stopping = EarlyStopping(min_delta=config.min_delta, patience=config.patience, verbose=1)
early_stopping.set_model(model)
early_stopping.on_train_begin()
csv_logger = CSVLogger(full_path(config.csv_log_file()))
csv_logger.on_train_begin()
if include_tensorboard:
tensorborad = TensorBoard(histogram_freq=10, write_images=True)
tensorborad.set_model(model)
else:
tensorborad = Callback()
epoch = 0
stop = False
while(epoch <= config.max_epochs and stop == False):
epoch_history = History()
epoch_history.on_train_begin()
valid_sizes = []
train_sizes = []
print("Epoch:", epoch)
for dataset in data.datasets:
print("dataset:", dataset.name)
model.reset_states()
dataset.reset_generators()
valid_sizes.append(dataset.valid_generators[0].size())
train_sizes.append(dataset.train_generators[0].size())
fit_history = model.fit_generator(dataset.train_generators[0],
dataset.train_generators[0].size(),
nb_epoch=1,
verbose=0,
validation_data=dataset.valid_generators[0],
nb_val_samples=dataset.valid_generators[0].size())
epoch_history.on_epoch_end(epoch, last_logs(fit_history))
train_sizes.append(dataset.train_generators[1].size())
fit_history = model.fit_generator(dataset.train_generators[1],
dataset.train_generators[1].size(),
nb_epoch=1,
verbose=0)
epoch_history.on_epoch_end(epoch, last_logs(fit_history))
epoch_logs = average_logs(epoch_history, train_sizes, valid_sizes)
model_history.on_epoch_end(epoch, logs=epoch_logs)
saver.on_epoch_end(epoch, logs=epoch_logs)
early_stopping.on_epoch_end(epoch, epoch_logs)
csv_logger.on_epoch_end(epoch, epoch_logs)
tensorborad.on_epoch_end(epoch, epoch_logs)
epoch+= 1
if early_stopping.stopped_epoch > 0:
stop = True
early_stopping.on_train_end()
csv_logger.on_train_end()
tensorborad.on_train_end({})