def train_model_2(config: ConfigParser, model: tf.keras.Sequential, data: Data,
save_path: Path, checkpoint_path: Path) -> None:
version = config['Model']['version']
callbacks = [
tfa.callbacks.AverageModelCheckpoint(filepath=str(checkpoint_path) +
'/cp-{epoch:04d}.ckpt',
update_weights=True),
tf.keras.callbacks.TensorBoard(log_dir=f'logs/{version}_model_2',
profile_batch='100, 110',
histogram_freq=1,
update_freq='batch')
]
optimizer = tf.keras.optimizers.SGD(
learning_rate=float(config['Model']['learning_rate']))
# 35 below obtained by inspecting the epoch at which convergence occurred on validation set with TensorBoard.
optimizer = tfa.optimizers.SWA(optimizer,
start_averaging=35,
average_period=int(
config['Model']['n_models']))
model.compile(
optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['accuracy'])
model.fit(data.training_dataset,
epochs=1000,
validation_data=data.validation_dataset,
callbacks=callbacks)
# Save the model
model.save(save_path)
# Remove the model from memory, since OOM might occur.
del model
def train_model(model: tf.keras.Sequential):
"""
Args:
model: current model
Returns: model trained
"""
# Training
model.fit(x=CMODEL.input_batch,
y=CMODEL.label_batch,
batch_size=CMODEL.batch_size,
epochs=2)
return model
def fit_model(
tf_model: tf.keras.Sequential,
training_data,
validation_data,
callbacks: None,
settings=None,
):
# class_weight_val = np.ones(training_data[1].shape[-1])
y = np.argmax(training_data[1], axis=-1)
class_weight_val = class_weight.compute_class_weight(
class_weight="balanced", classes=np.unique(y), y=y)
class_weights = dict(zip(np.unique(y), class_weight_val))
print("Class Weights: ", end="")
print(class_weights)
rtn_history = tf_model.fit(
x=training_data[0],
y=training_data[1],
validation_data=validation_data,
callbacks=callbacks,
class_weight=class_weights,
**settings,
)
return rtn_history
def train_epoch_plain(net: tf.keras.Sequential, x, y):
net.fit(x, y, epochs=1, batch_size=BATCH_SIZE, verbose=1)