def train_model(model: Model,
epochs: int,
batch_size: int,
use_wandb: bool = False) -> Model:
"""Train model."""
callbacks = []
if EARLY_STOPPING:
early_stopping = EarlyStopping(monitor="val_loss",
min_delta=0.01,
patience=3,
verbose=1,
mode="auto")
callbacks.append(early_stopping)
model.network.summary()
t = time()
_history = model.fit(dataset=dataset,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks)
print("Training took {:2f} s".format(time() - t))
return model
def train_model(model: Model,
dataset: Dataset,
epochs: int,
batch_size: int,
learning_rate: float,
gpu_ind: Optional[int] = None,
use_wandb=False) -> Model:
callbacks = []
callbacks.append(Metrics())
if EARLY_STOPPING:
early_stopping = EarlyStopping(monitor='val_loss',
min_delta=0.01,
patience=3,
verbose=1,
mode='auto')
callbacks.append(early_stopping)
if GPU_UTIL_SAMPLER and gpu_ind is not None:
gpu_utilization = GPUUtilizationSampler(gpu_ind)
callbacks.append(gpu_utilization)
if use_wandb:
wandb = WandbCallback()
callbacks.append(wandb)
model.network.summary()
t = time()
history = model.fit(dataset, batch_size, epochs, learning_rate, callbacks)
print('Training took {:2f} s'.format(time() - t))
if GPU_UTIL_SAMPLER and gpu_ind is not None:
gpu_utilizations = gpu_utilization.samples
print(
f'GPU utilization: {round(np.mean(gpu_utilizations), 2)} +- {round(np.std(gpu_utilizations), 2)}'
)
return model