def fit_generator(self, generator, epochs=1,
validation_data=None,
callbacks=None,
verbose=True):
method = self._model.optimizer.method
x0 = self._collect_weights()
history = History()
_callbacks = [BaseLogger(stateful_metrics=self._model.metrics_names)]
_callbacks += (callbacks or []) + [history]
callback_list = CallbackList(_callbacks)
callback_list.set_model(self._model)
callback_list.set_params({
'epochs': epochs,
'verbose': False,
'metrics': list(self._model.metrics_names),
})
state = {
'epoch': 0,
'verbose': verbose,
'callbacks': callback_list,
'in_epoch': False,
'epoch_logs': {},
}
min_options = {
'maxiter': epochs,
'maxfun': epochs*10,
'ftol': 1e-10,
'gtol': 1e-10,
'eps': 1e-8,
}
val_generator = None
if validation_data is not None:
if isinstance(validation_data, keras.utils.Sequence):
val_generator = validation_data
elif isinstance(validation_data, tuple) and len(validation_data) == 2:
val_generator = GeneratorWrapper(*validation_data)
def on_iteration_end(xk):
cb = state['callbacks']
if val_generator is not None:
self._validate(xk, val_generator, state)
cb.on_epoch_end(state['epoch'], state['epoch_logs'])
# if state['verbose']:
# epoch_logs = state['epoch_logs']
# print('epoch: ', state['epoch'],
# ', '.join([' {0}: {1:.3e}'.format(k, v) for k, v in epoch_logs.items()]))
state['epoch'] += 1
state['in_epoch'] = False
state['epoch_logs'] = {}
callback_list.on_train_begin()
result = minimize(
self._fun_generator, x0, method=method, jac=True, options=min_options,
callback=on_iteration_end, args=(generator, state))
self._update_weights(result['x'])
callback_list.on_train_end()
return history
def train(self, epochs):
"""
functional API with model.fit doesn't support sparse tensors with the current implementation =>
we write the training loop ourselves
"""
callbacks = CallbackList([
EvaluateCallback(self.valid_generator, prepend_str='val_'),
TensorBoard(self.log_dir, profile_batch=0),
ModelCheckpoint(self.model_save_path / 'best.h5py',
monitor='val_kendal',
save_best_only=True,
verbose=1,
mode='max'),
EarlyStopping(monitor='val_kendal',
patience=5,
mode='max',
restore_best_weights=True),
ReduceLROnPlateau(
monitor='val_kendal', patience=2, factor=0.5, mode='max'),
],
add_history=True,
add_progbar=True,
verbose=1,
model=self.model,
epochs=epochs,
steps=len(self.train_generator))
callbacks.on_train_begin()
for epoch in range(epochs):
if epoch % 5 == 0:
self.train_generator.gen_new_graphs()
self.valid_generator.gen_new_graphs()
callbacks.on_epoch_begin(epoch)
[c.on_train_begin() for c in callbacks]
for batch, (x, y) in enumerate(self.train_generator):
callbacks.on_train_batch_begin(batch)
logs = self.model.train_on_batch(x, y, return_dict=True)
callbacks.on_train_batch_end(batch, logs)
epoch_logs = copy.copy(logs)
callbacks.on_epoch_end(epoch, logs=epoch_logs)
pd.DataFrame(self.model.history.history).to_csv(self.log_dir /
'history.csv',
index=False)
if self.model.stop_training:
break
callbacks.on_train_end(copy.copy(epoch_logs))
print(self.model.history.history)
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
initial_epoch=0,
validation_split=0.,
validation_data=None,
shuffle=True,
callbacks=None):
"""
:param x: Numpy array of training data (if the model has a single input), or list of Numpy arrays (if the model has multiple inputs).If input layers in the model are named, you can also pass a
dictionary mapping input names to Numpy arrays.
:param y: Numpy array of target (label) data (if the model has a single output), or list of Numpy arrays (if the model has multiple outputs).
:param batch_size: Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 256.
:param epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided. Note that in conjunction with `initial_epoch`, `epochs` is to be understood as "final epoch". The model is not trained for a number of iterations given by `epochs`, but merely until the epoch of index `epochs` is reached.
:param verbose: Integer. 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
:param initial_epoch: Integer. Epoch at which to start training (useful for resuming a previous training run).
:param validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the `x` and `y` data provided, before shuffling.
:param validation_data: tuple `(x_val, y_val)` or tuple `(x_val, y_val, val_sample_weights)` on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. `validation_data` will override `validation_split`.
:param shuffle: Boolean. Whether to shuffle the order of the batches at the beginning of each epoch.
:param callbacks: List of `deepctr_torch.callbacks.Callback` instances. List of callbacks to apply during training and validation (if ). See [callbacks](https://tensorflow.google.cn/api_docs/python/tf/keras/callbacks). Now available: `EarlyStopping` , `ModelCheckpoint`
:return: A `History` object. Its `History.history` attribute is a record of training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable).
"""
if isinstance(x, dict):
x = [x[feature] for feature in self.feature_index]
do_validation = False
if validation_data:
do_validation = True
if len(validation_data) == 2:
val_x, val_y = validation_data
val_sample_weight = None
elif len(validation_data) == 3:
val_x, val_y, val_sample_weight = validation_data # pylint: disable=unpacking-non-sequence
else:
raise ValueError(
'When passing a `validation_data` argument, '
'it must contain either 2 items (x_val, y_val), '
'or 3 items (x_val, y_val, val_sample_weights), '
'or alternatively it could be a dataset or a '
'dataset or a dataset iterator. '
'However we received `validation_data=%s`' %
validation_data)
if isinstance(val_x, dict):
val_x = [val_x[feature] for feature in self.feature_index]
elif validation_split and 0. < validation_split < 1.:
do_validation = True
if hasattr(x[0], 'shape'):
split_at = int(x[0].shape[0] * (1. - validation_split))
else:
split_at = int(len(x[0]) * (1. - validation_split))
x, val_x = (slice_arrays(x, 0,
split_at), slice_arrays(x, split_at))
y, val_y = (slice_arrays(y, 0,
split_at), slice_arrays(y, split_at))
else:
val_x = []
val_y = []
for i in range(len(x)):
if len(x[i].shape) == 1:
x[i] = np.expand_dims(x[i], axis=1)
train_tensor_data = Data.TensorDataset(
torch.from_numpy(np.concatenate(x, axis=-1)), torch.from_numpy(y))
if batch_size is None:
batch_size = 256
model = self.train()
loss_func = self.loss_func
optim = self.optim
if self.gpus:
print('parallel running on these gpus:', self.gpus)
model = torch.nn.DataParallel(model, device_ids=self.gpus)
batch_size *= len(
self.gpus) # input `batch_size` is batch_size per gpu
else:
print(self.device)
train_loader = DataLoader(dataset=train_tensor_data,
shuffle=shuffle,
batch_size=batch_size)
sample_num = len(train_tensor_data)
steps_per_epoch = (sample_num - 1) // batch_size + 1
# configure callbacks
callbacks = (callbacks or []) + [self.history] # add history callback
callbacks = CallbackList(callbacks)
callbacks.on_train_begin()
callbacks.set_model(self)
if not hasattr(callbacks, 'model'):
callbacks.__setattr__('model', self)
callbacks.model.stop_training = False
# Train
print(
"Train on {0} samples, validate on {1} samples, {2} steps per epoch"
.format(len(train_tensor_data), len(val_y), steps_per_epoch))
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
start_time = time.time()
loss_epoch = 0
total_loss_epoch = 0
train_result = {}
try:
with tqdm(enumerate(train_loader), disable=verbose != 1) as t:
for _, (x_train, y_train) in t:
x = x_train.to(self.device).float()
y = y_train.to(self.device).float()
y_pred = model(x).squeeze()
optim.zero_grad()
loss = loss_func(y_pred, y.squeeze(), reduction='sum')
reg_loss = self.get_regularization_loss()
total_loss = loss + reg_loss + self.aux_loss
loss_epoch += loss.item()
total_loss_epoch += total_loss.item()
total_loss.backward()
optim.step()
if verbose > 0:
for name, metric_fun in self.metrics.items():
if name not in train_result:
train_result[name] = []
train_result[name].append(
metric_fun(
y.cpu().data.numpy(),
y_pred.cpu().data.numpy().astype(
"float64")))
except KeyboardInterrupt:
t.close()
raise
t.close()
# Add epoch_logs
epoch_logs["loss"] = total_loss_epoch / sample_num
for name, result in train_result.items():
epoch_logs[name] = np.sum(result) / steps_per_epoch
if do_validation:
eval_result = self.evaluate(val_x, val_y, batch_size)
for name, result in eval_result.items():
epoch_logs["val_" + name] = result
# verbose
if verbose > 0:
epoch_time = int(time.time() - start_time)
print('Epoch {0}/{1}'.format(epoch + 1, epochs))
eval_str = "{0}s - loss: {1: .4f}".format(
epoch_time, epoch_logs["loss"])
for name in self.metrics:
eval_str += " - " + name + \
": {0: .4f}".format(epoch_logs[name])
if do_validation:
for name in self.metrics:
eval_str += " - " + "val_" + name + \
": {0: .4f}".format(epoch_logs["val_" + name])
print(eval_str)
callbacks.on_epoch_end(epoch, epoch_logs)
if self.stop_training:
break
callbacks.on_train_end()
return self.history
def fit_dataset(self,
dataset,
steps_per_epoch=None,
batch_size=32,
epochs=1,
verbose=1,
callbacks=None,
on_sample=None,
on_scores=None):
"""Train the model on the given dataset for a given number of epochs.
Arguments
---------
dataset: Instance of `BaseDataset` that provides the data
to train on.
steps_per_epoch: int or None, number of gradient updates before
considering an epoch has passed. If None it is set
to be `len(dataset.train_data) / batch_size`.
batch_size: int, number of samples per gradient update
epochs: int, number of times to iterate `steps_per_epoch` times
verbose: {0, >0}, whether to employ the progress bar Keras
callback or not
callbacks: list of Keras callbacks to be called during training
on_sample: callable that accepts the sampler, idxs, w, scores
on_scores: callable that accepts the sampler and scores
"""
try:
if len(dataset.train_data) < batch_size:
raise ValueError(("The model cannot be trained with "
"batch_size > training set"))
except RuntimeError as e:
assert "no size" in str(e)
# Set steps_per_epoch properly
if steps_per_epoch is None:
steps_per_epoch = len(dataset.train_data) // batch_size
# Create the callbacks list
self.history = History()
callbacks = [BaseLogger()] + (callbacks or []) + [self.history]
if verbose > 0:
callbacks += [ProgbarLogger(count_mode="steps")]
callbacks = CallbackList(callbacks)
callbacks.set_model(self.original_model)
callbacks.set_params({
"epochs":
epochs,
"steps":
steps_per_epoch,
"verbose":
verbose,
"do_validation":
len(dataset.test_data) > 0,
"metrics":
self.metrics_names + ["val_" + n for n in self.metrics_names]
})
# Create the sampler
sampler = self.sampler(dataset, batch_size, steps_per_epoch, epochs)
# Start the training loop
epoch = 0
self.original_model.stop_training = False
callbacks.on_train_begin()
while epoch < epochs:
callbacks.on_epoch_begin(epoch)
for step in range(steps_per_epoch):
batch_logs = {"batch": step, "size": batch_size}
callbacks.on_batch_begin(step, batch_logs)
# Importance sampling is done here
idxs, (x, y), w = sampler.sample(batch_size)
# Train on the sampled data
loss, metrics, scores = self.model.train_batch(x, y, w)
# Update the sampler
sampler.update(idxs, scores)
values = map(lambda x: x.mean(), [loss] + metrics)
for l, o in zip(self.metrics_names, values):
batch_logs[l] = o
callbacks.on_batch_end(step, batch_logs)
if on_scores is not None and hasattr(self, "_latest_scores"):
on_scores(sampler, self._latest_scores)
if on_sample is not None:
on_sample(sampler, self._latest_sample_event["idxs"],
self._latest_sample_event["w"],
self._latest_sample_event["predicted_scores"])
if self.original_model.stop_training:
break
# Evaluate now that an epoch passed
epoch_logs = {}
if len(dataset.test_data) > 0:
val = self.model.evaluate(*dataset.test_data[:],
batch_size=batch_size)
epoch_logs = {
"val_" + l: o
for l, o in zip(self.metrics_names, val)
}
callbacks.on_epoch_end(epoch, epoch_logs)
if self.original_model.stop_training:
break
epoch += 1
callbacks.on_train_end()
return self.history
def fit_generator(self,
generator,
n_steps_per_epoch,
n_epochs=1,
validation_data=None,
n_validation_steps=None):
"""Train the network on batches of data generated from `generator`
:param generator: a generator yielding batches indefinitely, where each
batch is a tuple of (inputs, targets)
:type generator: generator
:param n_steps_per_epoch: number of batches to train on in one epoch
:type n_steps_per_epoch: int
:param n_epochs: number of epochs to train the model
:type n_epochs: int
:param validation_data: generator yielding batches to evaluate the loss
on at the end of each epoch, where each batch is a tuple of (inputs,
targets)
:type validation_data: generator
:param n_validation_steps: number of batches to evaluate on from
`validation_data`
:raises RuntimeError: if only one of `validation_data` and
`n_validation_steps` are passed in
"""
default_callbacks = self._default_callbacks()
callbacks = CallbackList(default_callbacks)
self._assert_compiled()
invalid_inputs = (
(validation_data is not None and n_validation_steps is None)
or (n_validation_steps is not None and validation_data is None))
if invalid_inputs:
msg = ('`validation_data` and `n_validation_steps` must both be '
'passed, or neither.')
raise RuntimeError(msg)
if self.device:
self.network.to(self.device)
callbacks.set_params({
'epochs': n_epochs,
'metrics': ['loss', 'val_loss'],
'steps': n_steps_per_epoch,
'verbose': True
})
callbacks.set_model(self)
callbacks.on_train_begin()
for idx_epoch in range(n_epochs):
if self.stop_training:
break
epoch_logs = {}
callbacks.on_epoch_begin(idx_epoch)
for idx_batch in range(n_steps_per_epoch):
batch_logs = {'batch': idx_batch, 'size': 1}
callbacks.on_batch_begin(idx_batch, batch_logs)
inputs, targets = next(generator)
loss = self.train_on_batch(inputs, targets)
batch_logs['loss'] = loss
callbacks.on_batch_end(idx_batch, batch_logs)
if self.stop_training:
break
if validation_data:
val_loss = self.evaluate_generator(validation_data,
n_validation_steps)
epoch_logs['val_loss'] = val_loss
callbacks.on_epoch_end(idx_epoch, epoch_logs)
callbacks.on_train_end()