def fit(self, X_train, y_train, X_val, y_val):
if X_train.ndim != 4:
raise Exception(
'ValueError: `X_train` is incompatible: expected ndim=4, found ndim='
+ str(X_train.ndim))
elif X_val.ndim != 4:
raise Exception(
'ValueError: `X_val` is incompatible: expected ndim=4, found ndim='
+ str(X_val.ndim))
self.input_shape = X_train.shape[1:]
if self.data_format == 'channels_first':
self.Chans = self.input_shape[1]
self.Samples = self.input_shape[2]
else:
self.Chans = self.input_shape[0]
self.Samples = self.input_shape[1]
csv_logger = CSVLogger(self.csv_dir)
time_callback = TimeHistory(self.time_log)
checkpointer = ModelCheckpoint(monitor=self.monitor,
filepath=self.weights_dir,
verbose=self.verbose,
save_best_only=self.save_best_only,
save_weight_only=self.save_weight_only)
reduce_lr = ReduceLROnPlateau(monitor=self.monitor,
patience=self.patience,
factor=self.factor,
mode=self.mode,
verbose=self.verbose,
min_lr=self.min_lr)
es = EarlyStopping(monitor=self.monitor,
mode=self.mode,
verbose=self.verbose,
patience=self.es_patience)
model = self.build()
model.compile(optimizer=self.optimizer,
loss=self.loss,
metrics=self.metrics)
model.summary()
print("The first kernel size is (1, {})".format(self.kernLength))
if self.class_balancing: # compute_class_weight if class_balancing is True
self.class_weight = compute_class_weight(y_train)
else:
self.class_weight = None
model.fit(
X_train,
y_train,
batch_size=self.batch_size,
shuffle=self.shuffle,
epochs=self.epochs,
validation_data=(X_val, y_val),
class_weight=self.class_weight,
callbacks=[checkpointer, csv_logger, reduce_lr, es, time_callback])
def fit(self, X_train, y_train, X_val, y_val):
if X_train.ndim != 5:
raise Exception(
'ValueError: `X_train` is incompatible: expected ndim=5, found ndim='
+ str(X_train.ndim))
elif X_val.ndim != 5:
raise Exception(
'ValueError: `X_val` is incompatible: expected ndim=5, found ndim='
+ str(X_val.ndim))
self.input_shape = (X_train.shape[2], X_train.shape[3],
X_train.shape[4])
self.n_subbands = X_train.shape[1]
csv_logger = CSVLogger(self.csv_dir)
time_callback = TimeHistory(self.time_log)
checkpointer = ModelCheckpoint(monitor=self.monitor,
filepath=self.weights_dir,
verbose=self.verbose,
save_best_only=self.save_best_only,
save_weight_only=self.save_weight_only)
reduce_lr = ReduceLROnPlateau(monitor=self.monitor,
patience=self.patience,
factor=self.factor,
mode=self.mode,
verbose=self.verbose,
min_lr=self.min_lr)
es = EarlyStopping(monitor=self.monitor,
mode=self.mode,
verbose=self.verbose,
patience=self.es_patience)
model = self.build()
model.compile(optimizer=self.optimizer,
loss=self.loss,
metrics=self.metrics)
model.summary()
if self.class_balancing: # compute_class_weight if class_balancing is True
self.class_weight = compute_class_weight(y_train)
else:
self.class_weight = None
model.fit(
[X_train[:, i, :, :, :] for i in range(self.n_subbands)],
y_train,
batch_size=self.batch_size,
shuffle=self.shuffle,
class_weight=self.class_weight,
epochs=self.epochs,
validation_data=([
X_val[:, i, :, :, :] for i in range(self.n_subbands)
], y_val),
callbacks=[checkpointer, csv_logger, reduce_lr, es, time_callback])
def fit(self, X_train, y_train, X_val, y_val):
if X_train.ndim != 4:
raise Exception(
'ValueError: `X_train` is incompatible: expected ndim=4, found ndim={}'
.format(X_train.ndim))
elif X_val.ndim != 4:
raise Exception(
'ValueError: `X_val` is incompatible: expected ndim=4, found ndim={}'
.format(X_val.ndim))
csv_logger = CSVLogger(self.csv_dir)
time_callback = TimeHistory(self.time_log)
checkpointer = ModelCheckpoint(monitor=self.monitor,
filepath=self.weights_dir,
verbose=self.verbose,
save_best_only=self.save_best_only,
save_weight_only=self.save_weight_only)
reduce_lr = ReduceLROnPlateau(monitor=self.monitor,
patience=self.patience,
factor=self.factor,
mode=self.mode,
verbose=self.verbose,
min_lr=self.min_lr)
es = EarlyStopping(monitor=self.monitor,
mode=self.mode,
verbose=self.verbose,
patience=self.es_patience)
model = self.build()
model.summary()
if self.class_balancing: # compute_class_weight if class_balancing is True
class_weight = compute_class_weight(y_train)
self.loss[-1] = SparseCategoricalCrossentropy(
class_weight=class_weight)
model.compile(optimizer=self.optimizer,
loss=self.loss,
metrics=self.metrics,
loss_weights=self.loss_weights)
model.fit(
x=X_train,
y=[y_train, y_train],
batch_size=self.batch_size,
shuffle=self.shuffle,
epochs=self.epochs,
validation_data=(X_val, [y_val, y_val]),
callbacks=[checkpointer, csv_logger, reduce_lr, es, time_callback])