def fit(self, train_x, train_y, validation_data_fit, train_loop_num,
**kwargs):
val_x, val_y = validation_data_fit
epochs = 5
patience = 2
batch_size = 32
# over_batch = len(train_x) % batch_size
# append_idx = np.random.choice(np.arange(len(train_x)), size=batch_size-over_batch, replace=False)
# train_x = np.concatenate([train_x, train_x[append_idx]], axis=0)
# train_y = np.concatenate([train_y, train_y[append_idx]], axis=0)
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
patience=patience)
]
self._model.fit(
train_x,
ohe2cat(train_y),
epochs=epochs,
callbacks=callbacks,
validation_data=(val_x, ohe2cat(val_y)),
verbose=1, # Logs once per epoch.
batch_size=batch_size,
shuffle=True)
def fit(self, train_x, train_y, validation_data_fit,
train_loop_num, **kwargs):
val_x, val_y = validation_data_fit
epochs = 10
patience = 2
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor='val_loss',
patience=patience)]
self._model.fit(train_x, ohe2cat(train_y),
epochs=epochs,
callbacks=callbacks,
validation_data=(val_x, ohe2cat(val_y)),
verbose=1, # Logs once per epoch.
batch_size=32,
shuffle=True)
def fit(self, train_x, train_y, validation_data_fit, train_loop_num,
**kwargs):
val_x, val_y = validation_data_fit
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
]
epochs = 10 if train_loop_num == 1 else 30
log('train_x: ' + str(train_x.shape) + '; train_y: ' +
str(train_y.shape) + '')
self._model.fit(
train_x,
ohe2cat(train_y),
epochs=epochs,
callbacks=callbacks,
validation_data=(val_x, ohe2cat(val_y)),
verbose=1, # Logs once per epoch.
batch_size=32,
shuffle=True)
def fit(self, train_x, train_y, validation_data_fit, round_num, **kwargs):
val_x, val_y = validation_data_fit
# if train_loop_num == 1:
# patience = 2
# epochs = 3
# elif train_loop_num == 2:
# patience = 3
# epochs = 10
# elif train_loop_num < 10:
# patience = 4
# epochs = 16
# elif train_loop_num < 15:
# patience = 4
# epochs = 24
# else:
# patience = 8
# epochs = 32
patience = 2
# epochs = self.epoch_cnt + 3
epochs = 10
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor='val_loss',
patience=patience)]
self._model.fit(train_x, ohe2cat(train_y),
epochs=epochs,
callbacks=callbacks,
validation_data=(val_x, ohe2cat(val_y)),
# validation_split=0.2,
verbose=VERBOSE, # Logs once per epoch.
batch_size=32,
shuffle=True,
# initial_epoch=self.epoch_cnt,
# use_multiprocessing=True
)
self.epoch_cnt += 3
def fit(self, train_x, train_y, validation_data_fit, train_loop_num,
**kwargs):
val_x, val_y = validation_data_fit
# if train_loop_num == 1:
# patience = 2
# epochs = 8
# elif train_loop_num == 2:
# patience = 3
# epochs = 10
# elif train_loop_num < 10:
# patience = 4
# epochs = 16
# elif train_loop_num < 15:
# patience = 4
# epochs = 24
# else:
# patience = 8
# epochs = 32
epochs = 3
patience = 2
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss',
patience=patience)
]
self._model.fit(
train_x,
ohe2cat(train_y),
epochs=epochs,
callbacks=callbacks,
validation_data=(val_x, ohe2cat(val_y)),
verbose=1, # Logs once per epoch.
batch_size=32,
shuffle=True)
def score(self, x_train, y_train):
return self._model.score(x_train, ohe2cat(y_train))
def fit(self, x_train, y_train, *args, **kwargs):
self._model.fit(x_train, ohe2cat(y_train))