本文整理汇总了Python中tensorflow.keras.callbacks.TensorBoard方法的典型用法代码示例。如果您正苦于以下问题:Python callbacks.TensorBoard方法的具体用法?Python callbacks.TensorBoard怎么用?Python callbacks.TensorBoard使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.keras.callbacks
的用法示例。
在下文中一共展示了callbacks.TensorBoard方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train
# 需要导入模块: from tensorflow.keras import callbacks [as 别名]
# 或者: from tensorflow.keras.callbacks import TensorBoard [as 别名]
def train():
depth = 6
filters = 25
block_filters = [filters] * depth
print(block_filters)
model = build_model(sequence_length=28 * 28,
channels=1,
num_classes=10,
filters=block_filters,
kernel_size=8)
model.compile(optimizer="Adam",
metrics=[metrics.SparseCategoricalAccuracy()],
loss=losses.SparseCategoricalCrossentropy())
print(model.summary())
#train_dataset, test_dataset = load_dataset()
"""
model.fit(train_dataset.batch(32),
validation_data=test_dataset.batch(32),
callbacks=[TensorBoard(str(Path("logs") / datetime.now().strftime("%Y-%m-%dT%H-%M_%S")))],
epochs=10)
"""
示例2: train
# 需要导入模块: from tensorflow.keras import callbacks [as 别名]
# 或者: from tensorflow.keras.callbacks import TensorBoard [as 别名]
def train(self, weights_only=True, call_back=False):
model = self._build_model()
if call_back:
early_stopping = EarlyStopping(monitor='val_loss', patience=30)
stamp = 'lstm_%d' % self.n_hidden
checkpoint_dir = os.path.join(
self.model_path, 'checkpoints/' + str(int(time.time())) + '/')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
bst_model_path = checkpoint_dir + stamp + '.h5'
if weights_only:
model_checkpoint = ModelCheckpoint(
bst_model_path, save_best_only=True, save_weights_only=True)
else:
model_checkpoint = ModelCheckpoint(
bst_model_path, save_best_only=True)
tensor_board = TensorBoard(
log_dir=checkpoint_dir + "logs/{}".format(time.time()))
callbacks = [early_stopping, model_checkpoint, tensor_board]
else:
callbacks = None
model_trained = model.fit([self.x_train['left'], self.x_train['right']],
self.y_train,
batch_size=self.batch_size,
epochs=self.epochs,
validation_data=([self.x_val['left'], self.x_val['right']], self.y_val),
verbose=1,
callbacks=callbacks)
if weights_only and not call_back:
model.save_weights(os.path.join(self.model_path, 'weights_only.h5'))
elif not weights_only and not call_back:
model.save(os.path.join(self.model_path, 'model.h5'))
self._save_config()
plot(model_trained)
return model
示例3: main
# 需要导入模块: from tensorflow.keras import callbacks [as 别名]
# 或者: from tensorflow.keras.callbacks import TensorBoard [as 别名]
def main(batch_size: int = 24,
epochs: int = 384,
train_path: str = 'train',
val_path: str = 'val',
weights=None,
workers: int = 8):
# We use an extra input during training to discount bounding box loss when a class is not present in an image.
discount_input = Input(shape=(7, 7), name='discount')
keras_model = MobileDetectNetModel.complete_model(extra_inputs=[discount_input])
keras_model.summary()
if weights is not None:
keras_model.load_weights(weights, by_name=True)
train_seq = MobileDetectNetSequence(train_path, stage="train", batch_size=batch_size)
val_seq = MobileDetectNetSequence(val_path, stage="val", batch_size=batch_size)
callbacks = []
def region_loss(classes):
def loss_fn(y_true, y_pred):
# Don't penalize bounding box errors when there is no object present
return 10 * (classes * K.abs(y_pred[:, :, :, 0] - y_true[:, :, :, 0]) +
classes * K.abs(y_pred[:, :, :, 1] - y_true[:, :, :, 1]) +
classes * K.abs(y_pred[:, :, :, 2] - y_true[:, :, :, 2]) +
classes * K.abs(y_pred[:, :, :, 3] - y_true[:, :, :, 3]))
return loss_fn
keras_model.compile(optimizer=Nadam(lr=0.001), loss=['mean_absolute_error',
region_loss(discount_input),
'binary_crossentropy'])
filepath = "weights-{epoch:02d}-{val_loss:.4f}-multi-gpu.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
callbacks.append(checkpoint)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=0.00001, verbose=1)
callbacks.append(reduce_lr)
try:
os.mkdir('logs')
except FileExistsError:
pass
tensorboard = TensorBoard(log_dir='logs/%s' % time.strftime("%Y-%m-%d_%H-%M-%S"))
callbacks.append(tensorboard)
keras_model.fit_generator(train_seq,
validation_data=val_seq,
epochs=epochs,
steps_per_epoch=np.ceil(len(train_seq) / batch_size),
validation_steps=np.ceil(len(val_seq) / batch_size),
callbacks=callbacks,
use_multiprocessing=True,
workers=workers,
shuffle=True)