恢复训练tf.keras Tensorboard

Question

当我继续训练我的模型并在tensorboard上可视化进展时，我遇到了一些问题。

我的问题是如何在不指定任何纪元的情况下从同一步骤恢复训练？如果可能的话，只需加载保存的模型，它就可以从保存的优化器中读取global_step并从那里继续训练。

我在下面提供了一些代码来重现类似的错误。

import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.models import load_model

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, callbacks=[Tensorboard()])
model.save('./final_model.h5', include_optimizer=True)

del model

model = load_model('./final_model.h5')
model.fit(x_train, y_train, epochs=10, callbacks=[Tensorboard()])

您可以使用以下命令运行tensorboard：

tensorboard --logdir ./logs

Answer 1

您可以将函数initial_epoch中的参数model.fit()设置为您希望训练开始的时期编号。考虑到模型训练直到达到指数epochs的时代（而不是由epochs给出的迭代次数）。在你的例子中，如果你想要训练10个以上的时代，它应该是：

model.fit(x_train, y_train, initial_epoch=9, epochs=19, callbacks=[Tensorboard()])

它将允许您以正确的方式在Tensorboard上可视化您的绘图。有关这些参数的更多信息可以在docs中找到。

Answer 2

这很简单。在训练模型时创建检查点，然后使用这些检查点从您离开的位置恢复训练。

import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.models import load_model

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, callbacks=[Tensorboard()])
model.save('./final_model.h5', include_optimizer=True)

model = load_model('./final_model.h5')

callbacks = list()

tensorboard = Tensorboard()
callbacks.append(tensorboard)

file_path = "model-{epoch:02d}-{loss:.4f}.hdf5"

# now here you can create checkpoints and save according to your need
# here period is the no of epochs after which to save the model every time during training
# another option is save_weights_only, for your case it should be false
checkpoints = ModelCheckpoint(file_path, monitor='loss', verbose=1, period=1, save_weights_only=False)
callbacks.append(checkpoints)

model.fit(x_train, y_train, epochs=10, callbacks=callbacks)

在此之后，只需加载您想要再次恢复训练的检查点

model = load_model(checkpoint_of_choice)
model.fit(x_train, y_train, epochs=10, callbacks=callbacks)

你完成了。

如果您对此有更多疑问，请与我们联系。

Answer 3

以下是有人需要的示例代码。它实现了Abhinav Anand提出的想法：

mca = ModelCheckpoint(join(dir, 'model_{epoch:03d}.h5'),
                      monitor = 'loss',
                      save_best_only = False)
tb = TensorBoard(log_dir = join(dir, 'logs'),
                 write_graph = True,
                 write_images = True)
files = sorted(glob(join(fold_dir, 'model_???.h5')))
if files:
    model_file = files[-1]
    initial_epoch = int(model_file[-6:-3])
    print('Resuming using saved model %s.' % model_file)
    model = load_model(model_file)
else:
    model = nn.model()
    initial_epoch = 0
model.fit(x_train,
          y_train,
          epochs = 100,
          initial_epoch = initial_epoch,
          callbacks = [mca, tb])

用您自己的函数替换nn.model()来定义模型。