Python tf.keras.callbacks.TensorBoard用法及代码示例

为 TensorBoard 启用可视化。

继承自：Callback

用法

tf.keras.callbacks.TensorBoard(
    log_dir='logs', histogram_freq=0, write_graph=True,
    write_images=False, write_steps_per_second=False, update_freq='epoch',
    profile_batch=0, embeddings_freq=0, embeddings_metadata=None, **kwargs
)

TensorBoard 是 TensorFlow 提供的可视化工具。

此回调记录 TensorBoard 的事件，包括：

• 指标汇总图
• 训练图可视化
• 激活直方图
• 采样分析

在 Model.evaluate 中使用时，除了纪元摘要之外，还会有一个摘要记录评估指标与编写的Model.optimizer.iterations。指标名称将以 evaluation 开头，其中 Model.optimizer.iterations 是可视化 TensorBoard 中的步骤。

如果你已经使用 pip 安装了 TensorFlow，你应该能够从命令行启动 TensorBoard：

tensorboard --logdir=path_to_your_logs

您可以在此处找到有关 TensorBoard 的更多信息。

例子：

基本用法：

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs")
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
# Then run the tensorboard command to view the visualizations.

子类模型中的自定义 batch-level 汇总：

class MyModel(tf.keras.Model):

  def build(self, _):
    self.dense = tf.keras.layers.Dense(10)

  def call(self, x):
    outputs = self.dense(x)
    tf.summary.histogram('outputs', outputs)
    return outputs

model = MyModel()
model.compile('sgd', 'mse')

# Make sure to set `update_freq=N` to log a batch-level summary every N batches.
# In addition to any `tf.summary` contained in `Model.call`, metrics added in
# `Model.compile` will be logged every N batches.
tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq=1)
model.fit(x_train, y_train, callbacks=[tb_callback])

函数 API 模型中的自定义 batch-level 摘要：

def my_summary(x):
  tf.summary.histogram('x', x)
  return x

inputs = tf.keras.Input(10)
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Lambda(my_summary)(x)
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', 'mse')

# Make sure to set `update_freq=N` to log a batch-level summary every N batches.
# In addition to any `tf.summary` contained in `Model.call`, metrics added in
# `Model.compile` will be logged every N batches.
tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq=1)
model.fit(x_train, y_train, callbacks=[tb_callback])

分析：

# Profile a single batch, e.g. the 5th batch.
tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir='./logs', profile_batch=5)
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])

# Profile a range of batches, e.g. from 10 to 20.
tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir='./logs', profile_batch=(10,20))
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])