利用Tensorboard实时监控训练，可视化模型体系结构

Question

我正在学习使用Tensorboard -- Tensorflow 2.0。

特别是，我希望实时监控学习曲线，并能直观地检查和交流我的模型的体系结构。

下面我将为一个可复制的示例提供代码。

我有三个问题：

，

1. ，虽然我在训练结束后得到了学习曲线，但我不知道如何在实时
2. 中监测它们。从Tensorboard获得的学习曲线与history.history的图不一致。实际上是很奇怪的，很难解释它的反转。

，

1. ，I不能理解这个图。我已经训练了一个连续的模型，其中有5个密集层和辍学层。Tensorboard向我展示的是其中更多的元素。--

我的代码如下：

from keras.datasets import boston_housing

(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()

inputs = Input(shape = (train_data.shape[1], ))
x1 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(inputs)
x1a = Dropout(0.5)(x1)
x2 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x1a)
x2a = Dropout(0.5)(x2)
x3 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x2a)
x3a = Dropout(0.5)(x3)
x4 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x3a)
x4a = Dropout(0.5)(x4)
x5 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x4a)
predictions = Dense(1)(x5)
model = Model(inputs = inputs, outputs = predictions)

model.compile(optimizer = 'Adam', loss = 'mse')

logdir="logs\\fit\\" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)

history = model.fit(train_data, train_targets,
          batch_size= 32,
          epochs= 20,
          validation_data=(test_data, test_targets),
          shuffle=True,
          callbacks=[tensorboard_callback ])

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])

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plt.plot(history.history['val_loss'])

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Answer 1

我认为您可以做的是在调用模型上的TensorBoard之前启动.fit()。如果您正在使用IPython (木星或Colab)，并且已经安装了TensorBoard，下面是您可以修改代码的方法；

from keras.datasets import boston_housing

(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()

inputs = Input(shape = (train_data.shape[1], ))
x1 = Dense(100, kernel_initializer = 'he_normal', activation = 'relu')(inputs)
x1a = Dropout(0.5)(x1)
x2 = Dense(100, kernel_initializer = 'he_normal', activation = 'relu')(x1a)
x2a = Dropout(0.5)(x2)
x3 = Dense(100, kernel_initializer = 'he_normal', activation = 'relu')(x2a)
x3a = Dropout(0.5)(x3)
x4 = Dense(100, kernel_initializer = 'he_normal', activation = 'relu')(x3a)
x4a = Dropout(0.5)(x4)
x5 = Dense(100, kernel_initializer = 'he_normal', activation = 'relu')(x4a)
predictions = Dense(1)(x5)
model = Model(inputs = inputs, outputs = predictions)

model.compile(optimizer = 'Adam', loss = 'mse')

logdir="logs\\fit\\" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)

在另一个牢房里，你可以运行；

# Magic func to use TensorBoard directly in IPython
%load_ext tensorboard

通过在另一个单元中运行此操作启动TensorBoard；

# Launch TensorBoard with objects in the log directory
# This should launch tensorboard in your browser, but you may not see your metadata.
%tensorboard --logdir=logdir 

最后，您可以在另一个单元中调用模型上的.fit()；

history = model.fit(train_data, train_targets,
          batch_size= 32,
          epochs= 20,
          validation_data=(test_data, test_targets),
          shuffle=True,
          callbacks=[tensorboard_callback ])

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])

如果您没有使用IPython，那么您可能只需要在培训您的模型期间或之前启动TensorBoard，以便实时监视它。