如何查看Google Colab生成的train_on_batch张量板日志文件？

Question

我知道如何在本地计算机上查看张量板图，同时我的神经网络使用以下代码在本地Jupyter Notebook中使用代码进行训练。当我使用Google Colab训练神经网络时，我需要做些什么？使用train_on_batch时，我看不到任何在线教程/示例。

定义完模型后（convnet）...

convnet.compile(loss='categorical_crossentropy',                                      
                optimizer=tf.keras.optimizers.Adam(0.001),
                metrics=['accuracy']
               )

# create tensorboard graph data for the model
tb = tf.keras.callbacks.TensorBoard(log_dir='Logs/Exp_15', 
                                    histogram_freq=0, 
                                    batch_size=batch_size, 
                                    write_graph=True, 
                                    write_grads=False)
tb.set_model(convnet)

num_epochs = 3
batches_processed_counter = 0

for epoch in range(num_epochs):

    for batch in range(int(train_img.samples/batch_size)): 
        batches_processed_counter = batches_processed_counter  + 1

        # get next batch of images & labels
        X_imgs, X_labels = next(train_img) 

        #train model, get cross entropy & accuracy for batch
        train_CE, train_acc = convnet.train_on_batch(X_imgs, X_labels) 

        # validation images - just predict
        X_imgs_val, X_labels_val = next(val_img)
        val_CE, val_acc = convnet.test_on_batch(X_imgs_val, X_labels_val) 

        # create tensorboard graph info for the cross entropy loss and training accuracies
        # for every batch in every epoch (so if 5 epochs and 10 batches there should be 50 accuracies )
        tb.on_epoch_end(batches_processed_counter, {'train_loss': train_CE, 'train_acc': train_acc})

        # create tensorboard graph info for the cross entropy loss and VALIDATION accuracies
        # for every batch in every epoch (so if 5 epochs and 10 batches there should be 50 accuracies )
        tb.on_epoch_end(batches_processed_counter, {'val_loss': val_CE, 'val_acc': val_acc})

        print('epoch', epoch, 'batch', batch, 'train_CE:', train_CE, 'train_acc:', train_acc)
        print('epoch', epoch, 'batch', batch, 'val_CE:', val_CE, 'val_acc:', val_acc)

tb.on_train_end(None)

我可以看到该日志文件已在Google Colab运行时中成功生成。如何在Tensorboard中查看此内容？我已经看到了描述将日志文件下载到本地计算机并在tensorboard中本地查看的解决方案，但这并没有显示任何内容。我的代码中缺少什么让它在本地的tensorboard上工作吗？和/或其他解决方案，可以在Google Colab的Tensorboard中查看日志数据？

如果它对于解决方案的细节很重要，那么我使用的是Mac。另外，我在网上看到的教程显示了在使用fit代码时如何将Tensorboard与Google Colab结合使用，但看不到如何修改不使用fit而是train_on_batch的代码。

Answer 1

!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
!unzip ngrok-stable-linux-amd64.zip

get_ipython().system_raw('tensorboard --logdir /content/trainingdata/objectdetection/ckpt_output/trainingImatges/ --host 0.0.0.0 --port 6006 &')

get_ipython().system_raw('./ngrok http 6006 &')

! curl -s http://localhost:4040/api/tunnels | python3 -c \
 "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])"

这将为您创建的日志文件提供一个张量板。这将为colab上的张量板创建一个隧道，并使其可通过ngrok提供的公共URL进行访问。运行最终命令时，将打印公共URL。它与TF1.13一起使用。我想您也可以对TF2使用相同的方法。