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2020-12-29 18:56:14

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wizardforcel
2020-12-29 18:56:15 +08:00
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@@ -482,7 +482,7 @@ Write a function to plot some images during training.
* The generator will then translate the input image into the output.
* Last step is to plot the predictions and **voila!**
<aside class="note">**Note:** The `training=True` is intentional here since we want the batch statistics while running the model on the test dataset. If we use training=False, we will get the accumulated statistics learned from the training dataset (which we don't want)</aside>
**Note:** The `training=True` is intentional here since we want the batch statistics while running the model on the test dataset. If we use training=False, we will get the accumulated statistics learned from the training dataset (which we don't want)
```
def generate_images(model, test_input, tar):
@@ -622,14 +622,14 @@ Time taken for epoch 150 is 16.14578342437744 sec
If you want to share the TensorBoard results *publicly* you can upload the logs to [TensorBoard.dev](https://tensorboard.dev/) by copying the following into a code-cell.
<aside class="note">**Note:** This requires a Google account.</aside>
**Note:** This requires a Google account.
```
tensorboard dev upload --logdir {log_dir}
```
<aside class="caution">**Caution:** This command does not terminate. It's designed to continuously upload the results of long-running experiments. Once your data is uploaded you need to stop it using the "interrupt execution" option in your notebook tool.</aside>
**Caution:** This command does not terminate. It's designed to continuously upload the results of long-running experiments. Once your data is uploaded you need to stop it using the "interrupt execution" option in your notebook tool.
You can view the [results of a previous run](https://tensorboard.dev/experiment/lZ0C6FONROaUMfjYkVyJqw) of this notebook on [TensorBoard.dev](https://tensorboard.dev/).