ee1e8abd87
* feat(tools): add seed/solution restore script * chore(curriculum): remove empty sections' markers * chore(curriculum): add seed + solution to Chinese * chore: remove old formatter * fix: update getChallenges parse translated challenges separately, without reference to the source * chore(curriculum): add dashedName to English * chore(curriculum): add dashedName to Chinese * refactor: remove unused challenge property 'name' * fix: relax dashedName requirement * fix: stray tag Remove stray `pre` tag from challenge file. Signed-off-by: nhcarrigan <nhcarrigan@gmail.com> Co-authored-by: nhcarrigan <nhcarrigan@gmail.com>
908 B
908 B
id, title, challengeType, videoId, dashedName
| id | title | challengeType | videoId | dashedName |
|---|---|---|---|---|
| 5e8f2f13c4cdbe86b5c72da2 | Natural Language Processing With RNNs: Training the Model | 11 | hEUiK7j9UI8 | natural-language-processing-with-rnns-training-the-model |
--question--
--text--
Fill in the blanks below to save your model's checkpoints in the ./checkpoints directory and call the latest checkpoint for training:
checkpoint_dir = __A__
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt_{epoch}')
checkpoint_callback = tf.keras.callbacks.__B__(
filepath=checkpoint_prefix,
save_weights_only=True
)
history = model.fit(data, epochs=2, callbacks=[__C__])
--answers--
A: './training_checkpoints'
B: ModelCheckpoint
C: checkpoint_prefix
A: './checkpoints'
B: ModelCheckpoint
C: checkpoint_callback
A: './checkpoints'
B: BaseLogger
C: checkpoint_callback
--video-solution--
2