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>
62 lines
965 B
Markdown
62 lines
965 B
Markdown
---
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id: 5e8f2f13c4cdbe86b5c72da1
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challengeType: 11
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videoId: 32WBFS7lfsw
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dashedName: natural-language-processing-with-rnns-building-the-model
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---
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# --question--
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## --text--
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Fill in the blanks below to complete the `build_model` function:
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```py
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def build_mode(vocab_size, embedding_dim, rnn_units, batch_size):
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model = tf.keras.Sequential([
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tf.keras.layers.Embedding(vocab_size,
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embedding_dim,
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batch_input_shape=[batch_size, None]),
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tf.keras.layers.__A__(rnn_units,
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return_sequences=__B__,
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recurrent_initializer='glorot_uniform),
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tf.keras.layers.Dense(__C__)
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])
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__D__
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```
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## --answers--
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A: `ELU`
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B: `True`
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C: `vocab_size`
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D: `return model`
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---
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A: `LSTM`
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B: `False`
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C: `batch_size`
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D: `return model`
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---
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A: `LSTM`
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B: `True`
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C: `vocab_size`
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D: `return model`
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## --video-solution--
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3
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