2020-08-13 12:00:20 +02:00
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---
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id: 5e8f2f13c4cdbe86b5c72da1
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challengeType: 11
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videoId: 32WBFS7lfsw
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2021-01-13 03:31:00 +01:00
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dashedName: natural-language-processing-with-rnns-building-the-model
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2020-08-13 12:00:20 +02:00
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---
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2020-12-16 00:37:30 -07:00
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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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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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## --answers--
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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A: `ELU`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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B: `True`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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C: `vocab_size`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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D: `return model`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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---
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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A: `LSTM`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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B: `False`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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C: `batch_size`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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D: `return model`
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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---
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2020-08-13 12:00:20 +02:00
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2020-12-16 00:37:30 -07:00
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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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