| 
									
										
										
										
											2020-04-21 11:19:42 -04:00
										 |  |  | --- | 
					
						
							|  |  |  | id: 5e8f2f13c4cdbe86b5c72da1 | 
					
						
							| 
									
										
										
										
											2020-04-24 05:52:42 -05:00
										 |  |  | title: 'Natural Language Processing With RNNs: Building the Model' | 
					
						
							| 
									
										
										
										
											2020-04-21 11:19:42 -04:00
										 |  |  | challengeType: 11 | 
					
						
							|  |  |  | videoId: 32WBFS7lfsw | 
					
						
							| 
									
										
										
										
											2021-01-13 03:31:00 +01:00
										 |  |  | dashedName: natural-language-processing-with-rnns-building-the-model | 
					
						
							| 
									
										
										
										
											2020-04-21 11:19:42 -04:00
										 |  |  | --- | 
					
						
							|  |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | # --question--
 | 
					
						
							|  |  |  | 
 | 
					
						
							|  |  |  | ## --text--
 | 
					
						
							|  |  |  | 
 | 
					
						
							|  |  |  | Fill in the blanks below to complete the `build_model` function: | 
					
						
							|  |  |  | 
 | 
					
						
							|  |  |  | ```py | 
					
						
							|  |  |  | def build_mode(vocab_size, embedding_dim, rnn_units, batch_size): | 
					
						
							|  |  |  |     model = tf.keras.Sequential([ | 
					
						
							|  |  |  |         tf.keras.layers.Embedding(vocab_size, | 
					
						
							|  |  |  |                                   embedding_dim, | 
					
						
							|  |  |  |                                   batch_input_shape=[batch_size, None]), | 
					
						
							|  |  |  |         tf.keras.layers.__A__(rnn_units, | 
					
						
							|  |  |  |                               return_sequences=__B__, | 
					
						
							|  |  |  |                               recurrent_initializer='glorot_uniform), | 
					
						
							|  |  |  |         tf.keras.layers.Dense(__C__) | 
					
						
							|  |  |  |     ]) | 
					
						
							|  |  |  |     __D__ | 
					
						
							|  |  |  | ``` | 
					
						
							| 
									
										
										
										
											2020-04-21 11:19:42 -04:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | ## --answers--
 | 
					
						
							| 
									
										
										
										
											2020-04-21 11:19:42 -04:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | A: `ELU` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | B: `True` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | C: `vocab_size` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | D: `return model` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | --- | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | A: `LSTM` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | B: `False` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | C: `batch_size` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | D: `return model` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | --- | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | A: `LSTM` | 
					
						
							| 
									
										
										
										
											2020-05-28 22:40:36 +09:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | B: `True` | 
					
						
							|  |  |  | 
 | 
					
						
							|  |  |  | C: `vocab_size` | 
					
						
							|  |  |  | 
 | 
					
						
							|  |  |  | D: `return model` | 
					
						
							|  |  |  | 
 | 
					
						
							|  |  |  | ## --video-solution--
 | 
					
						
							| 
									
										
										
										
											2020-04-21 11:19:42 -04:00
										 |  |  | 
 | 
					
						
							| 
									
										
										
										
											2020-11-27 19:02:05 +01:00
										 |  |  | 3 | 
					
						
							| 
									
										
										
										
											2020-04-21 11:19:42 -04:00
										 |  |  | 
 |