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										 |  |  | title: 'Natural Language Processing With RNNs: Training the Model' | 
					
						
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										 |  |  | challengeType: 11 | 
					
						
							|  |  |  | videoId: hEUiK7j9UI8 | 
					
						
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										 |  |  | dashedName: natural-language-processing-with-rnns-training-the-model | 
					
						
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										 |  |  | # --question--
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										 |  |  | ## --text--
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										 |  |  | Fill in the blanks below to save your model's checkpoints in the `./checkpoints` directory and call the latest checkpoint for training: | 
					
						
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										 |  |  | ```py | 
					
						
							|  |  |  | checkpoint_dir = __A__ | 
					
						
							|  |  |  | checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt_{epoch}') | 
					
						
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										 |  |  | checkpoint_callback = tf.keras.callbacks.__B__( | 
					
						
							|  |  |  |     filepath=checkpoint_prefix, | 
					
						
							|  |  |  |     save_weights_only=True | 
					
						
							|  |  |  | ) | 
					
						
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										 |  |  | history = model.fit(data, epochs=2, callbacks=[__C__]) | 
					
						
							|  |  |  | ``` | 
					
						
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										 |  |  | ## --answers--
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										 |  |  | A: `'./training_checkpoints'` | 
					
						
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										 |  |  | B: `ModelCheckpoint` | 
					
						
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										 |  |  | C: `checkpoint_prefix` | 
					
						
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										 |  |  | --- | 
					
						
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										 |  |  | A: `'./checkpoints'` | 
					
						
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										 |  |  | B: `ModelCheckpoint` | 
					
						
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							|  |  |  | C: `checkpoint_callback` | 
					
						
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							|  |  |  | A: `'./checkpoints'` | 
					
						
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							|  |  |  | B: `BaseLogger` | 
					
						
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							|  |  |  | C: `checkpoint_callback` | 
					
						
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							|  |  |  | ## --video-solution--
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