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  ## Model description
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- More information needed
 
 
 
 
 
 
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  ## Intended uses & limitations
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  ## Training and evaluation data
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- More information needed
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  ## Training procedure
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
 
 
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  - training_precision: float32
 
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  ## Training Metrics
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  ## Model description
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+ This repo contains the model which showcases the learning capabilities of LSTM using a simple example. A single-layer LSTM is made to learn to add two numbers, provided as strings. The model has been trained for adding two numbers where each number can have maximum of 5 digits.
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+ *Example:*
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+ Input: "535+61"
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+ Output: "596"
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+ Full credits to [Smerity](https://twitter.com/Smerity) and others for this work.
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  ## Intended uses & limitations
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  ## Training and evaluation data
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+ The data consists of generation of two random 5 digit numbers as input and their sum as output. These numbers (_and their sum)_ are encoded and fed as input to LSTM. The full data creation code is available within the [example](https://keras.io/examples/nlp/addition_rnn/).
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  ## Training procedure
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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+ - train_batch_size: 32
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+ - optimizer: {'name': 'Adam', 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
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  - training_precision: float32
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+ - num_epochs: 30
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  ## Training Metrics
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