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Merge pull request #153 from Hector-hedb12/issue_150_lstm_binary_text…
…_classifier Issue 150: Long Short-Term Memory (LSTM) Binary Classifier for texts
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mlprimitives/jsons/keras.Sequential.LSTMBinaryTextClassifier.json
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| { | ||
| "name": "keras.Sequential.LSTMBinaryTextClassifier", | ||
| "contributors": [ | ||
| "Hector Dominguez <hedb_12@hotmail.com>" | ||
| ], | ||
| "description": "Long Short-Term Memory (LSTM) Binary Classifier for texts", | ||
| "classifiers": { | ||
| "type": "estimator", | ||
| "subtype": "classifier" | ||
| }, | ||
| "modalities": [], | ||
| "primitive": "mlprimitives.adapters.keras.Sequential", | ||
| "fit": { | ||
| "method": "fit", | ||
| "args": [ | ||
| { | ||
| "name": "X", | ||
| "type": "ndarray" | ||
| }, | ||
| { | ||
| "name": "y", | ||
| "type": "array" | ||
| }, | ||
| { | ||
| "name": "vocabulary_size", | ||
| "type": "int", | ||
| "description": "vocabulary size + 1: number of known words in input." | ||
| } | ||
| ] | ||
| }, | ||
| "produce": { | ||
| "method": "predict", | ||
| "args": [ | ||
| { | ||
| "name": "X", | ||
| "type": "ndarray" | ||
| } | ||
| ], | ||
| "output": [ | ||
| { | ||
| "name": "y", | ||
| "type": "array" | ||
| } | ||
| ] | ||
| }, | ||
| "hyperparameters": { | ||
| "fixed": { | ||
| "classification": { | ||
| "type": "bool", | ||
| "default": true | ||
| }, | ||
| "loss": { | ||
| "type": "str", | ||
| "default": "keras.losses.binary_crossentropy" | ||
| }, | ||
| "optimizer": { | ||
| "type": "str", | ||
| "default": "keras.optimizers.RMSprop" | ||
| }, | ||
| "metrics": { | ||
| "type": "list", | ||
| "default": [ | ||
| "accuracy" | ||
| ] | ||
| }, | ||
| "epochs": { | ||
| "type": "int", | ||
| "default": 20 | ||
| }, | ||
| "verbose": { | ||
| "type": "int", | ||
| "default": 0, | ||
| "range": [ | ||
| 0, | ||
| 2 | ||
| ] | ||
| }, | ||
| "layers": { | ||
| "type": "list", | ||
| "default": [ | ||
| { | ||
| "class": "keras.layers.Embedding", | ||
| "parameters": { | ||
| "input_dim": "vocabulary_size", | ||
| "output_dim": "embedding_output_dim" | ||
| } | ||
| }, | ||
| { | ||
| "class": "keras.layers.LSTM", | ||
| "parameters": { | ||
| "units": "lstm_units" | ||
| } | ||
| }, | ||
| { | ||
| "class": "keras.layers.Dropout", | ||
| "parameters": { | ||
| "rate": "dropout_rate" | ||
| } | ||
| }, | ||
| { | ||
| "class": "keras.layers.Dense", | ||
| "parameters": { | ||
| "units": 2, | ||
| "activation": "sigmoid" | ||
| } | ||
| } | ||
| ] | ||
| } | ||
| }, | ||
| "tunable": { | ||
| "embedding_output_dim": { | ||
| "type": "int", | ||
| "default": 100, | ||
| "range": [ | ||
| 100, | ||
| 500 | ||
| ] | ||
| }, | ||
| "lstm_units": { | ||
| "type": "int", | ||
| "default": 100, | ||
| "range": [ | ||
| 100, | ||
| 500 | ||
| ] | ||
| }, | ||
| "dropout_rate": { | ||
| "type": "float", | ||
| "default": 0.5, | ||
| "range": [ | ||
| 0.0, | ||
| 0.75 | ||
| ] | ||
| } | ||
| } | ||
| } | ||
| } |
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| { | ||
| "metadata": { | ||
| "name": "keras.Sequential.LSTMBinaryTextClassifier", | ||
| "data_type": "text", | ||
| "task_type": "classification" | ||
| }, | ||
| "validation": { | ||
| "dataset": "personae" | ||
| }, | ||
| "primitives": [ | ||
| "mlprimitives.custom.counters.VocabularyCounter", | ||
| "mlprimitives.custom.feature_extraction.StringVectorizer", | ||
| "keras.Sequential.LSTMBinaryTextClassifier" | ||
| ], | ||
| "output_names": { | ||
| "mlprimitives.custom.counters.VocabularyCounter#1": { | ||
| "counts": "vocabulary_size" | ||
| } | ||
| }, | ||
| "init_params": { | ||
| "mlprimitives.custom.counters.VocabularyCounter#1": { | ||
| "add": 1 | ||
| }, | ||
| "keras.Sequential.LSTMBinaryTextClassifier#1": { | ||
| "epochs": 1 | ||
| } | ||
| } | ||
| } |