Source code for nlp_architect.models.most_common_word_sense

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# Copyright 2017-2018 Intel Corporation
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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import tensorflow as tf


[docs]class MostCommonWordSense(object): def __init__(self, epochs, batch_size, callback_args=None): self.optimizer = tf.keras.optimizers.SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) self.loss = 'mean_squared_error' self.epochs = epochs self.batch_size = batch_size self.model = None self.callback_args = callback_args
[docs] def build(self, input_dim): # setup model layers model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(100, activation='relu', input_dim=input_dim)) model.add(tf.keras.layers.Dropout(0.5)) model.add(tf.keras.layers.Dense(2, activation='softmax')) model.compile(loss=self.loss, optimizer=self.optimizer) self.model = model
[docs] def fit(self, train_set): self.model.fit(train_set['X'], train_set['y'], epochs=self.epochs, batch_size=self.batch_size)
[docs] def save(self, save_path): self.model.save(save_path)
[docs] def load(self, model_path): self.model = tf.keras.models.load_model(model_path)
[docs] def eval(self, valid_set): eval_rate = self.model.evaluate(valid_set['X'], valid_set['y'], batch_size=self.batch_size) return eval_rate
[docs] def get_outputs(self, valid_set): return self.model.predict(valid_set)