# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ****************************************************************************
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)