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"""
Ryan Tietjen
Sep 2024
Create best model for the demo
"""
import tensorflow as tf
from keras import layers
import tensorflow_hub as hub
class EmbeddingLayer(layers.Layer):
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Hardcode the module URL directly within the layer
# self.module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
url = "https://tfhub.dev/google/universal-sentence-encoder/4"
self.embed_model = hub.KerasLayer(url, trainable=False, name="universal_sentence_encoder")
def call(self, inputs):
return self.embed_model(inputs)
def get_config(self):
config = super().get_config()
# The URL is now a fixed part of the layer, so it can be included in the config for completeness
# config.update({'module_url': self.module_url})
return config
def create_token_model(token_embed):
input_layer = layers.Input(shape=[], dtype=tf.string)
embedding_layer = EmbeddingLayer()
token_embeddings = embedding_layer(input_layer)
output_layer = layers.Dense(128, activation="relu")(token_embeddings)
model = tf.keras.Model(input_layer, output_layer)
return model
def create_character_vectorizer_model(char_embed, char_vectorizer):
input_layer = layers.Input(shape=(1,), dtype=tf.string)
char_vectors = char_vectorizer(input_layer) # vectorize text inputs
char_embedding = char_embed(char_vectors) # create embedding
output_layer = layers.Bidirectional(layers.LSTM(32))(char_embedding)
model = tf.keras.Model(input_layer, output_layer)
return model
def create_line_number_model(input_shape, name):
input_layer = layers.Input(shape=(input_shape,), dtype=tf.int32, name=name)
output_layer = layers.Dense(32, activation="relu")(input_layer)
model = tf.keras.Model(input_layer, output_layer)
return model
def tribrid_model(num_classes, token_embed, char_embed, text_vectorizer):
token_model = create_token_model(token_embed)
character_vectorizer_model = create_character_vectorizer_model(char_embed, text_vectorizer)
line_number_model = create_line_number_model(15, "line_number")
total_lines_model = create_line_number_model(20, "total_lines")
hybrid_model = layers.Concatenate(name="hybrid")([token_model.output,
character_vectorizer_model.output])
dense_layer = layers.Dense(256, activation="relu")(hybrid_model)
dense_layer = layers.Dropout(0.5)(dense_layer)
tribrid_model = layers.Concatenate(name="tribrid") ([line_number_model.output, total_lines_model.output, dense_layer])
output_layer = layers.Dense(num_classes, activation="softmax")(tribrid_model)
model = tf.keras.Model([line_number_model.input, total_lines_model.input, token_model.input, character_vectorizer_model.input], output_layer)
model.compile(loss=tf.keras.losses.CategoricalCrossentropy(label_smoothing=0.2),
optimizer=tf.keras.optimizers.Adam(),
metrics=["accuracy"])
return model |