def build(self):
query_input = self.new_query_input(size=20)
url_input = self.new_url_input()
title_input = self.new_title_input()
body_input = self.new_body_input()
inputs = [query_input, url_input, title_input, body_input]
word_embedding = layers.Embedding(self.total_words, self.embedding_dim)
query = layers.GlobalMaxPooling1D()(word_embedding(query_input))
url = layers.GlobalMaxPooling1D()(word_embedding(url_input))
title = layers.GlobalMaxPooling1D()(word_embedding(title_input))
body = layers.GlobalMaxPooling1D()(word_embedding(body_input))
input_features = [query, url, title, body]
num_fields = len(input_features)
features = tf.concat(input_features, axis=1)
interactions = WeightedQueryFieldInteraction(num_fields, name='field_weights')(features)
features = []
for feature in input_features:
feature = layers.Dense(1, activation='relu')(feature)
features.append(feature)
features = layers.Add()(features)
features = AddBias0()(features)
output = layers.Activation('sigmoid', name='label')(features + interactions)
return tf.keras.Model(inputs=inputs, outputs=output, name=self.name)
def build(self):
text_inputs = [
self.new_query_input(size=20),
self.new_url_input(),
self.new_title_input(),
self.new_body_input(),
]
inputs = text_inputs
word_embedding = layers.Embedding(self.total_words, self.embedding_dim)
text_features = [word_embedding(text_input) for text_input in text_inputs]
text_features = [layers.GlobalMaxPooling1D()(feature) for feature in text_features]
input_features = text_features
interactions = []
for feature1, feature2 in itertools.combinations(input_features, 2):
interactions.append(layers.Dot(axes=1)([feature1, feature2]))
interactions = layers.Add()(interactions)
features = []
for feature in input_features:
feature = layers.Dense(1, activation='relu')(feature)
features.append(feature)
features = layers.Add()(features)
features = AddBias0()(features)
output = layers.Activation('sigmoid', name='label')(features + interactions)
return tf.keras.Model(inputs=inputs, outputs=output, name=self.name)
def build(self):
query_input = self.new_query_input(size=20)
url_input = self.new_url_input()
title_input = self.new_title_input()
body_input = self.new_body_input()
inputs = [query_input, url_input, title_input, body_input]
word_embedding = layers.Embedding(self.total_words, self.embedding_dim)
query = layers.GlobalMaxPooling1D()(word_embedding(query_input))
url = layers.GlobalMaxPooling1D()(word_embedding(url_input))
title = layers.GlobalMaxPooling1D()(word_embedding(title_input))
body = layers.GlobalMaxPooling1D()(word_embedding(body_input))
input_features = [query, url, title, body]
query_url = layers.Dot(axes=1)([query, url])
query_title = layers.Dot(axes=1)([query, title])
query_body = layers.Dot(axes=1)([query, body])
interactions = layers.Add()([query_url, query_title, query_body])
features = []
for feature in input_features:
feature = layers.Dense(1, activation='relu')(feature)
features.append(feature)
features = layers.Add()(features)
features = AddBias0()(features)
output = layers.Activation('sigmoid', name='label')(features + interactions)
return tf.keras.Model(inputs=inputs, outputs=output, name=self.name)
def build(self):
text_inputs = [
self.new_query_input(size=20),
self.new_url_input(),
self.new_title_input(),
self.new_body_input(),
]
inputs = text_inputs
word_embedding = layers.Embedding(self.total_words, self.embedding_dim, name='text_embedding')
text_features = [word_embedding(text_input) for text_input in text_inputs]
text_features = [layers.GlobalMaxPooling1D()(feature) for feature in text_features]
input_features = text_features
num_fields = len(input_features)
features = tf.concat(input_features, axis=1)
interactions = WeightedFeatureInteraction(num_fields, name='field_weights')(features)
features = []
for feature in input_features:
feature = layers.Dense(1, activation='relu')(feature)
features.append(feature)
features = layers.Add()(features)
features = AddBias0()(features)
output = layers.Activation('sigmoid', name='label')(features + interactions)
return tf.keras.Model(inputs=inputs, outputs=output, name=self.name)
def build(self):
query_input = self.new_query_input()
title_input = self.new_title_input()
ingredients_input = self.new_ingredients_input()
description_input = self.new_description_input()
country_input = self.new_country_input()
doc_id_input = self.new_doc_id_input()
inputs = [
query_input, title_input, ingredients_input, description_input,
country_input, doc_id_input
]
word_embedding = layers.Embedding(self.total_words,
self.embedding_dim,
name='word_embedding')
query = layers.GlobalMaxPooling1D()(word_embedding(query_input))
title = layers.GlobalMaxPooling1D()(word_embedding(title_input))
ingredients = layers.GlobalMaxPooling1D()(
word_embedding(ingredients_input))
description = layers.GlobalMaxPooling1D()(
word_embedding(description_input))
country_embedding = layers.Embedding(self.total_countries,
self.embedding_dim)
country = country_embedding(country_input)
country = tf.reshape(country, shape=(
-1,
self.embedding_dim,
))
image_embedding = self.load_pretrained_embedding(
embedding_filepath=
f'{project_dir}/data/raw/en_2020-03-16T00_04_34_recipe_image_tagspace5000_300.pkl',
embedding_dim=300,
name='image_embedding')
image = image_embedding(doc_id_input)
image = tf.reshape(image, shape=(
-1,
300,
))
image = layers.Dropout(.2)(image)
image = layers.Dense(self.embedding_dim)(image)
input_features = [
query, title, ingredients, description, country, image
]
num_fields = len(input_features)
features = tf.concat(input_features, axis=1)
interactions = WeightedSelectedFeatureInteraction(
num_fields, name='field_weights')(features)
features = []
for feature in input_features:
feature = layers.Dense(1, activation='relu')(feature)
features.append(feature)
features = layers.Add()(features)
features = AddBias0()(features)
output = layers.Activation('sigmoid',
name='label')(features + interactions)
return tf.keras.Model(inputs=inputs, outputs=output, name=self.name)
def build(self):
text_inputs = [
self.new_query_input(),
self.new_title_input(),
self.new_ingredients_input(),
self.new_description_input(),
]
country_input = self.new_country_input()
doc_id_input = self.new_doc_id_input()
inputs = text_inputs + [country_input, doc_id_input]
word_embedding = layers.Embedding(self.total_words,
self.embedding_dim,
name='word_embedding')
texts = [
layers.GlobalMaxPooling1D()(word_embedding(text_input))
for text_input in text_inputs
]
country_embedding = layers.Embedding(self.total_countries,
self.embedding_dim)
country = country_embedding(country_input)
country = tf.reshape(country, shape=(
-1,
self.embedding_dim,
))
image_embedding = self.load_pretrained_embedding(
embedding_filepath=
f'{project_dir}/data/raw/en_2020-03-16T00_04_34_recipe_image_tagspace5000_300.pkl',
embedding_dim=300,
name='image_embedding')
image = image_embedding(doc_id_input)
image = tf.reshape(image, shape=(
-1,
300,
))
image = layers.Dropout(.2)(image)
image = layers.Dense(self.embedding_dim)(image)
input_features = texts + [country, image]
interactions = []
for feature1, feature2 in itertools.combinations(input_features, 2):
interactions.append(layers.Dot(axes=1)([feature1, feature2]))
interactions = layers.Add()(interactions)
features = []
for feature in input_features:
feature = layers.Dense(1, activation='relu')(feature)
features.append(feature)
features = layers.Add()(features)
features = AddBias0()(features)
output = layers.Activation('sigmoid',
name='label')(features + interactions)
return tf.keras.Model(inputs=inputs, outputs=output, name=self.name)