def level_model_conv(df_meta_input, df_embeddings, name, bs):
input_embedding = Input(shape=(len(df_embeddings.columns),
len(df_embeddings.columns), 1),
name='embedding_input')
emb_x = Conv2D(64, 5, activation='relu')(input_embedding)
emb_x = MaxPool2D()(emb_x)
emb_x = Conv2D(32, 4, activation='relu')(emb_x)
emb_x = Conv2D(16, 3, activation='relu')(emb_x)
emb_x = Flatten()(emb_x)
emb_x_out = Dense(1, activation='sigmoid', name="aux")(emb_x)
meta_input = Input(shape=(len(df_meta_input.columns), ), name='meta_input')
x = keras.layers.concatenate([emb_x, meta_input])
x = Dense(128, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(32, activation='relu')(x)
x = Dense(16, activation='relu')(x)
x = Dense(8, activation='relu')(x)
predictions = Dense(1, activation='sigmoid', name="main")(x)
# This creates a model that includes
# the Input layer and three Dense layers
model_created = Model(inputs=[input_embedding, meta_input],
outputs=[emb_x_out, predictions])
model_created.Name = name
return model_created
def level_siames_merge_RNN_layer_soft(df_meta_input, df_embeddings_1,
df_embeddings_2, name, bs):
input_embedding_1 = Input(shape=(len(df_embeddings_1.columns), 1),
name='embedding_input_1')
input_embedding_2 = Input(shape=(len(df_embeddings_2.columns), 1),
name='embedding_input_2')
emb_x_1 = get_tensor_embedding_RNN_layer(input_embedding_1)
emb_x_2 = get_tensor_embedding_RNN_layer(input_embedding_2)
L1_layer = Lambda(lambda tensors: (tensors[0] - tensors[1]))
L1_distance = L1_layer([emb_x_1, emb_x_2])
emb_x_out = Dense(3, activation='softmax', name="aux")(L1_distance)
meta_input = Input(shape=(len(df_meta_input.columns), ), name='meta_input')
x = keras.layers.concatenate([L1_distance, meta_input])
x = Dense(256,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l1(0.0005))(x)
x = BatchNormalization()(x)
x = Dropout(0.3)(x)
x = Dense(128,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l1(0.0005))(x)
x = BatchNormalization()(x)
x = Dropout(0.3)(x)
x = Dense(64,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l1(0.0005))(x)
x = BatchNormalization()(x)
x = Dropout(0.2)(x)
x = Dense(64,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l1(0.0005))(x)
x = BatchNormalization()(x)
x = Dropout(0.2)(x)
x = Dense(32,
activation='relu',
kernel_regularizer=regularizers.l1(0.0005))(x)
x = Dropout(0.1)(x)
x = Dense(16,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l1(0.0005))(x)
x = BatchNormalization()(x)
predictions = Dense(3, activation='softmax', name="main")(x)
model_created = Model(
inputs=[input_embedding_1, input_embedding_2, meta_input],
outputs=[predictions, emb_x_out])
model_created.Name = name
return model_created
def level_model_rnn_soft(df_meta_input, df_embeddings, name, rnn_size, bs):
input_embedding = Input(shape=(len(df_embeddings.columns), 1),
name='embedding_input')
emb_x = GRU(128,
activation="relu",
use_bias=False,
recurrent_regularizer=regularizers.l1(0.0005),
kernel_regularizer=regularizers.l1(0.005))(input_embedding)
emb_x = BatchNormalization()(emb_x)
emb_x_out = Dense(3, activation='softmax', name="aux")(emb_x)
meta_input = Input(shape=(len(df_meta_input.columns), ), name='meta_input')
x = keras.layers.concatenate([emb_x, meta_input])
x = Dense(128, activation='relu', use_bias=False)(x)
x = BatchNormalization()(x)
x = Dropout(0.3)(x)
x = Dense(64,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l2(0.001))(x)
x = BatchNormalization()(x)
x = Dropout(0.3)(x)
x = Dense(32,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l2(0.001))(x)
x = BatchNormalization()(x)
x = Dropout(0.2)(x)
x = Dense(32,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l2(0.001))(x)
x = BatchNormalization()(x)
x = Dropout(0.2)(x)
x = Dense(16,
activation='relu',
kernel_regularizer=regularizers.l2(0.0005))(x)
x = Dropout(0.1)(x)
x = Dense(8,
activation='relu',
use_bias=False,
kernel_regularizer=regularizers.l2(0.0005))(x)
x = BatchNormalization()(x)
predictions = Dense(3, activation='softmax', name="main")(x)
# This creates a model that includes
# the Input layer and three Dense layers
model_created = Model(inputs=[input_embedding, meta_input],
outputs=[emb_x_out, predictions])
model_created.Name = name
return model_created
def flat_model(df_input, name):
inputs = Input(shape=(len(df_input.columns), ))
# a layer instance is callable on a tensor, and returns a tensor
x = Dense(1024,
activation='relu',
kernel_regularizer=regularizers.l2(0.005))(inputs)
x = Dropout(0.2)(x)
x = Dense(1024, activation='relu', use_bias=False)(x)
x = BatchNormalization()(x)
x = Dense(512,
activation='relu',
kernel_regularizer=regularizers.l2(0.005, ))(x)
x = Dropout(0.2)(x)
x = Dense(512, activation='relu', use_bias=False)(x)
x = BatchNormalization()(x)
x = Dense(256,
activation='relu',
kernel_regularizer=regularizers.l2(0.005))(x)
x = Dropout(0.2)(x)
x = Dense(256, activation='relu', use_bias=False)(x)
x = BatchNormalization()(x)
x = Dense(128,
activation='relu',
kernel_regularizer=regularizers.l2(0.00001))(x)
x = Dense(64,
activation='relu',
kernel_regularizer=regularizers.l2(0.00001))(x)
x = Dense(32,
activation='relu',
kernel_regularizer=regularizers.l2(0.00001))(x)
x = Dense(16,
activation='relu',
kernel_regularizer=regularizers.l2(0.00001))(x)
x = Dense(8,
activation='relu',
kernel_regularizer=regularizers.l2(0.00001))(x)
predictions = Dense(1, activation='sigmoid')(x)
# This creates a model that includes
# the Input layer and three Dense layers
model_created = Model(inputs=inputs, outputs=predictions)
model_created.Name = name
return model_created
