edunuke

#fixed model weights (set them as non trainable)
for m in model_1.layers:
    if m.name == 'layer_13':
        m.trainable = False
    else:
        m.trainable = False
        
#add the new model to the old model
mixed = keras.layers.add([model_1.get_layer(name='layer_13').output, nn2], name='concat')
result_mix_1 = keras.layers.Dense(1, activation='relu', name='result_mixed')(mixed)

edunuke

import keras

#Domain embedding
user_input = keras.layers.Input(shape=(10,), name='Domain_1')
user_vec = keras.layers.Flatten()(keras.layers.Embedding(11, 9, name='Domain_1_embed')(user_input))
user_vec = keras.layers.Dropout(0.5)(user_vec)

#Domain embedding
products_input = keras.layers.Input(shape=(10,), name='Domain_2')
products_vec = keras.layers.Flatten()(keras.layers.Embedding(50, 20, name='Domain_2_embed')(products_input))

edunuke

import keras

#Domain embedding
user_input = keras.layers.Input(shape=(10,), name='Domain_1')
user_vec= keras.layers.Dropout(0.8)(keras.layers.Dense(1200, activation='relu',)(user_input))


#Domain embedding
products_input = keras.layers.Input(shape=(10,), name='Domain_2')
products_vec= keras.layers.Dropout(0.8)(keras.layers.Dense(1200, activation='relu',)(products_input))

edunuke

import keras

#Domain embedding
user_input = keras.layers.Input(shape=(100,), name='Domain_1')
user_vec = keras.layers.Flatten()(keras.layers.Embedding(11, 9, name='Domain_1_embed')(user_input))
user_vec = keras.layers.Dropout(0.5)(user_vec)

#Domain embedding
products_input = keras.layers.Input(shape=(100,), name='Domain_3')
products_vec = keras.layers.Flatten()(keras.layers.Embedding(50, 20, name='Domain_3_embed')(products_input))

edunuke

import keras

user_input = keras.layers.Input(shape=(100,), name='Domain_1')
user_vec = keras.layers.Flatten()(keras.layers.Embedding(11, 9, name='Domain_1_embed')(user_input))
user_vec = keras.layers.Dropout(0.5)(user_vec)

#Domain embedding
products_input = keras.layers.Input(shape=(100,), name='Domain_3')
products_vec = keras.layers.Flatten()(keras.layers.Embedding(50, 20, name='Domain_3_embed')(products_input))
products_vec = keras.layers.Dropout(0.5)(products_vec)

edunuke

from keras import layers
from keras import models
import tensorflow as tf


#
# generator input params
#

rand_dim = (1, 1, 2048)  # dimension of the generator's input tensor (gaussian noise)