VAE

import ______ as np

import __________ as tf

from tensorflow.keras.________import Input, Dense, ________

from tensorflow.keras.______import Model

from tensorflow.keras.________import binary_crossentropy

from tensorflow.keras import ________ as __

from tensorflow.keras.datasets import _________

numpy

tensorflow

layers

Lambda

models

losses

backend

K

mnist

import numpy as np

import tensorflow as tf

from tensorflow.keras.layers import Input, Dense, Lambda

from tensorflow.keras.models import Model

from tensorflow.keras.losses import binary_crossentropy

from tensorflow.keras import backend as K

from tensorflow.keras.datasets import mnist

# VAE parameters

__________ = 784

___________ = 2

__________ = 256

input_dim

latent_dim

intermediate_dim

import numpy as np

import tensorflow as tf

from tensorflow.keras.layers import Input, Dense, Lambda

from tensorflow.keras.models import Model

from tensorflow.keras.losses import binary_crossentropy

from tensorflow.keras import backend as K

from tensorflow.keras.datasets import mnist

# VAE parameters

input_dim = 784

latent_dim = 2

intermediate_dim = 256

# Sampling function (reparameterization trick)

def sampling(args):

________, __________ = ______

epsilon = __.random_normal(shape=(________(z_mean)[0], latent_dim))

return z_mean + K.exp(0.5 z_log_var) * epsilon

z_mean

z_log_var

args

K

K.shape

import numpy as np

import tensorflow as tf

from tensorflow.keras.layers import Input, Dense, Lambda

from tensorflow.keras.models import Model

from tensorflow.keras.losses import binary_crossentropy

from tensorflow.keras import backend as K

from tensorflow.keras.datasets import mnist

# VAE parameters

input_dim = 784

latent_dim = 2

intermediate_dim = 256

# Encoder

inputs = _______(shape=(input_dim,))

h = ________(intermediate_dim, activation='relu')(inputs)

z_mean = ________(latent_dim)(h)

z_log_var = Dense(latent_dim)(h)

z = ________(sampling)([z_mean, z_log_var])

Input

Dense

Dense

Lambda

reconstruction_loss = _________________(inputs, outputs) * _____________

kl_loss = -0.5 * ________(1 + _______ - _________(z_mean) - _______(________), axis=-1)

binary_crossentropy
input_dim
K.sum
z_log_var
K.square
K.exp
z_log_var

reconstruction_loss = binary_crossentropy(inputs, outputs) * input_dim

kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)

vae_loss = K._____(__________ + ___________)

mean

reconstruction_loss

kl_loss