variational-sampler.py

# build your encoder upto here. It can simply be a series of dense layers, a convolutional network
# or even an LSTM decoder. Once made, flatten out the final layer of the encoder, call it hidden.

# we use Keras to build the graph

latent_size = 5
mean = Dense(latent_size)(hidden)

# we usually don't directly compute the stddev σ 
# but the log of the stddev instead, which is log(σ)
# the reasoning is similar to why we use softmax, instead of directly outputting
# numbers in fixed range [0, 1], the network can output a wider range of numbers which we can later compress down
log_stddev = Dense(latent_size)(hidden)

def sampler(mean, log_stddev):
    # we sample from the standard normal a matrix of batch_size * latent_size (taking into account minibatches)
    std_norm = K.random_normal(shape=(K.shape(mean)[0], latent_size), mean=0, stddev=1)
    # sampling from Z~N(μ, σ^2) is the same as sampling from μ + σX, X~N(0,1)
    return mean + K.exp(log_stddev) * std_norm
  
latent_vector = Lambda(sampler)([mean, log_stddev])
# pass latent_vector as input to decoder layers