dvgodoy

square = exponentiation_builder(2)
cube = exponentiation_builder(3)
fourth_power = exponentiation_builder(4)

# and so on and so forth...

dvgodoy

def exponentiation_builder(exponent):
  def skeleton_exponentiation(x):
    return x ** exponent

  return skeleton_exponentiation

dvgodoy

def skeleton_exponentiation(x):
  return x ** exponent

dvgodoy

def generic_exponentiation(x, exponent):
  return x ** exponent

dvgodoy

def square(x):
  return x ** 2
  
def cube(x):
  return x ** 3
  
def fourth_power(x):
  return x ** 4

# and so on and so forth...

dvgodoy

decoder_cnn = nn.Sequential(
    # z_size -> (n_filters*2)*7*7
    nn.Linear(z_size, (n_filters*2)*int(img_size/4)**2),
    
    # (n_filters*2)*7*7 -> (n_filters*2)@7x7
    nn.Unflatten(1, (n_filters*2, int(img_size/4), int(img_size/4))),
    
    # (n_filters*2)@7x7 -> (n_filters*2)@7x7
    nn.ConvTranspose2d(n_filters*2, n_filters*2, kernel_size=3, stride=1, padding=1, output_padding=0),
    nn.LeakyReLU(),

dvgodoy

set_seed(13)

z_size = 1
n_filters = 32
in_channels = 1
img_size = 28
input_shape = (in_channels, img_size, img_size)

base_model = nn.Sequential(
    # in_channels@28x28 -> n_filters@28x28

dvgodoy

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_vae_cnn = AutoEncoder(encoder_var_cnn, decoder_cnn)
model_vae_cnn.to(device)
loss_fn = nn.MSELoss(reduction='none')
optim = torch.optim.Adam(model_vae_cnn.parameters(), 0.0003)

num_epochs = 30

train_losses = []

dvgodoy

device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_vae.to(device)
loss_fn = nn.MSELoss(reduction='none')
optim = torch.optim.Adam(model_vae.parameters(), 0.0003)

num_epochs = 30

train_losses = []

reconstruction_loss_factor = 1

dvgodoy

set_seed(13)

z_size = 1
input_shape = (1, 28, 28)

base_model = nn.Sequential(
    # (C, H, W) -> C*H*W
    nn.Flatten(),
    # C*H*W -> 2048
    nn.Linear(np.prod(input_shape), 2048),