def decoder_fn(x):
mlp_decoding = snt.nets.MLP(name="mlp_decoder",
output_sizes=dec_units + [2 * n_inp],
activation=activation)
net = mlp_decoding(x)
net = tf.clip_by_value(net, -10, 10)
return generative_utils.QuantizedNormal(mu_log_sigma=net)
def decoder_fn(net):
"""Decoder for VAE."""
if batch["image"].shape.as_list()[1] == 28:
net = snt.Linear(7 * 7 * 32)(net)
net = tf.reshape(net, [-1, 7, 7, 32])
elif batch["image"].shape.as_list()[1] == 32:
net = snt.Linear(8 * 8 * 32)(net)
net = tf.reshape(net, [-1, 8, 8, 32])
else:
raise ValueError("Only 32x32 or 28x28 supported!")
net = snt.nets.ConvNet2DTranspose(
dec_units,
shapes,
kernel_shapes=[(3, 3)],
strides=[2, 2],
paddings=[snt.SAME],
activation=activation_fn,
activate_final=True)(
net)
outchannel = batch["image"].shape.as_list()[3]
net = snt.Conv2D(2 * outchannel, kernel_shape=(1, 1))(net)
net = tf.clip_by_value(net, -10, 10)
return generative_utils.QuantizedNormal(mu_log_sigma=net)
def decoder_fn(net):
hidden_units = cfg["dec_hidden_units"] + [
flat_img.shape.as_list()[1] * 2
]
mod = snt.nets.MLP(hidden_units,
activation=act_fn,
initializers=init)
net = mod(net)
net = tf.clip_by_value(net, -10, 10)
return generative_utils.QuantizedNormal(mu_log_sigma=net)
def decoder_fn(net):
"""Decoder for VAE."""
net = snt.Linear(4 * 4 * 32)(net)
net = tf.reshape(net, [-1, 4, 4, 32])
net = snt.nets.ConvNet2DTranspose(dec_units,
shapes,
kernel_shapes=[(3, 3)],
strides=[2, 2, 2],
paddings=[snt.SAME],
activation=activation_fn,
activate_final=True)(net)
outchannel = batch["image"].shape.as_list()[3]
net = snt.Conv2D(2 * outchannel, kernel_shape=(1, 1))(net)
net = tf.clip_by_value(net, -10, 10)
return generative_utils.QuantizedNormal(mu_log_sigma=net)