I'm trying to implement a 3D variational autoencoder following instructions in the book Generative Deep Learning and taking also some things from here link, those examples are in 2D so I have adapted it.
This is the code i'm using:
import tensorflow as tf
import tensorflow.keras as keras
from tensorflow.keras.layers import Input, BatchNormalization, Conv3D, Dense, Flatten, Lambda, Reshape, UpSampling3D
from tensorflow.keras.regularizers import l2
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
## Functional API
input_shape = (32, 32, 32, 1)
z_dim = 3000
########### Encoder ############
enc_in = Input(shape = input_shape, name="encoder_input")
enc_conv1 = tf.keras.layers.Conv3D(filters=8, kernel_size=5, strides=(1,1,1), padding='same', activation="ReLU", kernel_initializer="he_uniform", name="conv_1")(enc_in)
max1 = tf.keras.layers.MaxPool3D(pool_size=(2,2,2), strides=(2,2,2), padding='valid', name="max_pool_1")(enc_conv1)
enc_conv2 = tf.keras.layers.Conv3D(filters=16, kernel_size=3, padding='same', activation="ReLU", kernel_initializer="he_uniform", name="conv_2")(max1)
max2 = tf.keras.layers.MaxPool3D(pool_size=2, name="max_pool_2")(enc_conv2)
enc_fc1 = Dense(units = 512, kernel_initializer = 'he_uniform', activation = 'ReLU')(Flatten()(max2))
mu = Dense(units = z_dim, kernel_initializer = 'he_uniform', activation = None, name="mu")(enc_fc1)
log_variance = Dense(units = z_dim, kernel_initializer = 'he_uniform', activation = None, name="log_variance")(enc_fc1)
def sampling(args):
mu, log_variance = args
epsilon = K.random_normal(shape=K.shape(mu), mean= 0., stddev= 1.)
return mu + K.exp(0.5 * log_variance) * epsilon
z = Lambda(sampling, output_shape = (z_dim, ), name="z")([mu, log_variance])
encoder = Model(enc_in, [mu, log_variance, z], name="encoder")
########## Decoder ############
dec_in = Input(shape = (z_dim, ), name="decoder_input")
dec_fc1 = Dense(units = 512, kernel_initializer = 'he_uniform', activation = 'ReLU')(dec_in)
dec_unflatten = Reshape(target_shape = (8,8,8,1))(dec_fc1)
dec_conv1 = tf.keras.layers.Conv3D(filters=32, kernel_size=3, padding='same', activation="ReLU", kernel_initializer="he_uniform", name="deconv_1")(dec_unflatten)
ups1 = tf.keras.layers.UpSampling3D(size=(2, 2, 2), name="ups_1")(dec_conv1)
dec_conv2 = tf.keras.layers.Conv3D(filters=16, kernel_size=3, padding='same', activation="ReLU", kernel_initializer="he_uniform", name="deconv_2")(ups1)
ups2 = tf.keras.layers.UpSampling3D(size=(2, 2, 2), name="ups_2")(dec_conv2)
dec_conv4 = Conv3D(filters = 1, kernel_size = (3, 3, 3), padding = 'same', activation="ReLU", kernel_initializer = 'he_uniform', name='decorder_output')(ups2)
decoder = Model(dec_in, dec_conv4, name="decoder")
model_input = enc_in
model_output = decoder(z)
v_autoencoder = Model(model_input, model_output)
######################
def vae_r_loss(T1_input, model_output):
r_loss = K.mean(K.square(T1_input - model_output), axis = [1,2,3])
return r_loss_factor * r_loss
def vae_kl_loss(T1_input, model_output):
kl_loss = -0.5 * K.sum(1 + log_variance - K.square(mu) - K.exp(log_variance), axis = 1)
return kl_loss
def vae_loss(T1_input, model_output):
r_loss = vae_r_loss(T1_input, model_output)
kl_loss = vae_kl_loss(T1_input, model_output)
return r_loss + kl_loss
learning_rate=0.001
r_loss_factor = 500
optimizer = keras.optimizers.Adam(lr=learning_rate)
v_autoencoder.compile(optimizer=optimizer, loss = vae_loss, metrics = [vae_r_loss, vae_kl_loss])
v_autoencoder.summary()
But when I try to train the model:
history = v_autoencoder.fit(T1_input, T1_input, epochs=10, validation_split=0.2, shuffle=True)
I get this error:
TypeError: You are passing KerasTensor(type_spec=TensorSpec(shape=(), dtype=tf.float32, name=None), name='Placeholder:0', description="created by layer 'tf.cast_4'"), an intermediate Keras symbolic input/output, to a TF API that does not allow registering custom dispatchers, such as `tf.cond`, `tf.function`, gradient tapes, or `tf.map_fn`. Keras Functional model construction only supports TF API calls that *do* support dispatching, such as `tf.math.add` or `tf.reshape`. Other APIs cannot be called directly on symbolic Kerasinputs/outputs. You can work around this limitation by putting the operation in a custom Keras layer `call` and calling that layer on this symbolic input/output.
