ValueError: A KerasTensor cannot be used as input to a TensorFlow function

Short Course Q&A Vector Databases: from Embeddings to Applications
1

While running this code from example:

variational autoencoder (VAE) - to reconstruction input

reconstruction_loss = losses.binary_crossentropy(inputs_flat,
outputs_flat) * x_tr_flat.shape[1]

#kl_loss = Lambda(lambda x: 0.5 * K.sum(K.square(x[0]) + K.exp(x[1]) - x[1] - 1, axis=-1))([mu_flat, log_var_flat])

kl_loss = 0.5 * K.sum(K.square(mu_flat) + K.exp(log_var_flat) - log_var_flat - 1, axis = -1)
vae_flat_loss = reconstruction_loss + kl_loss

Build model

Ensure that the reconstructed outputs are as close to the inputs

vae_flat = Model(inputs_flat, outputs_flat)
vae_flat.add_loss(vae_flat_loss)
vae_flat.compile(optimizer=‘adam’)

got an error:
ValueError: A KerasTensor cannot be used as input to a TensorFlow function. A KerasTensor is a symbolic placeholder for a shape and dtype, used when constructing Keras Functional models or Keras Functions. You can only use it as input to a Keras layer or a Keras operation (from the namespaces keras.layers and keras.ops). You are likely doing something like:

x = Input(...)
...
tf_fn(x)  # Invalid.

What you should do instead is wrap tf_fn in a layer:

class MyLayer(Layer):
    def call(self, x):
        return tf_fn(x)

x = MyLayer()(x)

exploring the potential solution

2

is this code error in course provided environment?

Also can you share screenshot of complete error.

when I explored your error, the solution I got is to use tensorflow function in a layer by using tf.one_hot and then use call function to the new layer.

see this

1 Like
3

nope, it is not in the course provided environment. running it on latest phyton version supported by tensorflow.
Thanks for the advice @Deepti_Prasad .
I did these modifications and it working pretty good:

Added Lambda layer. Instead of computing the loss outside the model, integrated it inside a Lambda layer:

kl_loss = Lambda(lambda x: 0.5 * K.sum(K.square(x[0]) + K.exp(x[1]) - x[1] - 1, axis=-1))([mu_flat, log_var_flat])

was before kl_loss = 0.5 * K.sum(K.square(mu_flat) + K.exp(log_var_flat) - log_var_flat - 1, axis = -1)

accordingly
vae_loss = Lambda(lambda x: x[0] + x[1])([reconstruction_loss, kl_loss])

defined the loss function and pass it directly into compile().

def vae_loss(inputs_flat, outputs_flat, mu_flat, log_var_flat):
reconstruction_loss = K.sum(losses.binary_crossentropy(inputs_flat, outputs_flat), axis=-1)
kl_loss = 0.5 * K.sum(K.square(mu_flat) + K.exp(log_var_flat) - log_var_flat - 1, axis=-1)
return reconstruction_loss + kl_loss

Define VAE model

vae_flat = Model(inputs_flat, outputs_flat)

Compile with loss function directly

vae_flat.compile(optimizer=‘adam’, loss=lambda y_true, y_pred: vae_loss(inputs_flat, outputs_flat, mu_flat, log_var_flat))

seems working, if anyone thinks of a better solution please share. Happy to learn, thanks in advance.

1 Like
4

And accordingly above modification had impact on next parts of the code:

# train
vae_flat.fit(
    x_tr_flat,
    shuffle=True,
    epochs=n_epoch,
    batch_size=batch_size,
    validation_data=(x_te_flat, None),
    verbose=1
)

should be rewritten as,
Modify your loss function to take y_true and y_pred instead of inputs_flat and outputs_flat::

def vae_loss(y_true, y_pred, mu, log_var):
    reconstruction_loss = K.sum(losses.binary_crossentropy(y_true, y_pred), axis=-1)
    kl_loss = 0.5 * K.sum(K.square(mu) + K.exp(log_var) - log_var - 1, axis=-1)
    return reconstruction_loss + kl_loss

Then, modify how the loss function is passed to compile():

vae_flat.compile(optimizer=‘adam’, loss=lambda y_true, y_pred: vae_loss(y_true, y_pred, mu_flat, log_var_flat))

and all below parts accordingly.

1 Like
5

hi @volodymyr.trush

Can I know what kind of data are you working upon? are you working scalar data?

Choice of optimize would really depend on how large your dataset is.

The reason I am asking because you could have also used SGD in this case as Adam optimizer does give faster convergence but SGD would have given you better performance based on generalization.

Also can you point the source of your code?

are you using the VAE GitHub source code?

Regards
DP