Number of tensor mismatches for attachments in TensorBoard Keras callback

I am using the CIFAR-10 dataset, so there are 10,000 test images. I have successfully created a file .tsv

containing metadata: test suite labels (in human readable text, not indices) on each of 10,000 lines.

However, in TensorBoard, when I open the embed tab, I get this error:

The number of tensors (16128) does not match the number of rows in the metadata (10000).

But I would expect the test suites to inline, which are usually 10,000 long, like in the file .tsv

I made!

Here is the code I am using from this project :

K.set_learning_phase(1)

# [...]

model = build_model(hype_space)

# [...]

log_path = None

if log_for_tensorboard:
    log_path = os.path.join(TENSORBOARD_DIR, model_uuid)
    if not os.path.exists(log_path):
        os.makedirs(log_path)
    print("Tensorboard log files will be saved to: {}".format(log_path))

    embeddings_metadata = {
        # Dense layers only:
        l.name: "../test_classes.tsv"
        for l in model.layers if 'dense' in l.name.lower()
    }

    tb_callback = keras.callbacks.TensorBoard(
        log_dir=log_path,
        histogram_freq=1,
        write_graph=True,
        write_images=True
        embeddings_freq=3,
        embeddings_layer_names=list(embeddings_metadata.keys()),
        embeddings_metadata=embeddings_metadata
    )
    tb_callback.set_model(model)

    callbacks.append(tb_callback)

history = model.fit(
    [x_train],
    [y_train, y_train_c],
    batch_size=int(hype_space['batch_size']),
    epochs=EPOCHS,
    shuffle=True,
    verbose=1,
    callbacks=callbacks,
    validation_data=([x_test], [y_test, y_test_coarse])
).history

      

        
        
        
      

    

thank