Introduction
Have you encountered the ValueError stating that the layer expects 1 input but received 2 input tensors while building an Autoencoder using the KMNIST dataset in TensorFlow? This error can be perplexing, especially for beginners. In this article, we will dive into the cause of this error and provide solutions to resolve it.
Understanding the Error
The ValueError you’re experiencing occurs when the sequential model in TensorFlow expects a single input, but you are providing two input tensors. This issue often arises when there is a mismatch between the model’s input specifications and the actual input data.
Solution: Adjusting the Validation Data Format
The error can be resolved by modifying the format of the validation data. Let’s update the code to fix this issue:
pythonCopy code
history = stacked_ae.fit(img_train, img_train, epochs=10,
validation_data=(img_test, img_test))
By changing validation_data=[img_test, img_test] to validation_data=(img_test, img_test), we ensure that the validation data is passed as a tuple instead of a list. This aligns with the expected format and resolves the ValueError.
Explanation
In TensorFlow, the validation_data parameter of the fit() function expects a tuple of input data and target data. By using parentheses instead of square brackets, we correctly provide the validation data in the required format.
Conclusion
The ValueError stating that the sequential model expects 1 input but received 2 input tensors can be resolved by adjusting the format of the validation data. By changing the validation data parameter to a tuple format (img_test, img_test), we ensure compatibility with the model’s expectations.
Remember, understanding the expected input format and ensuring alignment with your data is crucial for successful model training and execution.
