We would have multiple types of input data for sequential Keras model, which includes:
Coming back to your question, for solving your encountering errors in autoencoders you have to go through the following steps:
Data Visualization & Preprocessing: Since we only have 3 layered features, we have to normalize the features to be between [0, 1].
Softmax Regression Mode: We will now train a Softmax Regression (SR) model to predict the labels as it achieves 97% training accuracy and a minimal loss.
ANN Model: Now we will build our ANN model. We will add 2 hidden layers with 32 and 16 nodes.
Cross-Validation: With a small sample size like our current situation, it’s especially important to perform cross-validation to get a better estimate on accuracy.
your problem arises in Dense as it takes an integer as an input (the number of neurons), you provided a tuple Try:
output_dim = 214 * 214 * 3
You need to flatten your inputs/outputs, the fully connected Dense layer expects a 1-dimension input/output.
For more details, you can check out this link:https://towardsdatascience.com/applied-deep-learning-part-2-real-world-case-studies-1bb4b142a585
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