We would have multiple types of input data for sequential Keras model, which includes:

**Numeric/continuous values**

**Categorical values**

**Image data**

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

autoencoder.add(Dense(output_dim, activation='softmax'))

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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