When training a Keras model, achieving an accuracy of 0 throughout the training process can be perplexing and frustrating. As perplexing as it might seem, this phenomenon can often be traced back to certain common issues faced while setting up the model or preparing the dataset. Below, we delve into the contributing factors, technical concepts, diagnostic tips, and ways to mitigate these issues.

Common Causes of Zero Accuracy

Data-Related Issues

1. Incorrect Labels: One of the first places to check is whether your labels are correct. If your labels are all zeros or ones, or entirely mismatched, your model might find no meaningful pattern to learn from. Also, make sure that the labels match the number of classes in your problem.
2. Class Imbalance: A heavily imbalanced dataset can cause your model to learn a biased prediction, which might result in zero accuracy. For instance, if 99% of your data belongs to one class, the model might predict the majority class most of the time, rendering an accuracy of zero if evaluated on the minority class.
3. Data Pre-processing: Poorly pre-processed data can lead to zero accuracy. Check if the input data is scaled/normalized properly and if categorical variables are encoded correctly.

Model Architecture Issues

1. Inappropriate Model Architecture: A model that is too simple may not capture the complexity of the dataset. Conversely, a too complex model may overfit, especially if not regularized properly. Both scenarios can lead to poor performance.
2. Activation Functions: Activation functions play a critical role in enabling the model to learn complex patterns. A mismatch between the chosen activation functions and the data characteristics might result in zero accuracy—for instance, using sigmoid in hidden layers of a deep network can sometimes lead to vanishing gradient problems in deeper layers.
3. Output Layer Configuration: For a multi-class classification problem, ensure that your output layer has a softmax activation function with the appropriate number of neurons corresponding to the number of classes.

Optimizer and Loss Function Issues

1. Learning Rate Problems: A very high learning rate might cause the model weights to diverge during training. Conversely, a very low learning rate could prevent the model from converging.
2. Loss Function: Ensure that the loss function is appropriate for the task. For binary classification, use binary_crossentropy, and for multi-class classification, use categorical_crossentropy or sparse_categorical_crossentropy.

Code-Related Errors

1. Batch Size and Epoch Configuration: Extremely large batch sizes can disrupt the learning, making convergence difficult. Similarly, running too few epochs might not be sufficient for the model to learn anything meaningful.
2. Randomness and Initialization: Random initial weights and variability due to different random seeds can sometimes lead to zero accuracy. Ensure consistent initialization and consider starting with pre-trained weights if available.

Diagnosing and Resolving Zero Accuracy in Keras

Step-by-Step Diagnosis

1. Print Shapes and Check Dimesionalities: Verify that the dimensions of your input data match the expectations of your model architecture.
2. Inspect Data Loading: Use functions like dataset.take(1) for TensorFlow datasets to visually inspect the first few data samples and their labels.
3. Visualize the Data: Plot your data to check for anomalies or patterns that might mislead the model. Visualization could assist you in identifying issues like class imbalance or incorrect preprocessing.
4. Simplify the Model: Temporarily simplify the model to identify whether the complexity of the model is a contributing factor. Simplifying might provide insights into what the model incorrectly learns.
5. Overfitting/Underfitting Check: Check for indications of underfitting by analyzing loss curves. Generally, a model that learns nothing has nearly unchanged loss over iterations.

Table of Diagnostic Points

Additional Considerations

• Utilize Callbacks: Implement callbacks like ModelCheckpoint and EarlyStopping to facilitate better training dynamics and prevent overfitting.
• Experiment Tracking: Keep track of different experimental setups using tools like TensorBoard, Neptune, or MLflow to identify what worked or didn't.
• Advanced Debugging: Use tf.print for TensorFlow models or callbacks.LambdaCallback to print values during training runs for an in-depth understanding.

In summary, obtaining zero accuracy during Keras model training is usually indicative of a fundamental setup issue rather than a trivial gradient descent problem. The steps and considerations outlined here should provide guidance in diagnosing and rectifying the root causes effectively.