I am working with a TensorFlow/Keras binary classification model and using SHAP to explain individual predictions. However, when I attempt to generate a force plot, I encounter the following error:

# Import SHAP
import shap

# Ensure data_for_prediction has the correct shape
data_for_prediction_reshaped = data_for_prediction.reshape(1, -1)

# Provide background data for DeepExplainer
background = X_train[:100]  # Use 100 samples from training data as background

# Initialize the DeepExplainer
explainer = shap.DeepExplainer(model, background)

# Compute SHAP values
shap_values = explainer.shap_values(data_for_prediction_reshaped)

# Generate force plot
shap.initjs()
shap.force_plot(explainer.expected_value[1], shap_values[1], data_for_prediction_reshaped)

Error:

InvalidArgumentError: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:CPU:0}} slice index 1 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/

Additional Details:

1. The model is a Keras Sequential model with the following architecture:

   • Multiple Dense layers with ReLU activations.

   • A Dropout layer after each dense layer.

   • An output layer with a sigmoid activation for binary classification.

2. Background Data:

   • X_train[:100] is a slice of my preprocessed training data (a NumPy array).

   3. Input for Prediction:

   • data_for_prediction_reshaped is a single sample reshaped to (1, n_features).

   4. Shapes:

   • shap_values[1].shape: Output shape of SHAP values (for class 1).

   • data_for_prediction_reshaped.shape: Input features reshaped to (1, n_features).

Questions:

1. What does the error “slice index 1 of dimension 0 out of bounds” mean in this context?
2. How should I adjust my code to ensure shap.force_plot works correctly with SHAP and TensorFlow/Keras models?
3. Are there specific compatibility issues between SHAP and TensorFlow/Keras that I should be aware of for this use case?