TensorFlow

InvalidArgumentError in TensorFlow typically arises when an argument passed to a TensorFlow operation has an unexpected or invalid value, such as out-of-bounds indices, incorrect data types, or shapes incompatible with the operation's requirements. To fix it, thoroughly inspect the shapes, data types, and value ranges of all input tensors passed to the failing operation, ensuring they conform to the operation's expected input requirements; use `tf.debugging.assert_*` ops for runtime validation. Verify that reduction axes are valid for `argmin/argmax` and that `buffer_size` and `vocab_size` are reasonable to prevent OOM issues when shuffling or creating index tables.

ResourceExhaustedError in TensorFlow usually means your operation tried to allocate more memory (typically GPU) than available. Reduce the batch size, image/sequence dimensions, model size, or number of parallel threads/processes to decrease memory usage. Consider using tf.data API for efficient memory management and data streaming; also, check for memory leaks or inefficient tensor operations.

TypeError in TensorFlow often arises when XLA compilation (jit_compile=True) encounters non-TensorFlow Python types (like Python booleans directly passed to tf.cond or when using >2 branches in tf.cond). To fix this, ensure that the conditional predicate is a TensorFlow boolean tensor (e.g., using tf.constant(True, dtype=tf.bool)) and convert intermediate values to Tensors when using multiple branches in `tf.cond`. For example, cast Python booleans to tf.constant and use tf.identity to enforce the same output type across different `tf.cond` branches.