NumPy NotImplementedError: Cannot convert a symbolic Tensor (dense_1_target:0) to a numpy array

Introduction

When working with TensorFlow and NumPy, it is not uncommon to encounter an error that reads “NotImplementedError: Cannot convert a symbolic Tensor (dense_1_target:0) to a numpy array”. This error is typically raised when an operation that expects a NumPy array is instead given a TensorFlow tensor, usually symbolic, that cannot be directly handled the same way as a NumPy array. This guide will walk you through understanding the reasons behind this error and offer actionable solutions to fix it.

Understanding the Error

The root cause of the NotImplementedError in the context of TensorFlow 2.x and Keras arises from an interaction between NumPy and TensorFlow’s eagerness to execute operations. TensorFlow tensors have fundamentally different properties from NumPy arrays, and functions that are designed to work only with NumPy arrays can’t handle symbolic TensorFlow tensors. Additionally, TensorFlow 2.x has adopted eager execution by default, which allows for more Pythonic coding but also increases the likelihood of these kinds of interoperability issues.

Solution 1: Convert Tensors to NumPy with .numpy()

One straightforward solution is to convert the TensorFlow tensor into a NumPy array using the .numpy() method. This is applicable when you are sure that the tensor you are dealing with is eager and does not partake in the computational graph.

Steps to implement:

1. Ensure that you’re working in eager execution mode (which is the default in TensorFlow 2.x).
2. Directly call .numpy() on the eager tensor to convert it to a NumPy array.

Code example:

import tensorflow as tf

# Assuming `tensor` is a TensorFlow EagerTensor
numpy_array = tensor.numpy()
print(numpy_array)

Notes: Using the .numpy() method is a simple and effective approach when you can guarantee that your tensors are eager. However, applying this method on symbolic tensors that are part of a graph will raise an AttributeError, as symbolic tensors do not possess a numpy method.

Solution 2: Using TensorFlow functions

Sometimes it’s not possible or desirable to convert a symbolic tensor to a NumPy array. In cases where the tensor is symbolic, using TensorFlow operations that are designed to work specifically with TensorFlow tensors is the more suitable approach.

Steps to implement:

1. Review your code to identify where you’re attempting to use a NumPy operation on a TensorFlow tensor.
2. Replace the NumPy operation with its TensorFlow equivalent. TensorFlow has a comprehensive API that often mirrors NumPy in functionality.

Code example:

import tensorflow as tf

# Example for TensorFlow operation
# instead of NumPy operation
result = tf.reduce_sum(tensor)
print(result)

Notes: TensorFlow’s functions often behave differently than their NumPy counterparts, especially concerning how the computational graph is managed. This method keeps the tensors within the TensorFlow ecosystem, hence will not cause NotImplementedErrors, but it requires a good understanding of TensorFlow operations.

Solution 3: EagerTensor Evaluation

Another approach is to ensure that tensors are evaluated as eager tensors before performing operations that do not handle symbolic tensors. This usually involves evaluating a tensor within a session or with the use of the tf.compat.v1.enable_eager_execution() in compatibility mode.

Steps to implement:

1. If you’re running TensorFlow in v1 compatibility mode, enable eager execution by calling tf.compat.v1.enable_eager_execution() at the start of your script.
2. Evaluate your tensor within a session or using the tensor.eval() method if you are in TensorFlow 1.x environment.

Code example:

import tensorflow as tf
tf.compat.v1.enable_eager_execution()

# Assuming `tensor` is a placeholder or symbolic in TensorFlow 1.x
with tf.compat.v1.Session() as sess:
    eager_tensor = tensor.eval()

Notes: This solution is aimed more at ensuring compatibility between TensorFlow 1.x and 2.x codebases. It works around the issue by leveraging eager execution in scenarios where it wasn’t the default behavior.

Conclusion

Encountering the NotImplementedError in the interaction between NumPy and TensorFlow tensors indicates a need for careful assessment of the operations being performed. Whether it means converting tensors to arrays when safe to do so, using TensorFlow’s own robust library of operations, or enforcing eager execution throughout your workflow, there are multiple ways to approach the error. Understanding these solutions and when to apply them will help streamline your machine learning development process and avoid compatibility issues.