Resolving AttributeError and ValueError when using Custom Layer and tf.concat() in TensorFlow Model

Keras
,
1

Hi!
Thanks in advance for any guidance, I appreciate your time efforts!

Here’s a block of code that demonstrates all four errors I have encountered:

#!/usr/bin/env python3

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense

class CustomLayer(tf.keras.layers.Layer):
    def __init__(self, axis=-1, **kwargs):
        super(CustomLayer, self).__init__(**kwargs)
        self.axis = axis

    def call(self, inputs):
        # Simulate returning a tuple instead of a tensor
        return (inputs[0], inputs[1])

def build_model():
    input_A = Input(shape=(1,), dtype=tf.float32)
    input_B = Input(shape=(1,), dtype=tf.float32)
    
    # Error 1: AttributeError: 'tuple' object has no attribute 'rank'
    concatenated = CustomLayer(axis=-1)([input_A, input_B])
    
    # Error 2: ValueError: A KerasTensor cannot be used as input to a TensorFlow function
    concatenated_tf = tf.concat([input_A, input_B], axis=-1)
    
    dense = Dense(64, activation='relu')(concatenated)
    output = Dense(1, activation='linear')(dense)
    
    model = Model(inputs=[input_A, input_B], outputs=output)
    return model

# Error 3: Warning: Could not find TensorRT
# This warning is related to TensorFlow's integration with NVIDIA TensorRT
# and can be safely ignored if you don't plan to use TensorRT.

try:
    model = build_model()
except AttributeError as e:
    # Error 4: AttributeError: 'tuple' object has no attribute 'rank'
    print("Error 4:", str(e))
except ValueError as e:
    print("Error 2:", str(e))

In this code:

  1. The CustomLayer simulates returning a tuple instead of a tensor in its call method, leading to the AttributeError: 'tuple' object has no attribute 'rank' error when passing the output of CustomLayer to the subsequent Dense layer.
  2. The line concatenated_tf = tf.concat([input_A, input_B], axis=-1) demonstrates the ValueError: A KerasTensor cannot be used as input to a TensorFlow function error when directly using a KerasTensor as input to a TensorFlow function like tf.concat.
  3. The comment # Error 3: Warning: Could not find TensorRT represents the warning related to TensorFlow’s integration with NVIDIA TensorRT. This warning can be safely ignored if you don’t plan to use TensorRT.
  4. The try-except block catches the AttributeError: 'tuple' object has no attribute 'rank' error that occurs when building the model.

IF I solve one problem I get one of the other two ( 1, 2, 4) , three is just annoying.

Anyone know how to fix the three main errors without causing one of the others?

greatly appreciate the help!

Regards,

SA

3

Hi @Steven_Anderson, The error might be due to as your customelayer is returning the tuple object. I have tried by changing the tuple object to the tensor and I did not face any error while building the model. please refer to this gist for working code example. Thank You.

5

Official TensorFlow 2.16 Repo via JARaaS Hybrid RAG - Documentation - code current 6/18/2024

Note: Sources at the end of the response

The issues you’re encountering revolve around using a custom layer in TensorFlow and the interactions between TensorFlow tensors and Keras tensors. Here’s how you can address each of the errors:

Error 1: AttributeError: ‘tuple’ object has no attribute ‘rank’

This error occurs because your custom layer returns a tuple, but a Keras layer expects a tensor. You should modify the call method of CustomLayer to return a single tensor instead of a tuple.

Error 2: ValueError: A KerasTensor cannot be used as input to a TensorFlow function

Keras tensors have to be used within the Keras functional API context. You can solve this by ensuring that all operations on Keras tensors stay within the functional API.

Error 3: Warning: Could not find TensorRT

This warning is unrelated to the main issues and can be ignored if you don’t use TensorRT. TensorRT is used for optimized inference on NVIDIA GPUs.

Resolution:

Here is the revised code with the explanations embedded:

#!/usr/bin/env python3  

import tensorflow as tf  
from tensorflow.keras.models import Model  
from tensorflow.keras.layers import Input, Dense, Concatenate

class CustomLayer(tf.keras.layers.Layer):  
    def __init__(self, axis=-1, **kwargs):  
        super(CustomLayer, self).__init__(**kwargs)  
        self.axis = axis  
 
    def call(self, inputs):  
        # Properly concatenate inputs within the custom layer
        return tf.concat(inputs, axis=self.axis)  

def build_model():  
    input_A = Input(shape=(1,), dtype=tf.float32)  
    input_B = Input(shape=(1,), dtype=tf.float32)  
      
    # Using the modified CustomLayer that correctly processes tensors
    concatenated = CustomLayer(axis=-1)([input_A, input_B])  

    # Ensure concatenation occurs in functional API
    concatenated_tf = Concatenate(axis=-1)([input_A, input_B])  
      
    dense = Dense(64, activation='relu')(concatenated)  
    output = Dense(1, activation='linear')(dense)  
      
    model = Model(inputs=[input_A, input_B], outputs=output)  
    return model  

try:  
    model = build_model()  
    model.summary()
except AttributeError as e:  
    print("AttributeError:", str(e))  
except ValueError as e:  
    print("ValueError:", str(e))

Explanation:

  1. Custom Layer Modification:

    def call(self, inputs):  
    # Properly concatenate inputs within the custom layer.
    return tf.concat(inputs, axis=self.axis)

    The call method now returns a single concatenated tensor instead of a tuple.

  2. Use Concatenate Layer:

    concatenated_tf = Concatenate(axis=-1)([input_A, input_B])

    The Concatenate layer from Keras ensures all operations stay within the Keras functional API.

Sources:

Internal documents:

  • TensorFlow Custom Layer: model_mapping.ipynb (internal document)
  • TensorFlow Installation Errors and Common Issues: errors.md (internal document)

This should resolve the main issues you’re encountering without causing one to appear when another is fixed.