AttributeError: Tensor.op is meaningless when eager execution is enabled

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I am trying to implement RESNET 50 from scratch. After accumulating all the layers, I call tf.keras.Model. However, it gives an error:

AttributeError: Tensor.op is meaningless when eager execution is enabled.

For testing, I am inputting a 4-D tensor. conv_diff_size and conv_same_size are two custom blocks having con2d and batch-normalization layers. I am using TensorFlow 2.0 on Google Colab.

def ResNet50(inputs, classes):
  X = tf.keras.layers.Conv2D(64, kernel_size = (7,7), strides=2, padding='valid', data_format='channels_last', input_shape = inputs.shape)(inputs)
  X = tf.keras.layers.BatchNormalization(axis=-1, momentum=0.9)(X)
  X = tf.keras.layers.MaxPool2D(pool_size=(3, 3), strides=2)(X)

  X = conv_diff_size(X, [64, 64, 256])
  X = conv_same_size(X, [64, 64, 256])
  X = conv_same_size(X, [64, 64, 256])
  
  X = conv_diff_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  
  X = conv_diff_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  
  X = conv_diff_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
              
  X = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), name = 'avg_pool')(X)
  X = tf.keras.layers.Flatten()(X)
  X = tf.keras.layers.Dense(classes, activation='relu')(X)
  
  model = tf.keras.Model(inputs=X, outputs = X)
  return model
resnet tensorflow2.0 conv-neural-network keras deep-learning
3 Answers

Sometimes this happens if you forgot to compile your model first. Make sure you're running model.compile(...) before you begin training.

user_25141300

If you are testing gradually your scripts in an interactive python environment (e.g. changing layers design after compilation or training), try to remove all the variables and start over.

user_157765660

the problem in your code is you are giving X as input as well as output.

Try this

import tensorflow as tf

def ResNet50(input_shape, classes):
  inputs = tf.keras.Input(shape=input_shape)#input_shape = (224,224,3)
  X = tf.keras.layers.Conv2D(64, kernel_size = (7,7), strides=2, padding='valid', data_format='channels_last', input_shape = inputs.shape)(inputs)
  X = tf.keras.layers.BatchNormalization(axis=-1, momentum=0.9)(X)
  X = tf.keras.layers.MaxPool2D(pool_size=(3, 3), strides=2)(X)

  X = conv_diff_size(X, [64, 64, 256])
  X = conv_same_size(X, [64, 64, 256])
  X = conv_same_size(X, [64, 64, 256])

  X = conv_diff_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])
  X = conv_same_size(X, [128, 128, 512])

  X = conv_diff_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])
  X = conv_same_size(X, [256, 256, 1024])

  X = conv_diff_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
  X = conv_same_size(X, [512, 512, 2048])
            
  X = tf.keras.layers.AveragePooling2D(pool_size=(2, 2), name = 'avg_pool')(X)
  X = tf.keras.layers.Flatten()(X)
  out = tf.keras.layers.Dense(classes, activation='relu')(X)

  model = tf.keras.Model(inputs=inputs, outputs = out)
  return model

I have added a input tensor layer and assigned it to variable inputs and the final layer to variable out

user_103840940
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