title: Image with 2 color channels?
body: <p>I have a dataset with over 12,000 images and while evaluating some of them visually they have an image shape which looks normal as I would expect with 3 color channels RGB</p>
  <pre><code>['10371.jpg']
   image shape: (375, 500, 3)
   array([[[128, 129, 134],
          [129, 130, 134],
          [130, 131, 135],
          ...,
          [209, 214, 218],
          [209, 214, 218],
          [209, 214, 218]],
  
         [[125, 126, 130],
          [126, 127, 131],
          [127, 128, 132],
          ...,
          [209, 214, 218],
          [209, 214, 218],
          [209, 214, 218]],
  
         [[123, 124, 128],
          [123, 124, 128],
          [124, 125, 129],
          ...,
          [206, 213, 219],
          [206, 213, 219],
          [206, 213, 219]],
  
         ...,
  
         [[196, 196, 196],
          [196, 196, 196],
          [196, 196, 196],
          ...,
          [ 81,  76,  70],
          [ 87,  82,  76],
          [ 88,  83,  77]],
  
         [[196, 196, 196],
          [196, 196, 196],
          [196, 196, 196],
          ...,
          [ 95,  90,  84],
          [106, 101,  95],
          [106, 101,  95]],
  
         [[196, 196, 196],
          [196, 196, 196],
          [196, 196, 196],
          ...,
          [ 99,  94,  88],
          [111, 106, 100],
          [109, 104,  98]]], dtype=uint8)
  </code></pre>
  <p>But then when trying to run through a model I get the error:</p>
  <pre><code>import tensorflow as tf
  from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Rescaling, Activation
  from tensorflow.keras import Sequential
  from tensorflow.keras.optimizers import Adam
  
  def baseline_model():
    model = Sequential([
    Rescaling(1.0/255.0),                    
    Conv2D(filters=10,
           kernel_size=3,
           strides=1,
           input_shape=(224, 224, 3)),
    Activation(activation='relu'),
    MaxPool2D(),
    Conv2D(filters=10,
           kernel_size=3,
           strides=1),
    Activation(activation='relu'),
    MaxPool2D(),
    Conv2D(filters=10,
           kernel_size=3,
           strides=1),
    Activation(activation='relu'),
    MaxPool2D(),
    Flatten(),
    Dense(1, activation='sigmoid')                                         
    ])
    return model
  
  baseline_model_0 = baseline_model()
  
  baseline_model_0.compile(loss='binary_crossentropy',
                           optimizer=Adam(learning_rate=0.001),
                           metrics=['accuracy'])
  
  
  history_0_baseline = baseline_model_0.fit(train_data,
                                            epochs=5,
                                            validation_data=test_data,
                                            steps_per_epoch=len(train_data),
                                            validation_steps=int(0.15 * len(test_data)),  # Evaluate on 15% of the testing data
                                            callbacks=create_tensorboard_callback(dir_name=tensorboard_dir_name,
                                                                                  experiment_name='baseline_model_0'))
  
  InvalidArgumentError: Graph execution error:
  
  Number of channels inherent in the image must be 1, 3 or 4, was 2
       [[{{node decode_image/DecodeImage}}]]
       [[IteratorGetNext]] [Op:__inference_train_function_1885]
  </code></pre>
  <p>How might I go about finding the image/images that are presenting the issue?</p>