title: Processing 4D input with 2D Convolution in Tensorflow
body: <p>I am having trouble understanding how 2D Conv calculations are done on 4D inputs. Basically, this is the situation, I have an image of height, width, channels = 128, 128, 103. I want each of these 103 channels to be processed individually as if I'm inputting them to the network one by one. Would the following line work?</p>
  <pre><code>import tensorflow.keras
  from tensorflow.keras.layers import Conv2D
  model1 = tensorflow.keras.models.Sequential()
  model1.add(Conv2D(1, kernel_size=(3,3), input_shape = (128, 128,103,1), padding='same'))
  </code></pre>
  <p><a href="https://i.stack.imgur.com/xlm3m.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/xlm3m.png" alt="enter image description here" /></a></p>
  <p>I want to avoid splitting the image and inputting it into the network as 103 batches of (128,128,1)</p>