title: Understanding the output shape of conv2d layer in keras
body: <p>I do not understand why the channel dimension is not included in the output dimension of a conv2D layer in Keras.</p>
  
  <p>I have the following model</p>
  
  <pre><code>def create_model():
      image = Input(shape=(128,128,3))
  
      x = Conv2D(24, kernel_size=(8,8), strides=(2,2), activation='relu', name='conv_1')(image)
      x = Conv2D(24, kernel_size=(8,8), strides=(2,2), activation='relu', name='conv_2')(x)
      x = Conv2D(24, kernel_size=(8,8), strides=(2,2), activation='relu', name='conv_3')(x)
      flatten = Flatten(name='flatten')(x)
  
      output = Dense(1, activation='relu', name='output')(flatten)
      model = Model(input=image, output=output)
      return model
  
  model = create_model()
  model.summary()
  </code></pre>
  
  <p>The model summary is given the figure at the end of my question. The input layer takes RGB images with width = 128 and height = 128. The first conv2D layer tells me the output dimension is (None, 61, 61, 24). I have used the kernel size of (8, 8), a stride of (2, 2) no padding. The values 61 = floor( (128 - 8 + 2 * 0)/2 + 1) and 24 (number of kernels/filters) makes sense. <strong>But why isn't the dimension for the different channels included in the dimension?</strong> As far as I can see the parameters for the 24 filters on each of the channels is included in the number of the parameters. <strong>So I would expect the output dimension to be (None, 61, 61, 24, 3) or (None, 61, 61, 24 * 3). Is this just a strange notation in Keras or am I confused about something else?</strong></p>
  
  <p><a href="https://i.stack.imgur.com/Ptyqi.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Ptyqi.png" alt="enter image description here"></a></p>