Keras Conv2D and input channels

Question

The Keras layer documentation specifies the input and output sizes for convolutional layers: https://keras.io/layers/convolutional/

Input shape: (samples, channels

Answer 1

I also needed to convince myself so I ran a simple example with a 3×3 RGB image.

# red    # green        # blue
1 1 1    100 100 100    10000 10000 10000
1 1 1    100 100 100    10000 10000 10000    
1 1 1    100 100 100    10000 10000 10000

The filter is initialised to ones:

1 1
1 1

I have also set the convolution to have these properties:

• no padding
• strides = 1
• relu activation function
• bias initialised to 0

We would expect the (aggregated) output to be:

40404 40404
40404 40404

Also, from the picture above, the no. of parameters is

3 separate filters (one for each channel) × 4 weights + 1 (bias, not shown) = 13 parameters

---

Here's the code.

Import modules:

import numpy as np
from keras.layers import Input, Conv2D
from keras.models import Model

Create the red, green and blue channels:

red   = np.array([1]*9).reshape((3,3))
green = np.array([100]*9).reshape((3,3))
blue  = np.array([10000]*9).reshape((3,3))

Stack the channels to form an RGB image:

img = np.stack([red, green, blue], axis=-1)
img = np.expand_dims(img, axis=0)

Create a model that just does a Conv2D convolution:

inputs = Input((3,3,3))
conv = Conv2D(filters=1, 
              strides=1, 
              padding='valid', 
              activation='relu',
              kernel_size=2, 
              kernel_initializer='ones', 
              bias_initializer='zeros', )(inputs)
model = Model(inputs,conv)

Input the image in the model:

model.predict(img)
# array([[[[40404.],
#          [40404.]],

#         [[40404.],
#          [40404.]]]], dtype=float32)

Run a summary to get the number of params:

model.summary()