Solution: Convolution Output Shape

Solution

The answer is 14x14x20 .

We can get the new height and width with the formula resulting in:

(32 - 8 + 2 * 1)/2 + 1 = 14
(32 - 8 + 2 * 1)/2 + 1 = 14

The new depth is equal to the number of filters, which is 20.

This would correspond to the following code:

input = tf.placeholder(tf.float32, (None, 32, 32, 3))
filter_weights = tf.Variable(tf.truncated_normal((8, 8, 3, 20))) # (height, width, input_depth, output_depth)
filter_bias = tf.Variable(tf.zeros(20))
strides = [1, 2, 2, 1] # (batch, height, width, depth)
padding = 'SAME'
conv = tf.nn.conv2d(input, filter_weights, strides, padding) + filter_bias

Note the output shape of conv will be [1, 16, 16, 20]. It's 4D to account for batch size, but more importantly, it's not [1, 14, 14, 20]. This is because the padding algorithm TensorFlow uses is not exactly the same as the one above. An alternative algorithm is to switch padding from 'SAME' to 'VALID' which would result in an output shape of [1, 13, 13, 20]. If you're curious how padding works in TensorFlow, read this document .

In summary TensorFlow uses the following equation for 'SAME' vs 'VALID'

SAME Padding , the output height and width are computed as:

out_height = ceil(float(in_height) / float(strides[1]))

out_width = ceil(float(in_width) / float(strides[2]))

VALID Padding , the output height and width are computed as:

out_height = ceil(float(in_height - filter_height + 1) / float(strides[1]))

out_width = ceil(float(in_width - filter_width + 1) / float(strides[2]))

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