You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hi in your convolutional_network.ipynb file. Do you mind how you come with the number of 7_7_64 in
'wd1': tf.Variable(tf.random_normal([7_7_64, 1024])) . I am still confuse as how you are calculating this number. I think it is not arbitary number right?
The text was updated successfully, but these errors were encountered:
Yes, it is calculated based on the previous layer, to know total number of parameters. Basically, you have:
input: (28, 28, 1) Pictures of size 28x28 with 1 color channel (grayscale)
conv1, 32 filters, strides=1:
(28, 28, 32)
maxpool1, kernel_size=2:
(14, 14, 32)
conv2, 64 filters, strides=1:
(14, 14, 64)
maxpool2, kernel_size=2:
(7, 7, 64) => 7x7x64 outputs
(that we flatten to connect to a dense layer)
(3136)
Hi in your convolutional_network.ipynb file. Do you mind how you come with the number of 7_7_64 in
'wd1': tf.Variable(tf.random_normal([7_7_64, 1024])) . I am still confuse as how you are calculating this number. I think it is not arbitary number right?
The text was updated successfully, but these errors were encountered: