I'd like to build an architecture that is similar to VGG16. If we ignore the computational cost without reducing the number of filters. Can I achieve p4m properties by simply inserting the following layers right after vgg5_3, please? (we retain all of CNN layers in VGG16)
Or, can i say that I can achieve p4m by training dense layers and above layers and then fine-tuning the whole networks?
After training, can i say they have properties, such as symmetry and orthogonal?
Suppose that we obtain a feature in size of (batch_size, height, width, channel*8), can we achieve transformation-invariant feature by reducing it into (batch_size, height, width, channel). If we can, which is better between reduce_mean and reduce_max?
Hi Dr. Cohen,
Thank you for releasing GrouPy.
I'd like to build an architecture that is similar to VGG16. If we ignore the computational cost without reducing the number of filters. Can I achieve p4m properties by simply inserting the following layers right after vgg5_3, please? (we retain all of CNN layers in VGG16)
gconv_indices, gconv_shape_info, w_shape = gconv2d_util( h_input='Z2', h_output='D4', in_channels=3, out_channels=64, ksize=3) w = tf.Variable(tf.truncated_normal(w_shape, stddev=1.)) y = gconv2d(input=x, filter=w, strides=[1, 1, 1, 1], padding='SAME', gconv_indices=gconv_indices, gconv_shape_info=gconv_shape_info)
gconv_indices, gconv_shape_info, w_shape = gconv2d_util( h_input='D4', h_output='D4', in_channels=64, out_channels=64, ksize=3) w = tf.Variable(tf.truncated_normal(w_shape, stddev=1.)) y = gconv2d(input=y, filter=w, strides=[1, 1, 1, 1], padding='SAME', gconv_indices=gconv_indices, gconv_shape_info=gconv_shape_info)
Or, can i say that I can achieve p4m by training dense layers and above layers and then fine-tuning the whole networks? After training, can i say they have properties, such as symmetry and orthogonal? Suppose that we obtain a feature in size of (batch_size, height, width, channel*8), can we achieve transformation-invariant feature by reducing it into (batch_size, height, width, channel). If we can, which is better between reduce_mean and reduce_max?
Thanks!