(x, name, num_features, kernel_size, stride, group)
| 24 | |
| 25 | |
| 26 | def conv2d(x, name, num_features, kernel_size, stride, group): |
| 27 | input_features = x.get_shape()[3] |
| 28 | w = tf.get_variable(name + "_W", [kernel_size, kernel_size, int(input_features) // group, num_features]) |
| 29 | b = tf.get_variable(name + "_b", [num_features]) |
| 30 | return conv(x, w, b, kernel_size, kernel_size, num_features, stride, stride, padding="SAME", group=group) |
| 31 | |
| 32 | |
| 33 | def linear(x, name, size): |