Visualizing activations of 3D convolutional filters using keras-vis library.
| Layer (type) |
Output Shape |
Param |
| conv1 (Conv3D) |
(None, 16, 112, 112, 64) |
5248 |
| pool1 (MaxPooling3D) |
(None, 16, 56, 56, 64) |
0 |
| conv2 (Conv3D) |
(None, 16, 56, 56, 128) |
221312 |
| pool2 (MaxPooling3D) |
(None, 8, 28, 28, 128) |
0 |
| conv3a (Conv3D) |
(None, 8, 28, 28, 256) |
884992 |
| conv3b (Conv3D) |
(None, 8, 28, 28, 256) |
1769728 |
| pool3 (MaxPooling3D) |
(None, 4, 14, 14, 256) |
0 |
| conv4a (Conv3D) |
(None, 4, 14, 14, 512) |
3539456 |
| conv4b (Conv3D) |
(None, 4, 14, 14, 512) |
7078400 |
| pool4 (MaxPooling3D) |
(None, 2, 7, 7, 512) |
0 |
| conv5a (Conv3D) |
(None, 2, 7, 7, 512) |
7078400 |
| conv5b (Conv3D) |
(None, 2, 7, 7, 512) |
7078400 |
| zero_padding3d_2 (ZeroPadding) |
(None, 2, 9, 9, 512) |
0 |
| pool5 (MaxPooling3D) |
(None, 1, 4, 4, 512) |
0 |
| flatten_2 (Flatten) |
(None, 8192) |
0 |
| fc6 (Dense) |
(None, 4096) |
33558528 |
| dropout_3 (Dropout) |
(None, 4096) |
0 |
| fc7 (Dense) |
(None, 4096) |
16781312 |
| dropout_4 (Dropout) |
(None, 4096) |
0 |
| fc8 (Dense) |
(None, 487) |
1995239 |
Possible to use the pre-trained model in Caffe format or convert it to Keras format or simply download model weights in Keras format from here.
3D CNN cativations of filters
conv1
conv2
conv3a
conv3b
conv4a
conv4b
conv5a
conv5b
