I have this framer motion-built marquee but it seems to skip before the text finishes going all the way through, any thoughts why? It animated fine but skips back to the beginning at the fifth word. Maybe be the values given for the x-coordinate?
.marquee {
position: relative;
width: 100vw;
max-width: 100%;
height: 206px;
overflow-x: hidden;
}
.track {
position: absolute;
white-space: nowrap;
}
.track > h1 {
margin: 20px 0;
font-size: 8rem;
font-family: machina;
-webkit-text-fill-color: rgba(255, 255, 255, 0);
-webkit-text-stroke-width: 2px;
-webkit-text-stroke-color: #f4955c;
text-transform: uppercase;
}
import React from 'react'
import { motion } from 'framer-motion'
const marqueeVariants = {
animate: {
x: [0, -1400],
transition: {
x: {
repeat: Infinity,
repeatType: "loop",
duration: 10,
ease: "linear",
},
},
},
};
const Marquee = () => {
return (
<div className='marquee machina'>
<motion.div
className='track'
variants={marqueeVariants}
animate='animate'>
<h1>Changing the way you view fitness by re-defining your holisitc RITUAL.</h1>
</motion.div>
</div>
)
}
export default Marquee
yes, you need to manually synchronize the two values. I suggest that you temporarily put a delay in the transition, to give you time to calibrate.
I have been using the AlexNet for a pixel-wise regression task (depth estimation). Now I wanted to replace the AlexNet with the VGG net since it is ought to be better.
This is the AlexNet I used:
layer {
name: "train-data"
type: "Data"
top: "data"
include {
phase: TRAIN
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
transform_param {
mean_value: 127
}
}
layer {
name: "train-depth"
type: "Data"
top: "gt"
include {
phase: TRAIN
}
transform_param {
# feature scaling coefficient: this maps [0, 255] to [0, 1]
scale: 0.00390625
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
}
layer {
name: "val-data"
type: "Data"
top: "data"
include {
phase: TEST
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
transform_param {
mean_value: 127
}
}
layer {
name: "val-depth"
type: "Data"
top: "gt"
include {
phase: TEST
}
transform_param {
# feature scaling coefficient: this maps [0, 255] to [0, 1]
scale: 0.00390625
}
data_param {
source: "../data/.."
batch_size: 4
backend: LMDB
}
}
# CONVOLUTIONAL
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
engine: CAFFE
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "norm2"
top: "conv3"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 0.02
decay_mult: 1
}
param {
lr_mult: 0.02
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
# MAIN
layer {
name: "fc-main"
type: "InnerProduct"
bottom: "pool5"
top: "fc-main"
param {
decay_mult: 1
}
param {
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "xavier"
std: 0.005
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc-main"
top: "fc-main"
relu_param {
engine: CAFFE
}
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc-main"
top: "fc-main"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc-depth"
type: "InnerProduct"
bottom: "fc-main"
top: "fc-depth"
param {
decay_mult: 1
lr_mult: 0.2
}
param {
lr_mult: 0.2
decay_mult: 0
}
inner_product_param {
num_output: 1369
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0.5
}
}
}
layer {
name: "reshape"
type: "Reshape"
bottom: "fc-depth"
top: "depth"
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: 1
dim: 37
dim: 37 # infer it from the other dimensions
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "depth"
bottom: "gt"
top: "loss"
loss_weight: 1
}
This is the VGG I am using:
layer {
bottom: "data"
top: "conv1_1"
name: "conv1_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv1_1"
top: "conv1_1"
name: "relu1_1"
type: "ReLU"
}
layer {
bottom: "conv1_1"
top: "conv1_2"
name: "conv1_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv1_2"
top: "conv1_2"
name: "relu1_2"
type: "ReLU"
}
layer {
bottom: "conv1_2"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool1"
top: "conv2_1"
name: "conv2_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv2_1"
top: "conv2_1"
name: "relu2_1"
type: "ReLU"
}
layer {
bottom: "conv2_1"
top: "conv2_2"
name: "conv2_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv2_2"
top: "conv2_2"
name: "relu2_2"
type: "ReLU"
}
layer {
bottom: "conv2_2"
top: "pool2"
name: "pool2"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool2"
top: "conv3_1"
name: "conv3_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv3_1"
top: "conv3_1"
name: "relu3_1"
type: "ReLU"
}
layer {
bottom: "conv3_1"
top: "conv3_2"
name: "conv3_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv3_2"
top: "conv3_2"
name: "relu3_2"
type: "ReLU"
}
layer {
bottom: "conv3_2"
top: "conv3_3"
name: "conv3_3"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv3_3"
top: "conv3_3"
name: "relu3_3"
type: "ReLU"
}
layer {
bottom: "conv3_3"
top: "pool3"
name: "pool3"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool3"
top: "conv4_1"
name: "conv4_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv4_1"
top: "conv4_1"
name: "relu4_1"
type: "ReLU"
}
layer {
bottom: "conv4_1"
top: "conv4_2"
name: "conv4_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv4_2"
top: "conv4_2"
name: "relu4_2"
type: "ReLU"
}
layer {
bottom: "conv4_2"
top: "conv4_3"
name: "conv4_3"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv4_3"
top: "conv4_3"
name: "relu4_3"
type: "ReLU"
}
layer {
bottom: "conv4_3"
top: "pool4"
name: "pool4"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool4"
top: "conv5_1"
name: "conv5_1"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv5_1"
top: "conv5_1"
name: "relu5_1"
type: "ReLU"
}
layer {
bottom: "conv5_1"
top: "conv5_2"
name: "conv5_2"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv5_2"
top: "conv5_2"
name: "relu5_2"
type: "ReLU"
}
layer {
bottom: "conv5_2"
top: "conv5_3"
name: "conv5_3"
type: "Convolution"
param {
lr_mult: 0.001
decay_mult: 1
}
param {
lr_mult: 0.001
decay_mult: 0
}
convolution_param {
num_output: 512
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
bottom: "conv5_3"
top: "conv5_3"
name: "relu5_3"
type: "ReLU"
}
layer {
bottom: "conv5_3"
top: "pool5"
name: "pool5"
type: "Pooling"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
bottom: "pool5"
top: "fc6"
name: "fc6"
type: "InnerProduct"
param {
lr_mult: 0.1
decay_mult: 1
}
param {
lr_mult: 0.1
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0.5
}
}
}
layer {
bottom: "fc6"
top: "fc6"
name: "relu6"
type: "ReLU"
relu_param {
engine: CAFFE
}
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
bottom: "fc6"
top: "fc7"
name: "fc7"
type: "InnerProduct"
param {
lr_mult: 0.1
decay_mult: 1
}
param {
lr_mult: 0.1
decay_mult: 0
}
inner_product_param {
num_output: 1369
weight_filler {
type: "gaussian"
std: 0.001
}
bias_filler {
type: "constant"
value: 0.5
}
}
}
layer {
name: "reshape"
type: "Reshape"
bottom: "fc7"
top: "depth"
reshape_param {
shape {
dim: 0 # copy the dimension from below
dim: 1
dim: 37
dim: 37 # infer it from the other dimensions
}
}
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "depth"
bottom: "gt"
top: "loss"
loss_weight: 1
}
learning_rate is: 0.0005
When training the AlexNet the loss converges to roughly 5 and when using the VGG the net does not converge at all. It always stays at 30, even though I have been decreasing the learning_rate and even decreased mult_lr. Does anyone have any ideas what else could be wrong? I am 100% sure that only the .prototxt files are different, everything else is exactly the same.
It is known that VGG is hard to train from scratch for large networks: in the paper of Simonyan and Zisserman, it is said in section 3.1. In practice, they first train a "small" (A) configuration with random weights then use these weights to initialize larger networks (C-D-E). Moreover, you may need more data to train VGG than AlexNet.
In your case, you may consider to fine-tune VGG16 rather than learning it from scratch. Or let use googlenet that is much lighter (and easier to train) and lead to similar performance on several problems I tested.
Now it is quite a long time (almost two months) that I was working on FCN32 for semantic segmentation of single channel images. I played around with different learning rates and even adding BatchNormalization layer. However, I was not successful to even see any output. I did not have any choice except to instantly ask for help here. I really do not know what I am doing wrong.
I am sending one image to the network as a batch.This the train-loss curve LR=1e-9 and lr_policy="fixed":
I increased the learning rate to 1e-4(the following figure). It seems that loss is falling down, however, the learning curve is not acting normal.
I reduced the layers of original FCN as follows:
(1) Conv64 – ReLU – Conv64 – ReLU – MaxPool
(2) Conv128 – ReLU – Conv128 – ReLU – MaxPool
(3) Conv256 – ReLU – Conv256 – ReLU – MaxPool
(4) Conv4096 – ReLU – Dropout0.5
(5) Conv4096 – ReLU – Dropout0.5
(6) Conv2
(7) Deconv32x – Crop
(8) SoftmaxWithLoss
layer {
name: "data"
type: "Data"
top: "data"
include {
phase: TRAIN
}
transform_param {
mean_file: "/jjj/FCN32_mean.binaryproto"
}
data_param {
source: "/jjj/train_lmdb/"
batch_size: 1
backend: LMDB
}
}
layer {
name: "label"
type: "Data"
top: "label"
include {
phase: TRAIN
}
data_param {
source: "/jjj/train_label_lmdb/"
batch_size: 1
backend: LMDB
}
}
layer {
name: "data"
type: "Data"
top: "data"
include {
phase: TEST
}
transform_param {
mean_file: "/jjj/FCN32_mean.binaryproto"
}
data_param {
source: "/jjj/val_lmdb/"
batch_size: 1
backend: LMDB
}
}
layer {
name: "label"
type: "Data"
top: "label"
include {
phase: TEST
}
data_param {
source: "/jjj/val_label_lmdb/"
batch_size: 1
backend: LMDB
}
}
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data"
top: "conv1_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 100
kernel_size: 3
stride: 1
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 128
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_2"
top: "pool3"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
layer {
name: "fc6"
type: "Convolution"
bottom: "pool3"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
pad: 0
kernel_size: 7
stride: 1
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "Convolution"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 4096
pad: 0
kernel_size: 1
stride: 1
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "score_fr"
type: "Convolution"
bottom: "fc7"
top: "score_fr"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 5 #21
pad: 0
kernel_size: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "upscore"
type: "Deconvolution"
bottom: "score_fr"
top: "upscore"
param {
lr_mult: 0
}
convolution_param {
num_output: 5 #21
bias_term: false
kernel_size: 64
stride: 32
group: 5 #2
weight_filler: {
type: "bilinear"
}
}
}
layer {
name: "score"
type: "Crop"
bottom: "upscore"
bottom: "data"
top: "score"
crop_param {
axis: 2
offset: 19
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "score"
bottom: "label"
top: "accuracy"
include {
phase: TRAIN
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "score"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "score"
bottom: "label"
top: "loss"
loss_param {
ignore_label: 255
normalize: true
}
}
and this is the solver definition:
net: "train_val.prototxt"
#test_net: "val.prototxt"
test_iter: 736
# make test net, but don't invoke it from the solver itself
test_interval: 2000 #1000000
display: 50
average_loss: 50
lr_policy: "step" #"fixed"
stepsize: 2000 #+
gamma: 0.1 #+
# lr for unnormalized softmax
base_lr: 0.0001
# high momentum
momentum: 0.99
# no gradient accumulation
iter_size: 1
max_iter: 10000
weight_decay: 0.0005
snapshot: 2000
snapshot_prefix: "snapshot/NET1"
test_initialization: false
solver_mode: GPU
At the beginning, the loss is starting to fall down, but again after some iterations, it is not showing good learning behavior:
I am a beginner in deep learning and caffe. I really do not understand why this happens. I really appreciate if those that have expertise, please have a look on the model definition and I will be very thankful if you help me.
The problem is that you are training from scratch.
Reading the FCN paper will tell you that they always use networks that are pretrained on ImageNet, it will NOT work if you train it from scratch, it has to be finetuned from a pretrained network. The optimization problem if you train from random weights just doesn't converge.
I have a page with some background image.
In body tag I have a svg element with only one inner path element.
How to add backdrop-filter to path element so it could blur background in non-rectangular shape?
$(function() {
var pattern = "M0,{offsetTop} C{ax1},{power},{ax2},{power},{width},{offsetTop} L{width},{height},0,{height}Z";
var $svg = $('svg#footer');
var $path = $svg.find('path');
var settings = {
width: 1200,
height: 200,
offsetTop: 200,
power: 200
}
settings.ax1 = settings.width / 3 * 1;
settings.ax2 = settings.width / 3 * 2;
function render() {
var newPath = pattern;
for (var i in settings) {
newPath = newPath.split('{' + i + '}').join(settings[i]);
}
$path.attr('d', newPath);
}
TweenMax.set($svg, {
force3D: true
})
var opened = false;
function open() {
if (opened) {
return
}
opened = true;
TweenMax.to(settings, 0.35, {
overwrite: true,
offsetTop: 80,
ease: Strong.easeOut,
onUpdate: render
})
TweenMax.to(settings, 1, {
power: 80,
ease: Elastic.easeOut,
onUpdate: render
})
}
function close() {
if (!opened) {
return
}
opened = false;
TweenMax.to(settings, 0.35, {
overwrite: true,
offsetTop: 200,
ease: Back.easeIn,
onUpdate: render
})
TweenMax.to(settings, 0.35, {
power: 200,
delay: 0.15,
ease: Back.easeOut,
onUpdate: render
})
}
$(window).on('mousedown touchstart', function(e) {
opened ? close() : open();
})
open();
})
html,
body {
margin: 0;
padding: 0;
width: 100%;
height: 100%;
}
body {
background-image: url('http://i839.photobucket.com/albums/zz314/mrkanpuc/stuffs/1PZ1.jpg');
background-repeat: no-repeat;
background-size: cover;
}
svg {
position: absolute;
bottom: 0;
width: 100%;
height: 200px;
}
svg path {
fill: rgba(0, 0, 0, 0.5);
}
<script src="https://code.jquery.com/jquery-1.11.3.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/1.18.0/TweenMax.min.js"></script>
<svg id="footer" viewBox="0 0 1200 200" preserveAspectRatio="none"><path/></svg>
Without doing too many changes to your code, you can achieve that by increasing your power and/or decreasing your offsetTop in the open function.
TweenMax.to(settings, 0.35, {overwrite: true, offsetTop: 80, ease: Strong.easeOut, onUpdate: render })
TweenMax.to(settings, 1, {power: 120, ease: Elastic.easeOut, onUpdate: render })
$(function() {
var pattern = "M0,{offsetTop} C{ax1},{power},{ax2},{power},{width},{offsetTop} L{width},{height},0,{height}Z";
var $svg = $('svg#footer');
var $path = $svg.find('path');
var settings = {
width: 1200,
height: 200,
offsetTop: 200,
power: 200
}
settings.ax1 = settings.width / 3 * 1;
settings.ax2 = settings.width / 3 * 2;
function render() {
var newPath = pattern;
for (var i in settings) {
newPath = newPath.split('{' + i + '}').join(settings[i]);
}
$path.attr('d', newPath);
}
TweenMax.set($svg, {
force3D: true
})
var opened = false;
function open() {
if (opened) {
return
}
opened = true;
TweenMax.to(settings, 0.35, {
overwrite: true,
offsetTop: 80,
ease: Strong.easeOut,
onUpdate: render
})
TweenMax.to(settings, 1, {
power: 150,
ease: Elastic.easeOut,
onUpdate: render
})
}
function close() {
if (!opened) {
return
}
opened = false;
TweenMax.to(settings, 0.35, {
overwrite: true,
offsetTop: 200,
ease: Back.easeIn,
onUpdate: render
})
TweenMax.to(settings, 0.35, {
power: 200,
delay: 0.15,
ease: Back.easeOut,
onUpdate: render
})
}
$(window).on('mousedown touchstart', function(e) {
opened ? close() : open();
})
open();
})
html,
body {
margin: 0;
padding: 0;
width: 100%;
height: 100%;
}
body {
background-image: url('http://i839.photobucket.com/albums/zz314/mrkanpuc/stuffs/1PZ1.jpg');
background-repeat: no-repeat;
background-size: cover;
}
svg {
position: absolute;
bottom: 0;
width: 100%;
height: 200px;
}
svg path {
fill: rgba(0, 0, 0, 0.5);
}
<script src="https://code.jquery.com/jquery-1.11.3.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/1.18.0/TweenMax.min.js"></script>
<svg id="footer" viewBox="0 0 1200 200" preserveAspectRatio="none"><path/></svg>
Quadratic Bézier curve
Another solution is to add a curved path (called quadratic Bézier curve) to your rectangle. The curve is built like this:
M{startWidth}, {startHeight} q {curvePeak}, {curveHeight}, {endWidth}, {endHeight}
startWidth - x-axis positioning of P0: x coordinate where the curve starts
startHeight - y-axis positioning of P0: y coordinate where the curve starts
curvePeak - x-axis positioning of P1: where the curve reach it's peak
curveHeight - y-axis positioning of P1: height of the curve
endWidth - x-axis positioning of P2: dimension of the curve
endHeight - y-axis positioning of P2: inclination of the curve
See also: Quadratic Bézier Curve: Calculate Points or click here for an interactive example of the quadratic Bézier curve.
Negative
This solution has some problems when using two different animations and duration, like in your case.
Strong.easeOut : 0.35s
Elastic.easeOut : 1.00s
$(function() {
var pattern = "M0,{offsetTop} C{ax1},{power},{ax2},{power},{width},{offsetTop} L{width},{height},0,{height}Z q 600, 100, 1200, 0";
var $svg = $('svg#footer');
var $path = $svg.find('path');
var settings = {
width: 1200,
height: 200,
offsetTop: 200,
power: 200
}
settings.ax1 = settings.width / 3 * 1;
settings.ax2 = settings.width / 3 * 2;
function render() {
var newPath = pattern;
for (var i in settings) {
newPath = newPath.split('{' + i + '}').join(settings[i]);
}
$path.attr('d', newPath);
}
TweenMax.set($svg, {
force3D: true
})
var opened = false;
function open() {
if (opened) {
return
}
opened = true;
TweenMax.to(settings, 0.35, {
overwrite: true,
offsetTop: 80,
ease: Strong.easeOut,
onUpdate: render
})
TweenMax.to(settings, 1, {
power: 80,
ease: Elastic.easeOut,
onUpdate: render
})
}
function close() {
if (!opened) {
return
}
opened = false;
TweenMax.to(settings, 0.35, {
overwrite: true,
offsetTop: 200,
ease: Back.easeIn,
onUpdate: render
})
TweenMax.to(settings, 0.35, {
power: 200,
delay: 0.15,
ease: Back.easeOut,
onUpdate: render
})
}
$(window).on('mousedown touchstart', function(e) {
opened ? close() : open();
})
open();
})
html,
body {
margin: 0;
padding: 0;
width: 100%;
height: 100%;
}
body {
background-image: url('http://i839.photobucket.com/albums/zz314/mrkanpuc/stuffs/1PZ1.jpg');
background-repeat: no-repeat;
background-size: cover;
}
svg {
position: absolute;
bottom: 0;
width: 100%;
height: 200px;
}
svg path {
fill: rgba(0, 0, 0, 0.5);
}
<script src="https://code.jquery.com/jquery-1.11.3.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/1.18.0/TweenMax.min.js"></script>
<svg id="footer" viewBox="0 0 1200 200" preserveAspectRatio="none"><path/></svg>
Positive
On the contrary it works great when using the same animation and duration.
Both with Elastic.easeOut : 1.00s
$(function() {
var pattern = "M0,{offsetTop} C{ax1},{power},{ax2},{power},{width},{offsetTop} L{width},{height},0,{height}Z q 600, 100, 1200, 0";
var $svg = $('svg#footer');
var $path = $svg.find('path');
var settings = {
width: 1200,
height: 200,
offsetTop: 200,
power: 200
}
settings.ax1 = settings.width / 3 * 1;
settings.ax2 = settings.width / 3 * 2;
function render() {
var newPath = pattern;
for (var i in settings) {
newPath = newPath.split('{' + i + '}').join(settings[i]);
}
$path.attr('d', newPath);
}
TweenMax.set($svg, {
force3D: true
})
var opened = false;
function open() {
if (opened) {
return
}
opened = true;
TweenMax.to(settings, 1, {
overwrite: true,
offsetTop: 80,
ease: Elastic.easeOut,
onUpdate: render
})
TweenMax.to(settings, 1, {
power: 80,
ease: Elastic.easeOut,
onUpdate: render
})
}
function close() {
if (!opened) {
return
}
opened = false;
TweenMax.to(settings, 0.35, {
overwrite: true,
offsetTop: 200,
ease: Back.easeIn,
onUpdate: render
})
TweenMax.to(settings, 0.35, {
power: 200,
ease: Back.easeIn,
onUpdate: render
})
}
$(window).on('mousedown touchstart', function(e) {
opened ? close() : open();
})
open();
})
html,
body {
margin: 0;
padding: 0;
width: 100%;
height: 100%;
}
body {
background-image: url('http://i839.photobucket.com/albums/zz314/mrkanpuc/stuffs/1PZ1.jpg');
background-repeat: no-repeat;
background-size: cover;
}
svg {
position: absolute;
bottom: 0;
width: 100%;
height: 200px;
}
svg path {
fill: rgba(0, 0, 0, 0.5);
}
<script src="https://code.jquery.com/jquery-1.11.3.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/1.18.0/TweenMax.min.js"></script>
<svg id="footer" viewBox="0 0 1200 200" preserveAspectRatio="none"><path/></svg>
I trained caffe's Alexnet model for testing with more efficient model. Since my training is for pedestrians my image size is 64 x 80 images. I made changes to prototxt files to match to my trained image size. According to this tutorial, it will be better to set the convolution filter size to match the input image size. So my filter sizes have slight changes from the original Alexnet's provided prototxt files (I trained and tested with Alexnet's original prototxt files and get the same error at the same line mentioned below).
According to my calculation, image sizes after passing each layer will be
80x64x3 -> Conv1 -> 38x30x96
38x30x96 -> Pools -> 18x14x96
18x14x96 -> Conv2 -> 19x15x256
19x15x256 -> Pool2 -> 9x7x256
9x7x256 -> Conv3 -> 9x7x384
9x7x384 -> Conv4 -> 9x7x384
9x7x384 -> Conv5 -> 9x7x256
9x7x256 -> Pool5 -> 4x3x256
The error is at fc6 layer and line number 714 of test_predict_imagenet.cpp. I use test_predict_imagenet.cpp file to test the model.
CHECK_EQ(target_blobs[j]->width(), source_layer.blobs(j).width());
The error is
F0816 22:58:28.328047 3432 net.cpp:714] Check failed: target_blobs[j]->width()
== source_layer.blobs(j).width() (5120 vs. 1024)
I don't understand why it is like that.
My two prototxt files are shown below.
train_val.prototxt
name: "AlexNet"
layers {
name: "data"
type: DATA
top: "data"
top: "label"
data_param {
source: "../../examples/Alexnet/Alexnet_train_leveldb"
batch_size: 200
}
transform_param {
crop_size: 48
mean_file: "../../examples/Alexnet/mean.binaryproto"
mirror: true
}
include: { phase: TRAIN }
}
layers {
name: "data"
type: DATA
top: "data"
top: "label"
data_param {
source: "../../examples/Alexnet/Alexnet_test_leveldb"
batch_size: 200
}
transform_param {
crop_size: 48
mean_file: "../../examples/Alexnet/mean.binaryproto"
mirror: false
}
include: { phase: TEST }
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 6
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "norm1"
type: LRN
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "pool1"
type: POOLING
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 4
stride: 2
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "pool1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 2
kernel_size: 4
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "norm2"
type: LRN
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "pool2"
type: POOLING
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "pool2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
bottom: "conv3"
top: "conv4"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layers {
name: "relu4"
type: RELU
bottom: "conv4"
top: "conv4"
}
layers {
name: "conv5"
type: CONVOLUTION
bottom: "conv4"
top: "conv5"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
top: "fc6"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "accuracy"
type: ACCURACY
bottom: "fc8"
bottom: "label"
top: "accuracy"
include: { phase: TEST }
}
layers {
name: "loss"
type: SOFTMAX_LOSS
bottom: "fc8"
bottom: "label"
top: "loss"
}
This is the testing file for the model.
deploy.txt
name: "AlexNet"
layers
{
name: "data"
type: MEMORY_DATA
top: "data"
top: "label"
memory_data_param
{
batch_size: 1
channels: 3
height: 80
width: 64
}
transform_param
{
crop_size: 64
mirror: false
mean_file: "../../examples/Alexnet/mean.binaryproto"
}
}
layers {
name: "conv1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 6
stride: 2
}
bottom: "data"
top: "conv1"
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "norm1"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
bottom: "conv1"
top: "norm1"
}
layers {
name: "pool1"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
bottom: "norm1"
top: "pool1"
}
layers {
name: "conv2"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 2
kernel_size: 4
group: 2
}
bottom: "pool1"
top: "conv2"
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "norm2"
type: LRN
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
bottom: "conv2"
top: "norm2"
}
layers {
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
bottom: "norm2"
top: "pool2"
}
layers {
name: "conv3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
bottom: "pool2"
top: "conv3"
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
bottom: "conv3"
top: "conv4"
}
layers {
name: "relu4"
type: RELU
bottom: "conv4"
top: "conv4"
}
layers {
name: "conv5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
bottom: "conv4"
top: "conv5"
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
bottom: "conv5"
top: "pool5"
}
layers {
name: "fc6"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
}
bottom: "pool5"
top: "fc6"
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
bottom: "fc6"
top: "fc6"
}
layers {
name: "fc7"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
}
bottom: "fc6"
top: "fc7"
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
bottom: "fc7"
top: "fc7"
}
layers {
name: "fc8"
type: INNER_PRODUCT
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 2
}
bottom: "fc7"
top: "fc8"
}
layers {
name: "prob"
type: SOFTMAX
bottom: "fc8"
top: "prob"
}
What is wrong with this error?
Those who have the same problem as i faced, please look at the prototxt files shown below. There are some modifications made compared to the original prototxt files provided in the downloaded folders. I used 80x64 image sizes for the input in training and testing.
Train_val.prototxt
name: "AlexNet"
layers {
name: "data"
type: DATA
top: "data"
top: "label"
data_param {
source: "../../examples/Alexnet_2/Alexnet_train_leveldb"
batch_size: 100
}
transform_param {
mean_file: "../../examples/Alexnet_2/mean.binaryproto"
}
include: { phase: TRAIN }
}
layers {
name: "data"
type: DATA
top: "data"
top: "label"
data_param {
source: "../../examples/Alexnet_2/Alexnet_test_leveldb"
batch_size: 100
}
transform_param {
mean_file: "../../examples/Alexnet_2/mean.binaryproto"
}
include: { phase: TEST }
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 11
stride: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm1"
type: LRN
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "norm1"
top: "conv2"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm2"
type: LRN
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "norm2"
top: "conv3"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
bottom: "conv3"
top: "conv4"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu4"
type: RELU
bottom: "conv4"
top: "conv4"
}
layers {
name: "conv5"
type: CONVOLUTION
bottom: "conv4"
top: "conv5"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
top: "fc6"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 1
}
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
inner_product_param {
num_output: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
name: "accuracy"
type: ACCURACY
bottom: "fc8"
bottom: "label"
top: "accuracy"
include: { phase: TEST }
}
layers {
name: "loss"
type: SOFTMAX_LOSS
bottom: "fc8"
bottom: "label"
top: "loss"
}
test.prototxt
name: "CaffeNet"
layers
{
name: "data"
type: MEMORY_DATA
top: "data"
top: "label"
memory_data_param
{
batch_size: 1
channels: 3
height: 80
width: 64
}
transform_param
{
crop_size: 64
mirror: false
mean_file: "../../examples/Alexnet_2/mean.binaryproto"
}
}
layers {
name: "conv1"
type: CONVOLUTION
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
kernel_size: 11
stride: 2
}
}
layers {
name: "relu1"
type: RELU
bottom: "conv1"
top: "conv1"
}
layers {
name: "pool1"
type: POOLING
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm1"
type: LRN
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv2"
type: CONVOLUTION
bottom: "norm1"
top: "conv2"
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
}
}
layers {
name: "relu2"
type: RELU
bottom: "conv2"
top: "conv2"
}
layers {
name: "pool2"
type: POOLING
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "norm2"
type: LRN
bottom: "pool2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layers {
name: "conv3"
type: CONVOLUTION
bottom: "norm2"
top: "conv3"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
}
}
layers {
name: "relu3"
type: RELU
bottom: "conv3"
top: "conv3"
}
layers {
name: "conv4"
type: CONVOLUTION
bottom: "conv3"
top: "conv4"
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
}
}
layers {
name: "relu4"
type: RELU
bottom: "conv4"
top: "conv4"
}
layers {
name: "conv5"
type: CONVOLUTION
bottom: "conv4"
top: "conv5"
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
}
}
layers {
name: "relu5"
type: RELU
bottom: "conv5"
top: "conv5"
}
layers {
name: "pool5"
type: POOLING
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
name: "fc6"
type: INNER_PRODUCT
bottom: "pool5"
top: "fc6"
inner_product_param {
num_output: 4096
}
}
layers {
name: "relu6"
type: RELU
bottom: "fc6"
top: "fc6"
}
layers {
name: "drop6"
type: DROPOUT
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc7"
type: INNER_PRODUCT
bottom: "fc6"
top: "fc7"
inner_product_param {
num_output: 4096
}
}
layers {
name: "relu7"
type: RELU
bottom: "fc7"
top: "fc7"
}
layers {
name: "drop7"
type: DROPOUT
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layers {
name: "fc8"
type: INNER_PRODUCT
bottom: "fc7"
top: "fc8"
inner_product_param {
num_output: 2
}
}
layers {
name: "prob"
type: SOFTMAX
bottom: "fc8"
top: "prob"
}
layers {
name: "output"
type: ARGMAX
bottom: "prob"
top: "output"
}