admin – Water's Home

How to embed Latex in blogs : WordPress

Posted on February 1, 2019February 1, 2019 by admin

Add Plugin : Mathjax-Latex

The default option is use “Use MathJax CDN Service”.
But sometimes it cannot work because of some reasons.
So you have to install the service on your own vps!

Install MathJax Service

wget https://github.com/mathjax/MathJax/archive/master.zip

```
unzip master.zip
```

mv MathJax-master /xxxxxx ....../wordpress/wp-content/plugins/mathjax-latex/

Check The MathJax Service is Okay

Custom MathJax location

“Settings”->”MathJax-LaTeX”->”Custom MathJax location”

/wp-content/plugins/mathjax-latex/MathJax/MathJax.js

Test It !

$E=mc^2$

```
[latex]E=mc^2[/latex]
```

$\frac{{ – b \pm \sqrt {{b^2} – 4ac} }}{{2a}}$

[latex]\frac{{ - b \pm \sqrt {{b^2} - 4ac} }}{{2a}}[/latex]

$\sum\limits_{i = 1}^n {{{({X_i} – \bar X)}^2}} $

[latex]\sum\limits_{i = 1}^n {{{({X_i} - \bar X)}^2}} [/latex]

$\left( {\begin{array}{*{20}{c}}
{{a_{11}}}&{{a_{12}}}&{{a_{13}}}\\
{{a_{21}}}&{{a_{22}}}&{{a_{23}}}\\
{{a_{31}}}&{{a_{32}}}&{{a_{33}}}
\end{array}} \right)$

```
[latex]\left( {\begin{array}{*{20}{c}}
```
```
{{a_{11}}}&{{a_{12}}}&{{a_{13}}}\\
```
```
{{a_{21}}}&{{a_{22}}}&{{a_{23}}}\\
```
```
{{a_{31}}}&{{a_{32}}}&{{a_{33}}}
```
```
\end{array}} \right)[/latex]
```

TensorFlow on Android (Refer to codelabs : tensorflow-for-poets)

Posted on January 23, 2019January 23, 2019 by admin

The google demo is good, but there are some error when you follow it, so I copied and update it.

Install TensorFlow As Normal

```
pip install tensorflow==1.7.1
```

Download Google Demo

git clone https://github.com/googlecodelabs/tensorflow-for-poets-2

```
cd tensorflow-for-poets-2
```

Download TF_Files

wget http://download.tensorflow.org/example_images/flower_photos.tgz

```
tar -xvf flower_photos.tgz -C tf_files
```

Set ARCHITECTURE

```
IMAGE_SIZE=224
```

ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"

Start TensorBoard

tensorboard --logdir tf_files/training_summaries &

Help : retraining script

```
python -m scripts.retrain -h
```

Train

```
python -m scripts.retrain \
```

  --bottleneck_dir=tf_files/bottlenecks \

```
  --how_many_training_steps=500 \
```
```
  --model_dir=tf_files/models/ \
```

  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \

  --output_graph=tf_files/retrained_graph.pb \

  --output_labels=tf_files/retrained_labels.txt \

```
  --architecture="${ARCHITECTURE}" \
```
```
  --image_dir=tf_files/flower_photos
```

Train with default 4,000 iterations

```
python -m scripts.retrain \
```

  --bottleneck_dir=tf_files/bottlenecks \

  --model_dir=tf_files/models/"${ARCHITECTURE}" \

  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \

  --output_graph=tf_files/retrained_graph.pb \

  --output_labels=tf_files/retrained_labels.txt \

```
  --architecture="${ARCHITECTURE}" \
```
```
  --image_dir=tf_files/flower_photos
```

Train with Your Own Data

--image_dir= The root folder of the subdirectories which is used as label names by the classification script
This image is copied from google’s codelabs !

The Retrained Model

```
tf_files/retrained_graph.pb
```
```
tf_files/retrained_labels.txt
```

Test

(venv) water@water-G11CD:~/tensorflow-for-poets-2$ python -m scripts.label_image     --graph=tf_files/retrained_graph.pb      --image=tf_files/flower_photos/daisy/21652746_cc379e0eea_m.jpg

```
 
```

2019-01-23 14:13:50.234939: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

```
 
```
```
Evaluation time (1-image): 0.094s
```
```
 
```
```
daisy (score=0.99938)
```
```
sunflowers (score=0.00033)
```
```
dandelion (score=0.00028)
```
```
roses (score=0.00000)
```
```
tulips (score=0.00000)
```

Now, Prepare for Android

Install PILLOW

```
pip install PILLOW
```

Convert the model to TFLite format

```
IMAGE_SIZE=224
```

toco   --input_file=tf_files/retrained_graph.pb   --output_file=tf_files/optimized_graph.lite   --input_format=TENSORFLOW_GRAPHDEF   --output_format=TFLITE   --input_shape=1,${IMAGE_SIZE},${IMAGE_SIZE},3   --input_array=input   --output_array=final_result   --inference_type=FLOAT   --input_data_type=FLOAT

The TFLite Model

```
tf_files/optimized_graph.lite
```

Install AndroidStudio

unzip android-studio-ide-182.5199772-linux.zip

```
cd android-studio/
```
```
cd bin/
```
```
./studio.sh
```

Maybe You Need to Set Proxy

If you use lantern, you can do it like this!

Open Project

Open an existing Android Studio project

Choose android/tflite

Gradle Sync, YES

Build, Make Project

android/tflite/app/build/outputs/apk/debug/app-debug.apk

Test on Your Phone

Add your model files to the project, and then Make Project, Test Again

cp tf_files/optimized_graph.lite android/tflite/app/src/main/assets/graph.lite

cp tf_files/retrained_labels.txt android/tflite/app/src/main/assets/labels.txt

How to train YOLOv3 to detect Peppa Pig

Posted on January 22, 2019January 23, 2019 by admin

Prepare

Collecting Data from the Web with Python If you like

PASCAL VOC

.
├── VOCdevkit
│ └── VOC2018
│ ├── Annotations
├── ImageSets
│ │ └── Main // name of *.jpg without “.jpg”
├── JPEGImages // *.jpg

Annotation

LabelImg is a graphical image annotation tool

Example : XML

```
<annotation>
```
```
	<folder>000</folder>
```
```
	<filename>000000.jpg</filename>
```

	<path>/home/water/Machine_Learning/Peppa_Pig/train/000/000000.jpg</path>

```
	<source>
```
```
		<database>Unknown</database>
```
```
	</source>
```
```
	<size>
```
```
		<width>410</width>
```
```
		<height>256</height>
```
```
		<depth>3</depth>
```
```
	</size>
```
```
	<segmented>0</segmented>
```
```
	<object>
```
```
		<name>Peppa</name>
```
```
		<pose>Unspecified</pose>
```
```
		<truncated>0</truncated>
```
```
		<difficult>0</difficult>
```
```
		<bndbox>
```
```
			<xmin>64</xmin>
```
```
			<ymin>87</ymin>
```
```
			<xmax>166</xmax>
```
```
			<ymax>226</ymax>
```
```
		</bndbox>
```
```
	</object>
```
```
	<object>
```
```
		<name>Peppa</name>
```
```
		<pose>Unspecified</pose>
```
```
		<truncated>0</truncated>
```
```
		<difficult>0</difficult>
```
```
		<bndbox>
```
```
			<xmin>290</xmin>
```
```
			<ymin>77</ymin>
```
```
			<xmax>333</xmax>
```
```
			<ymax>131</ymax>
```
```
		</bndbox>
```
```
	</object>
```
```
</annotation>
```

Generate Labels for VOC

Darknet wants a .txt file for each image with a line for each ground truth object in the image that looks like:

```
<object-class> <x> <y> <width> <height>
```

voc_label.py

```
import xml.etree.ElementTree as ET
```
```
import pickle
```
```
import os
```
```
from os import listdir, getcwd
```
```
from os.path import join
```
```
 
```

sets=[('2018', 'train'), ('2018', 'val')]

```
 
```
```
classes = ["Peppa"]
```
```
 
```
```
def convert(size, box):
```
```
    dw = 1./size[0]
```
```
    dh = 1./size[1]
```
```
    x = (box[0] + box[1])/2.0
```
```
    y = (box[2] + box[3])/2.0
```
```
    w = box[1] - box[0]
```
```
    h = box[3] - box[2]
```
```
    x = x*dw
```
```
    w = w*dw
```
```
    y = y*dh
```
```
    h = h*dh
```
```
    return (x,y,w,h)
```
```
 
```
```
def convert_annotation(year, image_id):
```

    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))

    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')

```
    tree=ET.parse(in_file)
```
```
    root = tree.getroot()
```
```
    size = root.find('size')
```
```
    w = int(size.find('width').text)
```
```
    h = int(size.find('height').text)
```
```
 
```
```
    for obj in root.iter('object'):
```

        difficult = obj.find('difficult').text

```
        cls = obj.find('name').text
```

        if cls not in classes or int(difficult) == 1:

```
            continue
```
```
        cls_id = classes.index(cls)
```
```
        xmlbox = obj.find('bndbox')
```

        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))

```
        bb = convert((w,h), b)
```

        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')

```
 
```
```
wd = getcwd()
```
```
 
```
```
for year, image_set in sets:
```

    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):

        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))

    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()

    list_file = open('%s_%s.txt'%(year, image_set), 'w')

```
    for image_id in image_ids:
```

        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))

        convert_annotation(year, image_id)

```
    list_file.close()
```

peppa.data

```
classes= 1
```

train  = /home/d/Downloads/Peppa_VOC/2018_train.txt

valid  = /home/d/Downloads/Peppa_VOC/2018_val.txt

```
names = data/peppa.names
```
```
backup = backup
```

yolov3-voc-peppa.cfg

```
[net]
```
```
# Testing
```
```
batch=1
```
```
subdivisions=1
```
```
# Training
```
```
 batch=64
```
```
 subdivisions=2
```
```
width=416
```
```
height=416
```
```
channels=3
```
```
momentum=0.9
```
```
decay=0.0005
```
```
angle=0
```
```
saturation = 1.5
```
```
exposure = 1.5
```
```
hue=.1
```
```
 
```
```
learning_rate=0.001
```
```
burn_in=1000
```
```
max_batches = 500200
```
```
policy=steps
```
```
steps=400000,450000
```
```
scales=.1,.1
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=16
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=32
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=64
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=128
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=256
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=512
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=1
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=1024
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
###########
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=256
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=512
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[convolutional]
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
filters=18
```
```
activation=linear
```
```
 
```
```
 
```
```
 
```
```
[yolo]
```
```
mask = 3,4,5
```

anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319

```
classes=1
```
```
num=6
```
```
jitter=.3
```
```
ignore_thresh = .7
```
```
truth_thresh = 1
```
```
random=1
```
```
 
```
```
[route]
```
```
layers = -4
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=128
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[upsample]
```
```
stride=2
```
```
 
```
```
[route]
```
```
layers = -1, 8
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=256
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[convolutional]
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
filters=18
```
```
activation=linear
```
```
 
```
```
[yolo]
```
```
mask = 0,1,2
```

anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319

```
classes=1
```
```
num=6
```
```
jitter=.3
```
```
ignore_thresh = .7
```
```
truth_thresh = 1
```
```
random=1
```

Train

./darknet detector train cfg/peppa.data cfg/yolov3-voc-peppa.cfg darknet53.conv.74

Test

Reference

```
@article{yolov3,
```

  title={YOLOv3: An Incremental Improvement},

  author={Redmon, Joseph and Farhadi, Ali},

```
  journal = {arXiv},
```
```
  year={2018}
```
```
}
```

Style Transfer : Caffe Example

Posted on January 17, 2019January 23, 2019 by admin

Default

Start

python nstyle.py

Example 1

Start with style-weight 2

python nstyle.py --content-image ~/Machine_Learning/Style_Transfer/src.png --style-image ~/Machine_Learning/Style_Transfer/style.jpg --output_dir ~/Machine_Learning/Style_Transfer/output_style-weight-2/ --save-epochs 200 --style-weight 2

Metric Learning : Caffe Example

Posted on January 17, 2019January 23, 2019 by admin

Prepare (MNIST Data)

```
python gen_pairwise_imglist.py
```

/home/water/caffe/build/tools/convert_imageset ./ train.txt train_lmdb --gray

/home/water/caffe/build/tools/convert_imageset ./ train_p.txt train_p_lmdb --gray

/home/water/caffe/build/tools/convert_imageset ./ val.txt val_lmdb --gray

/home/water/caffe/build/tools/convert_imageset ./ val_p.txt val_p_lmdb --gray

Based on Caffe siamese

Train loss vs iters

Visualize Result (Normal)

Visualize Result (τ- SNE)

Object Detection : MXNet SSD Example

Posted on January 17, 2019January 23, 2019 by admin

Prepare

```
cd /home/water/mxnet/example/ssd
```

wget https://github.com/zhreshold/mxnet-ssd/releases/download/v0.6/resnet50_ssd_512_voc0712_trainval.zi

unzip resnet50_ssd_512_voc0712_trainval.zip

python data/demo/download_demo_images.py

Test

```
python demo.py --gpu 0
```

python demo.py --epoch 0 --images ./data/demo/dog.jpg --thresh 0.5

python demo.py --cpu --network resnet50 --data-shape 512

```
. . . . . .
```

python demo.py --images ./data/demo/person.jpg --thresh 0.3

python demo.py --images ./data/demo/person.jpg --network inceptionv3

Result

Transfer learning & Fine tuning

Posted on January 15, 2019January 23, 2019 by admin

Based on CVGJ model

ResNet10_cvgj

Train Curve

Confusion Matrix

Precision-Recall, PR

Receiver operating characteristic curve, ROC

Visualize Activation

Collecting Data & Remove

Posted on January 15, 2019January 23, 2019 by admin

Collecting Data from the Web with Python If you like

Remove duplicate image

1. Remove invalid images

```
import os
```
```
import sys
```
```
import cv2
```
```
 
```

from collect_data import SUPPORTED_FORMATS

```
 
```
```
input_path = sys.argv[1]
```
```
 
```

for root, dirs, files in os.walk(input_path):

```
    for filename in files:
```

        ext = filename[filename.rfind('.')+1:].lower()

        if ext not in SUPPORTED_FORMATS:

```
            continue
```

        filepath = os.sep.join([root, filename])

        if cv2.imread(filepath) is None:

            os.system('rm {}'.format(filepath))

            print('{} is not a valid image file. Deleted!'.format(filepath))

2. Remove duplicate files

FDUPES is a program for identifying duplicate files residing
within specified directories.

```
sudo apt-get install fdupes
```
```
fdupes -rdN ./
```

3. Remove visually similar or duplicate images

3.1 Down scale

```
import os
```
```
import cv2
```
```
import sys
```
```
 
```
```
input_path = sys.argv[1].rstrip(os.sep)
```
```
 
```
```
target_short_edge = int(sys.argv[2])
```
```
 
```

for root, dirs, files in os.walk(input_path):

    print('scanning {} ...'.format(root))

```
    for filename in files:
```

        filepath = os.sep.join([root, filename])

```
        img = cv2.imread(filepath)
```
```
        h, w = img.shape[:2]
```
```
        short_edge = min(w, h)
```
```
 
```

        if short_edge > target_short_edge:

            scale = float(target_short_edge) / float(short_edge)

            new_w = int(round(w * scale))

            new_h = int(round(h * scale))

            print('Down sampling {} from {} x {} to {} x {} ...'.format(

                filepath, w, w, new_w, new_h

```
            ))
```

            img = cv2.resize(img, (new_w, new_h))

```
            cv2.imwrite(filepath, img)
```
```
print('Done!')
```

3.2 Find image dupes

```
sudo apt-get install findimagedupes
```
```
findimagedupes -R train > dup_list
```

3.3 Remove duplicates

```
import os
```
```
import sys
```
```
dup_list = sys.argv[1]
```
```
with open(dup_list, 'r') as f:
```
```
    lines = f.readlines()
```
```
    for line in lines:
```
```
        dups = line.split()
```

        print('Removing duplicates of {}'.format(dups[0]))

```
        for dup in dups[1:]:
```
```
            cmd = 'rm {}'.format(dup)
```
```
            os.system(cmd)
```

Regression : Caffe

Posted on January 15, 2019January 23, 2019 by admin

Based on LeNet-5

Deploy with GPU

```
name: "RegressionExample"
```
```
layer {
```
```
  name: "data"
```
```
  type: "Input"
```
```
  top: "data"
```
```
  input_param    {
```
```
    shape:  {
```
```
        dim:    100
```
```
        dim:    1
```
```
        dim:    100
```
```
        dim:    100
```
```
    }
```
```
  }
```
```
}
```
```
……
```

Convolution Visualization

MNIST : MXNet

Posted on January 15, 2019January 23, 2019 by admin

MNIST Data (refer to MNIST : Caffe)

Prepare imglist for MXNet

```
import os
```
```
import sys
```
```
 
```
```
input_path = sys.argv[1].rstrip(os.sep)
```
```
 
```
```
out_path = sys.argv[2]
```
```
 
```
```
filenames = os.listdir(input_path)
```
```
with open(out_path, 'w') as f:
```

    for i, filename in enumerate(filenames):

        filepath = os.sep.join([input_path, filename])

        label = filename[: filename.rfind('.')].split('_')[1]

```
 
```

        line = '{}\t{}\t{}\n'.format(i, label, filepath)

```
        f.write(line)
```

Convert to rec

python gen_mxnet_imglist.py mnist/train train.lst

python gen_mxnet_imglist.py mnist/val val.lst

python gen_mxnet_imglist.py mnist/test test.lst

```
 
```

/home/d/mxnet/bin/im2rec train.lst ./ train.rec color=0

/home/d/mxnet/bin/im2rec val.lst ./ val.rec color=0

/home/d/mxnet/bin/im2rec test.lst ./ test.rec color=0

Train (LeNet-5)

```
import mxnet as mx
```
```
 
```
```
data = mx.symbol.Variable('data')
```
```
 
```

conv1 = mx.symbol.Convolution(data=data, kernel=(5, 5), num_filter=20)

pool1 = mx.symbol.Pooling(data=conv1, pool_type="max", kernel=(2, 2), stride=(2, 2))

```
 
```

conv2 = mx.symbol.Convolution(data=pool1, kernel=(5, 5), num_filter=50)

pool2 = mx.symbol.Pooling(data=conv2, pool_type="max", kernel=(2, 2), stride=(2, 2))

```
 
```
```
flatten = mx.symbol.Flatten(data=pool2)
```

fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)

relu1 = mx.symbol.Activation(data=fc1, act_type="relu")

```
 
```

fc2 = mx.symbol.FullyConnected(data=relu1, num_hidden=10)

```
 
```

lenet5 = mx.symbol.SoftmaxOutput(data=fc2, name='softmax')

```
 
```

mod = mx.mod.Module(lenet5, context=mx.gpu(0))

```
 
```
```
#
```
```
 
```
```
train_dataiter = mx.io.ImageRecordIter(
```
```
    path_imgrec="train.rec",
```
```
    data_shape=(1, 28, 28),
```
```
    batch_size=50,
```
```
    mean_r=128,
```
```
    scale=0.00390625,
```
```
    rand_crop=True,
```
```
    min_crop_size=24,
```
```
    max_crop_size=28,
```
```
    max_rotate_angle=15,
```
```
    fill_value=0
```
```
)
```
```
val_dataiter = mx.io.ImageRecordIter(
```
```
    path_imgrec="val.rec",
```
```
    data_shape=(1, 28, 28),
```
```
    batch_size=100,
```
```
    mean_r=128,
```
```
    scale=0.00390625,
```
```
)
```
```
 
```
```
#
```
```
 
```
```
import logging
```
```
 
```

logging.getLogger().setLevel(logging.DEBUG)

fh = logging.FileHandler('train_mnist_lenet.log')

```
logging.getLogger().addHandler(fh)
```
```
 
```

lr_scheduler = mx.lr_scheduler.FactorScheduler(1000, factor=0.95)

```
optimizer_params = {
```
```
    'learning_rate': 0.01,
```
```
    'momentum': 0.9,
```
```
    'wd': 0.0005,
```
```
    'lr_scheduler': lr_scheduler
```
```
}
```
```
 
```

checkpoint = mx.callback.do_checkpoint('mnist_lenet', period=5)

```
 
```
```
mod.fit(train_dataiter,
```
```
        eval_data=val_dataiter,
```

        optimizer_params=optimizer_params,

```
        num_epoch=36,
```
```
        epoch_end_callback=checkpoint)
```

Log Visualization

Test Model Accuracy

```
import mxnet as mx
```
```
 
```
```
test_dataiter = mx.io.ImageRecordIter(
```
```
    path_imgrec="test.rec",
```
```
    data_shape=(1, 28, 28),
```
```
    batch_size=100,
```
```
    mean_r=128,
```
```
    scale=0.00390625,
```
```
)
```
```
 
```

mod = mx.mod.Module.load('mnist_lenet', 35, context=mx.gpu(0))

```
 
```
```
mod.bind(
```

    data_shapes=test_dataiter.provide_data,

    label_shapes=test_dataiter.provide_label,

```
    for_training=False)
```
```
 
```
```
metric = mx.metric.create('acc')
```
```
 
```
```
mod.score(test_dataiter, metric)
```
```
 
```

for name, val in metric.get_name_value():

    print('{} = {:.2f}%'.format(name, val * 100))

Test Model Time

```
import time
```
```
import mxnet as mx
```
```
 
```

benchmark_dataiter = mx.io.ImageRecordIter(

```
    path_imgrec="test.rec",
```
```
    data_shape=(1, 28, 28),
```
```
    batch_size=64,
```
```
    mean_r=128,
```
```
    scale=0.00390625,
```
```
)
```
```
 
```

mod = mx.mod.Module.load('mnist_lenet', 35, context=mx.gpu(0))

```
mod.bind(
```

    data_shapes=benchmark_dataiter.provide_data,

    label_shapes=benchmark_dataiter.provide_label,

```
    for_training=False)
```
```
 
```
```
start = time.time()
```
```
 
```

for i, batch in enumerate(benchmark_dataiter):

```
    mod.forward(batch)
```
```
 
```
```
time_elapsed = time.time() - start
```

msg = '{} batches iterated!\nAverage forward time per batch: {:.6f} ms'

print(msg.format(i + 1, 1000 * time_elapsed / float(i)))