Purpose

Compare MINST NN with CIFAR10 NN with Caltech101

Conclusion

CNN Layer channnel number is important.

Results

Preparation

Setup tensorflow-pi:

$ cd
$ git clone https://github.com/karaage0703/tensorflow-pi
$ cd tensorflow-pi/data
$ ./getCaltech101.sh 
$ python make_train_data.py 101_ObjectCategories

Clone this repository:

$ cd
$ git clone https://github.com/karaage0703/tensorflow-experiment

Like MNIST NN

Using cnn_mnist.py for NN

$ cp ~/tensorflow-experiment/20170815_experiment/cnn_mnist.py ~/tensorflow-pi/cnn.py

Train:

$ cd ~/tensorflow-pi/
$ python train.py

step 0, training accuracy 0.0508475, loss nan
step 1, training accuracy 0.0508475, loss nan

MNIST base NN using CIFAR10 CNN Layer

Using cnn_cifar10_01.py for NN

$ cp ~/tensorflow-experiment/20170815_experiment/cnn_cifar10_01.py ~/tensorflow-pi/cnn.py

Train:

$ cd ~/tensorflow-pi/
$ python train.py

elapsed_time:7920.29748917[sec]
test accuracy 0.429719

graph

MNIST base NN using CIFAR10 CNN Layer and Pooling Layer

Using cnn_cifar10_02.py for NN

$ cp ~/tensorflow-experiment/20170815_experiment/cnn_cifar10_01.py ~/tensorflow-pi/cnn.py

Train:

$ cd ~/tensorflow-pi/
$ python train.py

elapsed_time:9575.28440309[sec]
test accuracy 0.432842

graph