Description

This is a practice project that using Python with TensorFlow.

Requirements

• Python==3.7.0
• opencv-contrib-python==3.4.2.17
• matplotlib==3.1.1
• numpy==1.18.5

---

Show the results

1. Show train image

Show the Load Cifar10 training dataset and randomly show 10 images and labels.

2. Show training hyperparameters

3. Show VGG16 model

Or you can show this:

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 32, 32, 64)        1792      
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 32, 32, 64)        36928     
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 16, 16, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 16, 16, 128)       73856     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 16, 16, 128)       147584    
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 128)         0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 8, 8, 256)         295168    
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 8, 8, 256)         590080    
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 8, 8, 256)         590080    
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 4, 4, 256)         0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 4, 4, 512)         1180160   
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 4, 4, 512)         2359808   
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 4, 4, 512)         2359808   
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 2, 2, 512)         0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 2, 2, 512)         2359808   
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 2, 2, 512)         2359808   
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 2, 2, 512)         2359808   
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 1, 1, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 512)               0         
_________________________________________________________________
dense (Dense)                (None, 4096)              2101248   
_________________________________________________________________
dense_1 (Dense)              (None, 4096)              16781312  
_________________________________________________________________
dense_2 (Dense)              (None, 2)                 8194      
=================================================================
Total params: 33,605,442
Trainable params: 33,605,442
Non-trainable params: 0
_________________________________________________________________

4. Accuracy and training loss

5. Show test images

Random choose test image=1000 :