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https://github.com/abawchen/kaggle-rsna-pneumonia-detection-challenge
https://github.com/abawchen/kaggle-rsna-pneumonia-detection-challenge
Last synced: 11 days ago
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- Host: GitHub
- URL: https://github.com/abawchen/kaggle-rsna-pneumonia-detection-challenge
- Owner: abawchen
- Created: 2018-09-03T08:51:19.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:51:39.000Z (almost 2 years ago)
- Last Synced: 2024-10-12T04:09:46.911Z (28 days ago)
- Language: Jupyter Notebook
- Size: 3.04 MB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# kaggle-rsna-pneumonia-detection-challenge
### CNN References:
- [關於影像辨識，所有你應該知道的深度學習模型 | Medium @2018-02-04](https://medium.com/@syshen/%E7%89%A9%E9%AB%94%E5%81%B5%E6%B8%AC-object-detection-740096ec4540)
- [一文讀懂：R-CNN、Fast R-CNN、Faster R-CNN、YOLO、SSD @2018-05-02](https://hk.saowen.com/a/ea0b8f4a0266432ae2df9b75548929b77393a26141d06a70f8a3061025462b77)
- [如何评价 Kaiming He 最新的 Mask R-CNN? | 知乎 @2017-03-23](https://www.zhihu.com/question/57403701)
- [Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow | Medium @2018-05-20](https://engineering.matterport.com/splash-of-color-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46)
- [Deep Learning in Computer Vision | Coursera](https://zh-tw.coursera.org/lecture/deep-learning-in-computer-vision/region-based-convolutional-neural-network-yU6QP)
### Keras Tutorial
- [How to Use the Keras Functional API for Deep Learning @2017-10-27](https://machinelearningmastery.com/keras-functional-api-deep-learning/)
```python
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
visible = Input(shape=(10,))
hidden1 = Dense(10, activation='relu')(visible)
hidden2 = Dense(20, activation='relu')(hidden1)
hidden3 = Dense(10, activation='relu')(hidden2)
output = Dense(1, activation='sigmoid')(hidden3)
model = Model(inputs=visible, outputs=output)
print(model.summary())
```
```
Layer (type) Output Shape Param #
===========================================================================
input_1 (InputLayer) (None, 10) 0
___________________________________________________________________________
dense_1 (Dense) (None, 10) 110 =(10+1)*10
___________________________________________________________________________
dense_2 (Dense) (None, 20) 220 =(10+1)*20
___________________________________________________________________________
dense_3 (Dense) (None, 10) 210 =(20+1)*10
___________________________________________________________________________
dense_4 (Dense) (None, 1) 11 =(10+1)*1
===========================================================================
Total params: 551
Trainable params: 551
Non-trainable params: 0
```
- [How to calculate the number of parameters for convolutional neural network?](https://stackoverflow.com/a/42787467/9041712)
```python
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
visible = Input(shape=(64, 64, 1))
# fitler num: 32, filter shape: (4, 4)
conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(16, kernel_size=4, activation='relu')(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
hidden1 = Dense(10, activation='relu')(pool2)
output = Dense(1, activation='sigmoid')(hidden1)
model = Model(inputs=visible, outputs=output)
print(model.summary())
```
```
Layer (type) Output Shape Param #
===========================================================================
input_1 (InputLayer) (None, 64, 64, 1) 0
___________________________________________________________________________
# filter number 32, size 4*4. Say k=32, m=4, n=4, c=1
conv2d_1 (Conv2D) (None, 61, 61, 32) 544 = (4*4*1+1)*32
61=64-4+1 (m*n*c+1)*k
___________________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 30, 30, 32) 0
30=61/2
___________________________________________________________________________
# filter number 16, size 4*4. Say k=16, m=4, n=4, c=32
conv2d_2 (Conv2D) (None, 27, 27, 16) 8208 = (4*4*32+1)*16
27=30-4+1 (m*n*c+1)*k
___________________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 13, 13, 16) 0
13=27/2
___________________________________________________________________________
dense_1 (Dense) (None, 13, 13, 10) 170 = (16+1)*10
___________________________________________________________________________
dense_2 (Dense) (None, 13, 13, 1) 11
===========================================================================
Total params: 8,933
Trainable params: 8,933
Non-trainable params: 0
```