Back in 2014, the Soccer World Cup was being held in Brazil. Of course, England, the mother country of football, participated in the tournament. Chris Smalling, a popular defender of the Manchester United, was one of the members of the English team.
On the other hand, a company aiming to take advantage of the special demand for the World Cup naturally produced goods of this popular player. Here it is.
W杯おみやげ製造会社、スモーリングとオバマ大統領を間違えマグ2000個にプリント
It seems that they googled image for Smalling and commercialized a mug with the photo printed on it. However, for some reason, they printed a picture of Barack Obama, the president of the United States at the time. This ordinary mistake was widely reported by the Japanese football media, and I remember laughing at it.
Such a tragedy must never be repeated. I felt a strong sense of mission and decided to implement the Obama-Smalling Predictor.
I implemented the model by using Keras.
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 148, 148, 32) 896
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 72, 72, 64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 34, 34, 128) 73856
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 15, 15, 128) 147584
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 6272) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 6272) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 3211776
_________________________________________________________________
dense_2 (Dense) (None, 1) 513
=================================================================
Total params: 3,453,121
Trainable params: 3,453,121
Non-trainable params: 0
_________________________________________________________________
For more detail, see work/. Note that they are written in Japanese.
The parameters after learning were saved as model/obama_smalling_predictor.h5.
The repository obama_smalling_flask hosts the Flask app for Obama or Smalling Prediction.