MNIST_Recognition_MLP

import numpy as np
import pandas as pd
from keras import layers
from keras import models
from keras.datasets import mnist # 10 classes
from keras.utils import to_categorical # one-hot encoding

np.random.seed(7)

(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 資料前處理
# 將 (60000,28,28) 轉為 (60000,784) 才符合MLPRegressor 的格式
X_train = np.reshape(X_train, (60000,784))
#print(X_train[0])
#print(X_train.shape)

X_test = np.reshape(X_test, (10000,784))


# 開始做model, 預計跑 1-20次, 數據存在 r2_train
r2_train = []
r2_test = []

for i in (5,10,15,20):
    # 計算程式開始時間
    import time
    start = time.time()
    
    # i個神經元
    from sklearn.neural_network import MLPRegressor
    model_mlp = MLPRegressor(random_state=0, activation='relu', hidden_layer_sizes= i)
    model_mlp.fit(X_train, y_train)
    mlp_score=model_mlp.score(X_train,y_train)
    r2_train.append(mlp_score)
    #print('score:',mlp_score)
    test_score=model_mlp.score(X_test,y_test)
    r2_test.append(test_score)

    # 結束時間
    end = time.time()
    print("執行時間：%f 秒" % (end - start))

    
        
print(r2_train)
print(r2_test)
    
### 繪圖顯示訓練集與核驗集的R2 score 
import matplotlib.pyplot as plt
%matplotlib inline

layers = (5,10,15,20)

plt.plot(layers, r2_train, 'bo', label='Training r2_score')
plt.plot(layers, r2_test, 'ro', label='Validation r2_score')
plt.title('Training and test r2_score')
plt.legend()
plt.show()