Keras多層感知器：印第安糖尿病人診斷
阿新 • • 發佈：2018-11-03
例項中使用的是Pima Indians Diabetes資料集，資料集有八項屬性和對應輸出：
（1）懷孕次數
（2）2小時口服葡萄糖耐量實驗中血漿葡萄糖濃度
（3）舒張壓
（4）三頭肌皮褶皺厚度
（5）2小時血清胰島素
（6）身體質量指數
（7）糖尿病譜系功能
（8）年齡
（9）是否是糖尿病
第九項是我們的輸出層。
資料集下載：資料集下載地址
下面程式碼：
from keras.models import Sequential from keras.layers import Dense import numpy as np #設定隨機種子 np.random.seed(7) dataset = np.loadtxt(r'F:\Python\pycharm\keras_deeplearning\datasets\PimaIndiansdiabetes.csv', delimiter=',',skiprows=1) #分割輸入變數和輸出變數，0-7為輸入x， 8為輸出Y x = dataset[:, 0:8] Y = dataset[:, 8] #建立模型——引數為“神經元數，input_dim只有第一層有，表示有多少個輸入量， #activation為啟用函式，前兩層用relu，二分類的輸出層要用sigmoid model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='sigmoid')) #編譯模型——二分類中損失函式定義為“二進位制交叉熵”，優化器Adam為有效的梯度下降演算法 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) #訓練模型——epochs引數表示對資料集進行固定次數的迭代 #batch_size表示在執行神經網路中的權重更新的每個批次中所用例項的個數 model.fit(x=x, y=Y, epochs=150, batch_size=10) #評估模型——這裡簡化用，訓練集評估 scores = model.evaluate(x=x, y=Y) print('\n%s: %.2f%%' %(model.metrics_names[1], scores[1]*100))
輸出結果：
Using TensorFlow backend. Epoch 1/150 2018-10-29 16:26:09.934286: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 2018-10-29 16:26:09.935682: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 8. Tune using inter_op_parallelism_threads for best performance. 10/768 [..............................] - ETA: 31s - loss: 1.6118 - acc: 0.9000 500/768 [==================>...........] - ETA: 0s - loss: 4.6762 - acc: 0.6260 768/768 [==============================] - 0s 644us/step - loss: 3.6799 - acc: 0.5964 Epoch 2/150 10/768 [..............................] - ETA: 0s - loss: 0.4365 - acc: 0.8000 520/768 [===================>..........] - ETA: 0s - loss: 0.9445 - acc: 0.6038 768/768 [==============================] - 0s 100us/step - loss: 0.9296 - acc: 0.6016 Epoch 3/150 10/768 [..............................] - ETA: 0s - loss: 0.7402 - acc: 0.9000 520/768 [===================>..........] - ETA: 0s - loss: 0.7585 - acc: 0.6481 768/768 [==============================] - 0s 100us/step - loss: 0.7461 - acc: 0.6380 Epoch 4/150 10/768 [..............................] - ETA: 0s - loss: 0.7285 - acc: 0.8000 490/768 [==================>...........] - ETA: 0s - loss: 0.6964 - acc: 0.6449 768/768 [==============================] - 0s 103us/step - loss: 0.7103 - acc: 0.6549 Epoch 5/150 . . . . . . . 10/768 [..............................] - ETA: 0s - loss: 0.3279 - acc: 0.9000 520/768 [===================>..........] - ETA: 0s - loss: 0.4545 - acc: 0.7981 768/768 [==============================] - 0s 100us/step - loss: 0.4701 - acc: 0.7826 Epoch 142/150 10/768 [..............................] - ETA: 0s - loss: 0.5173 - acc: 0.7000 510/768 [==================>...........] - ETA: 0s - loss: 0.4799 - acc: 0.7745 768/768 [==============================] - 0s 102us/step - loss: 0.4800 - acc: 0.7734 Epoch 143/150 10/768 [..............................] - ETA: 0s - loss: 0.4776 - acc: 0.6000 510/768 [==================>...........] - ETA: 0s - loss: 0.4408 - acc: 0.8020 768/768 [==============================] - 0s 104us/step - loss: 0.4720 - acc: 0.7760 Epoch 144/150 10/768 [..............................] - ETA: 0s - loss: 0.6618 - acc: 0.6000 510/768 [==================>...........] - ETA: 0s - loss: 0.4592 - acc: 0.8039 768/768 [==============================] - 0s 100us/step - loss: 0.4738 - acc: 0.7786 Epoch 145/150 10/768 [..............................] - ETA: 0s - loss: 0.6643 - acc: 0.6000 510/768 [==================>...........] - ETA: 0s - loss: 0.4932 - acc: 0.7569 768/768 [==============================] - 0s 102us/step - loss: 0.4862 - acc: 0.7630 Epoch 146/150 10/768 [..............................] - ETA: 0s - loss: 0.5036 - acc: 0.7000 500/768 [==================>...........] - ETA: 0s - loss: 0.4992 - acc: 0.7580 768/768 [==============================] - 0s 103us/step - loss: 0.4918 - acc: 0.7708 Epoch 147/150 10/768 [..............................] - ETA: 0s - loss: 0.7728 - acc: 0.6000 510/768 [==================>...........] - ETA: 0s - loss: 0.4734 - acc: 0.7824 768/768 [==============================] - 0s 100us/step - loss: 0.4818 - acc: 0.7799 Epoch 148/150 10/768 [..............................] - ETA: 0s - loss: 0.3585 - acc: 0.8000 510/768 [==================>...........] - ETA: 0s - loss: 0.4805 - acc: 0.7745 768/768 [==============================] - 0s 102us/step - loss: 0.4686 - acc: 0.7812 Epoch 149/150 10/768 [..............................] - ETA: 0s - loss: 0.5303 - acc: 0.7000 500/768 [==================>...........] - ETA: 0s - loss: 0.4795 - acc: 0.7660 768/768 [==============================] - 0s 101us/step - loss: 0.4722 - acc: 0.7643 Epoch 150/150 10/768 [..............................] - ETA: 0s - loss: 0.3492 - acc: 0.9000 490/768 [==================>...........] - ETA: 0s - loss: 0.4477 - acc: 0.7980 768/768 [==============================] - 0s 103us/step - loss: 0.4769 - acc: 0.7799 32/768 [>.............................] - ETA: 1s 768/768 [==============================] - 0s 81us/step acc: 78.12%
最後可以看到準確率。