训练

data.py

@@ -106,14 +106,14 @@ def load_pay_data(textNum = 80):
     lastday_v = collect_pay[0]
     for cur_v in collect_pay[1:]:
     
-        total_coin = 0
-        users = 0
-        last_total_coin = 0
+        total_coin = 0.0
+        users = 0.0
+        last_total_coin = 0.0
 
         for v2 in lastday_v:
             last_total_coin += v2[0] + v2[1]
 
-        count = 0
+        count = 0.0
         for v1, v2 in zip(cur_v,lastday_v):
             total_coin += v1[0] + v1[1]
             users += v1[2]
@@ -125,10 +125,10 @@ def load_pay_data(textNum = 80):
             # print(compare)
     
             # 时刻. 前一个小时 时刻. 当前支付总币数. 当前支付总币数 昨天币数
-            x_train.append([count  ,total_coin / last_total_coin , total_coin])
+            x_train.append([count,    total_coin, last_total_coin ])
             count+=1
 
-        for i in range(count):
+        for i in range(int(count)):
             y_train.append(total_coin)
 
         lastday_v = cur_v

predict_pay.py

@@ -2,10 +2,12 @@ import numpy
 from keras.models import load_model
 from data import load_pay_data, load_gift_data
 
+import matplotlib
+
 import matplotlib.pyplot as plt
 
 
-x_train, y_train, tx_train, ty_train, _ = load_pay_data(160)
+x_train, y_train, tx_train, ty_train, _ = load_pay_data(320)
 model = load_model("./predict_pay")
 
 p_data = model.predict(tx_train)

train_pay.py

@@ -16,6 +16,8 @@ import numpy
 from data import load_pay_data
 
 def mean_squared_error(y_true, y_pred):
+    print(dir(y_true), y_true.consumers)
+    print(y_true, y_pred)
     return K.mean(K.square(y_pred - y_true), axis=-1)
 
 if __name__ == "__main__":
@@ -31,9 +33,9 @@ if __name__ == "__main__":
     # model.add(Dropout(0.1))
     model.add(Dense(1))
     model.summary()
-    model.compile(loss = 'msle', optimizer = 'adam')
+    model.compile(loss = "mse", optimizer = 'adam')
 
-    model.fit(x_train, y_train, batch_size=96, epochs=600)
+    model.fit(x_train, y_train, batch_size=128, epochs=500)
     model.save("./predict_pay")
 
     p_data = model.predict(tx_train)