From 65bb5b387d315078c5d88d7cf47f38a13e841539 Mon Sep 17 00:00:00 2001
From: eson <474420502@qq.com>
Date: Fri, 2 Apr 2021 18:40:15 +0800
Subject: [PATCH] =?UTF-8?q?=E8=AE=AD=E7=BB=83?=
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---
 data.py        | 12 ++++++------
 predict_pay.py |  4 +++-
 train_pay.py   |  6 ++++--
 3 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/data.py b/data.py
index 71b4e89..fab802e 100644
--- a/data.py
+++ b/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
diff --git a/predict_pay.py b/predict_pay.py
index 0a82827..2dc9234 100644
--- a/predict_pay.py
+++ b/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)
diff --git a/train_pay.py b/train_pay.py
index 190fb59..5d06d9b 100644
--- a/train_pay.py
+++ b/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)