瞎搞搞

stock/cnn_predict.py

@@ -0,0 +1,44 @@
+import keras
+# -*- encoding:utf-8 -*-
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense,Dropout
+import random
+from keras.models import load_model
+
+lines = []
+def read_data(path):
+    with open(path) as f:
+        for line in f.readlines()[:2000]:
+            lines.append(eval(line.strip()))
+
+    train_x=[s[:-2] for s in lines]
+    train_y=[s[-1] for s in lines]
+    return np.array(train_x),np.array(train_y)
+
+
+test_x,test_y=read_data("D:\\data\\quantization\\stock6_test.log")
+
+path="15min_model_seq.h5"
+model=load_model(path)
+score = model.evaluate(test_x, test_y)
+print(score)
+
+result=model.predict(test_x)
+print(result)
+
+up_num = 0
+up_right = 0
+i = 0
+for r in result:
+    fact = test_y[i]
+    if fact[0] == 1:
+        if r[0] > 0.5:
+            up_right = up_right + 1
+        elif r[1] > 0.5:
+            up_right = up_right + 0.5
+        up_num = up_num + 1
+
+    i = i + 1
+
+print(up_right, up_num, up_right/up_num)

stock/cnn_train.py

@@ -10,11 +10,9 @@ from keras.utils import np_utils
 
 
 def read_data(path):
-    train_x=[]
-    train_y=[]
     lines = []
     with open(path) as f:
-        for x in range(3000):
+        for x in range(40000):
             lines.append(eval(f.readline().strip()))
 
     random.shuffle(lines)
@@ -22,10 +20,9 @@ def read_data(path):
 
     d=int(0.95*len(lines))
 
-    size = len(lines[0])
-    train_x=[s[:size - 2] for s in lines[0:d]]
+    train_x=[s[:-2] for s in lines[0:d]]
     train_y=[s[-1] for s in lines[0:d]]
-    test_x=[s[:size - 2] for s in lines[d:]]
+    test_x=[s[:-2] for s in lines[d:]]
     test_y=[s[-1] for s in lines[d:]]
 
     print('转换数据完毕')
@@ -53,7 +50,7 @@ model.add(Dense(units=3, activation='softmax'))
 model.compile(loss='categorical_crossentropy', optimizer="adam",metrics=['accuracy'])
 
 print("Starting training ")
-h=model.fit(train_x, train_y, batch_size=64, epochs=6, shuffle=True)
+h=model.fit(train_x, train_y, batch_size=64, epochs=14, shuffle=True)
 score = model.evaluate(test_x, test_y)
 print(score)
 print('Test score:', score[0])