CNN预测

stock/cnn_predict.py

@@ -18,6 +18,7 @@ def read_data(path):
 
 
 test_x,test_y=read_data("D:\\data\\quantization\\stock6_test.log")
+test_x = test_x.reshape(test_x.shape[0], 1,80,5)
 
 path="15min_model_seq.h5"
 model=load_model(path)
@@ -36,7 +37,7 @@ for r in result:
         if r[0] > 0.5:
             up_right = up_right + 1
         elif r[1] > 0.5:
-            up_right = up_right + 0.5
+            up_right = up_right + 0.9
         up_num = up_num + 1
 
     i = i + 1

stock/cnn_train.py

@@ -42,11 +42,7 @@ def read_data(path):
 train_x,train_y,test_x,test_y=read_data("D:\\data\\quantization\\stock6.log")
 train_x = train_x.reshape(train_x.shape[0], 1,80,5)
 test_x = test_x.reshape(test_x.shape[0], 1,80,5)
-# train_y = train_y.reshape(train_y.shape[0],3)
-# 调用此次训练的数据集
-from keras.datasets import mnist
-import numpy as np
-np.random.seed(1337)
+
 
 # 加载数据
 # (X_train,Y_train),(X_test,Y_test) = mnist.load_data()
@@ -60,7 +56,7 @@ model = Sequential()
 model.add(Conv2D(
     nb_filter=32,  # 第一层设置32个滤波器
     nb_row=3,
-    nb_col=15,  # 设置滤波器的大小为5*5
+    nb_col=20,  # 设置滤波器的大小为5*5
     padding='same',  # 选择滤波器的扫描方式，即是否考虑边缘
     input_shape=(1,80,5),  # 设置输入的形状
     # batch_input_shape=(64, 1, 28, 28),
@@ -91,7 +87,7 @@ model.compile(optimizer=adam,
     metrics=['accuracy'])
 
 print("Starting training ")
-h=model.fit(train_x, train_y, batch_size=64, epochs=14, shuffle=True)
+h=model.fit(train_x, train_y, batch_size=64, epochs=10, shuffle=True)
 score = model.evaluate(test_x, test_y)
 print(score)
 print('Test score:', score[0])