先用dnn试试

stock/cnn_train.py

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+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
+from imblearn.over_sampling import RandomOverSampler
+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):
+            lines.append(eval(f.readline().strip()))
+
+    random.shuffle(lines)
+    print('读取数据完毕')
+
+    d=int(0.95*len(lines))
+
+    size = len(lines[0])
+    train_x=[s[:size - 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_y=[s[-1] for s in lines[d:]]
+
+    print('转换数据完毕')
+
+    ros = RandomOverSampler(random_state=0)
+    X_resampled, y_resampled = ros.fit_sample(np.array(train_x), np.array(train_y))
+
+    print('数据重采样完毕')
+
+    return X_resampled,y_resampled,np.array(test_x),np.array(test_y)
+
+train_x,train_y,test_x,test_y=read_data("D:\\data\\quantization\\stock6.log")
+
+model = Sequential()
+model.add(Dense(units=635, input_dim=400,  activation='relu'))
+model.add(Dense(units=525, activation='relu'))
+model.add(Dense(units=525, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(units=525, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(units=425, activation='relu'))
+model.add(Dropout(0.2))
+model.add(Dense(units=366, activation='relu'))
+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)
+score = model.evaluate(test_x, test_y)
+print(score)
+print('Test score:', score[0])
+print('Test accuracy:', score[1])
+
+path="15min_model_seq.h5"
+model.save(path)
+model=None
+
+
+model=load_model(path)
+result=model.predict(test_x)
+print(result)
+print(test_y)