加入正则项

stock/dnn_train.py

@@ -4,6 +4,7 @@ import numpy as np
 from keras.models import Sequential
 from keras.layers import Dense,Dropout
 import random
+from keras import regularizers
 from keras.models import load_model
 from imblearn.over_sampling import RandomOverSampler
 
@@ -11,7 +12,7 @@ from imblearn.over_sampling import RandomOverSampler
 def read_data(path):
     lines = []
     with open(path) as f:
-        for x in range(10000):
+        for x in range(100000):
             lines.append(eval(f.readline().strip()))
 
     random.shuffle(lines)
@@ -39,20 +40,20 @@ def train(input_dim=400, result_class=3, file_path="D:\\data\\quantization\\stoc
     train_x,train_y,test_x,test_y=read_data(file_path)
 
     model = Sequential()
-    model.add(Dense(units=425, input_dim=input_dim,  activation='relu'))
-    model.add(Dense(units=325, activation='relu'))
-    model.add(Dense(units=325, activation='relu'))
+    model.add(Dense(units=120+input_dim, input_dim=input_dim,  activation='relu'))
+    # model.add(Dense(units=60+int(input_dim/2), activation='relu'))
+    model.add(Dense(units=60+input_dim, activation='relu',kernel_regularizer=regularizers.l2(0.01)))
     model.add(Dropout(0.2))
-    model.add(Dense(units=325, activation='relu'))
-    model.add(Dropout(0.2))
-    model.add(Dense(units=325, activation='relu'))
+    model.add(Dense(units=60+input_dim, activation='relu',kernel_regularizer=regularizers.l2(0.01)))
     model.add(Dropout(0.2))
+    model.add(Dense(units=60+input_dim, activation='relu'))
+    # model.add(Dropout(0.2))
     model.add(Dense(units=512, activation='relu'))
     model.add(Dense(units=result_class, 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=26, shuffle=True)
+    h=model.fit(train_x, train_y, batch_size=64, epochs=126, shuffle=True)
     score = model.evaluate(test_x, test_y)
     print(score)
     print('Test score:', score[0])
@@ -68,5 +69,5 @@ def train(input_dim=400, result_class=3, file_path="D:\\data\\quantization\\stoc
 
 
 if __name__ == '__main__':
-    # train(input_dim=46, result_class=5, file_path="D:\\data\\quantization\\stock6_5.log", model_name='5d_dnn_seq.h5')
-    train(input_dim=400, result_class=3, file_path="D:\\data\\quantization\\stock6.log", model_name='15m_dnn_seq.h5')
+    train(input_dim=46, result_class=5, file_path="D:\\data\\quantization\\stock6_5.log", model_name='5d_dnn_seq.h5')
+    # train(input_dim=400, result_class=3, file_path="D:\\data\\quantization\\stock6.log", model_name='15m_dnn_seq.h5')