IC lstm

linear/train_jqxx_lstm.py

@@ -22,7 +22,7 @@ from keras import regularizers
 from keras.callbacks import EarlyStopping
 
 epochs= 330
-early_stopping = EarlyStopping(monitor='accuracy', patience=5, verbose=2)
+early_stopping = EarlyStopping(monitor='accuracy', patience=30, verbose=2)
 
 
 def curce_data(x,y,y_pred):
@@ -102,9 +102,11 @@ def demo_1(file, model_file):
     # our final FC layer head will have two dense layers, the final one
     # being our regression head
     x = Dense(128, activation="relu", kernel_regularizer=regularizers.l1(0.003))(cnn_0.input)
-    x = Dropout(0.2)(x)
-    x = Dense(128, activation="relu")(x)
-    x = Dense(256, activation="relu")(x)
+    x = Dropout(0.1)(x)
+    x = Dense(56, activation="relu")(x)
+    x = Dense(56, activation="relu")(x)
+    x = Dense(56, activation="relu")(x)
+    x = Dense(56, activation="relu")(x)
     x = Flatten()(x)
     # 在建设一层
     x = Dense(2, activation="sigmoid")(x)
@@ -129,7 +131,7 @@ def demo_1(file, model_file):
     print("[INFO] training model...")
     model.fit(
         [train_x_a], Ytrain,
-        # validation_data=([testAttrX, testImagesX], testY),
+        validation_data=([test_x_a], Ytest),
         # epochs=int(3*train_x_a.shape[0]/1300),
         epochs=epochs,
         batch_size=1024, shuffle=True,
@@ -143,11 +145,11 @@ def demo_1(file, model_file):
 
 
 windows = 5
-x_lenth = 15
+x_lenth = 19
 
 if __name__ == '__main__':
     root_dir = 'D:\\data\\quantization\\jqxx2\\'
     model_dir = 'D:\\data\\quantization\\jqxx2_svm_model\\'
-    m = '000004.SH.log'  # 12
+    m = '000007.SH.log'  # 12
     demo_1(root_dir + m, model_dir + str(m)[:6] + '.pkl')