modify train_file

Windows/mlp_model.py

@@ -9,7 +9,7 @@ import joblib
 
 
 class MLPClassifier:
-    def __init__(self, num_inputs=2, num_outputs=4, num_hiddens=10, lr=0.01, num_epochs=400, batch_size=2010,
+    def __init__(self, num_inputs=2, num_outputs=2, num_hiddens=10, lr=0.001, num_epochs=300, batch_size=2010,
                  data_dir='Data/Data'):
         self.num_inputs = num_inputs
         self.num_outputs = num_outputs
@@ -36,7 +36,7 @@ class MLPClassifier:
         return H @ self.w2 + self.b2
 
     def load_data(self):
-        classes = ['HandClosed', 'HandOpen', 'WristExtension', 'WristFlexation']
+        classes = ['HandClosed', 'HandOpen']
         data = []
         labels = []
         for label, class_name in enumerate(classes):
@@ -51,6 +51,7 @@ class MLPClassifier:
 
         data = torch.tensor(np.vstack(data), dtype=torch.float32)
         labels = torch.tensor(labels, dtype=torch.long)
+        print(labels)
         return data, labels
 
     def load_data_loaders(self):
@@ -119,18 +120,30 @@ class MLPClassifier:
 
 if __name__ == '__main__':
     mlp_classifier = MLPClassifier()
+    #mlp_classifier.run()
     file_path='trained_mlp.pkl'
     if not os.path.exists(file_path):
      mlp_classifier.run()
-    test_data = torch.tensor([[97,58], [25,34], [42,7],[298,9],[10,39]], dtype=torch.float32)
+    '''
+    np.random.seed(42)
+    num_random_points = 1000
+    random_data = np.random.uniform(low=0, high=100, size=(num_random_points, 2))
+    test_data = torch.tensor(random_data, dtype=torch.float32)
+
+    predictions = mlp_classifier.predict(test_data)
+    print("Predictions:", predictions.numpy())
+    '''
+
+
+
+    test_data = torch.tensor([[32,5], [20,5],[30,6],[29,23],[24,6]], dtype=torch.float32)
 
-    # 进行预测
     predictions = mlp_classifier.predict(test_data)
 
-    # 输出预测结果
     print("Predictions:", predictions.numpy())
 
 
 
 
 
+