fix: fix indexing of labels in mfcc json

Handle_emg_data.py

@@ -675,7 +675,7 @@ class DL_data_handler:
                 mfcc_frame_list.append(mfcc.shape[0])
 
                 #data["mfcc"].append(mfcc.tolist())
-                data["labels"].append(key)
+                data["labels"].append(key-1)
                 print("sample:{} is done".format(i+1))
 
         minimum = min(mfcc_frame_list)
@@ -711,7 +711,7 @@ class DL_data_handler:
                 # process all samples per subject
                 for i, sample in enumerate(value):
 
-                    data["labels"].append(key)
+                    data["labels"].append(key-1)
                     data["mfcc"].append(sample)
                     print("sample:{} is done".format(i+1))
                     #print(np.array(mfcc_data).shape)

Neural_Network_Analysis.py

@@ -17,13 +17,12 @@ def load_data(data_path):
         data = json.load(fp)
 
     # convert lists to numpy arraysls
-    #print('\n', data['mfcc'], '\n')
     X = np.array(data['mfcc'])
+    X = X.reshape(X.shape[0], 1, X.shape[1])
     print(X.shape)
-    #print((len(X), len(X[0]), len(X[0][0])))
-    #print((len(X), len(X[5]), len(X[5][0])))
-
+    
     y = np.array(data["labels"])
+    y = y.reshape(y.shape[0], 1)
     print(y.shape)
     
 
@@ -109,7 +108,8 @@ if __name__ == "__main__":
 
     print(X_train.shape[1], X_train.shape[2])
     # create network
-    input_shape = (X_train.shape[1], X_train.shape[2])  # 18, 13
+    
+    input_shape = (X_train.shape[1], X_train.shape[2])  # 1, 208
     model = build_model(input_shape)
 
     # compile model
@@ -121,7 +121,7 @@ if __name__ == "__main__":
     model.summary()
 
     # train model
-    history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=100, epochs=30)
+    history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=128, epochs=30)
 
     # plot accuracy/error for training and validation
     plot_history(history)
@@ -129,5 +129,6 @@ if __name__ == "__main__":
     # evaluate model on test set
     test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
     print('\nTest accuracy:', test_acc)
+    
 
 

Present_data.py

@@ -222,7 +222,7 @@ def main():
     csv_handler.load_data('soft')
     dl_data_handler = DL_data_handler(csv_handler)
     dl_data_handler.store_mfcc_samples()
-    dl_data_handler.save_json_reg()
+    dl_data_handler.save_json_mfcc()
 
     '''
     dict = dl_data_handler.get_mfcc_samples_dict()