ajout result exp1

Exp2_Aug_urbanclass_fold_v1.py → Exp1_NoAug_urbanclass_fold.py

@@ -75,7 +75,7 @@ def train_test_split(fold_k, data, X_dim=(128, 128, 1)):
 
 def show_results(tot_history):
     """Show accuracy and loss graphs for train and test sets."""
-    pngName = ['resultaugmented/res1.png','resultaugmented/res2.png','resultaugmented/res3.png','resultaugmented/res4.png','resultaugmented/res5.png','resultaugmented/res6.png','resultaugmented/res7.png','resultaugmented/res8.png','resultaugmented/res9.png','resultaugmented/res10.png']
+    pngName = ['Exp1_result/res1.png','Exp1_result/res2.png','Exp1_result/res3.png','Exp1_result/res4.png','Exp1_result/res5.png','Exp1_result/res6.png','Exp1_result/res7.png','Exp1_result/res8.png','Exp1_result/res9.png','Exp1_result/res10.png']
     for i, history in enumerate(tot_history):
         print('\n({})'.format(i+1))
         fig = plt.figure()
@@ -99,7 +99,7 @@ def show_results(tot_history):
             
         plt.savefig(pngName[i])
         
-        file = open('resultaugmented/result.txt','a')
+        file = open('Exp1_result/result.txt','a')
         file.write('\n=========FOLD%s=========\n'% (i+1))
         file.write('\nMax validation accuracy: %.4f %%' % (np.max(history.history['val_accuracy']) * 100))
         file.write('\nMin validation loss: %.5f' % np.min(history.history['val_loss']))
@@ -129,7 +129,7 @@ def process_fold(fold_k, data, epochs=100, num_batch_size=32):
 
 
 if __name__ == "__main__":
-    us8k_df = pd.read_pickle("us8k_augmented_df.pkl")
+    us8k_df = pd.read_pickle("./pkl/us8k_df.pkl")
     history1 = []
     for i in range(10) : 
         FOLD = i+1