modif

urbanclass_fold.py

@@ -75,8 +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 = ['result/res1.png','result/res2.png','result/res3.png','result/res4.png','result/res5.png','result/res6.png','result/res7.png',
-               'result/res8.png','result/res9.png','result/res10.png']
+    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']
     for i, history in enumerate(tot_history):
         print('\n({})'.format(i+1))
         fig = plt.figure()
@@ -100,8 +99,8 @@ def show_results(tot_history):
             
         plt.savefig(pngName[i])
         
-        file = open('result/result.txt','a')
-        file.write('\n=========FOLD%s=========\n'% i+1)
+        file = open('resultaugmented/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']))
 
@@ -117,7 +116,7 @@ def process_fold(fold_k, data, epochs=100, num_batch_size=32):
     model = init_model()
 
     # pre-training accuracy
-    log_dir = "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
+    log_dir = "logs/fit/folds/" + datetime.now().strftime("%Y%m%d-%H%M%S")
 
     tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir = log_dir, histogram_freq = 1)
 
@@ -130,7 +129,7 @@ def process_fold(fold_k, data, epochs=100, num_batch_size=32):
 
 
 if __name__ == "__main__":
-    us8k_df = pd.read_pickle("us8k_df.pkl")
+    us8k_df = pd.read_pickle("us8k_augmented_df.pkl")
     history1 = []
     for i in range(10) : 
         FOLD = i+1