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chrisjay
/

mnist-adversarial

Runtime error

@@ -22,7 +22,7 @@ learning_rate = 0.01
 momentum = 0.5
 log_interval = 10
 random_seed = 1
-TRAIN_CUTOFF = 5
 WHAT_TO_DO=WHAT_TO_DO.format(num_samples=TRAIN_CUTOFF)
 METRIC_PATH = './metrics.json'
 REPOSITORY_DIR = "data"
@@ -216,8 +216,8 @@ def test():
         test_losses.append(test_loss)
         acc = 100. * correct / len(test_loader.dataset)
         acc = acc.item()
-        test_metric = '〽Current test metric - Avg. loss: `{:.4f}`, Accuracy: `{}/{}` (`{:.0f}%`)\n'.format(
-        test_loss, correct, len(test_loader.dataset),acc  )
         return test_metric,acc
@@ -349,7 +349,7 @@ def flag(input_image,correct_result,adversarial_number):
         test_metric_ = train_and_test()
         test_metric = f"<html> {test_metric_} </html>"
         output = f'<div> ✔ ({adversarial_number}) Successfully saved your adversarial data and trained the model on adversarial data! </div>'
-    return output,test_metric,adversarial_number
 def get_number_dict(DATA_DIR):
     files = [f.name for f in os.scandir(DATA_DIR)]
@@ -376,10 +376,11 @@ def get_statistics():
     DATA_DIR = './data_mnist/data'
     numbers_count_keys,numbers_count_values = get_number_dict(DATA_DIR)
     plt_digits = plot_bar(numbers_count_values,numbers_count_keys,'Number of adversarial samples',"Digit",f"Distribution of adversarial samples over digits")
-    fig_d, ax_d = plt.subplots(figsize=(10,4),tight_layout=True)
     if os.path.exists(METRIC_PATH):
         metric_dict = read_json(METRIC_PATH)
@@ -401,7 +402,7 @@ def get_statistics():
                 </div>
                """
-    return plt_digits,fig_d,done_html
@@ -417,7 +418,7 @@ def main():
         with gr.Tabs():
             with gr.TabItem('MNIST'):
                 gr.Markdown(WHAT_TO_DO)
-                test_metric = gr.outputs.HTML(DEFAULT_TEST_METRIC)
                 with gr.Row():
@@ -435,23 +436,20 @@ def main():
                 submit.click(image_classifier,inputs = [image_input],outputs=[label_output])
-                flag_btn.click(flag,inputs=[image_input,number_dropdown,adversarial_number],outputs=[output_result,test_metric,adversarial_number])
             with gr.TabItem('Dashboard') as dashboard:
                 notification = gr.HTML("""<div style="color: green">
                                         <p> ⌛ Creating statistics... </p>
                                         </div>
                                     """)
-                _,numbers_count_values_ = get_number_dict('./data_mnist/data')
-                STATS_EXPLANATION_ = STATS_EXPLANATION.format(num_adv_samples = sum(numbers_count_values_))
-                gr.Markdown(STATS_EXPLANATION_)
                 stat_adv_image =gr.Plot(type="matplotlib")
                 gr.Markdown(DASHBOARD_EXPLANATION)
                 test_results=gr.Plot(type="matplotlib")
-            dashboard.select(get_statistics,inputs=[],outputs=[stat_adv_image,test_results,notification])

 momentum = 0.5
 log_interval = 10
 random_seed = 1
+TRAIN_CUTOFF = 10
 WHAT_TO_DO=WHAT_TO_DO.format(num_samples=TRAIN_CUTOFF)
 METRIC_PATH = './metrics.json'
 REPOSITORY_DIR = "data"
         test_losses.append(test_loss)
         acc = 100. * correct / len(test_loader.dataset)
         acc = acc.item()
+        test_metric = '〽Current test metric -> Avg. loss: `{:.4f}`, Accuracy: `{:.0f}%`\n'.format(
+        test_loss,acc)
         return test_metric,acc
         test_metric_ = train_and_test()
         test_metric = f"<html> {test_metric_} </html>"
         output = f'<div> ✔ ({adversarial_number}) Successfully saved your adversarial data and trained the model on adversarial data! </div>'
+    return output,adversarial_number
 def get_number_dict(DATA_DIR):
     files = [f.name for f in os.scandir(DATA_DIR)]
     DATA_DIR = './data_mnist/data'
     numbers_count_keys,numbers_count_values = get_number_dict(DATA_DIR)
+    STATS_EXPLANATION_ = STATS_EXPLANATION.format(num_adv_samples = sum(numbers_count_values))
     plt_digits = plot_bar(numbers_count_values,numbers_count_keys,'Number of adversarial samples',"Digit",f"Distribution of adversarial samples over digits")
+    fig_d, ax_d = plt.subplots(tight_layout=True)
     if os.path.exists(METRIC_PATH):
         metric_dict = read_json(METRIC_PATH)
                 </div>
                """
+    return plt_digits,fig_d,done_html,STATS_EXPLANATION_
         with gr.Tabs():
             with gr.TabItem('MNIST'):
                 gr.Markdown(WHAT_TO_DO)
+                #test_metric = gr.outputs.HTML("")
                 with gr.Row():
                 submit.click(image_classifier,inputs = [image_input],outputs=[label_output])
+                flag_btn.click(flag,inputs=[image_input,number_dropdown,adversarial_number],outputs=[output_result,adversarial_number])
             with gr.TabItem('Dashboard') as dashboard:
                 notification = gr.HTML("""<div style="color: green">
                                         <p> ⌛ Creating statistics... </p>
                                         </div>
                                     """)
+                stats = gr.Markdown()
                 stat_adv_image =gr.Plot(type="matplotlib")
                 gr.Markdown(DASHBOARD_EXPLANATION)
                 test_results=gr.Plot(type="matplotlib")
+            dashboard.select(get_statistics,inputs=[],outputs=[stat_adv_image,test_results,notification,stats])

data_mnist CHANGED Viewed

	@@ -1 +1 @@
1	- Subproject commit ~~eb1e3cf9de597112c1da3b921ffcd07c8e4419c1~~


1	+ Subproject commit c6d1292ac6318c7c44131ca2fb18d37535ae1383

metrics.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"all": [10.55875015258789], "0": [0.0], "1": [0.0], "2": [0.0], "3": [43.33333206176758], "4": [86.66666412353516], "5": [0.0], "6": [0.0], "7": [0.0], "8": [0.0], "9": [0.0]}

model.pth CHANGED Viewed

@@ -1,3 +1,3 @@
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 size 89871

optimizer.pth CHANGED Viewed

@@ -1,3 +1,3 @@
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 size 89807

utils.py CHANGED Viewed

@@ -22,7 +22,7 @@ WHAT_TO_DO="""
 MODEL_IS_WRONG = """
 ---
-> Did the model get it wrong? Choose the correct prediction below and flag it. When you flag it, the instance is saved to our dataset and the model is trained on it.
 """
 DEFAULT_TEST_METRIC = "<html> Current test metric - Avg. loss: 1000, Accuracy: 30/1000 (30%) </html>"
@@ -65,7 +65,7 @@ def dump_json(thing,file):
 def plot_bar(value,name,x_name,y_name,title):
-    fig, ax = plt.subplots(figsize=(10,4),tight_layout=True)
     ax.set(xlabel=x_name, ylabel=y_name,title=title)

 MODEL_IS_WRONG = """
 ---
+### Did the model get it wrong? Choose the correct prediction below and flag it. When you flag it, the instance is saved to our dataset and the model is trained on it.
 """
 DEFAULT_TEST_METRIC = "<html> Current test metric - Avg. loss: 1000, Accuracy: 30/1000 (30%) </html>"
 def plot_bar(value,name,x_name,y_name,title):
+    fig, ax = plt.subplots(tight_layout=True)
     ax.set(xlabel=x_name, ylabel=y_name,title=title)