clean up imports. fix naming, force CPU to fill the cache faster with images using 20 workers.

5_test_models.py

@@ -15,15 +15,13 @@ ImageFile.LOAD_TRUNCATED_IMAGES = True
 accuracies = []
 losses = []
 filenames = []
-test_idg = ImageDataGenerator(
-)
 
 input_shape = (224, 224, 3)
 batch_size = 32
 
-test_gen = test_idg.flow_from_directory(
-    # './data/test',
-    './single_image_test_set',
+test_gen = ImageDataGenerator().flow_from_directory(
+    './data/test',
+    # './single_image_test_set',
     target_size=(input_shape[0], input_shape[1]),
     batch_size=batch_size,
     shuffle=False
@@ -34,17 +32,15 @@ for file  in glob("./models/keras/*"):
     print(file)
     model = load_model(file)
 
-
-
-    predictions = model.predict(test_gen, verbose=True, workers=12, steps=len(test_gen))
+    predictions = model.predict(test_gen, verbose=True, workers=12)
 
     print(predictions)
     print(type(predictions))
     print(predictions.shape)
+
     # Process the predictions
     predictions = np.argmax(predictions,
                             axis=1)
-    # test_gen.reset()
     label_index = {v: k for k, v in test_gen.class_indices.items()}
     predictions = [label_index[p] for p in predictions]
     reals = [label_index[p] for p in test_gen.classes]
@@ -69,24 +65,12 @@ for file  in glob("./models/keras/*"):
     print("Confusion Matrix", conf_mat)
 
     accuracies.append(acc)
-    # df_cm = pd.DataFrame(conf_mat, index=[i for i in list(set(reals))],
-    #                      columns=[i for i in list(set(reals))])
-    # print("made dataframe")
-    # plt.figure(figsize=(10, 7))
-    # print("made plot")
-    # # sn.heatmap(df_cm, annot=True)
-    # print("showing plot")
-    # plt.show()
-
-
-    # with open("labels.txt", "w") as f:
-    #     for label in label_index.values():
-    #         f.write(label + "\n")
 
 overall_df = pd.DataFrame(list(zip(filenames, accuracies)),
                columns =['model', 'acc']).sort_values('acc')
 
 print(overall_df)
-overall_df.to_csv("all_model_output.csv")
-overall_df.plot.bar(x="model", y="acc", rot=0)
+overall_df.plot.bar(y="acc", rot=90)
+plt.tight_layout()
 plt.show()
+overall_df.to_csv("all_model_output.csv")