renaming all files - moving training to be a single file for transfer vs not transfer learning. Made the testing file test all models. Needs to be updated to only update with new models.

5_test_models.py

@@ -0,0 +1,92 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import load_model
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+from glob import glob
+
+from PIL import ImageFile
+
+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',
+    target_size=(input_shape[0], input_shape[1]),
+    batch_size=batch_size,
+    shuffle=False
+)
+
+for file  in glob("./models/keras/*"):
+    filenames.append(file)
+    print(file)
+    model = load_model(file)
+
+
+
+    predictions = model.predict(test_gen, verbose=True, workers=12, steps=len(test_gen))
+
+    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]
+
+    # Save the results
+    print(label_index)
+    print(test_gen.classes)
+    print(test_gen.classes.shape)
+    print(type(test_gen.classes))
+    df = pd.DataFrame(columns=['fname', 'prediction', 'true_val'])
+    df['fname'] = [x for x in test_gen.filenames]
+    df['prediction'] = predictions
+    df["true_val"] = reals
+    df.to_csv("sub1_non_transfer.csv", index=False)
+
+    # Processed the saved results
+
+    acc = accuracy_score(reals, predictions)
+    conf_mat = confusion_matrix(reals, predictions)
+    print(classification_report(reals, predictions, labels=[l for l in label_index.values()]))
+    print("Testing accuracy score is ", acc)
+    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)
+plt.show()