Adding in new trained models, as well as new tools for reviewing the results.

Added in testing flow for testing our unfininshed/finished models. Also adding a test dataset with one picture of every pokemon in the game.
2019-04-30 13:00:24 -05:00
parent 601f4f7633
commit fbac5d519a
17 changed files with 1643 additions and 73 deletions
@@ -0,0 +1,76 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sn
+import numpy as np
+
+from keras.applications.inception_v3 import preprocess_input
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import load_model
+from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
+
+
+from PIL import ImageFile
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+model = load_model("./Models/mobilenetv2-stock-all-fixed-v2/mobilenetv2.hdf5")
+
+input_shape = (224, 224, 3)
+batch_size = 96
+
+test_idg = ImageDataGenerator(
+    preprocessing_function=preprocess_input,
+)
+
+test_gen = test_idg.flow_from_directory(
+    # './data/test',
+    './SingleImageTestSet',
+    target_size=(input_shape[0], input_shape[1]),
+    batch_size=batch_size,
+    shuffle=False
+
+)
+
+predicts = model.predict_generator(test_gen, verbose=True, workers=1, steps=len(test_gen))
+
+print(predicts)
+print(type(predicts))
+print(predicts.shape)
+# Process the predictions
+predicts = np.argmax(predicts,
+                     axis=1)
+# test_gen.reset()
+label_index = {v: k for k, v in test_gen.class_indices.items()}
+predicts = [label_index[p] for p in predicts]
+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'] = predicts
+df["true_val"] = reals
+df.to_csv("sub1_non_transfer.csv", index=False)
+
+# Processed the saved results
+
+acc = accuracy_score(reals, predicts)
+conf_mat = confusion_matrix(reals, predicts)
+print(classification_report(reals, predicts, [l for l in label_index.values()]))
+print("Testing accuracy score is ", acc)
+print("Confusion Matrix", conf_mat)
+
+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))
+sn.heatmap(df_cm, annot=True)
+plt.show()
+
+with open("labels.txt", "w") as f:
+    for label in label_index.values():
+        f.write(label + "\n")
+