Lucas
93 lines
2.6 KiB
Python
93 lines
2.6 KiB
Python
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()
|