Lucas
79 lines
2.1 KiB
Python
79 lines
2.1 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sn
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import numpy as np
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from keras.applications.inception_v3 import preprocess_input
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from keras.preprocessing.image import ImageDataGenerator
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from keras.models import load_model
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from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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model = load_model("./Models/mobilenetv2-stock-all-fixed-v2/mobilenetv2.hdf5")
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input_shape = (224, 224, 3)
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batch_size = 96
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test_idg = ImageDataGenerator(
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preprocessing_function=preprocess_input,
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)
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test_gen = test_idg.flow_from_directory(
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# './data/test',
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'./SingleImageTestSet',
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target_size=(input_shape[0], input_shape[1]),
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batch_size=batch_size,
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shuffle=False
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)
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predicts = model.predict_generator(test_gen, verbose=True, workers=1, steps=len(test_gen))
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print(predicts)
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print(type(predicts))
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print(predicts.shape)
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# Process the predictions
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predicts = np.argmax(predicts,
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axis=1)
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# test_gen.reset()
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label_index = {v: k for k, v in test_gen.class_indices.items()}
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predicts = [label_index[p] for p in predicts]
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reals = [label_index[p] for p in test_gen.classes]
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# Save the results
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print(label_index)
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print(test_gen.classes)
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print(test_gen.classes.shape)
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print(type(test_gen.classes))
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df = pd.DataFrame(columns=['fname', 'prediction', 'true_val'])
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df['fname'] = [x for x in test_gen.filenames]
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df['prediction'] = predicts
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df["true_val"] = reals
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df.to_csv("sub1_non_transfer.csv", index=False)
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# Processed the saved results
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acc = accuracy_score(reals, predicts)
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conf_mat = confusion_matrix(reals, predicts)
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print(classification_report(reals, predicts, labels=[l for l in label_index.values()]))
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print("Testing accuracy score is ", acc)
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print("Confusion Matrix", conf_mat)
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df_cm = pd.DataFrame(conf_mat, index=[i for i in list(set(reals))],
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columns=[i for i in list(set(reals))])
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print("made dataframe")
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plt.figure(figsize=(10, 7))
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print("made plot")
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# sn.heatmap(df_cm, annot=True)
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print("showing plot")
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plt.show()
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with open("labels.txt", "w") as f:
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for label in label_index.values():
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f.write(label + "\n")
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