Lucas Oskorep
62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
from glob import glob
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import matplotlib.pyplot as plt
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import pandas as pd
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from PIL import ImageFile
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from keras.models import load_model
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from keras.preprocessing.image import ImageDataGenerator
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from modeling_utils import get_metrics
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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accuracies = []
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losses = []
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filenames = []
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input_shape = (224, 224, 3)
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batch_size = 32
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metrics_df = pd.read_csv("all_model_output.csv")
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test_gen = ImageDataGenerator().flow_from_directory(
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'./data/test',
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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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single_gen = ImageDataGenerator().flow_from_directory(
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'./single_image_test_set',
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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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for file in glob("./models/keras/*.hdf5"):
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print(file)
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if file in metrics_df.values:
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continue
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model = load_model(file)
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test_acc, test_ll = get_metrics(test_gen, model)
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single_acc, single_ll = get_metrics(single_gen, model, file[:-5] + ".csv")
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metrics_df = metrics_df.append({
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"model": file,
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"test_acc": test_acc,
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"test_loss": test_ll,
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"single_acc": single_acc,
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"single_loss": single_ll,
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}, ignore_index=True)
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# Save the results
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metrics_df.to_csv("all_model_output.csv", index=False)
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print(metrics_df)
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metrics_df = metrics_df.sort_values('single_acc')
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metrics_df.plot.bar(y=["test_acc", "single_acc"], rot=90)
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metrics_df = metrics_df.sort_values('test_acc')
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metrics_df.plot.bar(y=["test_acc", "single_acc"], rot=90)
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plt.tight_layout()
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plt.show()
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