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from sklearn.metrics import roc_curve |
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from sklearn.metrics import auc |
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from matplotlib import pyplot as plt |
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from scipy import interp |
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import sys |
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import pickle |
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import numpy as np |
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import warnings |
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warnings.filterwarnings("ignore") |
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import os |
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plt.rcParams.update({"font.size": 14}) |
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prefix = +"logs/" |
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def learning_curves(percent, epsilon, exp, curve_type): |
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acc_list = [] |
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"Ten fold CVs of ResNet" |
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BS = 64 |
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init = (32, 32, 1) |
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sgd = SGD(lr=0.1, momentum=0.9) |
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model_name = prefix + exp + "/ResNet_" + str(epsilon) + "_" + str(percent) |
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for j in range(10): |
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hist_name = model_name + "_" + curve_type + "" + "_" + str(j) + ".pickle" |
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with open(hist_name, "rb") as f: |
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acc = pickle.load(f) |
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acc_list.append(acc) |
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return acc_list |
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def learning_curves_plot(train_sizes, mean_acc, std_acc, exp, curve_type): |
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plt.figure(dpi=80) |
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plt.fill_between( |
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train_sizes, mean_acc - std_acc, mean_acc + std_acc, alpha=0.1, color="r" |
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) |
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plt.plot(mean_acc, "-", color="r", label="ResNet") |
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plt.fill_between( |
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train_sizes, |
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mean_list[0] - std_list[0], |
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mean_list[0] + std_list[0], |
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alpha=0.1, |
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color="darkgreen", |
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) |
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plt.plot(mean_list[0], "-", color="darkgreen", label="ResNet-0.25 AEs") |
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plt.fill_between( |
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train_sizes, |
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mean_list[1] - std_list[1], |
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mean_list[1] + std_list[1], |
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alpha=0.1, |
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color="darkblue", |
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) |
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plt.plot(mean_list[1], "-", color="darkblue", label="ResNet-0.5 AEs") |
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plt.fill_between( |
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train_sizes, |
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mean_list[2] - std_list[2], |
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mean_list[2] + std_list[2], |
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alpha=0.1, |
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color="darkorange", |
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) |
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plt.plot(mean_list[2], "-", color="darkorange", label="ResNet-0.75 AEs") |
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plt.fill_between( |
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train_sizes, |
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mean_list[3] - std_list[3], |
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mean_list[3] + std_list[3], |
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alpha=0.1, |
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color="magenta", |
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) |
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plt.plot(mean_list[3], "-", color="magenta", label="ResNet-1.0 AEs") |
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plt.title("Learning Curves of Models (epsilon={})".format(epsilon)) |
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plt.xlabel("Epoch"), plt.ylabel(curve_type), plt.legend(loc="best") |
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plt.tight_layout() |
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plt.savefig(prefix + exp + curve_type + "/" + str(epsilon) + ".png") |
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train_sizes = [i for i in range(50)] |
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epsilons = [0.001, 0.003, 0.005, 0.01, 0.03] |
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percents = [0.25, 0.5, 0.75, 1.0] |
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Curve_Types = ["loss", "acc"] |
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Experiment = ["AEModels", "RandomNoisemodels"] |
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for exp in Experiment: |
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for curve_type in Curve_Types: |
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for epsilon in epsilons: |
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mean_list, std_list = [], [] |
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train_mean_list, train_std_list = [], [] |
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for percent in percents: |
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acc_list = learning_curves(percent, epsilon, exp, curve_type) |
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mean_list.append(np.mean(acc_list, axis=0)) |
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std_list.append(np.std(acc_list, axis=0)) |
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learning_curves_plot(train_sizes, mean_acc, std_list, exp, curve_type) |
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