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import numpy as np |
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from cleverhans.future.tf2.attacks import fast_gradient_method |
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import pandas as pd |
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from sklearn.model_selection import KFold |
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import sys |
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import tensorflow |
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import tensorflow as tf |
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from _utility import print_test, get_adversarial_examples |
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import pickle |
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folder_name = "./adversarial_examples_parseval_net/src/logs/saved_models/" |
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def train( |
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instance, |
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X_train, |
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Y_train, |
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X_test, |
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y_test, |
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epochs, |
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BS, |
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sgd, |
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generator, |
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callbacks_list, |
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model_name="ResNet", |
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): |
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kfold = KFold(n_splits=10, random_state=42, shuffle=False) |
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for j, (train, val) in enumerate(kfold.split(X_train)): |
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model = instance.create_wide_residual_network() |
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model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=["acc"]) |
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print("Finished compiling") |
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x_train, y_train = X_train[train], Y_train[train] |
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x_val, y_val = X_train[val], Y_train[val] |
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hist = model.fit( |
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generator.flow(x_train, y_train, batch_size=BS), |
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steps_per_epoch=len(x_train) // BS, |
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epochs=epochs, |
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callbacks=callbacks_list, |
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validation_data=(x_val, y_val), |
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validation_steps=x_val.shape[0] // BS, |
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) |
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with open("history_" + model_name + str(j), "wb") as file_pi: |
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pickle.dump(hist.history, file_pi) |
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model_name = folder_name + model_name + "_" + str(j) + ".h5" |
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model.save(model_name) |
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