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