From 7b1aca308b719c8d2c572a6d3222db02d8cf2006 Mon Sep 17 00:00:00 2001 From: xs018 <82354373+xs018@users.noreply.github.com> Date: Mon, 14 Aug 2023 14:32:34 -0500 Subject: [PATCH] Add files via upload --- .../HW1-Xiaotong Sun-oct1.pdf | Bin 0 -> 587151 bytes MLP_Regression&Classification_code/dataset.py | 2 + MLP_Regression&Classification_code/hw1_1_1.py | 68 ++++++++++++++ MLP_Regression&Classification_code/hw1_1_1.sh | 22 +++++ .../hw1_1_1mse.png | Bin 0 -> 18121 bytes MLP_Regression&Classification_code/hw1_1_2.py | 83 ++++++++++++++++++ MLP_Regression&Classification_code/hw1_2.sh | 22 +++++ .../hw1_2ROC.png | Bin 0 -> 13183 bytes .../hw1_2_train.png | Bin 0 -> 10743 bytes .../hw1_2confusion_matrix.png | Bin 0 -> 20507 bytes .../hw1_2confusion_prediction.png | Bin 0 -> 10861 bytes .../visualization.py | 35 ++++++++ 12 files changed, 232 insertions(+) create mode 100644 MLP_Regression&Classification_code/HW1-Xiaotong Sun-oct1.pdf create mode 100644 MLP_Regression&Classification_code/dataset.py create mode 100644 MLP_Regression&Classification_code/hw1_1_1.py create mode 100644 MLP_Regression&Classification_code/hw1_1_1.sh create mode 100644 MLP_Regression&Classification_code/hw1_1_1mse.png create mode 100644 MLP_Regression&Classification_code/hw1_1_2.py create mode 100644 MLP_Regression&Classification_code/hw1_2.sh create mode 100644 MLP_Regression&Classification_code/hw1_2ROC.png create mode 100644 MLP_Regression&Classification_code/hw1_2_train.png create mode 100644 MLP_Regression&Classification_code/hw1_2confusion_matrix.png create mode 100644 MLP_Regression&Classification_code/hw1_2confusion_prediction.png create mode 100644 MLP_Regression&Classification_code/visualization.py diff --git a/MLP_Regression&Classification_code/HW1-Xiaotong Sun-oct1.pdf b/MLP_Regression&Classification_code/HW1-Xiaotong Sun-oct1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..fed0900b320087e5b9774ff2f668ac517024cdb8 GIT 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Sequential +from tensorflow.keras.layers import Dense +from tensorflow.keras.callbacks import EarlyStopping +from sklearn import metrics +import matplotlib.pyplot as plt +from sklearn.model_selection import KFold +from keras import losses +import statistics +import time +import tensorflow as tf + +tf.random.set_seed(1234) +def create_mlp(dim, regress=False): + model = Sequential() + model.add(Dense(256, input_dim=dim, activation="relu")) + model.add(Dense(512, activation="relu")) + model.add(Dense(128, activation="relu")) + model.add(Dense(64, activation="relu")) + if regress: + model.add(Dense(1, activation="linear")) + return model + +def main(): + file_path = '/ocean/projects/mch210006p/shared/HW1/Regression/boiling-32_temp_heat_flux.txt' + data = pd.read_csv(file_path, delimiter = "\t", names=['Temperature(C)', 'Heat flux(W/cm2)'], header=0) + x = zscore(data['Temperature(C)']) + y = data['Heat flux(W/cm2)'].values + # x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) + start=time.time() + model = create_mlp(1, True) + print(model.summary()) + model.compile(loss='mean_squared_error', optimizer='adam') + monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, + patience=5, verbose=1, mode='auto', restore_best_weights=True) + + best_loss = np.Infinity + best_losses_train = None + best_losses_validation = None + kf = KFold(100, shuffle=True, random_state=42) + fold=0 + for train, test in kf.split(x): + x_train = x[train] + y_train = y[train] + x_test = x[test] + y_test = y[test] + fold+=1 + print(f"Fold #{fold}") + # print(history.history) + history = model.fit(x_train,y_train, validation_data=(x_test,y_test), callbacks=[monitor], verbose=2, epochs=100) + if statistics.mean(history.history['val_loss']) < best_loss: + best_loss = statistics.mean(history.history['val_loss']) + best_losses_train = history.history['loss'] + best_losses_validation = history.history['val_loss'] + end=time.time() + print(f"Eclapse time: {end-start}s") + plt.figure() + plt.plot(best_losses_train, label='training_loss') + plt.plot(best_losses_validation, label='val_loss') + plt.savefig('res/hw1_1_2mse.png') + # plt.figure() + # plt.plot(history.history['rmse'], label='training accuracy') + # plt.plot(history.history['val_rmse'], label='val_accuracy') + # plt.savefig('res/hw1_1_rmse.png') + # Predict + pred = model.predict(x_test) + + # Measure MSE error. + score_mse = metrics.mean_squared_error(pred, y_test) + + # Measure RMSE error. RMSE is common for regression. + score_rmse = np.sqrt(score_mse) + + print(f"Mean Square Error: {score_mse}") + print(f"Rooted Mean Square Error: {score_rmse}") + + + +if __name__ == '__main__': + main() diff --git a/MLP_Regression&Classification_code/hw1_2.sh b/MLP_Regression&Classification_code/hw1_2.sh new file mode 100644 index 0000000..28c8746 --- /dev/null +++ b/MLP_Regression&Classification_code/hw1_2.sh @@ -0,0 +1,22 @@ +#!/bin/bash +######## --send email ######## +#SBATCH --mail-type=begin +#SBATCH --mail-type=end +#SBATCH --mail-user=xs018@uark.edu + +######## Job Name: Train_Job ######## +#SBATCH -J HW1_Job +#SBATCH -o log/HW1_Job.o%j +#SBATCH -e log/HW1_Job.e%j + +#SBATCH -p GPU-shared +#SBATCH -N 1 +#SBATCH --export=ALL +#SBATCH --gres=gpu:1 +#SBATCH -t 02:00:00 + +module load AI/anaconda3-tf2.2020.11 +conda activate /jet/home/xs018/envs +cd /jet/home/xs018/code + +python hw1_2.py \ No newline at end of file diff --git a/MLP_Regression&Classification_code/hw1_2ROC.png 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