Commit 0ed845fa authored by Zhangkai Wu's avatar Zhangkai Wu
Browse files

Change the paths for Assignments

parent d94f33a8

Assignments/8_1_template_assignment.ipynb

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%% Cell type:code id: tags:



``` python

# You are encouraged to follow this template as a guide, but feel free to make reasonable modifications as needed.

# The key requirement is that your code runs successfully and produces the expected results.

```



%% Cell type:code id: tags:



``` python

# NOTE:

# You may choose to use ChatGPT (or any AI-based tool) to assist with your assignment,

# but you must ensure that you fully understand the entire code.

# You are solely responsible for the work you submit.

# Please keep in mind: ChatGPT will not be available during the exam.

```



%% Cell type:code id: tags:



``` python

import numpy as np

import pandas





def init_layers():

    # TODO: Implement this function

    return 0





def activation_function():

    # TODO: Implement this function

    return 0





def activation_derivative():

    # TODO: Implement this function

    return 0





def single_layer_forward_propagation():

    # TODO: Implement this function

    return 0





def full_forward_propagation():

    # TODO: Implement this function

    return 0





def get_loss_value():

    # TODO: Implement this function

    return 0



def single_layer_backward_propagation():

    # TODO: Implement this function

    return 0





def full_backward_propagation():

    # TODO: Implement this function

    return 0





def update():

    # TODO: Implement this function

    return 0





def train():

    # TODO: Implement this function

    return 0





def preprocess_dataset(dataset):

    filtered_dataset = dataset.loc[

        (dataset.iloc[:, 0] == "phytophthora-rot")

        | (dataset.iloc[:, 0] == "alternarialeaf-spot")

    ]



    filtered_dataset = filtered_dataset.replace("?", -1)



    filtered_dataset.iloc[:, 0] = filtered_dataset.iloc[:, 0].astype("category")

    cat_columns = filtered_dataset.select_dtypes(["category"]).columns

    filtered_dataset[cat_columns] = filtered_dataset[cat_columns].apply(

        lambda x: x.cat.codes

    )



    filtered_dataset = filtered_dataset.astype("float64")



    X = filtered_dataset.iloc[:, 1:]

    y = filtered_dataset.iloc[:, 0]



    return X, y





# an auxiliary function that converts probability into class

def convert_prob_into_class(probs):

    probs_ = np.copy(probs)

    probs_[probs_ > 0.5] = 1

    probs_[probs_ <= 0.5] = 0

    return probs_





def get_accuracy_value(Y_hat, Y):

    Y_hat_ = convert_prob_into_class(Y_hat)

    return (Y_hat_ == Y).all(axis=0).mean()





if __name__ == "__main__":

    # Main function of script

    train_data = pandas.read_csv("/home/jovyan/data/IntroML/Chapter8_data/Assignment_1/soybean-large.data.csv", header=None)

    train_data = pandas.read_csv("/home/jovyan/work/Chapter8/Chapter8_data/Assignment_1/soybean-large.data.csv", header=None)



    test_data = pandas.read_csv("/home/jovyan/data/IntroML/Chapter8_data/Assignment_1/soybean-large.test.csv", header=None)

    test_data = pandas.read_csv("/home/jovyan/work/Chapter8/Chapter8_data/Assignment_1/soybean-large.test.csv", header=None)



    X_train, y_train = preprocess_dataset(train_data)



    X_test, y_test = preprocess_dataset(test_data)



    # TODO: Implement learning and prediction

```

Assignments/8_2_pytorch_lightning_template_assignment.ipynb

+2 −2
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

# You are encouraged to follow this template as a guide, but feel free to make reasonable modifications as needed.

# The key requirement is that your code runs successfully and produces the expected results.

```



%% Cell type:code id: tags:



``` python

# NOTE:

# You may choose to use ChatGPT (or any AI-based tool) to assist with your assignment,

# but you must ensure that you fully understand the entire code.

# You are solely responsible for the work you submit.

# Please keep in mind: ChatGPT will not be available during the exam.

```



%% Cell type:code id: tags:



``` python

import pandas

import pytorch_lightning as pl

import torch

import torch.nn as nn

from pytorch_lightning.callbacks import EarlyStopping

from sklearn.metrics import accuracy_score

from torch.utils.data import DataLoader, TensorDataset, random_split





class SoybeanDiseaseClassification(pl.LightningModule):

    def __init__(self):

        super(SoybeanDiseaseClassification, self).__init__()

        # TODO: Implement this function



    def forward(self, x):

        # TODO: Implement this function

        return 0



    def configure_optimizers(self):

        # TODO: Implement this function

        return 0



    def training_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def validation_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def test_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def test_epoch_end(self, outputs):

        # TODO: Implement this function

        return 0





def preprocess_dataset(dataset):

    dataset = dataset.replace("?", -1)



    dataset.iloc[:, 0] = dataset.iloc[:, 0].astype("category")

    cat_columns = dataset.select_dtypes(["category"]).columns

    dataset[cat_columns] = dataset[cat_columns].apply(lambda x: x.cat.codes)



    dataset = dataset.astype("float64")



    X = dataset.iloc[:, 1:]

    y = dataset.iloc[:, 0]



    data = torch.Tensor(X.to_numpy()).float()

    labels = torch.Tensor(y.to_numpy()).long()



    dataset = TensorDataset(data, labels)



    return dataset





if __name__ == "__main__":

    # Main function of script

    train = pandas.read_csv("/home/jovyan/data/IntroML/Chapter8_data/Assignment_1/soybean-large.data.csv", header=None)

    train = pandas.read_csv("/home/jovyan/work/Chapter8/Chapter8_data/Assignment_1/soybean-large.data.csv", header=None)



    test = pandas.read_csv("/home/jovyan/data/IntroML/Chapter8_data/Assignment_1/soybean-large.test.csv", header=None)

    test = pandas.read_csv("/home/jovyan/work/Chapter8/Chapter8_data/Assignment_1/soybean-large.test.csv", header=None)



    train_set = preprocess_dataset(train)

    train_set, val_set = random_split(train_set, [267, 40])



    test_set = preprocess_dataset(test)



    # TODO: Implement data loader, learning and prediction

```

Assignments/8_2_pytorch_template_assignment.ipynb

+2 −2
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

# You are encouraged to follow this template as a guide, but feel free to make reasonable modifications as needed.

# The key requirement is that your code runs successfully and produces the expected results.

```



%% Cell type:code id: tags:



``` python

# NOTE:

# You may choose to use ChatGPT (or any AI-based tool) to assist with your assignment,

# but you must ensure that you fully understand the entire code.

# You are solely responsible for the work you submit.

# Please keep in mind: ChatGPT will not be available during the exam.

```



%% Cell type:code id: tags:



``` python

import pandas

import torch

import torch.nn as nn

from sklearn.metrics import accuracy_score

from torch.autograd import Variable





class Model(nn.Module):

    def __init__(self, input_features, hidden_layer1, hidden_layer2, output_features):

        super().__init__()

        # TODO: Implement this function



    def forward(self, x):

        # TODO: Implement this function

        return 0





def preprocess_dataset(dataset):

    dataset = dataset.replace("?", -1)



    dataset.iloc[:, 0] = dataset.iloc[:, 0].astype("category")

    cat_columns = dataset.select_dtypes(["category"]).columns

    dataset[cat_columns] = dataset[cat_columns].apply(lambda x: x.cat.codes)



    dataset = dataset.astype("float64")



    X = dataset.iloc[:, 1:]

    y = dataset.iloc[:, 0]



    return X, y





if __name__ == "__main__":

    # Main function of script

    train = pandas.read_csv("/home/jovyan/data/IntroML/Chapter8_data/Assignment_1/soybean-large.data.csv", header=None)

    train = pandas.read_csv("/home/jovyan/work/Chapter8/Chapter8_data/Assignment_1/soybean-large.data.csv", header=None)



    test = pandas.read_csv("/home/jovyan/data/IntroML/Chapter8_data/Assignment_1/soybean-large.test.csv", header=None)

    test = pandas.read_csv("/home/jovyan/work/Chapter8/Chapter8_data/Assignment_1/soybean-large.test.csv", header=None)



    X_train, y_train = preprocess_dataset(train)



    X_test, y_test = preprocess_dataset(test)



    # TODO: Implement learning and prediction

```

Assignments/8_4_custom_cnn_template_assignment.ipynb

+2 −2
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

# You are encouraged to follow this template as a guide, but feel free to make reasonable modifications as needed.

# The key requirement is that your code runs successfully and produces the expected results.

```



%% Cell type:code id: tags:



``` python

# NOTE:

# You may choose to use ChatGPT (or any AI-based tool) to assist with your assignment,

# but you must ensure that you fully understand the entire code.

# You are solely responsible for the work you submit.

# Please keep in mind: ChatGPT will not be available during the exam.

```



%% Cell type:code id: tags:



``` python

import pytorch_lightning as pl

import torch

import torch.nn as nn

import torch.nn.functional as F

import torchvision as torchvision

from pytorch_lightning.callbacks import EarlyStopping

from sklearn.metrics import accuracy_score

from torch.utils.data import DataLoader, random_split





class PLANTSCNN(pl.LightningModule):

    def __init__(self):

        super(PLANTSCNN, self).__init__()

        # TODO: Implement this function



    def forward(self, x):

        # TODO: Implement this function

        return 0



    def configure_optimizers(self):

        # TODO: Implement this function

        return 0



    def training_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def validation_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def test_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def test_epoch_end(self, outputs):

        avg_loss = torch.stack([x["test_loss"] for x in outputs]).mean()

        avg_test_acc = torch.stack([x["test_acc"] for x in outputs]).mean()

        logs = {"test_loss": avg_loss, "test_acc": avg_test_acc}

        results = {

            "avg_test_loss": avg_loss,

            "avg_test_acc": avg_test_acc,

            "log": logs,

            "progress_bar": logs,

        }

        self.log_dict(results)

        return results





if __name__ == "__main__":

    # Main function of script

    # TODO: Implement data loading, learning and prediction

    # data path:

    # train "/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/train_crop_images"

    # test "/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/test_crop_images"

    # train "/home/jovyan/work/Chapter8/Chapter8_data/Assignment_4/train_crop_images"

    # test "/home/jovyan/work/Chapter8/Chapter8_data/Assignment_4/test_crop_images"

```

Assignments/8_4_transfer_template_assignment.ipynb

+2 −2
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

# You are encouraged to follow this template as a guide, but feel free to make reasonable modifications as needed.

# The key requirement is that your code runs successfully and produces the expected results.

```



%% Cell type:code id: tags:



``` python

# NOTE:

# You may choose to use ChatGPT (or any AI-based tool) to assist with your assignment,

# but you must ensure that you fully understand the entire code.

# You are solely responsible for the work you submit.

# Please keep in mind: ChatGPT will not be available during the exam.

```



%% Cell type:code id: tags:



``` python

import pytorch_lightning as pl

import torch

import torch.nn as nn

import torchvision as torchvision

import torchvision.models as models

from pytorch_lightning.callbacks import EarlyStopping

from sklearn.metrics import accuracy_score

from torch.utils.data import DataLoader, random_split





class TransferCNN(pl.LightningModule):

    def __init__(self):

        super(TransferCNN, self).__init__()

        # TODO: Implement this function



    def forward(self, x):

        # TODO: Implement this function

        return 0



    def configure_optimizers(self):

        # TODO: Implement this function

        return 0



    def training_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def validation_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def test_step(self, batch, batch_idx):

        # TODO: Implement this function

        return 0



    def test_epoch_end(self, outputs):

        avg_loss = torch.stack([x["test_loss"] for x in outputs]).mean()

        avg_test_acc = torch.stack([x["test_acc"] for x in outputs]).mean()

        logs = {"test_loss": avg_loss, "test_acc": avg_test_acc}

        results = {

            "avg_test_loss": avg_loss,

            "avg_test_acc": avg_test_acc,

            "log": logs,

            "progress_bar": logs,

        }

        self.log_dict(results)

        return results





if __name__ == "__main__":

    # Main function of script

    # TODO: Implement data loading, learning and prediction

    # data path:

    # train "/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/train_crop_images"

    # test "/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/test_crop_images"

    # train "/home/jovyan/work/Chapter8/Chapter8_data/Assignment_4/train_crop_images"

    # test "/home/jovyan/work/Chapter8/Chapter8_data/Assignment_4/test_crop_images"

```