diff --git a/4_Resnet_Solar_panel_defect_Identification/train.py b/4_Resnet_Solar_panel_defect_Identification/train.py
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+++ b/4_Resnet_Solar_panel_defect_Identification/train.py
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+import torch as t
+import torch.utils.data
+
+from data import ChallengeDataset
+from trainer import Trainer
+from matplotlib import pyplot as plt
+import numpy as np
+import model
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+
+# load the data from the csv file and perform a train-test-split
+# this can be accomplished using the already imported pandas and sklearn.model_selection modules
+# TODO
+dataframe = pd.read_csv('data.csv', sep=";")
+
+# set up data loading for the training and validation set each using t.utils.data.DataLoader and ChallengeDataset objects
+# TODO
+train_set, validation_set = train_test_split(dataframe, shuffle=True)
+
+# create an instance of our ResNet model
+# TODO
+model = model.ResNet()
+batch_size = 32
+# set up a suitable loss criterion (you can find a pre-implemented loss functions in t.nn)
+# set up the optimizer (see t.optim)
+# create an object of type Trainer and set its early stopping criterion
+# TODO
+train_challenge = torch.utils.data.DataLoader(ChallengeDataset(train_set, 'train'), batch_size=batch_size)
+
+validation_challenge = torch.utils.data.DataLoader(ChallengeDataset(validation_set, 'val'), batch_size=batch_size)
+loss_fn = t.nn.BCELoss()
+optimizer = t.optim.SGD(model.parameters(), lr=0.009, momentum=0.8)
+trainer = Trainer(model, loss_fn, optimizer, train_challenge, validation_challenge, cuda = True,early_stopping_patience=-1)
+
+
+# go, go, go... call fit on trainer
+res = trainer.fit(epochs=50)
+
+# plot the results
+plt.plot(np.arange(len(res[0])), res[0], label='train loss')
+plt.plot(np.arange(len(res[1])), res[1], label='val loss')
+plt.yscale('log')
+plt.legend()
+plt.savefig('losses.png')
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