This dataset will be used by the dataloader to pass our data into our model. We’ve now reached what we all had been waiting for! Custom Datasetįirst up, let’s define a custom dataset. ![]() fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(25,7)) # Train sns.barplot(data = pd.om_dict().melt(), x = "variable", y="value", hue="variable", ax=axes).set_title('Class Distribution in Train Set') # Validation sns.barplot(data = pd.om_dict().melt(), x = "variable", y="value", hue="variable", ax=axes).set_title('Class Distribution in Val Set') # Test sns.barplot(data = pd.om_dict().melt(), x = "variable", y="value", hue="variable", ax=axes).set_title('Class Distribution in Test Set')Ĭlass distribution in train, val, and test sets ] Neural Network melt() our convert our dataframe into the long format and finally use sns.barplot() to build the plots. The make the plot, we first convert our dictionary to a dataframe using pd.om_dict(). Once we have the dictionary count, we use Seaborn library to plot the bar charts. class2idx = for i in obj: if i = 0: count_dict += 1 elif i = 1: count_dict += 1 elif i = 2: count_dict += 1 elif i = 3: count_dict += 1 elif i = 4: count_dict += 1 elif i = 5: count_dict += 1 else: print("Check classes.") return count_dict To create the reverse mapping, we create a dictionary comprehension and simply reverse the key and value. Let’s also create a reverse mapping called idx2class which converts the IDs back to their original classes. replace() method from the Pandas library to change it. To do that, let’s create a dictionary called class2idx and use the. ![]() We need to remap our labels to start from 0. That needs to change because PyTorch supports labels starting from 0. Next, we see that the output labels are from 3 to 8. ![]() Class distribution bar plot ] Encode Output Class
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