How to get samples per class for TensorFlow Dataset

With np.fromiter you can create a 1-D array from an iterable object.

import tensorflow_datasets as tfds
import numpy as np
import seaborn as sns

dataset = tfds.load('cifar10', split='train', as_supervised=True)

labels, counts = np.unique(np.fromiter(dataset.map(lambda x, y: y), np.int32), 
                       return_counts=True)

plt.ylabel('Counts')
plt.xlabel('Labels')
sns.barplot(x = labels, y = counts) 

Update: You can also count the labels like below:

labels = []
for x, y in dataset:
  # Not one hot encoded
  labels.append(y.numpy())

  # If one hot encoded, then apply argmax
  # labels.append(np.argmax(y, axis = -1))
labels = np.concatenate(labels, axis = 0) # Assuming dataset was batched.

Then you can plot them using the labels array.