How do convert data type of Tensorflow Dataset [EMNIST/balanced] (From uint8 to float32) - tensorflow

Question

I am using Tensorflow dataset "emnist/balanced". The data type of features value is uint8 by default. However, Tensorflow model accept only float values.
How can I convert the features and labels data type to float32.
The code is here:
#########################################################3
import tensorflow as tf
import tensorflow_datasets as tfds
datasets, info = tfds.load(name="emnist/balanced", with_info=True, as_supervised=True)
emnist_train, emnist_test = datasets['train'], datasets['test']
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history = model.fit(emnist_train, epochs = 10)
#validation
test_loss, test_acc = model.evaluate(emnist_test, verbose=2)
print(test_acc)
Error --
2
3
----> 4 history = model.fit(emnist_train, epochs = 10)
5
6 #validation
TypeError: Value passed to parameter 'features' has DataType uint8 not in list of allowed values: float16, bfloat16, float32, float64
TypeError: Value passed to parameter 'features' has DataType uint8 not in list of allowed values: float16, bfloat16, float32, float64

Answer 1

Please refer working code to train a ANN for MNIST dataset
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
from __future__ import absolute_import, division, print_function, unicode_literals
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
print("T/F Version:",tf.__version__)
#### Import the Fashion MNIST dataset
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
##Scale these values to a range of 0 to 1 before feeding them to the neural network model
train_images = train_images / 255.0
test_images = test_images / 255.0
###Build the model
##the neural network requires configuring the layers of the model
##Set up the layers
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10)
])
###Compile the model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
###Train the model
##Feed the model
model.fit(train_images, train_labels, epochs=10)
###Evaluate accuracy
##compare how the model performs on the test dataset
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
output:
T/F Version: 2.1.0
Train accuracy:91.06
Test accuracy: 0.8871