Building a CNN model for Fashion MNIST dataset

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D

import random
random.seed(1)
np.random.seed(1)
tf.random.set_seed(1)

(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()

# checking unique labels
np.unique(y_train)

# reshaping to one channel color
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)

# normalizing 
X_train_normalized = X_train.astype('float32')/255.0
X_test_normalized = X_test.astype('float32')/255.0

#encoding label classes
y_train_encoded = tf.keras.utils.to_categorical(y_train)
y_test_encoded = tf.keras.utils.to_categorical(y_test)

#building CNN model
model = Sequential(
    [
        Conv2D(64, kernel_size=(3,3), input_shape=(28, 28, 1), activation='relu', padding='same'),
        MaxPooling2D(pool_size=(2, 2), padding='same'),
        Flatten(),
        Dense(100, activation='relu'),
        Dense(10, activation='softmax')
    ]
)

# checking trainable parameters
model.summary()

#compiling model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# fitting with 5 epochs
model.fit(X_train_normalized, y_train_encoded, epochs=5)

# checking training set accuracy
model.evaluate(X_train_normalized, y_train_encoded)

# checking test set accuracy
model.evaluate(X_test_normalized, y_test_encoded)