main.py

import tensorflow as tf

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

import matplotlib.pyplot as plt

image_index = 7777  # You may select anything up to 60,000
print(y_train[image_index])  # The label is 8
plt.imshow(x_train[image_index], cmap='Greys')

# Reshaping the array to 4-dims so that it can work with the Keras API
x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
input_shape = (28, 28, 1)
# Converting values to floats so we can get decimal points after division
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# Normalizing the RGB codes by dividing it to the max RGB value.
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print('Number of images in x_train', x_train.shape[0])
print('Number of images in x_test', x_test.shape[0])


def custom_mean_squared_error(y_true, y_pred):
    return tf.math.reduce_mean(tf.square(y_true - y_pred))


# Importing the required Keras modules containing model and layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D

# Creating a Sequential Model and adding the layers
model = Sequential()
model.add(Conv2D(28, kernel_size=(3, 3), input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())  # Flattening the 2D arrays for fully connected layers
model.add(Dense(32, activation=tf.nn.relu))  # adds a layer with 32 neurons
model.add(Dense(16, activation=tf.nn.relu))  # adds a layer with 16 neurons
model.add(Dropout(0.2))
model.add(Dense(10, activation=tf.nn.softmax))

model.compile(optimizer='adam',
              loss=custom_mean_squared_error,
              metrics=['accuracy', 'mse'])
y_train_one_hot = tf.one_hot(y_train, depth=10)
model.fit(x=x_train, y=y_train_one_hot, epochs=5)

predictions = model.predict(x_test[:3])
print(predictions)
y_test_one_hot = tf.one_hot(y_test, depth=10)
print(model.evaluate(x_test, y_test_one_hot))