mnist_cnn.py

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

from PIL import Image
import numpy as np
import sys

input_image = np.array(Image.open(sys.argv[1]).convert('L').resize((28, 28), 0)).reshape(1, 28, 28, 1)

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

x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

model = Sequential()
model.add(Conv2D(28, kernel_size=(3,3), input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation=tf.nn.relu))
model.add(Dropout(0.2))
model.add(Dense(10,activation=tf.nn.softmax))

tb_log_dir = "./cnn_tb"
file_writer = tf.summary.create_file_writer(tb_log_dir)
file_writer.set_as_default()
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=tb_log_dir, profile_batch=0)

model.compile(optimizer='adam', 
              loss='sparse_categorical_crossentropy', 
              metrics=['accuracy'])

model.fit(x=x_train,y=y_train, epochs=10, callbacks=[tensorboard_callback])

model.evaluate(x_test, y_test)

pred = model.predict(input_image)

print (pred)

print("The number is = ", pred.argmax())