test.py
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	import tensorflow as tf
	from tensorflow import keras
	import numpy as np

	nb = 1000

	((train_input, train_target),
	(test_input, test_target)) = tf.keras.datasets.mnist.load_data()

	train_input = train_input.reshape((60000, 28, 28, 1))
	test_input = test_input.reshape((10000, 28, 28, 1))

	train_input = train_input/255.0

	test_input = test_input/255.0

	######################################################################


	model = tf.keras.Sequential([
	tf.keras.layers.Conv2D(32, (3,3), activation=tf.nn.relu, input_shape=(28, 28, 1)),
	tf.keras.layers.MaxPooling2D((2, 2)),
	tf.keras.layers.Conv2D(64, (5,5), activation=tf.nn.relu),
	tf.keras.layers.MaxPooling2D((2, 2)),
	tf.keras.layers.Flatten(),
	tf.keras.layers.Dense(128, activation=tf.nn.relu),
	tf.keras.layers.Dense(10, activation=tf.nn.softmax)])


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

	model.summary()

	model.fit(train_input, train_target, batch_size = 100, epochs=5)

	test_loss, test_acc = model.evaluate(test_input, test_target)

	print('Test accuracy:', test_acc)