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Handwritten-Digit-Recognition

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Handwritten-Digit-Recognition / model.py

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	import tensorflow as tf
	import numpy as np

	# Load the pre prepared data from TensofFlow
	(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

	# Preprocess the 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

	# Define a CNN model with data augmentation
	model = tf.keras.models.Sequential([
	tf.keras.layers.Conv2D(64, (3, 3), activation="relu", input_shape=(28, 28, 1)),
	tf.keras.layers.MaxPooling2D(2, 2),
	tf.keras.layers.Conv2D(64, (3, 3), activation="relu"),
	tf.keras.layers.MaxPooling2D(2, 2),
	tf.keras.layers.Flatten(),
	tf.keras.layers.Dense(512, activation="relu"),
	# Dropout to avoid from the overfitting
	tf.keras.layers.Dropout(0.5),

	# There are 10 different (0-9) output.
	# So use 10 units dense layer with softmax activation
	tf.keras.layers.Dense(10, activation="softmax"),
	])

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

	# Data Augmentation
	datagen = tf.keras.preprocessing.image.ImageDataGenerator(
	rotation_range=10,
	width_shift_range=0.1,
	height_shift_range=0.1,
	shear_range=0.1,
	zoom_range=0.1
	)

	datagen.fit(x_train)

	# Train the model with data augmentation
	model.fit(datagen.flow(x_train, y_train, batch_size=128),
	steps_per_epoch=len(x_train) / 128,
	epochs=10,
	validation_data=(x_test, y_test))