Handwriting Recognition with Deep Learning

In this post, we will go over an example to apply deep learning, in particular convolutional neural network, to recognize handwritten numbers from 0 to 9 using TensorFlow backend Keras. If you don't have deep learning environment setup yet, checkout this post.

Thankfully, there is publicly available handwritten digits and its labels on the web that we can use. For more info, check out MNIST website. Even better, Keras has MNIST data module, so we will use  it.

Use your favorite editor and create mnist.py file with the following:

	from tensorflow.examples.tutorials.mnist import input_data
	mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
	from keras.models import Sequential
	from keras.layers.core import Flatten, Dense, Lambda, Dropout, Activation
	from keras.optimizers import SGD, Adam
	from keras.layers.convolutional import Conv2D
	from keras.layers.pooling import MaxPooling2D
	X_train = mnist.train.images.reshape(-1, 28, 28, 1)
	y_train = mnist.train.labels
	X_test = mnist.test.images.reshape(-1, 28, 28, 1)
	y_test = mnist.test.labels
	input_shape = (28,28,1)
	model = Sequential()
	model.add(Conv2D(24, 5, 5, input_shape=input_shape, init='he_normal')) # output = 24x24x24
	model.add(Activation('elu'))
	model.add(MaxPooling2D((2,2), border_mode='same')) # output = 12x12x24
	model.add(Conv2D(32, 5, 5, input_shape=input_shape, init='he_normal')) # output = 8x8x32
	model.add(Activation('elu'))
	model.add(MaxPooling2D((2,2), border_mode='same')) # output = 4x4x32
	model.add(Conv2D(48, 4, 4, input_shape=input_shape, init='he_normal')) # output = 1x1x48
	model.add(Activation('elu'))
	model.add(Flatten()) # output = 48
	model.add(Dense(24, init='he_normal')) # output = 24
	model.add(Activation('elu'))
	model.add(Dropout(0.5))
	model.add(Dense(10, init='he_normal')) # output = 10
	model.add(Activation('softmax'))
	adam = Adam(lr=0.001)
	model.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])
	model.fit(X_train, y_train, nb_epoch=10, batch_size=100)
	loss_and_metrics = model.evaluate(X_test, y_test)
	print('test accuracy: ' + str(loss_and_metrics[1])) # Accuracy
	import numpy as np
	idx = np.random.randint(0, X_test.shape[0])
	img = X_test[idx].reshape(28,28)
	label = y_test[idx]
	label = np.argmax(label)
	import matplotlib.pyplot as plt
	fig = plt.figure()
	print('label = ' + str(label))
	pred = np.argmax(model.predict(X_test[idx].reshape(1,28,28,1)))
	print('prediction = ' + str(pred))
	plt.imshow(img, cmap='gray')
	plt.show()

view raw mnist.py hosted with ❤ by GitHub

Upon running the file with
$ python mnist.py

you will see convolutional network being trained with data and tested with unseen data with accuracy of about 99%!

Setup Deep Learning Development Environment in Ubuntu

In this tutorial, I will go through step by step instructions to setup deep learning development environment for Ubuntu. We will install the following python packages on fresh Ubuntu 16.04:
opencv
tensorflow
keras
matplotlib
numpy
scipy
sklearn
tk

Let's dig it! First, I would install virtualenv, in case you need multiple python environments.
$ sudo apt-get install virtualenv -y

To create an environment, simply run
$ virtualenv ENV
where replace ENV with the deep learning environment name you would like.

To activate the new environment,
$ source ~/ENV/bin/activate
where again replace ENV with the name chosen above.

Next, we need to install pip, which helps us install these python packages with ease.
$ sudo apt-get install python-pip -y

You may want to upgrade pip to the latest version:
$ pip install --upgrade pip

Next, let's install python packages within the environment.
$ pip install tensorflow keras numpy scipy matplotlib sklearn

For OpenCV and TK, we need to install it from apt-get:
$ sudo apt-get install libopencv-dev python-opencv python-tk -y

That's it! Now you are ready to develop your neural network with tensorflow backend keras! If you want to test out if your environment is successfully setup, check out this post.

Displaying Exact Values for Digital Color Meter on Mac OS X

Mac OS X's built-in app Digital Color Meter is an extremely useful tool for me, and I love it. However, the only problem is that it is not exact.

I manually created an image with RGB values of arbitrary numbers, say (38, 205, 86) using OpenCV. I then opened up the image with Preview, and measured the color with Digital Color Meter. Unfortunately, its output was close but not exact.

I also noticed that there is an drop down menu, such as
Display in native values
Display in P3
...

After playing around with all of these options, I am very happy to report that the option Display in sRGB produces exact match with the pixel!