This post basically takes the tutorial on Classifying MNIST digits using Logistic Regression which is primarily written for Theano and attempts to port it to Keras.
What is Keras?
This is what the official Keras site says.
Keras is a minimalist, highly modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
So, what better way to put that claim to the test than to write some code!
Keras comes with great documentation. One can really get up and running in a matter of minutes. Everything needed to accomplish the goal can be found on the Guide to Sequential Model page (assuming of course the initial setup and configuration is all taken care of).
Getting the data
Keras also offers a collection of datasets that can be used to train and test the model. The MNIST set is a part of the available datasets and can be loaded as shown below.
from keras.datasets import mnist
# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()Reshaping and normalizing the inputs
input_dim = 784 #28*28
X_train = X_train.reshape(60000, input_dim)
X_test = X_test.reshape(10000, input_dim)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255Convert class vectors to binary class matrices
from keras.utils import np_utils
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)Build the model
from keras.models import Sequential
from keras.layers import Dense, Activation
output_dim = nb_classes = 10
model = Sequential()
model.add(Dense(output_dim, input_dim=input_dim, activation='softmax'))
batch_size = 128
nb_epoch = 20Compile the model
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])Save model and weights
json_string = model.to_json() # as json open('mnist_Logistic_model.json', 'w').write(json_string) yaml_string = model.to_yaml() #as yaml open('mnist_Logistic_model.yaml', 'w').write(yaml_string) # save the weights in h5 format model.save_weights('mnist_Logistic_wts.h5') # uncomment the code below (and modify accordingly) to read a saved model and weights
# model = model_from_json(open('my_model_architecture.json').read())# if json
# model = model_from_yaml(open('my_model_architecture.yaml').read())# if yaml
# model.load_weights('my_model_weights.h5')
And that’s it! Full source code available here
Originally published at the1ju.tumblr.com.
