======Sample code======
<file python keras_example.py>
#basic MLP network
# nice documentation is available at https://keras.io/

# Step 1
# import classes and functions 
import numpy							#package for scientific computing, support for large array etc.
from keras.datasets import mnist	 	#dataset
from keras.models import Sequential		#model
from keras.layers import Dense			#layer
from keras.layers import Dropout		#layer
from keras.utils import np_utils		#for transforming data 
import matplotlib.pyplot as plt			#to plot images

# Step 2: load MNIST data set
(X_train, y_train), (X_test, y_test) = mnist.load_data() 	#Keras function

# Print shape of dataset..it will print three tuples, namely the no. of images in dataset, height and width(60000, 28, 28)
# uncomment if do not want to print
print X_train.shape 

# plot images...subplot function is being used...nice documentation is available on the official webpage of matplotlib
# arguments to subplot functions are number of rows, number of columns and number of subplots in the plot...comma is mandatory if values are less than 10
# you can experiment
# uncomment if do not want to print
plt.subplot(221)	
plt.imshow(X_train[0], cmap=plt.get_cmap('gray')) # ploting first image of training data set
plt.subplot(222)
plt.imshow(X_train[134], cmap=plt.get_cmap('gray'))	# ploting 135th image in training data set
plt.subplot(223)
plt.imshow(X_test[2444], cmap=plt.get_cmap('gray'))	# ploting 2445th image of test date set
plt.subplot(224)
plt.imshow(X_test[3], cmap=plt.get_cmap('gray'))	# ploting 4th image of test data set
# show the plot
plt.show()

# Step 3: Preprocess input data for Keras

# flatten 28*28 images to a 784 vector for each image and pixel precision set to 32 bit
num_pixels = X_train.shape[1] * X_train.shape[2]
X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')

# normalize inputs from 0-255 to 0-1
X_train = X_train / 255
X_test = X_test / 255

# Step 4: Preprocess class labels
# check shape of our class label data
# uncomment if do not want to print
print y_train.shape
#We should have 10 different classes, one for each digit, but it looks like we only have a 1-dimensional array.
#check labels for the first 10 training samples:
	
print y_train[:10]
# output of the form [5 0 4 1 9 2 1 3 1 4]
#The y_train and y_test data are not split into 10 distinct class labels, but rather are represented as a single array with the class values.

	
# Convert 1-dimensional class arrays to 10-dimensional class matrices
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]

# check again	
print y_train.shape
# (60000, 10)
print y_train[:10]


# Step 5:  Define model architecture
# a very simple model is being created in next few lines...this is the most important part => creating a good network

model = Sequential()
model.add(Dense(num_pixels, input_dim=num_pixels, init='normal', activation='relu')) #only one hidden layer with relu as activation function
model.add(Dense(num_classes, init='normal', activation='softmax'))					#output layer with softmax as activation function


# Step 6: Compile model
# define optimizer, loss function, meterics => very important step which will determine the performance of your network
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Step 7: Train model
model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=2, batch_size=200, verbose=1)

# Step 8: Evaluate model
scores = model.evaluate(X_test, y_test)
print("Error: %.2f%%" % (100-scores[1]*100))
</file>