# Import the required modules
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
from sklearn import datasets
from lasagne import layers
from lasagne import init
from lasagne import nonlinearities
from lasagne.updates import sgd,nesterov_momentum

from nolearn.lasagne import NeuralNet
import numpy as np
%pylab inline

# Load the training and testing set
dataset = datasets.fetch_mldata("MNIST Original")
(trainX, testX, trainY, testY) = train_test_split((dataset.data / 255.0).astype(np.float32), dataset.target.astype(np.int32), test_size = 0.33)

# Create the classifier
clf = NeuralNet(
    layers=[
        ('input', layers.InputLayer),
        ('hidden', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],
    input_shape = (None, trainX.shape[1]),
    hidden_num_units=100,
    output_num_units=10,
    output_nonlinearity=nonlinearities.softmax,
    update=nesterov_momentum,
    #update=sgd,
    update_learning_rate=0.01,
    update_momentum=0.9,
    regression=False,
    max_epochs=20,
    verbose=1,
    #W=init.Uniform()
    )

# Perform the training
clf.fit(trainX, trainY)

# Perform the predictions
preds = clf.predict(testX)
# and show the precision, recall and f1-score
print classification_report(testY, preds)

# Sidekick Function to display the image in python notebook
def imshow(im_title, im):
    ''' This is function to display the image'''
    plt.figure()  
    plt.title(im_title)
    plt.axis("off")
    if len(im.shape) == 2:
        plt.imshow(im, cmap = "gray")
    else:
        im_display = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
        plt.imshow(im_display)
    plt.show()

# randomly select a few of the test instances
for i in np.random.choice(np.arange(0, len(testY)), size = (10,)):
	# classify the digit
	pred = clf.predict(np.atleast_2d(testX[i]))
 
	# reshape the feature vector to be a 28x28 pixel image, then change
	# the data type to be an unsigned 8-bit integer
	image = (testX[i] * 255).reshape((28, 28)).astype("uint8")
 
	# show the image and prediction
	imshow("Actual digit is {0}, predicted {1}".format(testY[i], pred[0]), image)