theano_demo.py

import theano
import theano.tensor as T
import numpy

# Again, make 100 points in numpy
x_data = numpy.float32(numpy.random.rand(2, 100))
y_data = numpy.dot([0.100, 0.200], x_data) + 0.3

# Intialise the Theano model
X = T.matrix()
Y = T.vector()
b = theano.shared(numpy.random.uniform(-1, 1), name="b")
W = theano.shared(numpy.random.uniform(-1.0, 1.0, (1, 2)), name="W")
y = W.dot(X) + b 

# Compute the gradients WRT the mean-squared-error for each parameter
cost = T.mean(T.sqr(y - Y))
gradientW = T.grad(cost=cost, wrt=W)
gradientB = T.grad(cost=cost, wrt=b)
updates = [[W, W - gradientW * 0.5], [b, b - gradientB * 0.5]] 

train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True) 

for i in xrange(0, 201):
    train(x_data, y_data)
    print W.get_value(), b.get_value()