AdaGrad using theano.scan()

theano_scan_adagrad.py

import theano
import theano.tensor as T

# AdaGrad using theano.scan()

def fn(x, r, learning_rate):
    y = x ** 2 - x
    g = T.grad(y, x)
    r += g ** 2
    return x - learning_rate / T.sqrt(r) * g, r

init_x = T.dscalar()
init_r = T.dscalar()
result, updates = theano.scan(fn=fn,
                              outputs_info=(init_x, init_r),
                              non_sequences=0.3,
                              n_steps=100)
f2 = theano.function([init_x], result[0][-1], givens={init_r: 1e-8}, updates=updates)

print(f2(1))  # 0.5