def test_validity2(self):
theano.config.on_unused_input = 'warn'
a0_var = T.dmatrix('a0')
r0_var = T.dmatrix('r0')
fri_var = T.dmatrix("fri")
out = T.dmatrix("out")
out_stale = T.dmatrix("out_stale")
f = theano.function([a0_var, r0_var, fri_var, out, out_stale],
dqn.build_loss(out, out_stale, a0_var, r0_var, fri_var, gamma=0.5))
sqr_mean, mean, y, q = f(np.array([[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1]]),
np.array([[1],
[0],
[5]]),
np.array([[1],
[1],
[0]]),
np.array([[-5, 1, 2, 3, 4, 7],
[1, 4, 3, 4, 5, 9],
[0, 9, 0, 3, 2, 1]]),
np.array([[-5, 1, 2, 3, 4, 5],
[1, 2, 3, 4, 5, 6],
[8, 0, -1, -1, 2, 3]]))
print(y, q)
def test_validity2(self):
theano.config.on_unused_input = 'warn'
a0_var = T.dmatrix('a0')
r0_var = T.dmatrix('r0')
fri_var = T.dmatrix("fri")
out = T.dmatrix("out")
out_stale = T.dmatrix("out_stale")
f = theano.function([a0_var, r0_var, fri_var, out, out_stale],
dqn.build_loss(out,
out_stale,
a0_var,
r0_var,
fri_var,
gamma=0.5))
sqr_mean, mean, y, q = f(
np.array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1]]), np.array([[1], [0], [5]]),
np.array([[1], [1], [0]]),
np.array([[-5, 1, 2, 3, 4, 7], [1, 4, 3, 4, 5, 9],
[0, 9, 0, 3, 2, 1]]),
np.array([[-5, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 6],
[8, 0, -1, -1, 2, 3]]))
print(y, q)
def test_validity(self):
theano.config.on_unused_input = 'warn'
a0_var = T.dmatrix('a0')
r0_var = T.dmatrix('r0')
fri_var = T.dmatrix("fri")
out = T.dmatrix("out")
out_stale = T.dmatrix("out_stale")
f = theano.function([a0_var, r0_var, fri_var, out, out_stale],
dqn.build_loss(out, out_stale, a0_var, r0_var, fri_var, gamma=0.5))
loss, not_loss, y, q = f(np.array([[1, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0]]),
np.array([[1],
[0]]),
np.array([[1],
[1]]),
np.array([[-5, 1, 2, 3, 4, 7],
[1, 4, 3, 4, 5, 9]]),
np.array([[-5, 1, 2, 3, 4, 5],
[1, 2, 3, 4, 5, 6]]))
self.assertTrue(np.all(y == [[3.5], [3]]))
self.assertTrue(np.all(q == [[-5], [4]]))
print(loss)
print(not_loss)
self.assertTrue(loss == 8.5)
def test_validity(self):
theano.config.on_unused_input = 'warn'
a0_var = T.dmatrix('a0')
r0_var = T.dmatrix('r0')
fri_var = T.dmatrix("fri")
out = T.dmatrix("out")
out_stale = T.dmatrix("out_stale")
f = theano.function([a0_var, r0_var, fri_var, out, out_stale],
dqn.build_loss(out,
out_stale,
a0_var,
r0_var,
fri_var,
gamma=0.5))
loss, not_loss, y, q = f(
np.array([[1, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0]]),
np.array([[1], [0]]), np.array([[1], [1]]),
np.array([[-5, 1, 2, 3, 4, 7], [1, 4, 3, 4, 5, 9]]),
np.array([[-5, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 6]]))
self.assertTrue(np.all(y == [[3.5], [3]]))
self.assertTrue(np.all(q == [[-5], [4]]))
print(loss)
print(not_loss)
self.assertTrue(loss == 8.5)