def check_affine(f, *nu_inputs):
types = ",".join(["{%s,%s}" % (x.dtype, x.ndim) for x in nu_inputs])
cgt.utils.colorprint(cgt.utils.Color.YELLOW,
"Testing %s(%s)\n" % (f.__name__, types))
sy_inputs = map(tensor_like, nu_inputs)
for (i, sy) in enumerate(sy_inputs):
sy.name = "x%i" % i
sy_result = f(*sy_inputs)
def maybeprint(msg):
if DISPLAY: print msg
maybeprint("Function:")
if DISPLAY: cgt.print_tree([sy_result])
f_cgt = cgt.function(sy_inputs, sy_result)
sy_grads = cgt.grad(sy_result, sy_inputs)
gradf_cgt = cgt.function(sy_inputs, sy_grads)
sy_result_simple = core.simplify([sy_result])
sy_grads_simple = core.simplify(sy_grads)
maybeprint("Gradient:")
if DISPLAY: cgt.print_tree(sy_grads)
maybeprint("Gradient after simplification:")
if DISPLAY: cgt.print_tree(sy_grads_simple)
out_true = f(*nu_inputs)
out_cgt = f_cgt(*nu_inputs)
grads_true = gradients_affine(f_cgt,
nu_inputs,
h=1e-4 if "max" in f.__name__ else 1e-1)
grads_cgt = gradf_cgt(*nu_inputs)
rtol = {"single": 1e-3, "double": 1e-5}[cgt.get_precision()]
np.testing.assert_allclose(out_cgt, out_true, rtol=rtol)
for (g_cgt, g_true) in zip(grads_cgt, grads_true):
np.testing.assert_allclose(g_cgt, g_true, rtol=rtol)
result_count = cgt.count_nodes(sy_result_simple)
grad_count = cgt.count_nodes(sy_grads_simple)
maybeprint("Result before: %i. after: %i" %
(cgt.count_nodes([sy_result]), result_count))
maybeprint("Grad before: %i. after: %i" %
(cgt.count_nodes(sy_grads), grad_count))
PROB2RESULT[f.__name__] = {}
PROB2RESULT[f.__name__]["fn"] = result_count
PROB2RESULT[f.__name__]["grad"] = grad_count
def check_affine(f, *nu_inputs):
types = ",".join(["{%s,%s}" % (x.dtype, x.ndim) for x in nu_inputs])
cgt.utils.colorprint(cgt.utils.Color.YELLOW, "Testing %s(%s)\n" % (f.__name__, types))
sy_inputs = map(tensor_like, nu_inputs)
for (i, sy) in enumerate(sy_inputs):
sy.name = "x%i" % i
sy_result = f(*sy_inputs)
def maybeprint(msg):
if DISPLAY:
print msg
maybeprint("Function:")
if DISPLAY:
cgt.print_tree([sy_result])
f_cgt = cgt.function(sy_inputs, sy_result)
sy_grads = cgt.grad(sy_result, sy_inputs)
gradf_cgt = cgt.function(sy_inputs, sy_grads)
sy_result_simple = core.simplify([sy_result])
sy_grads_simple = core.simplify(sy_grads)
maybeprint("Gradient:")
if DISPLAY:
cgt.print_tree(sy_grads)
maybeprint("Gradient after simplification:")
if DISPLAY:
cgt.print_tree(sy_grads_simple)
out_true = f(*nu_inputs)
out_cgt = f_cgt(*nu_inputs)
grads_true = gradients_affine(f_cgt, nu_inputs, h=1e-4 if "max" in f.__name__ else 1e-1)
grads_cgt = gradf_cgt(*nu_inputs)
rtol = {"single": 1e-3, "double": 1e-5}[cgt.get_precision()]
np.testing.assert_allclose(out_cgt, out_true, rtol=rtol)
for (g_cgt, g_true) in zip(grads_cgt, grads_true):
np.testing.assert_allclose(g_cgt, g_true, rtol=rtol)
result_count = cgt.count_nodes(sy_result_simple)
grad_count = cgt.count_nodes(sy_grads_simple)
maybeprint("Result before: %i. after: %i" % (cgt.count_nodes([sy_result]), result_count))
maybeprint("Grad before: %i. after: %i" % (cgt.count_nodes(sy_grads), grad_count))
PROB2RESULT[f.__name__] = {}
PROB2RESULT[f.__name__]["fn"] = result_count
PROB2RESULT[f.__name__]["grad"] = grad_count
X_tnk = cgt.tensor3("X")
cell = gru.GRUCell([dim_x], mem_size)
Minit_nk = cgt.zeros((X_tnk.shape[0], X_tnk.shape[1]), cgt.floatX)
M = Minit_nk
for t in xrange(horizon):
M = cell(M, X_tnk[t])
# cgt.print_tree(M)
print "simplifying..."
M_simp = cgt.simplify([M])
print "done"
# cgt.print_tree(M_simp)
print "fn before:", cgt.count_nodes(M)
print "fn after:", cgt.count_nodes(M_simp)
gs = cgt.grad(cgt.sum(M), cell.params())
print "grad before", cgt.count_nodes(gs)
g_simp = cgt.simplify(gs)
print "grad after", cgt.count_nodes(g_simp)
# M = cgt.simplify(M)
elapsed.append(time() - tstart)
import matplotlib.pyplot as plt
plt.plot(horizons, elapsed, 'x-')
plt.show()
X_tnk = cgt.tensor3("X")
cell = gru.GRUCell([dim_x], mem_size)
Minit_nk = cgt.zeros((X_tnk.shape[0], X_tnk.shape[1]),cgt.floatX)
M = Minit_nk
for t in xrange(horizon):
M = cell(M, X_tnk[t])
# cgt.print_tree(M)
print "simplifying..."
M_simp = cgt.simplify([M])
print "done"
# cgt.print_tree(M_simp)
print "fn before:",cgt.count_nodes(M)
print "fn after:",cgt.count_nodes(M_simp)
gs = cgt.grad(cgt.sum(M), cell.params())
print "grad before", cgt.count_nodes(gs)
g_simp = cgt.simplify(gs)
print "grad after",cgt.count_nodes(g_simp)
# M = cgt.simplify(M)
elapsed.append(time()-tstart)
import matplotlib.pyplot as plt
plt.plot(horizons,elapsed,'x-')
plt.show()
