def test_operation_and_placeholder_gradients():
a_array = np.ones([10, 5])
b_array = np.ones([5, 10])
a_sp = sp.Variable(a_array)
b_sp = sp.Variable(b_array)
a = sp.Placeholder()
b = sp.Placeholder()
y = sp.Lazy(sp.matmul)(a, b)
a.assign_value(a_sp)
b.assign_value(b_sp)
result = y.run()
grads = sp.get_gradients(result)
sp_results = [result.array, grads[a_sp], grads[b_sp]]
def func(a, b):
return np.matmul(a, b)
y_np = func(a_array, b_array)
args = [a_array, b_array]
num_grads = numgrads(func, args, n=1, delta=1)
num_results = [y_np, num_grads[0], num_grads[1]]
for spval, numval in zip(sp_results, num_results):
error = rmse(spval, numval)
assert error < EPS, f"rmse = {error}"
def test_operation_and_placeholder_result():
a_array = np.ones([10, 5])
b_array = np.ones([5, 10])
a = sp.Placeholder()
b = sp.Placeholder()
c = sp.Lazy(sp.matmul)(a, b)
d = sp.Lazy(sp.mul)(c, sp.Variable(np.array(5)))
a.assign_value(sp.Variable(a_array))
b.assign_value(sp.Variable(b_array))
result = d.run() # runs the graph
assert np.all(result.array - np.matmul(a_array, b_array) * 5) == 0, "invalid result"
def test_learnable_and_get_learnables():
"""Test that sp.get_learnables correctly obtains
sp.Variables that have been flagged by sp.learnable().
"""
param_1 = sp.learnable(sp.Variable(np.array(5)))
param_2 = sp.learnable(sp.Variable(np.array(10)))
a = sp.Placeholder()
b = sp.Placeholder()
y = sp.Lazy(sp.matmul)(a, b)
y = sp.Lazy(sp.mul)(y, param_1)
y = sp.Lazy(sp.add)(y, param_2)
params = sp.get_learnables(y)
assert params == [param_1, param_2], "Did not get params."
def test_operation_and_placeholder_basic():
x_in = sp.Placeholder()
x = sp.Lazy(lambda x: x ** 2)(x_in)
x_in.assign_value(3)
assert x.run() == 9, "Expecting a value of 9"