def test_nested_value_2():
npr.seed(11)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 20)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X, axis=1)
Z = kayak.MatMean(Y)
assert np.all(close_float(Y.value.ravel(), np.mean(npX, axis=1)))
assert close_float(Z.value, np.mean(npX))
def test_keepdims_value_1():
npr.seed(9)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 20)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X, axis=0, keepdims=False)
Z = kayak.MatMean(Y)
assert Y.shape == np.mean(npX, axis=0, keepdims=False).shape
assert np.all(
close_float(Y.value, np.mean(npX, axis=0, keepdims=False)))
assert close_float(Z.value, np.mean(npX))
def test_keepdims_grad_2():
npr.seed(10)
for ii in xrange(NUM_TRIALS):
npW = npr.randn(5, 10, 20)
npX = npr.randn(5, 10, 20)
W = kayak.Parameter(npW)
X = kayak.Parameter(npX)
Y = W * X
Z = kayak.MatMean(Y, axis=2, keepdims=False)
S = kayak.MatMean(Z)
assert S.grad(W).shape == npW.shape
assert_less(kayak.util.checkgrad(X, S), MAX_GRAD_DIFF)
def test_keepdims_grad_1():
npr.seed(10)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 20)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X, axis=0, keepdims=False)
Z = kayak.MatMean(Y)
assert Z.grad(X).shape == npX.shape
assert np.all(
close_float(Z.grad(X),
1.0 / float(np.prod(npX.shape)) * np.ones(npX.shape)))
assert_less(kayak.util.checkgrad(X, Z), MAX_GRAD_DIFF)
def test_vector_value_1():
npr.seed(3)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 1)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X)
# Verify the sum.
assert close_float(Y.value, np.mean(npX))
def test_matrix_value():
npr.seed(7)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 20)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X)
# Verify the value.
assert close_float(Y.value, np.mean(npX))
def test_scalar_value():
npr.seed(1)
for ii in xrange(NUM_TRIALS):
npX = npr.randn()
X = kayak.Parameter(npX)
Y = kayak.MatMean(X)
# Verify that a scalar is reproduced.
assert close_float(Y.value, npX)
def test_tensor_value_4():
npr.seed(16)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 20, 30)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X, axis=0)
assert np.all(
close_float(Y.value, np.expand_dims(np.mean(npX, axis=0), axis=0)))
def test_tensor_value_1():
npr.seed(13)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 20, 30)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X)
assert X.shape == npX.shape
assert close_float(Y.value, np.mean(npX))
def test_scalar_grad():
npr.seed(2)
for ii in xrange(NUM_TRIALS):
npX = npr.randn()
X = kayak.Parameter(npX)
Y = kayak.MatMean(X)
# Verify that the gradient is one.
Y.value
assert Y.grad(X) == 1.0
assert_less(kayak.util.checkgrad(X, Y), MAX_GRAD_DIFF)
def test_vector_grad_1():
npr.seed(4)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 1)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X)
# Verify the gradient.
Y.value
assert Y.grad(X).shape == npX.shape
assert np.all(
close_float(Y.grad(X), 1.0 / float(npX.size) * np.ones(npX.shape)))
assert_less(kayak.util.checkgrad(X, Y), MAX_GRAD_DIFF)
def test_matrix_grad():
npr.seed(8)
for ii in xrange(NUM_TRIALS):
npX = npr.randn(10, 20)
X = kayak.Parameter(npX)
Y = kayak.MatMean(X)
# Verify the value.
Y.value
assert Y.grad(X).shape == npX.shape
assert np.all(
close_float(Y.grad(X),
1.0 / float(np.prod(npX.shape)) * np.ones(npX.shape)))
assert_less(kayak.util.checkgrad(X, Y), MAX_GRAD_DIFF)
