def check_log():
flow = myelin.Flow()
f = flow.define("log")
x = f.var("x", dtype, shape)
y = f.log(x, "y")
gradcheck(f, [x], [y], 0.0, 10.0)
def tanh_test(n):
flow = myelin.Flow()
f = flow.define("tanh")
x = f.var("x", dt, [n])
y = f.tanh(x)
check(flow, n)
def acosh_test(n):
flow = myelin.Flow()
f = flow.define("acosh")
x = f.var("x", dt, [n])
y = f.acosh(x)
check(flow, n, 1.0, 10.0, atol=1e-6)
def asin_test(n):
flow = myelin.Flow()
f = flow.define("asin")
x = f.var("x", dt, [n])
y = f.asin(x)
check(flow, n, -1.0, 1.0)
def atan_test(n):
flow = myelin.Flow()
f = flow.define("atan")
x = f.var("x", dt, [n])
y = f.atan(x)
check(flow, n)
def round_test(n):
flow = myelin.Flow()
f = flow.define("round")
x = f.var("x", dt, [n])
y = f.round(x)
check(flow, n)
def exp_test(n):
flow = myelin.Flow()
f = flow.define("exp")
x = f.var("x", dt, [n])
y = f.exp(x)
check(flow, n)
def check_normalize():
flow = myelin.Flow()
f = flow.define("normalize")
x = f.var("x", dtype, shape)
y = f.normalize(x, "y")
gradcheck(f, [x], [y], tol=1e-2)
def check_softmax():
flow = myelin.Flow()
f = flow.define("softmax")
x = f.var("x", dtype, shape)
y = f.softmax(x, "y")
gradcheck(f, [x], [y])
def check_relu():
flow = myelin.Flow()
f = flow.define("relu")
x = f.var("x", dtype, shape)
y = f.relu(x, "y")
gradcheck(f, [x], [y], lo=-1.0, hi=1.0)
def check_norm():
flow = myelin.Flow()
f = flow.define("norm")
x = f.var("x", dtype, shape)
y = f.norm(x, "y")
gradcheck(f, [x], [y], eps=1e-2)
def check_erf():
flow = myelin.Flow()
f = flow.define("erf")
x = f.var("x", dtype, shape)
y = f.erf(x, "y")
gradcheck(f, [x], [y])
def check_sigmoid():
flow = myelin.Flow()
f = flow.define("sigmoid")
x = f.var("x", dtype, shape)
y = f.sigmoid(x, "y")
gradcheck(f, [x], [y])
def check_tanh():
flow = myelin.Flow()
f = flow.define("tanh")
x = f.var("x", dtype, shape)
y = f.tanh(x, "y")
gradcheck(f, [x], [y])
def floor_test(n):
flow = myelin.Flow()
f = flow.define("floor")
x = f.var("x", dt, [n])
y = f.floor(x)
check(flow, n)
def check_sum():
flow = myelin.Flow()
f = flow.define("sum")
x = f.var("x", dtype, shape)
y = f.sum(x, "y")
gradcheck(f, [x], [y], tol=1e-3)
def ceil_test(n):
flow = myelin.Flow()
f = flow.define("ceil")
x = f.var("x", dt, [n])
y = f.ceil(x)
check(flow, n)
def check_min():
flow = myelin.Flow()
f = flow.define("min")
x = f.var("x", dtype, shape)
y = f.min(x, "y")
gradcheck(f, [x], [y])
def trunc_test(n):
flow = myelin.Flow()
f = flow.define("trunc")
x = f.var("x", dt, [n])
y = f.trunc(x)
check(flow, n)
def transpose_test(m, k, n, perm):
flow = myelin.Flow()
f = flow.define("transpose")
x = f.var("x", dt, [m, k, n])
y = f.transpose(x, perm=perm)
check(flow, (m, k, n, perm))
def erf_test(n):
flow = myelin.Flow()
f = flow.define("erf")
x = f.var("x", dt, [n])
y = f.erf(x)
check(flow, n, -2.0, 2.0, 1e-6, 1e-4)
def neg_test(n):
flow = myelin.Flow()
f = flow.define("neg")
x = f.var("x", dt, [n])
y = f.neg(x)
check(flow, n)
def acos_test(n):
flow = myelin.Flow()
f = flow.define("acos")
x = f.var("x", dt, [n])
y = f.acos(x)
check(flow, n, -1.0, 1.0)
def rcp_test(n):
flow = myelin.Flow()
f = flow.define("rcp")
x = f.var("x", dt, [n])
y = f.rcp(x)
check(flow, n)
def cosh_test(n):
flow = myelin.Flow()
f = flow.define("cosh")
x = f.var("x", dt, [n])
y = f.cosh(x)
check(flow, n)
def abs_test(n):
flow = myelin.Flow()
f = flow.define("abs")
x = f.var("x", dt, [n])
y = f.abs(x)
check(flow, n, -10.0, 10.0)
def asinh_test(n):
flow = myelin.Flow()
f = flow.define("asinh")
x = f.var("x", dt, [n])
y = f.asinh(x)
check(flow, n, -1.0, 1.0, rtol=1e-3, atol=1e-6)
def sign_test(n):
flow = myelin.Flow()
f = flow.define("sign")
x = f.var("x", dt, [n])
y = f.sign(x)
check(flow, n, -10.0, 10.0)
def sqrt_test(n):
flow = myelin.Flow()
f = flow.define("sqrt")
x = f.var("x", dt, [n])
y = f.sqrt(x)
check(flow, n, 0.1, 10.0)
def check_exp():
flow = myelin.Flow()
f = flow.define("exp")
x = f.var("x", dtype, shape)
y = f.exp(x, "y")
gradcheck(f, [x], [y], -3.0, 3.0)
