def minimize(self, obj):
variables = obj.list_input_variables()
grads = _base.gradients(obj, variables)
updates = []
for i, v in enumerate(variables):
self.m.append(_base.Variable(_sym.zeros_like(v), self.name + '_m' + str(i)))
self.v.append(_base.Variable(_sym.zeros_like(v), self.name + '_v' + str(i)))
update_t = _sym.assign(self.t, self.t + 1)
rate = _sym.sqrt(1 - self.beta2 ** update_t) / (1 - self.beta1 ** update_t)
lr_t = self.learning_rate * rate
for var, g, m, v in zip(variables, grads, self.m, self.v):
update_m = _sym.assign(m, self.beta1 * m + (1 - self.beta1) * g)
update_v = _sym.assign(v, self.beta2 * v + (1 - self.beta2) * g * g)
update_var = _sym.assign(var,
var - lr_t * update_m / (_sym.sqrt(update_v) + self.epsilon))
updates.append(update_var)
return _base.group(*updates)
def minimize(self, obj):
variables = obj.list_input_variables()
grads = _base.gradients(obj, variables)
updates = []
for i, v in enumerate(variables):
self.m.append(
_base.Variable(_sym.zeros_like(v), self.name + '_m' + str(i)))
self.v.append(
_base.Variable(_sym.zeros_like(v), self.name + '_v' + str(i)))
update_t = _sym.assign(self.t, self.t + 1)
rate = _sym.sqrt(1 - self.beta2**update_t) / (1 - self.beta1**update_t)
lr_t = self.learning_rate * rate
for var, g, m, v in zip(variables, grads, self.m, self.v):
update_m = _sym.assign(m, self.beta1 * m + (1 - self.beta1) * g)
update_v = _sym.assign(v,
self.beta2 * v + (1 - self.beta2) * g * g)
update_var = _sym.assign(
var,
var - lr_t * update_m / (_sym.sqrt(update_v) + self.epsilon))
updates.append(update_var)
return _base.group(*updates)
def test_full():
shape = (3, 4, 5)
value = 7
dtype = "float32"
for target, ctx in ctx_list():
data = sym.Variable("data", dtype=dtype)
# full_like
s = sym.full_like(data=data, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target, {"data": shape})
m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose(out.asnumpy(),
np.full(shape,
fill_value=value,
dtype=dtype),
atol=1e-5,
rtol=1e-5)
# ones_like
s = sym.ones_like(data=data, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target, {"data": shape})
m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose(out.asnumpy(),
np.full(shape, fill_value=1, dtype=dtype),
atol=1e-5,
rtol=1e-5)
# zeros_like
s = sym.zeros_like(data=data, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target, {"data": shape})
m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose(out.asnumpy(),
np.full(shape, fill_value=0, dtype=dtype),
atol=1e-5,
rtol=1e-5)
# full
s = sym.full(shape=shape, dtype=dtype, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target)
m = graph_runtime.create(graph, lib, ctx)
m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose(out.asnumpy(),
np.full(shape,
fill_value=value,
dtype=dtype),
atol=1e-5,
rtol=1e-5)
# ones
s = sym.ones(shape=shape, dtype=dtype, name="s")
graph, lib, _ = nnvm.compiler.build(s, target)
m = graph_runtime.create(graph, lib, ctx)
m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose(out.asnumpy(),
np.full(shape, fill_value=1, dtype=dtype),
atol=1e-5,
rtol=1e-5)
# zeros
s = sym.zeros(shape=shape, dtype=dtype, name="s")
graph, lib, _ = nnvm.compiler.build(s, target)
m = graph_runtime.create(graph, lib, ctx)
m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
np.testing.assert_allclose(out.asnumpy(),
np.full(shape, fill_value=0, dtype=dtype),
atol=1e-5,
rtol=1e-5)
def test_full():
shape = (3, 4, 5)
value = 7
dtype = "float32"
for target, ctx in ctx_list():
data = sym.Variable("data", dtype=dtype)
# full_like
s = sym.full_like(data=data, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target, {"data": shape})
m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
tvm.testing.assert_allclose(
out.asnumpy(),
np.full(shape, fill_value=value, dtype=dtype),
atol=1e-5, rtol=1e-5)
# ones_like
s = sym.ones_like(data=data, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target, {"data": shape})
m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
tvm.testing.assert_allclose(
out.asnumpy(),
np.full(shape, fill_value=1, dtype=dtype),
atol=1e-5, rtol=1e-5)
# zeros_like
s = sym.zeros_like(data=data, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target, {"data": shape})
m = graph_runtime.create(graph, lib, ctx)
m.run(data=np.random.uniform(size=shape).astype(dtype))
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
tvm.testing.assert_allclose(
out.asnumpy(),
np.full(shape, fill_value=0, dtype=dtype),
atol=1e-5, rtol=1e-5)
# full
s = sym.full(shape=shape, dtype=dtype, fill_value=value, name="s")
graph, lib, _ = nnvm.compiler.build(s, target)
m = graph_runtime.create(graph, lib, ctx)
m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
tvm.testing.assert_allclose(
out.asnumpy(),
np.full(shape, fill_value=value, dtype=dtype),
atol=1e-5, rtol=1e-5)
# ones
s = sym.ones(shape=shape, dtype=dtype, name="s")
graph, lib, _ = nnvm.compiler.build(s, target)
m = graph_runtime.create(graph, lib, ctx)
m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
tvm.testing.assert_allclose(
out.asnumpy(),
np.full(shape, fill_value=1, dtype=dtype),
atol=1e-5, rtol=1e-5)
# zeros
s = sym.zeros(shape=shape, dtype=dtype, name="s")
graph, lib, _ = nnvm.compiler.build(s, target)
m = graph_runtime.create(graph, lib, ctx)
m.run()
out = m.get_output(0, tvm.nd.empty(shape, dtype=dtype))
tvm.testing.assert_allclose(
out.asnumpy(),
np.full(shape, fill_value=0, dtype=dtype),
atol=1e-5, rtol=1e-5)