def test_unary():
x = sym.Variable('x')
x = sym.exp(x)
x = sym.log(x)
x = sym.sigmoid(x)
x = sym.tanh(x)
x = sym.relu(x)
assert x.list_input_names() == ['x']
def test_unary():
x = sym.Variable('x')
x = sym.exp(x)
x = sym.log(x)
x = sym.sigmoid(x)
x = sym.tanh(x)
x = sym.relu(x)
assert x.list_input_names() == ['x']
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
def forward(x):
return 1.0 / (1.0 + np.exp(-x))
def backward(head_grads, x):
y_np = forward(x)
return [y_np * (1 - y_np) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
def forward(x):
return 1.0 / (1.0 + np.exp(-x))
def backward(head_grads, x):
y_np = forward(x)
return [y_np *(1 - y_np) * head_grads]
shape = {'x': (1, 3, 32, 32)}
check_function(y, forward, backward, shape=shape)
def compile(self, **kwargs):
if kwargs['op'] == 'dense':
return sym.dense(data=kwargs['data'],
weight=kwargs['weight'],
bias=kwargs['bias'],
units=kwargs['units'])
elif kwargs['op'] == 'relu':
return sym.relu(data=kwargs['data'])
elif kwargs['op'] == 'leaky_relu':
return sym.leaky_relu(data=kwargs['data'], alpha=kwargs['alpha'])
elif kwargs['op'] == 'sigmoid':
return sym.sigmoid(data=kwargs['data'])
else:
raise RuntimeError('invalid operator')
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
dtype = "float32"
dshape = (1, 3, 32, 32)
oshape = dshape
for target, ctx in ctx_list():
graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
m = graph_runtime.create(graph, lib, ctx)
data = np.random.uniform(size=dshape).astype(dtype)
m.run(x=data)
out = m.get_output(0, tvm.nd.empty(oshape, dtype))
y_np = 1.0 / (1.0 + np.exp(-data))
np.testing.assert_allclose(out.asnumpy(), y_np, atol=1e-5, rtol=1e-5)
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
def forward(x):
return 1.0 / (1.0 + np.exp(-x))
def backward(head_grads, x):
y_np = forward(x)
return [y_np * (1 - y_np) * head_grads]
dtype = "float32"
dshape = (1, 3, 32, 32)
inputs = [('x', dshape, x)]
helper(y, inputs, dtype, forward, backward)
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
def forward(x):
return 1.0 / (1.0 + np.exp(-x))
def backward(x):
y_np = forward(x)
return y_np *(1 - y_np)
dtype = "float32"
dshape = (1, 3, 32, 32)
inputs = {'x': (dshape, x)}
helper(y, inputs, dtype, forward, backward)
def test_sigmoid():
x = sym.Variable("x")
y = sym.sigmoid(x)
def forward(x):
return 1.0 / (1.0 + np.exp(-x))
def backward(x):
y_np = forward(x)
return y_np * (1 - y_np)
dtype = "float32"
dshape = (1, 3, 32, 32)
inputs = {'x': (dshape, x)}
helper(y, inputs, dtype, forward, backward)
