def forward(self, x):
for i in range(self.number_of_layers):
x = self._param_encoder[i](x)
x = F.selu(x)
if self._dp_drop_prob > 0: # apply dropout only on code layer
x = self.drop(x)
for i in range(self.number_of_layers):
x = self._param_decoder[i](x)
x = F.selu(x)
return x
def test_errors(self):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.selu, 1)
# The input dtype must be float16, float32, float64.
x_int32 = paddle.fluid.data(
name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, F.selu, x_int32)
# The scale must be greater than 1.0
x_fp32 = paddle.fluid.data(
name='x_fp32', shape=[12, 10], dtype='float32')
self.assertRaises(ValueError, F.selu, x_fp32, -1.0)
# The alpha must be no less than 0
self.assertRaises(ValueError, F.selu, x_fp32, 1.6, -1.0)
# support the input dtype is float16
x_fp16 = paddle.fluid.data(
name='x_fp16', shape=[12, 10], dtype='float16')
F.selu(x_fp16)
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.selu(x, self.scale, self.alpha)
selu = paddle.nn.SELU(self.scale, self.alpha)
out2 = selu(x)
out_ref = ref_selu(self.x_np, self.scale, self.alpha)
for r in [out1, out2]:
self.assertEqual(np.allclose(out_ref, r.numpy()), True)
paddle.enable_static()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.fluid.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.selu(x, self.scale, self.alpha)
selu = paddle.nn.SELU(self.scale, self.alpha)
out2 = selu(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_selu(self.x_np, self.scale, self.alpha)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
