def call(self, inputs):
x = relu(self.linear1(inputs))
x = relu(self.linear2(x))
x = relu(self.linear3(x))
x = self.linear4(x)
z_mean = x[:, :self.latent_dim]
z_log_var = softplus(x[:, self.latent_dim:])
return z_mean, z_log_var
def test_softplus(self):
def softplus(x):
return np.log(np.ones_like(x) + np.exp(x))
x = backend.placeholder(ndim=2)
f = backend.function([x], [activations.softplus(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
expected = softplus(test_values)
self.assertAllClose(result, expected, rtol=1e-05)
def call(self, inputs, **kwargs):
mu = self.dense_mu(inputs)
pre_sigma = self.dense_pre_sigma(inputs)
sigma = softplus(pre_sigma)
return mu, sigma