# Generate test z0 and x0
z0 = np.array([0.10, 0.50])
x0 = model(z0, sigma=0.)
x_dim = len(x0)
z_dim = len(z0)
# Initialize loss list
losses = []
# Generate 1-dim posteriors
xz = swyft.init_xz(model, n_sims=n_sims, n_dim=z_dim)
network = swyft.MLP(x_dim, z_dim, n_hidden, xz_init=xz)
losses += swyft.train(network,
xz,
n_steps=n_steps,
lr=1e-3,
n_particles=n_particles)
losses += swyft.train(network,
xz,
n_steps=n_steps,
lr=1e-4,
n_particles=n_particles)
# Generate 2-dim posteriors
network_2d = swyft.MLP_2d(x_dim, n_hidden, xz_init=xz)
losses += swyft.train(network_2d,
xz,
n_steps=n_steps,
lr=1e-3,
n_particles=n_particles)
# Generate test z0 and x0
z0 = np.array([0.10, 0.50])
x0 = model(z0)
x_dim = len(x0)
z_dim = len(z0)
# Initialize loss list
losses = []
# And the first run
xz1 = swyft.init_xz(model, n_sims=n_sims, n_dim=z_dim)
network1 = swyft.MLP(x_dim, z_dim, n_hidden, xz_init=xz1)
losses += swyft.train(network1,
xz1,
n_steps=n_steps,
lr=1e-3,
n_particles=n_particles)
losses += swyft.train(network1,
xz1,
n_steps=n_steps,
lr=1e-4,
n_particles=n_particles)
xz2 = swyft.update_xz(xz1,
network1,
x0,
model,
n_sims=n_sims,
lnL_th=np.log(1e-5),
append=False)