How to use the netket.optimizer.Sgd function in NetKet

# Hilbert space of spins on the graph
hi = nk.hilbert.Spin(s=0.5, graph=g)

# Ising spin hamiltonian at the critical point
ha = nk.operator.Ising(h=3.0, hilbert=hi)

# RBM Spin Machine
ma = nk.machine.RbmSpin(alpha=1, hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)

# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(machine=ma)

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)

# Stochastic reconfiguration
gs = nk.variational.Vmc(
    hamiltonian=ha,
    sampler=sa,
    optimizer=op,
    n_samples=1000,
    diag_shift=0.1,
    method="Sr",
)

gs.run(output_prefix="test", n_iter=1000)

# Spin based Hilbert Space
hi = nk.hilbert.Spin(s=0.5, total_sz=0.0, graph=g)

# Custom Hamiltonian operator
op = nk.operator.GraphOperator(hi, bondops=bond_operator, bondops_colors=bond_color)

# Restricted Boltzmann Machine
ma = nk.machine.RbmSpin(hi, alpha=1)
ma.init_random_parameters(seed=1234, sigma=0.01)

# Sampler
sa = nk.sampler.MetropolisHamiltonianPt(machine=ma, hamiltonian=op, n_replicas=16)

# Optimizer
opt = nk.optimizer.Sgd(learning_rate=0.01)

# Variational Monte Carlo
gs = nk.variational.Vmc(
    hamiltonian=op,
    sampler=sa,
    optimizer=opt,
    n_samples=1000,
    use_iterative=True,
    method="Sr",
)

gs.run(output_prefix="test", n_iter=10000)

# Hilbert space of spins on the graph
hi = nk.hilbert.Spin(s=0.5, graph=g)

# Ising spin hamiltonian
ha = nk.operator.Ising(h=1.0, hilbert=hi)

# RBM Spin Machine
ma = nk.machine.RbmSpinReal(alpha=1, hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)


# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(machine=ma, n_chains=16, sweep_size=20)

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)

# Stochastic reconfiguration
gs = nk.variational.Vmc(
    hamiltonian=ha,
    sampler=sa,
    optimizer=op,
    n_samples=1000,
    diag_shift=0.1,
    method="Sr",
)

gs.run(output_prefix="test", n_iter=300)

# Hilbert space of spins from given graph
hi = nk.hilbert.Spin(s=0.5, graph=g)

# Hamiltonian
ha = nk.operator.Ising(h=1.0, hilbert=hi)

# Machine
ma = nk.machine.RbmSpin(hilbert=hi, alpha=1)
ma.init_random_parameters(seed=SEED, sigma=0.01)

# Sampler
sa = nk.sampler.MetropolisLocal(machine=ma)
sa.seed(SEED)

# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)

# Variational Monte Carlo
vmc = nk.variational.Vmc(
    hamiltonian=ha,
    sampler=sa,
    optimizer=op,
    n_samples=1000,
    diag_shift=0.1,
    method="Sr",
)

vmc.run(output_prefix="test", n_iter=300, save_params_every=10)