def test_ising_derivatives():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.IsingLayer(num_visible_units)
hid_layer = layers.IsingLayer(num_hidden_units)
rbm = hidden.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].int_params.loc[:] = a
rbm.layers[1].int_params.loc[:] = b
rbm.weights[0].int_params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
vdata_scaled = rbm.layers[0].rescale(vdata)
# compute the mean of the hidden layer
rbm.layers[1].update([vdata], [rbm.weights[0].W()])
hid_mean = rbm.layers[1].mean()
hid_mean_scaled = rbm.layers[1].rescale(hid_mean)
# compute the derivatives
d_visible_loc = -be.mean(vdata, axis=0)
d_hidden_loc = -be.mean(hid_mean_scaled, axis=0)
d_W = -be.batch_outer(vdata, hid_mean_scaled) / len(vdata)
# compute the derivatives using the layer functions
vis_derivs = rbm.layers[0].derivatives(vdata, [hid_mean_scaled],
[rbm.weights[0].W()])
hid_derivs = rbm.layers[1].derivatives(hid_mean, [vdata_scaled],
[rbm.weights[0].W_T()])
weight_derivs = rbm.weights[0].derivatives(vdata, hid_mean_scaled)
assert be.allclose(d_visible_loc, vis_derivs.loc), \
"derivative of visible loc wrong in ising-ising rbm"
assert be.allclose(d_hidden_loc, hid_derivs.loc), \
"derivative of hidden loc wrong in ising-ising rbm"
assert be.allclose(d_W, weight_derivs.matrix), \
"derivative of weights wrong in ising-ising rbm"
def test_ising_conditional_params():
ly = layers.IsingLayer(num_vis)
w = layers.Weights((num_vis, num_hid))
scaled_units = [be.randn((num_samples, num_hid))]
weights = [w.W_T()]
beta = be.rand((num_samples, 1))
ly._conditional_params(scaled_units, weights, beta)
def test_ising_build_from_config():
ly = layers.IsingLayer(num_vis)
ly.add_constraint({'loc': constraints.non_negative})
p = penalties.l2_penalty(0.37)
ly.add_penalty({'log_var': p})
ly_new = layers.Layer.from_config(ly.get_config())
assert ly_new.get_config() == ly.get_config()
def test_ising_update():
ly = layers.IsingLayer(num_vis)
w = layers.Weights((num_vis, num_hid))
scaled_units = [be.randn((num_samples, num_hid))]
weights = [w.W_T()]
beta = be.rand((num_samples, 1))
ly.update(scaled_units, weights, beta)
def test_ising_derivatives():
ly = layers.IsingLayer(num_vis)
w = layers.Weights((num_vis, num_hid))
vis = ly.random((num_samples, num_vis))
hid = [be.randn((num_samples, num_hid))]
weights = [w.W_T()]
beta = be.rand((num_samples, 1))
ly.derivatives(vis, hid, weights, beta)
def test_ising_conditional_params():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.IsingLayer(num_visible_units)
hid_layer = layers.IsingLayer(num_hidden_units)
rbm = model.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].params.loc[:] = a
rbm.layers[1].params.loc[:] = b
rbm.weights[0].params.matrix[:] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
hdata = rbm.layers[1].random((batch_size, num_hidden_units))
# compute conditional parameters
hidden_field = be.dot(vdata, W) # (batch_size, num_hidden_units)
hidden_field += be.broadcast(b, hidden_field)
visible_field = be.dot(hdata,
be.transpose(W)) # (batch_size, num_visible_units)
visible_field += be.broadcast(a, visible_field)
# compute the conditional parameters using the layer functions
hidden_field_func = rbm.layers[1]._conditional_params([vdata],
[rbm.weights[0].W()])
visible_field_func = rbm.layers[0]._conditional_params(
[hdata], [rbm.weights[0].W_T()])
assert be.allclose(hidden_field, hidden_field_func), \
"hidden field wrong in ising-ising rbm"
assert be.allclose(visible_field, visible_field_func), \
"visible field wrong in ising-ising rbm"
def test_ising_update():
num_visible_units = 100
num_hidden_units = 50
batch_size = 25
# set a seed for the random number generator
be.set_seed()
# set up some layer and model objects
vis_layer = layers.IsingLayer(num_visible_units)
hid_layer = layers.IsingLayer(num_hidden_units)
rbm = hidden.Model([vis_layer, hid_layer])
# randomly set the intrinsic model parameters
a = be.randn((num_visible_units, ))
b = be.randn((num_hidden_units, ))
W = be.randn((num_visible_units, num_hidden_units))
rbm.layers[0].int_params['loc'] = a
rbm.layers[1].int_params['loc'] = b
rbm.weights[0].int_params['matrix'] = W
# generate a random batch of data
vdata = rbm.layers[0].random((batch_size, num_visible_units))
hdata = rbm.layers[1].random((batch_size, num_hidden_units))
# compute extrinsic parameters
hidden_field = be.dot(vdata, W) # (batch_size, num_hidden_units)
hidden_field += be.broadcast(b, hidden_field)
visible_field = be.dot(hdata,
be.transpose(W)) # (batch_size, num_visible_units)
visible_field += be.broadcast(a, visible_field)
# update the extrinsic parameter using the layer functions
rbm.layers[1].update(vdata, rbm.weights[0].W())
rbm.layers[0].update(hdata, be.transpose(rbm.weights[0].W()))
assert be.allclose(hidden_field, rbm.layers[1].ext_params['field']), \
"hidden field wrong in ising-ising rbm"
assert be.allclose(visible_field, rbm.layers[0].ext_params['field']), \
"visible field wrong in ising-ising rbm"
def test_Ising_creation():
layers.IsingLayer(num_vis)
def test_ising_online_param_update():
ly = layers.IsingLayer(num_vis)
vis = ly.random((num_samples, num_vis))
ly.online_param_update(vis)
def test_ising_log_partition_function():
ly = layers.IsingLayer(num_vis)
vis = ly.random((num_samples, num_vis))
ly.log_partition_function(vis)
def test_ising_energy():
ly = layers.IsingLayer(num_vis)
vis = ly.random((num_samples, num_vis))
ly.energy(vis)