def test_exponential_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.ExponentialLayer(num_visible_units) hid_layer = layers.ExponentialLayer(num_hidden_units) rbm = model.Model([vis_layer, hid_layer]) # randomly set the intrinsic model parameters # for the exponential layers, we need a > 0, b > 0, and W < 0 a = be.rand((num_visible_units, )) b = be.rand((num_hidden_units, )) W = -be.rand((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_rate = -be.dot(vdata, W) # (batch_size, num_hidden_units) hidden_rate += be.broadcast(b, hidden_rate) visible_rate = -be.dot(hdata, be.transpose(W)) # (batch_size, num_visible_units) visible_rate += be.broadcast(a, visible_rate) # compute the conditional parameters using the layer functions hidden_rate_func = rbm.layers[1]._conditional_params([vdata], [rbm.weights[0].W()]) visible_rate_func = rbm.layers[0]._conditional_params( [hdata], [rbm.weights[0].W_T()]) assert be.allclose(hidden_rate, hidden_rate_func), \ "hidden rate wrong in exponential-exponential rbm" assert be.allclose(visible_rate, visible_rate_func), \ "visible rate wrong in exponential-exponential rbm"
def test_bernoulli_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.BernoulliLayer(num_visible_units) hid_layer = layers.BernoulliLayer(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 conditional parameters with layer funcitons hidden_field_layer = rbm.layers[1]._conditional_params( [vdata], [rbm.weights[0].W()]) visible_field_layer = rbm.layers[0]._conditional_params( [hdata], [rbm.weights[0].W_T()]) assert be.allclose(hidden_field, hidden_field_layer), \ "hidden field wrong in bernoulli-bernoulli rbm" assert be.allclose(visible_field, visible_field_layer), \ "visible field wrong in bernoulli-bernoulli rbm"
def test_exponential_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.ExponentialLayer(num_visible_units) hid_layer = layers.ExponentialLayer(num_hidden_units) rbm = hidden.Model([vis_layer, hid_layer]) # randomly set the intrinsic model parameters # for the exponential layers, we need a > 0, b > 0, and W < 0 a = be.rand((num_visible_units, )) b = be.rand((num_hidden_units, )) W = -be.rand((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_rate = -be.dot(vdata, W) # (batch_size, num_hidden_units) hidden_rate += be.broadcast(b, hidden_rate) visible_rate = -be.dot(hdata, be.transpose(W)) # (batch_size, num_visible_units) visible_rate += be.broadcast(a, visible_rate) # 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_rate, rbm.layers[1].ext_params['rate']), \ "hidden rate wrong in exponential-exponential rbm" assert be.allclose(visible_rate, rbm.layers[0].ext_params['rate']), \ "visible rate wrong in exponential-exponential 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], [rbm.weights[0].W_T()]) 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_gaussian_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.GaussianLayer(num_visible_units) hid_layer = layers.GaussianLayer(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, )) log_var_a = 0.1 * be.randn((num_visible_units, )) log_var_b = 0.1 * 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.layers[0].int_params.log_var[:] = log_var_a rbm.layers[1].int_params.log_var[:] = log_var_b rbm.weights[0].int_params.matrix[:] = W # generate a random batch of data vdata = rbm.layers[0].random((batch_size, num_visible_units)) visible_var = be.exp(log_var_a) vdata_scaled = vdata / be.broadcast(visible_var, vdata) # compute the mean of the hidden layer rbm.layers[1].update([vdata_scaled], [rbm.weights[0].W()]) hidden_var = be.exp(log_var_b) hid_mean = rbm.layers[1].mean() hid_mean_scaled = rbm.layers[1].rescale(hid_mean) # compute the derivatives d_vis_loc = -be.mean(vdata_scaled, axis=0) d_vis_logvar = -0.5 * be.mean(be.square(be.subtract(a, vdata)), axis=0) d_vis_logvar += be.batch_dot( hid_mean_scaled, be.transpose(W), vdata, axis=0) / len(vdata) d_vis_logvar /= visible_var d_hid_loc = -be.mean(hid_mean_scaled, axis=0) d_hid_logvar = -0.5 * be.mean( be.square(hid_mean - be.broadcast(b, hid_mean)), axis=0) d_hid_logvar += be.batch_dot(vdata_scaled, W, hid_mean, axis=0) / len(hid_mean) d_hid_logvar /= hidden_var d_W = -be.batch_outer(vdata_scaled, hid_mean_scaled) / len(vdata_scaled) # compute the derivatives using the layer functions rbm.layers[1].update([vdata_scaled], [rbm.weights[0].W()]) rbm.layers[0].update([hid_mean_scaled], [rbm.weights[0].W_T()]) 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_scaled, hid_mean_scaled) assert be.allclose(d_vis_loc, vis_derivs.loc), \ "derivative of visible loc wrong in gaussian-gaussian rbm" assert be.allclose(d_hid_loc, hid_derivs.loc), \ "derivative of hidden loc wrong in gaussian-gaussian rbm" assert be.allclose(d_vis_logvar, vis_derivs.log_var, rtol=1e-05, atol=1e-01), \ "derivative of visible log_var wrong in gaussian-gaussian rbm" assert be.allclose(d_hid_logvar, hid_derivs.log_var, rtol=1e-05, atol=1e-01), \ "derivative of hidden log_var wrong in gaussian-gaussian rbm" assert be.allclose(d_W, weight_derivs.matrix), \ "derivative of weights wrong in gaussian-gaussian rbm"
def test_gaussian_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.GaussianLayer(num_visible_units) hid_layer = layers.GaussianLayer(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, )) log_var_a = 0.1 * be.randn((num_visible_units, )) log_var_b = 0.1 * 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.layers[0].int_params.log_var[:] = log_var_a rbm.layers[1].int_params.log_var[:] = log_var_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 the variance visible_var = be.exp(log_var_a) hidden_var = be.exp(log_var_b) # rescale the data vdata_scaled = vdata / be.broadcast(visible_var, vdata) hdata_scaled = hdata / be.broadcast(hidden_var, hdata) # test rescale assert be.allclose(vdata_scaled, rbm.layers[0].rescale(vdata)),\ "visible rescale wrong in gaussian-gaussian rbm" assert be.allclose(hdata_scaled, rbm.layers[1].rescale(hdata)),\ "hidden rescale wrong in gaussian-gaussian rbm" # compute the mean hidden_mean = be.dot(vdata_scaled, W) # (batch_size, num_hidden_units) hidden_mean += be.broadcast(b, hidden_mean) visible_mean = be.dot(hdata_scaled, be.transpose(W)) # (batch_size, num_hidden_units) visible_mean += be.broadcast(a, visible_mean) # update the extrinsic parameters using the layer functions rbm.layers[0].update([hdata_scaled], [rbm.weights[0].W_T()]) rbm.layers[1].update([vdata_scaled], [rbm.weights[0].W()]) assert be.allclose(visible_var, rbm.layers[0].ext_params.variance),\ "visible variance wrong in gaussian-gaussian rbm" assert be.allclose(hidden_var, rbm.layers[1].ext_params.variance),\ "hidden variance wrong in gaussian-gaussian rbm" assert be.allclose(visible_mean, rbm.layers[0].ext_params.mean),\ "visible mean wrong in gaussian-gaussian rbm" assert be.allclose(hidden_mean, rbm.layers[1].ext_params.mean),\ "hidden mean wrong in gaussian-gaussian rbm"