Cleaning comments for posterior_push_mean

research/lfads/lfads.py

@@ -490,11 +490,6 @@ class LFADS(object):
     pf_pairs_out_fac_Ws = zip(preds, fns_out_fac_Ws)
     pf_pairs_out_fac_bs = zip(preds, fns_out_fac_bs)
 
-    # def _case_with_no_default(pairs):
-    #   def _default_value_fn():
-    #     with tf.control_dependencies([tf.Assert(False, ["Reached default"])]):
-    #       return tf.identity(pairs[0][1]())
-    #   return tf.case(pairs, _default_value_fn, exclusive=True)
     this_in_fac_W = tf.case(pf_pairs_in_fac_Ws, exclusive=True)
     this_in_fac_b = tf.case(pf_pairs_in_fac_bs, exclusive=True)
     this_out_fac_W = tf.case(pf_pairs_out_fac_Ws, exclusive=True)
@@ -931,7 +926,7 @@ class LFADS(object):
       tvars2 = \
         tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                           scope='LFADS/z/ic_enc_*')
-      
+
       self.train_vars = tvars = tvars1 + tvars2
     # train all variables
     else:
@@ -1765,7 +1760,6 @@ class LFADS(object):
     E, T, D  = data_extxd.shape
     E_to_process = hps.ps_nexamples_to_process
     if E_to_process > E:
-      print("Setting number of posterior samples to process to : ", E)
       E_to_process = E
 
     if hps.ic_dim > 0:
@@ -1843,12 +1837,16 @@ class LFADS(object):
 
   def eval_model_runs_push_mean(self, data_name, data_extxd,
                                 ext_input_extxi=None):
-    """Returns the value for goodies for the entire model using the means
+    """Returns values of interest for the  model by pushing the means through
 
-    The expected value is taken over hidden (z) variables, namely the initial
-    conditions and the control inputs, by pushing the mean values for both
-    through the model rather than by sampling (as in eval_model_runs_avg_epoch)
-    A total of batch_size trials are run at a time.
+    The mean values for both initial conditions and the control inputs are
+    pushed through the model instead of sampling (as is done in
+    eval_model_runs_avg_epoch).
+    This is a quick and approximate version of estimating these values instead
+    of sampling from the posterior many times and then averaging those values of
+    interest.
+
+    Internally, a total of batch_size trials are run through the model at once.
 
     Args:
       data_name: The name of the data dict, to select which in/out matrices
@@ -1859,7 +1857,7 @@ class LFADS(object):
         shape: # examples x # time steps x # external input dims
 
     Returns:
-      A dictionary with the averaged outputs of the model decoder, namely:
+      A dictionary with the estimated outputs of the model decoder, namely:
         prior g0 mean, prior g0 variance, approx. posterior mean, approx
         posterior mean, the generator initial conditions, the control inputs (if
         enabled), the state of the generator, the factors, and the output
@@ -1897,6 +1895,9 @@ class LFADS(object):
     nll_bound_iwaes = np.zeros(E_to_process)
     train_steps = np.zeros(E_to_process)
 
+    # generator that will yield 0:N in groups of per items, e.g.
+    # (0:per-1), (per:2*per-1), ..., with the last group containing <= per items
+    # this will be used to feed per=batch_size trials into the model at a time
     def trial_batches(N, per):
       for i in range(0, N, per):
         yield np.arange(i, min(i+per, N), dtype=np.int32)
@@ -1949,6 +1950,9 @@ class LFADS(object):
     model_runs['gen_states'] = gen_states
     model_runs['factors'] = factors
     model_runs['output_dist_params'] = out_dist_params
+
+    # You probably do not want the LL associated values when pushing the mean
+    # instead of sampling.
     model_runs['costs'] = costs
     model_runs['nll_bound_vaes'] = nll_bound_vaes
     model_runs['nll_bound_iwaes'] = nll_bound_iwaes