Support for --kind=posterior_push_mean altnerative to sample and average

research/lfads/lfads.py

@@ -295,7 +295,8 @@ class LFADS(object):
       datasets: a dictionary of named data_dictionaries, see top of lfads.py
     """
     print("Building graph...")
-    all_kinds = ['train', 'posterior_sample_and_average', 'prior_sample']
+    all_kinds = ['train', 'posterior_sample_and_average', 'posterior_push_mean',
+                 'prior_sample']
     assert kind in all_kinds, 'Wrong kind'
     if hps.feedback_factors_or_rates == "rates":
       assert len(hps.dataset_names) == 1, \
@@ -622,7 +623,8 @@ class LFADS(object):
         self.posterior_zs_g0 = \
             DiagonalGaussianFromInput(ic_enc, ic_dim, "ic_enc_2_post_g0",
                                       var_min=hps.ic_post_var_min)
-        if kind in ["train", "posterior_sample_and_average"]:
+        if kind in ["train", "posterior_sample_and_average",
+                    "posterior_push_mean"]:
           zs_g0 = self.posterior_zs_g0
         else:
           zs_g0 = self.prior_zs_g0
@@ -665,7 +667,7 @@ class LFADS(object):
                                 recurrent_collections=['l2_con_reg'])
       with tf.variable_scope("con", reuse=False):
         self.con_ics = tf.tile(
-            tf.Variable(tf.zeros([1, hps.con_dim*con_cell.state_multiplier]), \
+            tf.Variable(tf.zeros([1, hps.con_dim*con_cell.state_multiplier]),
                         name="c0"),
             tf.stack([batch_size, 1]))
         self.con_ics.set_shape([None, con_cell.state_size]) # tile loses shape
@@ -711,8 +713,7 @@ class LFADS(object):
       else:
         assert False, "NIY"
 
-
-    # We support mulitple output distributions, for example Poisson, and also
+    # We support multiple output distributions, for example Poisson, and also
     # Gaussian. In these two cases respectively, there are one and two
     # parameters (rates vs. mean and variance).  So the output_dist_params
     # tensor will variable sizes via tf.concat and tf.split, along the 1st
@@ -769,6 +770,8 @@ class LFADS(object):
           u_t[t] = posterior_zs_co[t].sample
         elif kind == "posterior_sample_and_average":
           u_t[t] = posterior_zs_co[t].sample
+        elif kind == "posterior_push_mean":
+          u_t[t] = posterior_zs_co[t].mean
         else:
           u_t[t] = prior_zs_ar_con.samples_t[t]
 
@@ -836,7 +839,7 @@ class LFADS(object):
     self.recon_cost = tf.constant(0.0) # VAE reconstruction cost
     self.nll_bound_vae = tf.constant(0.0)
     self.nll_bound_iwae = tf.constant(0.0) # for eval with IWAE cost.
-    if kind in ["train", "posterior_sample_and_average"]:
+    if kind in ["train", "posterior_sample_and_average", "posterior_push_mean"]:
       kl_cost_g0_b = 0.0
       kl_cost_co_b = 0.0
       if ic_dim > 0:
@@ -1595,6 +1598,9 @@ class LFADS(object):
                             do_eval_cost=False, do_average_batch=False):
     """Returns all the goodies for the entire model, per batch.
 
+    If data_bxtxd and ext_input_bxtxi can have fewer than batch_size along dim 1
+    in which case this handles the padding and truncating automatically
+
     Args:
       data_name: The name of the data dict, to select which in/out matrices
         to use.
@@ -1614,6 +1620,19 @@ class LFADS(object):
         enabled), the state of the generator, the factors, and the rates.
     """
     session = tf.get_default_session()
+
+    # if fewer than batch_size provided, pad to batch_size
+    hps = self.hps
+    batch_size = hps.batch_size
+    E, _, _ = data_bxtxd.shape
+    if E < hps.batch_size:
+      data_bxtxd = np.pad(data_bxtxd, ((0, hps.batch_size-E), (0, 0), (0, 0)),
+                          mode='constant', constant_values=0)
+      if ext_input_bxtxi is not None:
+        ext_input_bxtxi = np.pad(ext_input_bxtxi,
+                                 ((0, hps.batch_size-E), (0, 0), (0, 0)),
+                                 mode='constant', constant_values=0)
+
     feed_dict = self.build_feed_dict(data_name, data_bxtxd,
                                      ext_input_bxtxi, keep_prob=1.0)
 
@@ -1663,6 +1682,7 @@ class LFADS(object):
     factors = list_t_bxn_to_tensor_bxtxn(factors)
     out_dist_params = list_t_bxn_to_tensor_bxtxn(out_dist_params)
     if self.hps.ic_dim > 0:
+      # select first time point
       prior_g0_mean = prior_g0_mean[0]
       prior_g0_logvar = prior_g0_logvar[0]
       post_g0_mean = post_g0_mean[0]
@@ -1670,6 +1690,21 @@ class LFADS(object):
     if self.hps.co_dim > 0:
       controller_outputs = list_t_bxn_to_tensor_bxtxn(controller_outputs)
 
+    # slice out the trials in case < batch_size provided
+    if E < hps.batch_size:
+      idx = np.arange(E)
+      gen_ics = gen_ics[idx, :]
+      gen_states = gen_states[idx, :]
+      factors = factors[idx, :, :]
+      out_dist_params = out_dist_params[idx, :, :]
+      if self.hps.ic_dim > 0:
+        prior_g0_mean = prior_g0_mean[idx, :]
+        prior_g0_logvar = prior_g0_logvar[idx, :]
+        post_g0_mean = post_g0_mean[idx, :]
+        post_g0_logvar = post_g0_logvar[idx, :]
+      if self.hps.co_dim > 0:
+        controller_outputs = controller_outputs[idx, :, :]
+
     if do_average_batch:
       gen_ics = np.mean(gen_ics, axis=0)
       gen_states = np.mean(gen_states, axis=0)
@@ -1806,7 +1841,121 @@ class LFADS(object):
     model_runs['train_steps'] = train_steps
     return model_runs
 
-  def write_model_runs(self, datasets, output_fname=None):
+  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
+
+    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.
+
+    Args:
+      data_name: The name of the data dict, to select which in/out matrices
+        to use.
+      data_extxd:  Numpy array training data with shape:
+        # examples x # time steps x # dimensions
+      ext_input_extxi (optional): Numpy array training external input with
+        shape: # examples x # time steps x # external input dims
+
+    Returns:
+      A dictionary with the averaged 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
+        distribution parameters, e.g. (rates or mean and variances).
+    """
+    hps = self.hps
+    batch_size = hps.batch_size
+    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:
+      prior_g0_mean = np.zeros([E_to_process, hps.ic_dim])
+      prior_g0_logvar = np.zeros([E_to_process, hps.ic_dim])
+      post_g0_mean = np.zeros([E_to_process, hps.ic_dim])
+      post_g0_logvar = np.zeros([E_to_process, hps.ic_dim])
+
+    if hps.co_dim > 0:
+      controller_outputs = np.zeros([E_to_process, T, hps.co_dim])
+    gen_ics = np.zeros([E_to_process, hps.gen_dim])
+    gen_states = np.zeros([E_to_process, T, hps.gen_dim])
+    factors = np.zeros([E_to_process, T, hps.factors_dim])
+
+    if hps.output_dist == 'poisson':
+      out_dist_params = np.zeros([E_to_process, T, D])
+    elif hps.output_dist == 'gaussian':
+      out_dist_params = np.zeros([E_to_process, T, D+D])
+    else:
+      assert False, "NIY"
+
+    costs = np.zeros(E_to_process)
+    nll_bound_vaes = np.zeros(E_to_process)
+    nll_bound_iwaes = np.zeros(E_to_process)
+    train_steps = np.zeros(E_to_process)
+
+    def trial_batches(N, per):
+      for i in range(0, N, per):
+        yield np.arange(i, min(i+per, N), dtype=np.int32)
+
+    for batch_idx, es_idx in enumerate(trial_batches(E_to_process,
+                                                     hps.batch_size)):
+      print("Running trial batch %d with %d trials" % (batch_idx+1,
+                                                       len(es_idx)))
+      data_bxtxd, ext_input_bxtxi = self.get_batch(data_extxd,
+                                                   ext_input_extxi,
+                                                   batch_size=batch_size,
+                                                   example_idxs=es_idx)
+      model_values = self.eval_model_runs_batch(data_name, data_bxtxd,
+                                                ext_input_bxtxi,
+                                                do_eval_cost=True,
+                                                do_average_batch=False)
+
+      if self.hps.ic_dim > 0:
+        prior_g0_mean[es_idx,:] = model_values['prior_g0_mean']
+        prior_g0_logvar[es_idx,:] = model_values['prior_g0_logvar']
+        post_g0_mean[es_idx,:] = model_values['post_g0_mean']
+        post_g0_logvar[es_idx,:] = model_values['post_g0_logvar']
+      gen_ics[es_idx,:] = model_values['gen_ics']
+
+      if self.hps.co_dim > 0:
+        controller_outputs[es_idx,:,:] = model_values['controller_outputs']
+      gen_states[es_idx,:,:] = model_values['gen_states']
+      factors[es_idx,:,:] = model_values['factors']
+      out_dist_params[es_idx,:,:] = model_values['output_dist_params']
+
+      # TODO
+      # model_values['costs'] and other costs come out as scalars, summed over
+      # all the trials in the batch. what we want is the per-trial costs
+      costs[es_idx] = model_values['costs']
+      nll_bound_vaes[es_idx] = model_values['nll_bound_vaes']
+      nll_bound_iwaes[es_idx] = model_values['nll_bound_iwaes']
+
+      train_steps[es_idx] = model_values['train_steps']
+
+    model_runs = {}
+    if self.hps.ic_dim > 0:
+      model_runs['prior_g0_mean'] = prior_g0_mean
+      model_runs['prior_g0_logvar'] = prior_g0_logvar
+      model_runs['post_g0_mean'] = post_g0_mean
+      model_runs['post_g0_logvar'] = post_g0_logvar
+    model_runs['gen_ics'] = gen_ics
+
+    if self.hps.co_dim > 0:
+      model_runs['controller_outputs'] = controller_outputs
+    model_runs['gen_states'] = gen_states
+    model_runs['factors'] = factors
+    model_runs['output_dist_params'] = out_dist_params
+    model_runs['costs'] = costs
+    model_runs['nll_bound_vaes'] = nll_bound_vaes
+    model_runs['nll_bound_iwaes'] = nll_bound_iwaes
+    model_runs['train_steps'] = train_steps
+    return model_runs
+
+  def write_model_runs(self, datasets, output_fname=None, push_mean=False):
     """Run the model on the data in data_dict, and save the computed values.
 
     LFADS generates a number of outputs for each examples, and these are all
@@ -1822,6 +1971,11 @@ class LFADS(object):
     Args:
       datasets: a dictionary of named data_dictionaries, see top of lfads.py
       output_fname: a file name stem for the output files.
+      push_mean: if False (default), generates batch_size samples for each trial
+        and averages the results. if True, runs each trial once without noise,
+        pushing the posterior mean initial conditions and control inputs through
+        the trained model. False is used for posterior_sample_and_average, True
+        is used for posterior_push_mean.
     """
     hps = self.hps
     kind = hps.kind
@@ -1838,8 +1992,12 @@ class LFADS(object):
           fname = output_fname + data_name + '_' + data_kind + '_' + kind
 
         print("Writing data for %s data and kind %s." % (data_name, data_kind))
-        model_runs = self.eval_model_runs_avg_epoch(data_name, data_extxd,
-                                                    ext_input_extxi)
+        if push_mean:
+          model_runs = self.eval_model_runs_push_mean(data_name, data_extxd,
+                                                      ext_input_extxi)
+        else:
+          model_runs = self.eval_model_runs_avg_epoch(data_name, data_extxd,
+                                                      ext_input_extxi)
         full_fname = os.path.join(hps.lfads_save_dir, fname)
         write_data(full_fname, model_runs, compression='gzip')
         print("Done.")

research/lfads/run_lfads.py

@@ -99,6 +99,7 @@ flags = tf.app.flags
 flags.DEFINE_string("kind", "train",
                     "Type of model to build {train, \
                     posterior_sample_and_average, \
+                    posterior_push_mean, \
                     prior_sample, write_model_params")
 flags.DEFINE_string("output_dist", OUTPUT_DISTRIBUTION,
                     "Type of output distribution, 'poisson' or 'gaussian'")
@@ -318,11 +319,10 @@ flags.DEFINE_boolean("do_train_io_only", DO_TRAIN_IO_ONLY,
 
 # This flag is used for an experiment where one wants to know if the dynamics
 # learned by the generator generalize across conditions. In that case, you might
-# train up a model on one set of data, and then only further train the encoder on 
-# another set of data (the conditions to be tested) so that the model is forced
-# to use the same dynamics to describe that data.
-# If you don't care about that particular experiment, this flag should always be
-# false.
+# train up a model on one set of data, and then only further train the encoder
+# on another set of data (the conditions to be tested) so that the model is
+# forced to use the same dynamics to describe that data. If you don't care about
+# that particular experiment, this flag should always be false.
 flags.DEFINE_boolean("do_train_encoder_only", DO_TRAIN_ENCODER_ONLY,
                      "Train only the encoder weights.")
 
@@ -449,11 +449,11 @@ def build_model(hps, kind="train", datasets=None):
     saver.restore(session, ckpt.model_checkpoint_path)
   else:
     print("Created model with fresh parameters.")
-    if kind in ["posterior_sample_and_average", "prior_sample",
-                "write_model_params"]:
+    if kind in ["posterior_sample_and_average", "posterior_push_mean",
+                "prior_sample", "write_model_params"]:
       print("Possible error!!! You are running ", kind, " on a newly \
       initialized model!")
-      # cant print ckpt.model_check_point path if no ckpt
+      # cannot print ckpt.model_check_point path if no ckpt
       print("Are you sure you sure a checkpoint in ", hps.lfads_save_dir,
             " exists?")
 
@@ -609,7 +609,7 @@ def train(hps, datasets):
   model.train_model(datasets)
 
 
-def write_model_runs(hps, datasets, output_fname=None):
+def write_model_runs(hps, datasets, output_fname=None, push_mean=False):
   """Run the model on the data in data_dict, and save the computed values.
 
   LFADS generates a number of outputs for each examples, and these are all
@@ -627,9 +627,14 @@ def write_model_runs(hps, datasets, output_fname=None):
     datasets: A dictionary of data dictionaries.  The dataset dict is simply a
       name(string)-> data dictionary mapping (See top of lfads.py).
     output_fname (optional): output filename stem to write the model runs.
+    push_mean: if False (default), generates batch_size samples for each trial
+      and averages the results. if True, runs each trial once without noise,
+      pushing the posterior mean initial conditions and control inputs through
+      the trained model. False is used for posterior_sample_and_average, True
+      is used for posterior_push_mean.
   """
   model = build_model(hps, kind=hps.kind, datasets=datasets)
-  model.write_model_runs(datasets, output_fname)
+  model.write_model_runs(datasets, output_fname, push_mean)
 
 
 def write_model_samples(hps, datasets, dataset_name=None, output_fname=None):
@@ -759,8 +764,8 @@ def main(_):
 
   # Read the data, if necessary.
   train_set = valid_set = None
-  if kind in ["train", "posterior_sample_and_average", "prior_sample",
-              "write_model_params"]:
+  if kind in ["train", "posterior_sample_and_average", "posterior_push_mean",
+              "prior_sample", "write_model_params"]:
     datasets = load_datasets(hps.data_dir, hps.data_filename_stem)
   else:
     raise ValueError('Kind {} is not supported.'.format(kind))
@@ -792,7 +797,11 @@ def main(_):
       if kind == "train":
         train(hps, datasets)
       elif kind == "posterior_sample_and_average":
-        write_model_runs(hps, datasets, hps.output_filename_stem)
+        write_model_runs(hps, datasets, hps.output_filename_stem,
+                         push_mean=False)
+      elif kind == "posterior_push_mean":
+        write_model_runs(hps, datasets, hps.output_filename_stem,
+                         push_mean=True)
       elif kind == "prior_sample":
         write_model_samples(hps, datasets, hps.output_filename_stem)
       elif kind == "write_model_params":