Fix typos and indentation in lfads.py (#10699)

research/lfads/lfads.py

@@ -37,15 +37,15 @@ The nested dictionary is the DATA DICTIONARY, which has the following keys:
     'train_ext_input' and 'valid_ext_input', if there are know external inputs
       to the system being modeled, these take on dimensions:
       ExTxI, E - # examples, T - # time steps, I = # dimensions in input.
-   'alignment_matrix_cxf' - If you are using multiple days data, it's possible
-     that one can align the channels (see manuscript).  If so each dataset will
-     contain this matrix, which will be used for both the input adapter and the
-     output adapter for each dataset. These matrices, if provided, must be of
-     size [data_dim x factors] where data_dim is the number of neurons recorded
-     on that day, and factors is chosen and set through the '--factors' flag.
-   'alignment_bias_c' - See alignment_matrix_cxf.  This bias will used to
-     the offset for the alignment transformation.  It will *subtract* off the
-     bias from the data, so pca style inits can align factors across sessions.
+    'alignment_matrix_cxf' - If you are using multiple days data, it's possible
+      that one can align the channels (see manuscript).  If so each dataset will
+      contain this matrix, which will be used for both the input adapter and the
+      output adapter for each dataset. These matrices, if provided, must be of
+      size [data_dim x factors] where data_dim is the number of neurons recorded
+      on that day, and factors is chosen and set through the '--factors' flag.
+    'alignment_bias_c' - See alignment_matrix_cxf.  This bias will used to
+      the offset for the alignment transformation.  It will *subtract* off the
+      bias from the data, so pca style inits can align factors across sessions.
 
 
   If one runs LFADS on data where the true rates are known for some trials,
@@ -85,13 +85,13 @@ class GRU(object):
     """Create a GRU object.
 
     Args:
-      num_units: Number of units in the GRU
+      num_units: Number of units in the GRU.
       forget_bias (optional): Hack to help learning.
-      weight_scale (optional): weights are scaled by ws/sqrt(#inputs), with
-       ws being the weight scale.
-      clip_value (optional): if the recurrent values grow above this value,
+      weight_scale (optional): Weights are scaled by ws/sqrt(#inputs), with
+        ws being the weight scale.
+      clip_value (optional): If the recurrent values grow above this value,
         clip them.
-      collections (optional): List of additonal collections variables should
+      collections (optional): List of additional collections variables should
         belong to.
     """
     self._num_units = num_units
@@ -171,17 +171,17 @@ class GenGRU(object):
     """Create a GRU object.
 
     Args:
-      num_units: Number of units in the GRU
+      num_units: Number of units in the GRU.
       forget_bias (optional): Hack to help learning.
-      input_weight_scale (optional): weights are scaled ws/sqrt(#inputs), with
+      input_weight_scale (optional): Weights are scaled ws/sqrt(#inputs), with
         ws being the weight scale.
-      rec_weight_scale (optional): weights are scaled ws/sqrt(#inputs),
+      rec_weight_scale (optional): Weights are scaled ws/sqrt(#inputs),
         with ws being the weight scale.
-      clip_value (optional): if the recurrent values grow above this value,
+      clip_value (optional): If the recurrent values grow above this value,
         clip them.
-      input_collections (optional): List of additonal collections variables
+      input_collections (optional): List of additional collections variables
         that input->rec weights should belong to.
-      recurrent_collections (optional): List of additonal collections variables
+      recurrent_collections (optional): List of additional collections variables
         that rec->rec weights should belong to.
     """
     self._num_units = num_units
@@ -271,7 +271,7 @@ class LFADS(object):
   various factors, such as an initial condition, a generative
   dynamical system, inferred inputs to that generator, and a low
   dimensional description of the observed data, called the factors.
-  Additoinally, the observations have a noise model (in this case
+  Additionally, the observations have a noise model (in this case
   Poisson), so a denoised version of the observations is also created
   (e.g. underlying rates of a Poisson distribution given the observed
   event counts).
@@ -291,8 +291,8 @@ class LFADS(object):
 
     Args:
       hps: The dictionary of hyper parameters.
-      kind: the type of model to build (see above).
-      datasets: a dictionary of named data_dictionaries, see top of lfads.py
+      kind: The type of model to build (see above).
+      datasets: A dictionary of named data_dictionaries, see top of lfads.py
     """
     print("Building graph...")
     all_kinds = ['train', 'posterior_sample_and_average', 'posterior_push_mean',
@@ -905,7 +905,7 @@ class LFADS(object):
     if kind != "train":
       # save every so often
       self.seso_saver = tf.train.Saver(tf.global_variables(),
-                                       max_to_keep=hps.max_ckpt_to_keep)
+                                      max_to_keep=hps.max_ckpt_to_keep)
       # lowest validation error
       self.lve_saver = tf.train.Saver(tf.global_variables(),
                                       max_to_keep=hps.max_ckpt_to_keep_lve)
@@ -952,7 +952,7 @@ class LFADS(object):
         zip(grads, tvars), global_step=self.train_step)
 
     self.seso_saver = tf.train.Saver(tf.global_variables(),
-                                     max_to_keep=hps.max_ckpt_to_keep)
+                                    max_to_keep=hps.max_ckpt_to_keep)
 
     # lowest validation error
     self.lve_saver = tf.train.Saver(tf.global_variables(),
@@ -963,7 +963,7 @@ class LFADS(object):
     self.example_image = tf.placeholder(tf.float32, shape=[1,None,None,3],
                                         name='image_tensor')
     self.example_summ = tf.summary.image("LFADS example", self.example_image,
-                                         collections=["example_summaries"])
+                                        collections=["example_summaries"])
 
     # general training summaries
     self.lr_summ = tf.summary.scalar("Learning rate", self.learning_rate)
@@ -1032,8 +1032,8 @@ class LFADS(object):
     Args:
       train_name: The key into the datasets, to set the tf.case statement for
         the proper readin / readout matrices.
-      data_bxtxd: The data tensor
-      ext_input_bxtxi (optional): The external input tensor
+      data_bxtxd: The data tensor.
+      ext_input_bxtxi (optional): The external input tensor.
       keep_prob: The drop out keep probability.
 
     Returns:
@@ -1066,7 +1066,7 @@ class LFADS(object):
         # examples x # time steps x # dimensions
       ext_input_extxi (optional): The external inputs, numpy tensor with shape:
         # examples x # time steps x # external input dimensions
-      batch_size:  The size of the batch to return
+      batch_size: The size of the batch to return.
       example_idxs (optional): The example indices used to select examples.
 
     Returns:
@@ -1123,8 +1123,8 @@ class LFADS(object):
     is managed by drawing randomly from 1:nexamples.
 
     Args:
-      nexamples: number of examples to randomize
-      batch_size: number of elements in batch
+      nexamples: Number of examples to randomize.
+      batch_size: Number of elements in batch.
 
     Returns:
       The randomized, properly shaped indicies.
@@ -1148,7 +1148,7 @@ class LFADS(object):
     enough to pick up dynamics that you may not want.
 
     Args:
-      data_bxtxd: numpy array of spike count data to be shuffled.
+      data_bxtxd: Numpy array of spike count data to be shuffled.
     Returns:
     S_bxtxd, a numpy array with the same dimensions and contents as
       data_bxtxd, but shuffled appropriately.
@@ -1231,7 +1231,7 @@ class LFADS(object):
     Args:
       datasets: A dict of data dicts.  The dataset dict is simply a
         name(string)-> data dictionary mapping (See top of lfads.py).
-      batch_size (optional):  The batch_size to use
+      batch_size (optional): The batch_size to use.
       do_save_ckpt (optional): Should the routine save a checkpoint on this
         training epoch?
 
@@ -1283,7 +1283,7 @@ class LFADS(object):
         name(string)-> data dictionary mapping (See top of lfads.py).
       ops_to_eval: A list of tensorflow operations that will be evaluated in
         the tf.session.run() call.
-      batch_size (optional):  The batch_size to use
+      batch_size (optional): The batch_size to use.
       do_collect (optional): Should the routine collect all session.run
         output as a list, and return it?
       keep_prob (optional): The dropout keep probability.
@@ -1330,7 +1330,7 @@ class LFADS(object):
 
     Args:
       datasets, the dictionary of datasets used in the study.
-      summary_values:  These summary values are created from the training loop,
+      summary_values: These summary values are created from the training loop,
       and so summarize the entire set of datasets.
     """
     hps = self.hps
@@ -1599,12 +1599,12 @@ class LFADS(object):
     Args:
       data_name: The name of the data dict, to select which in/out matrices
         to use.
-      data_bxtxd:  Numpy array training data with shape:
+      data_bxtxd: Numpy array training data with shape:
         batch_size x # time steps x # dimensions
       ext_input_bxtxi: Numpy array training external input with shape:
         batch_size x # time steps x # external input dims
       do_eval_cost (optional): If true, the IWAE (Importance Weighted
-         Autoencoder) log likeihood bound, instead of the VAE version.
+        Autoencoder) log likeihood bound, instead of the VAE version.
       do_average_batch (optional): average over the batch, useful for getting
       good IWAE costs, and model outputs for a single data point.
 
@@ -1743,7 +1743,7 @@ class LFADS(object):
     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:
+      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
@@ -1837,7 +1837,7 @@ class LFADS(object):
 
   def eval_model_runs_push_mean(self, data_name, data_extxd,
                                 ext_input_extxi=None):
-    """Returns values of interest for the  model by pushing the means through
+    """Returns values of interest for the model by pushing the means through
 
     The mean values for both initial conditions and the control inputs are
     pushed through the model instead of sampling (as is done in
@@ -1851,7 +1851,7 @@ class LFADS(object):
     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:
+      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
@@ -1966,16 +1966,16 @@ class LFADS(object):
     saved.  They are:
       The mean and variance of the prior of g0.
       The mean and variance of approximate posterior of g0.
-      The control inputs (if enabled)
+      The control inputs (if enabled).
       The initial conditions, g0, for all examples.
       The generator states for all time.
       The factors for all time.
       The output distribution parameters (e.g. rates) for all time.
 
     Args:
-      datasets: a dictionary of named data_dictionaries, see top of lfads.py
+      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
+      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
@@ -2013,7 +2013,7 @@ class LFADS(object):
     LFADS generates a number of outputs for each sample, and these are all
     saved.  They are:
       The mean and variance of the prior of g0.
-      The control inputs (if enabled)
+      The control inputs (if enabled).
       The initial conditions, g0, for all examples.
       The generator states for all time.
       The factors for all time.
@@ -2148,7 +2148,7 @@ class LFADS(object):
     """Randomly spikify underlying rates according a Poisson distribution
 
     Args:
-      rates_bxtxd: a numpy tensor with shape:
+      rates_bxtxd: A numpy tensor with shape:
 
     Returns:
       A numpy array with the same shape as rates_bxtxd, but with the event