Formatting fixes

research/lfads/lfads.py

@@ -366,9 +366,11 @@ class LFADS(object):
       if datasets and 'alignment_matrix_cxf' in datasets[name].keys():
         dataset = datasets[name]
         if hps.do_train_readin:
-            print("Initializing trainable readin matrix with alignment matrix provided for dataset:", name)
+            print("Initializing trainable readin matrix with alignment matrix \
+                  provided for dataset:", name)
         else:
-            print("Setting non-trainable readin matrix to alignment matrix provided for dataset:", name)
+            print("Setting non-trainable readin matrix to alignment matrix \
+                  provided for dataset:", name)
         in_mat_cxf = dataset['alignment_matrix_cxf'].astype(np.float32)
         if in_mat_cxf.shape != (data_dim, factors_dim):
           raise ValueError("""Alignment matrix must have dimensions %d x %d
@@ -378,9 +380,11 @@ class LFADS(object):
       if datasets and 'alignment_bias_c' in datasets[name].keys():
         dataset = datasets[name]
         if hps.do_train_readin:
-          print("Initializing trainable readin bias with alignment bias provided for dataset:", name)
+          print("Initializing trainable readin bias with alignment bias \
+                provided for dataset:", name)
         else:
-          print("Setting non-trainable readin bias to alignment bias provided for dataset:", name)
+          print("Setting non-trainable readin bias to alignment bias \
+                provided for dataset:", name)
         align_bias_c = dataset['alignment_bias_c'].astype(np.float32)
         align_bias_1xc = np.expand_dims(align_bias_c, axis=0)
         if align_bias_1xc.shape[1] != data_dim:

research/lfads/run_lfads.py

@@ -212,9 +212,9 @@ flags.DEFINE_float("co_prior_var_scale", CO_PRIOR_VAR_SCALE,
                    "Variance of control input prior distribution.")
 
 
-flags.DEFINE_float("prior_ar_atau",  PRIOR_AR_AUTOCORRELATION, 
+flags.DEFINE_float("prior_ar_atau",  PRIOR_AR_AUTOCORRELATION,
                    "Initial autocorrelation of AR(1) priors.")
-flags.DEFINE_float("prior_ar_nvar", PRIOR_AR_PROCESS_VAR, 
+flags.DEFINE_float("prior_ar_nvar", PRIOR_AR_PROCESS_VAR,
                    "Initial noise variance for AR(1) priors.")
 flags.DEFINE_boolean("do_train_prior_ar_atau", DO_TRAIN_PRIOR_AR_ATAU,
                      "Is the value for atau an init, or the constant value?")
@@ -257,13 +257,13 @@ flags.DEFINE_boolean("do_causal_controller",
 # Strictly speaking, feeding either the factors or the rates to the controller
 # violates causality, since the g0 gets to see all the data. This may or may not
 # be only a theoretical concern.
-flags.DEFINE_boolean("do_feed_factors_to_controller", 
-                     DO_FEED_FACTORS_TO_CONTROLLER, 
+flags.DEFINE_boolean("do_feed_factors_to_controller",
+                     DO_FEED_FACTORS_TO_CONTROLLER,
                      "Should factors[t-1] be input to controller at time t?")
 flags.DEFINE_string("feedback_factors_or_rates", FEEDBACK_FACTORS_OR_RATES,
                     "Feedback the factors or the rates to the controller? \
                      Acceptable values: 'factors' or 'rates'.")
-flags.DEFINE_integer("controller_input_lag", CONTROLLER_INPUT_LAG, 
+flags.DEFINE_integer("controller_input_lag", CONTROLLER_INPUT_LAG,
                      "Time lag on the encoding to controller t-lag for \
                      forward, t+lag for reverse.")
 
@@ -320,10 +320,13 @@ flags.DEFINE_boolean("do_reset_learning_rate", DO_RESET_LEARNING_RATE,
 
 
 # for multi-session "stitching" models, the per-session readin matrices map from
-# neurons to input factors which are fed into the shared encoder. These are initialized
-# by alignment_matrix_cxf and alignment_bias_c in the input .h5 files. They can be fixed or 
-# made trainable.
-flags.DEFINE_boolean("do_train_readin", DO_TRAIN_READIN, "Whether to train the readin matrices and bias vectors. False leaves them fixed at their initial values specified by the alignment matrices / vectors.")
+# neurons to input factors which are fed into the shared encoder. These are
+# initialized by alignment_matrix_cxf and alignment_bias_c in the input .h5
+# files. They can be fixed or made trainable.
+flags.DEFINE_boolean("do_train_readin", DO_TRAIN_READIN, "Whether to train the \
+                     readin matrices and bias vectors. False leaves them fixed \
+                     at their initial values specified by the alignment \
+                     matrices and vectors.")
 
 
 # OVERFITTING
@@ -439,8 +442,9 @@ def build_model(hps, kind="train", datasets=None):
                 "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 
-      print("Are you sure you sure a checkpoint in ", hps.lfads_save_dir, " exists?")
+      # cant print ckpt.model_check_point path if no ckpt
+      print("Are you sure you sure a checkpoint in ", hps.lfads_save_dir,
+            " exists?")
 
     tf.global_variables_initializer().run()
 
@@ -462,7 +466,7 @@ def jsonify_dict(d):
   Creates a shallow-copied dictionary first, then accomplishes string
   conversion.
 
-  Args: 
+  Args:
     d: hyperparameter dictionary
 
   Returns: hyperparameter dictionary with bool's as strings
@@ -787,4 +791,3 @@ def main(_):
 
 if __name__ == "__main__":
     tf.app.run()
- 

research/lfads/utils.py

@@ -91,7 +91,7 @@ def init_linear(in_size, out_size, do_bias=True, mat_init_value=None,
   Args:
     in_size: The integer size of the non-batc input dimension. [(x),y]
     out_size: The integer size of non-batch output dimension. [x,(y)]
-    do_bias (optional): Add a (learnable) bias vector to the operation, 
+    do_bias (optional): Add a (learnable) bias vector to the operation,
       if false, b will be an appropriately sized, non-trainable vector
     mat_init_value (optional): numpy constant for matrix initialization, if None
       , do random, with additional parameters.
@@ -132,7 +132,8 @@ def init_linear(in_size, out_size, do_bias=True, mat_init_value=None,
     if collections:
       w_collections += collections
     if mat_init_value is not None:
-      w = tf.Variable(mat_init_value, name=wname, collections=w_collections, trainable=trainable)
+      w = tf.Variable(mat_init_value, name=wname, collections=w_collections,
+                      trainable=trainable)
     else:
       w = tf.get_variable(wname, [in_size, out_size], initializer=mat_init,
                           collections=w_collections, trainable=trainable)
@@ -142,7 +143,8 @@ def init_linear(in_size, out_size, do_bias=True, mat_init_value=None,
     if collections:
       w_collections += collections
     if mat_init_value is not None:
-      w = tf.Variable(mat_init_value, name=wname, collections=w_collections, trainable=trainable)
+      w = tf.Variable(mat_init_value, name=wname, collections=w_collections,
+                      trainable=trainable)
     else:
       w = tf.get_variable(wname, [in_size, out_size], initializer=mat_init,
                           collections=w_collections, trainable=trainable)