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

@@ -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
@@ -440,7 +443,8 @@ def build_model(hps, kind="train", datasets=None):
       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?")
+      print("Are you sure you sure a checkpoint in ", hps.lfads_save_dir,
+            " exists?")
 
     tf.global_variables_initializer().run()
 
@@ -787,4 +791,3 @@ def main(_):
 
 if __name__ == "__main__":
     tf.app.run()
- 

research/lfads/utils.py

@@ -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)