Drop TF as requirement to allow general use

Only `see_rnn.visuals_gen` will work

requirements-test.txt

@@ -4,4 +4,5 @@ coverage
 pytest
 pytest-cov
 pycodestyle
+tensorflow
 Keras

requirements.txt

@@ -1,3 +1,2 @@
 numpy
-matplotlib
-tensorflow
+matplotlib

see_rnn/__init__.py

@@ -22,13 +22,17 @@ def scalefig(fig):
 ##############################################################################
 
 from . import visuals_gen
-from . import visuals_rnn
-from . import inspect_gen
-from . import inspect_rnn
-
 from .visuals_gen import *
-from .visuals_rnn import *
-from .inspect_gen import *
-from .inspect_rnn import *
+try:
+    from . import visuals_rnn
+    from .visuals_rnn import *
+    from . import inspect_gen
+    from .inspect_gen import *
+    from . import inspect_rnn
+    from .inspect_rnn import *
+except:
+    # handled in _backend.py
+    pass
+
 
-__version__ = '1.15.0'
+__version__ = '1.15.1'

see_rnn/_backend.py

@@ -3,16 +3,19 @@
 
 
 TF_KERAS = os.environ.get("TF_KERAS", '1') == '1'
-
 WARN = colored("WARNING:", 'red')
 NOTE = colored("NOTE:", 'blue')
 
-
-if TF_KERAS:
-    from tensorflow.python.keras import backend as K
-    from tensorflow.keras.layers import Layer
-    from tensorflow.keras.models import Model
-else:
-    from keras import backend as K
-    from keras.layers import Layer
-    from keras.models import Model
+try:
+    if TF_KERAS:
+        from tensorflow.python.keras import backend as K
+        from tensorflow.keras.layers import Layer
+        from tensorflow.keras.models import Model
+    else:
+        from keras import backend as K
+        from keras.layers import Layer
+        from keras.models import Model
+except:
+    print("WARNING: failed to import TensorFlow or Keras; functionality "
+          "is restricted to see_rnn.visuals_gen")
+    K, Layer, Model = None, None, None

see_rnn/inspect_gen.py

@@ -479,7 +479,7 @@ def _get_by_name(model, name):
     if as_dict:
         return weights
     weights = list(weights.values())
-    return weights[0] if len(_ids) == 1 else weights
+    return weights[0] if (len(_ids) == 1 and len(weights) == 1) else weights
 
 
 def detect_nans(data, include_inf=True):
@@ -664,7 +664,8 @@ def _cell_penalties(cell):
 
             if _lambda is not None:
                 weight_name = cell.weights[weight_idx].name
-                l1_l2 = (float(_lambda.l1), float(_lambda.l2))
+                l1_l2 = (float(getattr(_lambda, 'l1', 0)),
+                         float(getattr(_lambda, 'l2', 0)))
                 penalties.append([weight_name, l1_l2])
         return penalties
 

see_rnn/utils.py

@@ -1,10 +1,13 @@
 import numpy as np
-import tensorflow as tf
 from copy import deepcopy
 from pathlib import Path
-
 from ._backend import WARN, NOTE, TF_KERAS, Layer
 
+try:
+    import tensorflow as tf
+except:
+    pass  # handled in __init__ via _backend.py
+
 
 def _kw_from_configs(configs, defaults):
     def _fill_absent_defaults(kw, defaults):

see_rnn/visuals_gen.py

@@ -2,8 +2,8 @@
 import matplotlib.pyplot as plt
 from matplotlib import cm
 
-from .utils import _kw_from_configs, clipnums
 from ._backend import NOTE
+from .utils import _kw_from_configs, clipnums
 from . import scalefig