fix: Remove check on keras version

* To support Python 3.7+, require tensorflow v2.5.0+ in the extras, which will
  correctly manage compatible versions of keras.

* The check on the version of keras was required to deal with issues between
  Keras v2.2.4 and v2.2.5. As the minimum required Keras version for
  tensorflow v2.5.0 is keras v2.5.0.dev then all Keras versions installed
  through the EnergyFlow extras will be newer than v2.2.5 and so the
  guard check is no longer necessary.

   - c.f. https://github.com/tensorflow/tensorflow/blob/a4dfb8d1a71385bd6d122e4f27f86dcebb96712d/tensorflow/tools/pip_package/setup.py#L107

energyflow/archs/efn.py

@@ -15,7 +15,6 @@
 import numpy as np
 
 import tensorflow.keras.backend as K
-from keras import __version__ as __keras_version__
 from tensorflow.keras.layers import Concatenate, Dense, Dot, Dropout, Input, Lambda, TimeDistributed
 from tensorflow.keras.models import Model
 from tensorflow.keras.regularizers import l2
@@ -33,20 +32,13 @@
     # weight mask constructor functions
     #'construct_efn_weight_mask', 'construct_pfn_weight_mask',
 
-    # network consstructor functions
-    #'construct_distributed_dense', 'construct_latent', 'construct_dense', 
+    # network constructor functions
+    #'construct_distributed_dense', 'construct_latent', 'construct_dense',
 
     # full model classes
     'EFN', 'PFN'
 ]
 
-################################################################################
-# Keras 2.2.5 fixes bug in 2.2.4 that affects our usage of the Dot layer
-################################################################################
-
-if __keras_version__.endswith('-tf'):
-    __keras_version__ = __keras_version__[:-3]
-keras_version_tuple = tuple(map(int, __keras_version__.split('.')))
 DOT_AXIS = 1
 
 ################################################################################
@@ -533,20 +525,19 @@ def eval_filters(self, patch, n=100, prune=True):
         XY = np.asarray([X, Y]).reshape((1, 2, nx*ny)).transpose((0, 2, 1))
 
         # handle weirdness of Keras/tensorflow
-        old_keras = (keras_version_tuple <= (2, 2, 5))
         s = self.Phi_sizes[-1] if len(self.Phi_sizes) else self.input_dim
         in_t, out_t = self.inputs[1], self._tensors[self._tensor_inds['latent'][0] - 1]
 
         # construct function
-        kf = K.function([in_t] if old_keras else in_t, [out_t] if old_keras else out_t)
+        kf = K.function(in_t, out_t)
 
         # evaluate function
-        Z = kf([XY] if old_keras else XY)[0].reshape(nx, ny, s).transpose((2, 0, 1))
+        Z = kf(XY)[0].reshape(nx, ny, s).transpose((2, 0, 1))
 
         # prune filters that are off
         if prune:
             return X, Y, Z[[not (z == 0).all() for z in Z]]
-        
+
         return X, Y, Z
 
 

setup.cfg

@@ -13,7 +13,7 @@ long_description = file: README.md
 long_description_content_type = text/markdown
 
 url = https://energyflow.network
-project_urls = 
+project_urls =
     Source Code = https://github.com/pkomiske/EnergyFlow
     Issues = https://github.com/pkomiske/EnergyFlow/issues
 
@@ -98,25 +98,25 @@ generation =
     python-igraph
 
 examples =
-    tensorflow > 2.0.0
+    tensorflow >= 2.5.0
     scikit-learn
     matplotlib
 
 archs =
-    tensorflow > 2.0.0
+    tensorflow >= 2.5.0
     scikit-learn
 
 tests =
     pot >= 0.8.0
     pytest
     python-igraph
     python-igraph == 0.8.3; python_version=='2.7'
-    tensorflow > 2.0.0
+    tensorflow >= 2.5.0
     scikit-learn
 
 all =
     python-igraph
-    tensorflow > 2.0.0
+    tensorflow >= 2.5.0
     scikit-learn
 
 [bdist_wheel]