Update TF NN

NN/TF/Networks.py

@@ -250,7 +250,6 @@ def get_rs(self, x, predict=True, pipe=False):
 
     @NNTiming.timeit(level=4, prefix="[API] ")
     def add(self, layer, *args, **kwargs):
-
         # Init kwargs
         kwargs["apply_bias"] = kwargs.get("apply_bias", True)
         kwargs["position"] = kwargs.get("position", len(self._layers) + 1)
@@ -571,8 +570,9 @@ def _draw_detailed_network(self, radius=6, width=1200, height=800, padding=0.2,
         input_x, input_y = np.meshgrid(xf, yf)
         input_xs = np.c_[input_x.ravel().astype(np.float32), input_y.ravel().astype(np.float32)]
 
-        _activations = [activation.eval(feed_dict={self._tfx: input_xs}).T.reshape(units[i + 1], plot_num, plot_num)
-                        for i, activation in enumerate(self._activations)]
+        _activations = self._sess.run(self._activations, {self._tfx: input_xs})
+        _activations = [activation.T.reshape(units[i + 1], plot_num, plot_num)
+                        for i, activation in enumerate(_activations)]
         _graphs = []
         for j, activation in enumerate(_activations):
             _graph_group = []
@@ -726,10 +726,10 @@ def build(self, units="load"):
                 raise BuildLayerError("At least 2 layers are needed")
             _input_shape = (units[0], units[1])
             self.initialize()
-            self.add(ReLU(_input_shape))
+            self.add("ReLU", _input_shape)
             for unit_num in units[2:]:
-                self.add(ReLU((unit_num,)))
-            self.add(CrossEntropy((units[-1],)))
+                self.add("ReLU", (unit_num,))
+            self.add("CrossEntropy", (units[-1],))
 
     @NNTiming.timeit(level=4, prefix="[API] ")
     def split_data(self, x, y, x_test, y_test,