fixed Minimum - CategoricalCrossentropy #901

Author: Oceania2018

src/TensorFlowNET.Core/Graphs/Graph.Control.cs

@@ -81,10 +81,7 @@ public _ControlDependenciesController control_dependencies(ITensorOrOperation[]
         /// </summary>
         public _ControlDependenciesController control_dependencies(object[] control_inputs)
         {
-            if (tf.Context.executing_eagerly())
-                return new _ControlDependenciesController(this, null);
-
-            if (control_inputs == null)
+            if (control_inputs == null || tf.Context.executing_eagerly())
                 return new _ControlDependenciesController(this, null);
 
             var control_ops = new List<ITensorOrOperation>();

src/TensorFlowNET.Keras/BackendImpl.cs

@@ -264,15 +264,15 @@ public Tensor categorical_crossentropy(Tensor target, Tensor output, bool from_l
             if (output.op != null && output.op.type == "Softmax")
             {
                 if (output.op.inputs.Length != 1) throw new ApplicationException();
-                var o = output = output.op.inputs[0];
+                var o = output.op.inputs[0];
                 return tf.nn.softmax_cross_entropy_with_logits_v2(labels: target, logits: o, axis: axis);
             }
 
             // scale preds so that the class probas of each sample sum to 1
             output = output / math_ops.reduce_sum(output, new Axis(axis), true);
             // Compute cross entropy from probabilities.
             var epsilon_ = constant_op.constant(epsilon(), output.dtype.as_base_dtype());
-            output = clip_ops.clip_by_value(output, epsilon_, 1.0 - epsilon_);
+            output = clip_ops.clip_by_value(output, epsilon_, 1.0f - epsilon_);
             return -math_ops.reduce_sum(target * math_ops.log(output), new Axis(axis));
         }