Merge branch 'master' of https://github.com/SciSharp/TensorFlow.NET

src/TensorFlowNET.Core/APIs/tf.math.cs

@@ -528,5 +528,7 @@ namespace Tensorflow
 
         public Tensor square(Tensor x, string name = null)
             => gen_math_ops.square(x, name: name);
+        public Tensor squared_difference(Tensor x, Tensor y, string name = null)
+            => gen_math_ops.squared_difference(x: x, y: y, name: name);
     }
 }

src/TensorFlowNET.Core/APIs/tf.nn.cs

@@ -116,6 +116,8 @@ namespace Tensorflow
             public IActivation relu() => new relu();
             public IActivation swish() => new swish();
             public IActivation tanh() => new tanh();
+
+            public IActivation softmax() => new softmax();
             public Tensor tanh(Tensor x, string name = null)
                 => gen_nn_ops.tanh(x, name);
 

src/TensorFlowNET.Core/APIs/tf.random.cs

@@ -38,6 +38,12 @@ namespace Tensorflow
                 TF_DataType dtype = TF_DataType.TF_FLOAT,
                 int? seed = null,
                 string name = null) => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
+            public Tensor categorical(
+                Tensor logits,
+                int num_samples,
+                int? seed = null,
+                string name = null,
+                TF_DataType output_dtype = TF_DataType.DtInvalid) => random_ops.multinomial(logits, num_samples, seed: seed, name: name, output_dtype: output_dtype);
         }
 
         public Tensor random_uniform(TensorShape shape,

src/TensorFlowNET.Core/APIs/tf.train.cs

@@ -38,8 +38,8 @@ namespace Tensorflow
             public Optimizer GradientDescentOptimizer(Tensor learning_rate)
                 => new GradientDescentOptimizer(learning_rate);
 
-            public Optimizer AdamOptimizer(float learning_rate, string name = "Adam") 
-                => new AdamOptimizer(learning_rate, name: name);
+            public Optimizer AdamOptimizer(float learning_rate, float epsilon = 1e-8f, string name = "Adam") 
+                => new AdamOptimizer(learning_rate, epsilon:epsilon, name: name);
 
             public Optimizer AdamOptimizer(float learning_rate, TF_DataType dtype, string name = "Adam")
                 => new AdamOptimizer(learning_rate, name: name, dtype: dtype);