lib/tensorflow/math.rb in tensorflow-0.1.1 vs lib/tensorflow/math.rb in tensorflow-0.1.2

@@ -222,12 +222,14 @@
 
       def log1p(x)
         RawOps.log1p(x: x)
       end
 
-      # def log_sigmoid
-      # end
+      def log_sigmoid(x)
+        x = TensorFlow.convert_to_tensor(x)
+        negative(RawOps.softplus(features: -x))
+      end
 
       def log_softmax(logits)
         RawOps.log_softmax(logits: logits)
       end
 
@@ -241,12 +243,13 @@
 
       def logical_or(x, y)
         RawOps.logical_or(x: x, y: y)
       end
 
-      # def logical_xor
-      # end
+      def logical_xor(x, y)
+        logical_and(logical_or(x, y), logical_not(logical_and(x, y)))
+      end
 
       def maximum(x, y)
         RawOps.maximum(x: x, y: y)
       end
 
@@ -260,15 +263,17 @@
 
       def multiply(x, y)
         RawOps.mul(x: x, y: y)
       end
 
-      # def multiply_no_nan
-      # end
+      def multiply_no_nan(x, y)
+        RawOps.mul_no_nan(x: x, y: y)
+      end
 
-      # def negative
-      # end
+      def negative(x)
+        RawOps.neg(x: x)
+      end
 
       # def nextafter
       # end
 
       def not_equal(x, y)
@@ -298,39 +303,62 @@
       # end
 
       # def reduce_all
       # end
 
-      # def reduce_any
-      # end
+      def reduce_any(input_tensor, axis: nil, keepdims: false)
+        input_tensor = TensorFlow.convert_to_tensor(input_tensor)
+        axis ||= reduction_dims(input_tensor)
+        RawOps.any(input: input_tensor, reduction_indices: axis, keep_dims: keepdims)
+      end
 
       # def reduce_euclidean_norm
       # end
 
       # def reduce_logsumexp
       # end
 
-      # def reduce_max
-      # end
+      def reduce_max(input_tensor, axis: nil, keepdims: false)
+        input_tensor = TensorFlow.convert_to_tensor(input_tensor)
+        axis ||= reduction_dims(input_tensor)
+        RawOps.max(input: input_tensor, reduction_indices: axis, keep_dims: keepdims)
+      end
 
-      # def reduce_mean
-      # end
+      def reduce_mean(input_tensor, axis: nil, keepdims: false)
+        input_tensor = TensorFlow.convert_to_tensor(input_tensor)
+        axis ||= reduction_dims(input_tensor)
+        RawOps.mean(input: input_tensor, reduction_indices: axis, keep_dims: keepdims)
+      end
 
-      # def reduce_min
-      # end
+      def reduce_min(input_tensor, axis: nil, keepdims: false)
+        input_tensor = TensorFlow.convert_to_tensor(input_tensor)
+        axis ||= reduction_dims(input_tensor)
+        RawOps.min(input: input_tensor, reduction_indices: axis, keep_dims: keepdims)
+      end
 
-      # def reduce_prod
-      # end
+      def reduce_prod(input_tensor, axis: nil, keepdims: false)
+        input_tensor = TensorFlow.convert_to_tensor(input_tensor)
+        axis ||= reduction_dims(input_tensor)
+        RawOps.prod(input: input_tensor, reduction_indices: axis, keep_dims: keepdims)
+      end
 
-      # def reduce_std
-      # end
+      def reduce_std(input_tensor, axis: nil, keepdims: false)
+        variance = reduce_variance(input_tensor, axis: axis, keepdims: keepdims)
+        sqrt(variance)
+      end
 
-      # def reduce_sum
-      # end
+      def reduce_sum(input_tensor, axis: nil, keepdims: false)
+        input_tensor = TensorFlow.convert_to_tensor(input_tensor)
+        axis ||= reduction_dims(input_tensor)
+        RawOps.sum(input: input_tensor, reduction_indices: axis, keep_dims: keepdims)
+      end
 
-      # def reduce_variance
-      # end
+      def reduce_variance(input_tensor, axis: nil, keepdims: false)
+        means = reduce_mean(input_tensor, axis: axis, keepdims: true)
+        squared_deviations = RawOps.square(x: input_tensor - means)
+        reduce_mean(squared_deviations, axis: axis, keepdims: keepdims)
+      end
 
       def rint(x)
         RawOps.rint(x: x)
       end
 
@@ -457,9 +485,16 @@
       # def zero_fraction
       # end
 
       def zeta(x, q)
         RawOps.zeta(x: x, q: q)
+      end
+
+      private
+
+      def reduction_dims(input_tensor)
+        rank = RawOps.rank(input: input_tensor).value
+        (0...rank).to_a
       end
     end
   end
 end