Refactor to add generic to base class and change signature of call me…

…thod to <U extends T>. Changed all subclasses to match these signatures.
tensorflow · JimClarke5 · Oct 8, 2020 · Oct 27, 2020 · Nov 17, 2020 · Nov 19, 2020
commit d0e8de92efcd37d103492dd6a8f5bb8afb3918c0
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Activation.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Activation.java
@@ -19,7 +19,7 @@
 import org.tensorflow.types.family.TNumber;
 
 /** Abstract base class for Activations */
-public abstract class Activation {
+public abstract class Activation<T extends TNumber> {
 
   /** The TensorFlow Ops */
   protected Ops tf;
@@ -55,8 +55,8 @@ protected void setTF(Ops tf) {
    * Gets the calculation operation for the activation.
    *
    * @param input the input tensor
-   * @param <T> the data type of the input and result
+   * @param <U> the data type of the input and result
    * @return The operand for the activation
    */
-  public abstract <T extends TNumber> Operand<T> call(Operand<T> input);
+  public abstract <U extends T> Operand<U> call(Operand<U> input);
 }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/ELU.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/ELU.java
@@ -18,7 +18,6 @@
 import org.tensorflow.op.Ops;
 import org.tensorflow.types.TBool;
 import org.tensorflow.types.family.TFloating;
-import org.tensorflow.types.family.TNumber;
 
 /**
  * Exponential linear unit.
@@ -48,8 +47,7 @@
  * @see <a href="https://arxiv.org/abs/1511.07289">Clevert et al, 2016, Fast and Accurate Deep
  *     Network Learning by Exponential Linear Units (ELUs)</a>
  */
-// TFloating
-public class ELU extends Activation {
+public class ELU extends Activation<TFloating> {
 
   private static final double ALPHA_DEFAULT = 1.0;
 
@@ -79,19 +77,14 @@ public ELU(Ops tf, double alpha) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
+  public <U extends TFloating> Operand<U> call(Operand<U> input) {
 
-    if (!TFloating.class.isAssignableFrom(input.type()) ) {
-      throw new IllegalArgumentException(
-              "Tensor type must be numeric or boolean: " + input.type().getSimpleName());
-    }
-
-    Operand<T> result = tf.nn.elu(input);
+    Operand<U> result = tf.nn.elu(input);
     if (alpha == 1.0) {
       return result;
     } else {
-      Class<T> inputType = input.type();
-      Operand<T> y = tf.math.mul(result, tf.dtypes.cast(tf.constant(alpha), inputType));
+      Class<U> inputType = input.type();
+      Operand<U> y = tf.math.mul(result, tf.dtypes.cast(tf.constant(alpha), inputType));
       Operand<TBool> cond = tf.math.greater(result, tf.dtypes.cast(tf.constant(0), inputType));
       return tf.select(cond, result, y);
     }

diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Exponential.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Exponential.java
@@ -17,7 +17,6 @@
 import org.tensorflow.Operand;
 import org.tensorflow.op.Ops;
 import org.tensorflow.types.family.TFloating;
-import org.tensorflow.types.family.TNumber;
 
 /**
  * Exponential activation function.
@@ -32,8 +31,7 @@
  *   // result is [0.04978707f,  0.36787945f,  1.f,  2.7182817f, 20.085537f]
  * </pre>
  */
-// TFloating
-public class Exponential extends Activation {
+public class Exponential extends Activation<TFloating> {
 
   /**
    * Creates an Exponential activation.
@@ -48,15 +46,12 @@ public Exponential(Ops tf) {
    * Calculates the Exponential activation.
    *
    * @param input the input tensor
-   * @param <T> the data type of the input and result
+   * @param <U> the data type of the input and result
    * @return an Operand for the exponential activation: <code>exp(x)</code>.
    */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
-    if (!TFloating.class.isAssignableFrom(input.type()) ) {
-      throw new IllegalArgumentException(
-              "Tensor type must be numeric or boolean: " + input.type().getSimpleName());
-    }
+  public <U extends TFloating> Operand<U> call(Operand<U> input) {
+
 
     return tf.math.exp(input);
   }

diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/GeLU.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/GeLU.java
@@ -15,13 +15,8 @@
 package org.tensorflow.framework.activations;
 
 import org.tensorflow.Operand;
-import org.tensorflow.Session;
 import org.tensorflow.op.Ops;
-import org.tensorflow.types.TFloat64;
 import org.tensorflow.types.family.TFloating;
-import org.tensorflow.types.family.TNumber;
-
-import java.util.Arrays;
 
 import static org.tensorflow.framework.utils.CastHelper.cast;
 
@@ -39,16 +34,15 @@
  * <p>or, if <code>approximate</code> is <code>false</code>.
  *
  * <pre>
- *     x * P(X <= x) = 0.5 * x * (1 + erf(x / sqrt(2))),
+ *     x * P(X &lt;= x) = 0.5 * x * (1 + erf(x / sqrt(2))),
  * </pre>
  *
  * where <code>P(X) ~ N(0, 1)</code>.
  *
  * @see <a href="https://arxiv.org/abs/1606.08415">Hendrycks, Dan and Gimpel, Kevin, 2016-2020,
  *     Gaussian Error Linear Units (GELUs)</a>
  */
-// TFloating
-public class GeLU extends Activation {
+public class GeLU extends Activation<TFloating> {
 
   private final boolean approximate;
 
@@ -74,21 +68,18 @@ public GeLU(Ops tf, boolean approximate) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
-    if (!TFloating.class.isAssignableFrom(input.type())) {
-      throw new IllegalArgumentException(
-          "Tensor type must be numeric or boolean: " + input.type().getSimpleName());
-    }
+  public <U extends TFloating> Operand<U> call(Operand<U> input) {
+
     if (approximate) {
       /*
               coeff = math_ops.cast(0.044715, features.dtype)
               return 0.5 * features * (
                     1.0 + math_ops.tanh(0.7978845608028654 *
                               (features + coeff * math_ops.pow(features, 3))))
       */
-      Operand<T> coeff = cast(tf, tf.constant(0.044715), input.type());
-      Operand<T> point5 = cast(tf, tf.constant(0.5), input.type());
-      Operand<T> one = cast(tf, tf.constant(1.0), input.type());
+      Operand<U> coeff = cast(tf, tf.constant(0.044715), input.type());
+      Operand<U> point5 = cast(tf, tf.constant(0.5), input.type());
+      Operand<U> one = cast(tf, tf.constant(1.0), input.type());
 
       return tf.math.mul(
           point5,
@@ -128,5 +119,4 @@ public <T extends TNumber> Operand<T> call(Operand<T> input) {
                           input, cast(tf, tf.constant(1.4142135623730951), input.type()))))));
     }
   }
-
 }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/HardSigmoid.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/HardSigmoid.java
@@ -41,7 +41,7 @@
  *     // result is [0.f , 0.3f, 0.5f, 0.7f, 1.f]
  * </pre>
  */
-public class HardSigmoid extends Activation {
+public class HardSigmoid extends Activation<TNumber> {
 
   /**
    * Creates Hard sigmoid activation.
@@ -54,12 +54,12 @@ public HardSigmoid(Ops tf) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
-    Class<T> inputType = input.type();
-    Operand<T> point2 = tf.dtypes.cast(tf.constant(0.2), inputType);
-    Operand<T> point5 = tf.dtypes.cast(tf.constant(0.5), inputType);
+  public <U extends TNumber> Operand<U> call(Operand<U> input) {
+    Class<U> inputType = input.type();
+    Operand<U> point2 = tf.dtypes.cast(tf.constant(0.2), inputType);
+    Operand<U> point5 = tf.dtypes.cast(tf.constant(0.5), inputType);
 
-    Operand<T> x = tf.math.add(tf.math.mul(input, point2), point5);
+    Operand<U> x = tf.math.add(tf.math.mul(input, point2), point5);
     return tf.clipByValue(
         x, tf.dtypes.cast(tf.constant(0), inputType), tf.dtypes.cast(tf.constant(1), inputType));
   }

diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Linear.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Linear.java
@@ -33,7 +33,7 @@
  *    // result is [-3.0f,-1.0f, 0.0f,1.0f,3.0f]
  * </pre>
  */
-public class Linear extends Activation {
+public class Linear extends Activation<TNumber> {
 
   /**
    * Creates a linear activation.
@@ -46,7 +46,7 @@ public Linear(Ops tf) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
+  public <U extends TNumber> Operand<U> call(Operand<U> input) {
     return input;
   }
 }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/ReLU.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/ReLU.java
@@ -56,8 +56,7 @@
  *     // result is [-0.f, -0.f,  0.f,  0.f, 10.f]
  * </pre>
  */
-// TFloating
-public class ReLU extends Activation {
+public class ReLU extends Activation<TNumber> {
 
   public static final float ALPHA_DEFAULT = 0.0f;
   public static final float MAX_VALUE_DEFAULT = Float.NaN;
@@ -95,11 +94,11 @@ public ReLU(Ops tf, float alpha, float maxValue, float threshold) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
-    Class<T> inputType = input.type();
+  public <U extends TNumber> Operand<U> call(Operand<U> input) {
+    Class<U> inputType = input.type();
 
     boolean clipMax = !Float.isNaN(maxValue);
-    Operand<T> negativePart = null;
+    Operand<U> negativePart = null;
     if (alpha != 0) {
       if (Float.isNaN(maxValue) && threshold == 0) {
         return tf.nn.leakyRelu(input, LeakyRelu.alpha(alpha));
@@ -113,7 +112,7 @@ public <T extends TNumber> Operand<T> call(Operand<T> input) {
       }
     }
 
-    Operand<T> lInput;
+    Operand<U> lInput;
     if (threshold != 0) {
       // computes input for input > threshold else 0
       Greater greater = tf.math.greater(input, tf.dtypes.cast(tf.constant(threshold), inputType));
@@ -126,8 +125,8 @@ public <T extends TNumber> Operand<T> call(Operand<T> input) {
       lInput = tf.nn.relu(input);
     }
     if (clipMax) {
-      Operand<T> lmaxValue = tf.dtypes.cast(tf.constant(maxValue), inputType);
-      Operand<T> zero = tf.dtypes.cast(tf.constant(0), inputType);
+      Operand<U> lmaxValue = tf.dtypes.cast(tf.constant(maxValue), inputType);
+      Operand<U> zero = tf.dtypes.cast(tf.constant(0), inputType);
       lInput = tf.clipByValue(lInput, zero, lmaxValue);
     }
 

diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/SELU.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/SELU.java
@@ -16,7 +16,7 @@
 
 import org.tensorflow.Operand;
 import org.tensorflow.op.Ops;
-import org.tensorflow.types.family.TNumber;
+import org.tensorflow.types.family.TFloating;
 
 /**
  * Scaled Exponential Linear Unit (SELU).
@@ -44,8 +44,7 @@
  *
  * @see <a href="https://arxiv.org/abs/1706.02515">Klambauer et al., 2017</a>
  */
-// TFloating
-public class SELU extends Activation {
+public class SELU extends Activation<TFloating> {
 
   /**
    * Creates a Scaled Exponential Linear Unit (SELU) activation.
@@ -58,7 +57,7 @@ public SELU(Ops tf) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
+  public <U extends TFloating> Operand<U> call(Operand<U> input) {
     return tf.nn.selu(input);
   }
 }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Sigmoid.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Sigmoid.java
@@ -16,7 +16,7 @@
 
 import org.tensorflow.Operand;
 import org.tensorflow.op.Ops;
-import org.tensorflow.types.family.TNumber;
+import org.tensorflow.types.family.TFloating;
 
 /**
  * Sigmoid activation. <code>sigmoid(x) = 1 / (1 + exp(-x))</code>.
@@ -39,8 +39,7 @@
  *     //                 5.0000000e-01f,7.3105860e-01f, 1.f]
  * </pre>
  */
-// TFloating
-public class Sigmoid extends Activation {
+public class Sigmoid extends Activation<TFloating> {
 
   /**
    * Creates a Sigmoid activation.
@@ -53,7 +52,7 @@ public Sigmoid(Ops tf) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
+  public <U extends TFloating> Operand<U> call(Operand<U> input) {
     return tf.math.sigmoid(input);
   }
 }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Softmax.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Softmax.java
@@ -19,7 +19,7 @@
 import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.ReduceMax;
 import org.tensorflow.op.core.ReduceSum;
-import org.tensorflow.types.family.TNumber;
+import org.tensorflow.types.family.TFloating;
 
 /**
  * Softmax converts a real vector to a vector of categorical probabilities.
@@ -36,8 +36,7 @@
  *
  * <p>The input values in are the log-odds of the resulting probability.
  */
-// TFloating
-public class Softmax extends Activation {
+public class Softmax extends Activation<TFloating> {
 
   private static final int AXIS_DEFAULT = -1;
 
@@ -66,16 +65,16 @@ public Softmax(Ops tf, int axis) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
+  public <U extends TFloating> Operand<U> call(Operand<U> input) {
     Shape shape = input.shape();
     int numDimensions = shape.numDimensions();
     if (numDimensions == 2) {
       return tf.nn.softmax(input);
     } else {
-      Operand<T> e =
+      Operand<U> e =
           tf.math.exp(
               tf.math.sub(input, tf.reduceMax(input, tf.constant(axis), ReduceMax.keepDims(true))));
-      Operand<T> s = tf.reduceSum(e, tf.constant(axis), ReduceSum.keepDims(true));
+      Operand<U> s = tf.reduceSum(e, tf.constant(axis), ReduceSum.keepDims(true));
       return tf.math.div(e, s);
     }
   }

diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Softplus.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Softplus.java
@@ -16,7 +16,7 @@
 
 import org.tensorflow.Operand;
 import org.tensorflow.op.Ops;
-import org.tensorflow.types.family.TNumber;
+import org.tensorflow.types.family.TFloating;
 
 /**
  * Softplus activation function, <code>softplus(x) = log(exp(x) + 1)</code>.
@@ -32,8 +32,7 @@
  *     //                 1.3132616e+00f, 2.0000000e+01f]
  * </pre>
  */
-// TFloating
-public class Softplus extends Activation {
+public class Softplus extends Activation<TFloating> {
 
   /**
    * Creates a Softplus activation function.
@@ -46,7 +45,7 @@ public Softplus(Ops tf) {
 
   /** {@inheritDoc} */
   @Override
-  public <T extends TNumber> Operand<T> call(Operand<T> input) {
+  public <U extends TFloating> Operand<U> call(Operand<U> input) {
     return tf.math.softplus(input);
   }
 }