activations, constraints, initializers, losses, regularizers: move Ops param from CTOR to call method (#329)

* Move Ops from CTOR to call method

* Move Ops from CTOR to call method

* Move Ops from CTOR to call method

* JavaDoc fixes including Dataset

* Results of Run mvn spotless:apply

Author: JimClarke5

tensorflow-framework/src/main/java/org/tensorflow/framework/activations/AbstractActivation.java

@@ -0,0 +1,46 @@
+/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+=======================================================================*/
+package org.tensorflow.framework.activations;
+
+import org.tensorflow.op.Ops;
+import org.tensorflow.types.family.TNumber;
+
+/** Abstract base class for Activations */
+public abstract class AbstractActivation<T extends TNumber> implements Activation<T> {
+
+  /** The TensorFlow Ops */
+  protected Ops tf;
+
+  /** Creates the abstract class for an AbstractActivation */
+  protected AbstractActivation() {}
+
+  /**
+   * Gets the TensorFlow Ops
+   *
+   * @return the TensorFlow Ops
+   */
+  protected Ops getTF() {
+    return this.tf;
+  }
+
+  /**
+   * Sets the TensorFlow Ops
+   *
+   * @param tf the TensorFlow Ops
+   */
+  protected void setTF(Ops tf) {
+    this.tf = tf;
+  }
+}

tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Activation.java

@@ -1,4 +1,4 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -19,50 +19,19 @@
 import org.tensorflow.types.family.TNumber;
 
 /**
- * Abstract base class for Activations
+ * Interface for Activations
  *
- * <p><b>Note:</b> The {@link #tf} attribute must be set prior to invoking the call method. See
- * {@link #setTF(Ops)} and the constructor {@link #Activation(Ops)}.
- *
- * @param <T> the data type of the activation
+ * @param <T> the data type of the input and the result
  */
-public abstract class Activation<T extends TNumber> {
-
-  /** The TensorFlow Ops */
-  protected Ops tf;
-
-  /**
-   * Creates the abstract class for an Activation
-   *
-   * @param tf the TensorFlow Ops
-   */
-  protected Activation(Ops tf) {
-    this.tf = tf;
-  }
-
-  /**
-   * Sets the TensorFlow Ops
-   *
-   * @param tf the TensorFlow Ops
-   */
-  protected void setTF(Ops tf) {
-    this.tf = tf;
-  }
-
-  /**
-   * Gets the TensorFlow Ops
-   *
-   * @return the TensorFlow Ops
-   */
-  protected Ops getTF() {
-    return this.tf;
-  }
+@FunctionalInterface
+public interface Activation<T extends TNumber> {
 
   /**
    * Gets the calculation operation for the activation.
    *
+   * @param tf the TensorFlow Ops
    * @param input the input tensor
    * @return The operand for the activation
    */
-  public abstract Operand<T> call(Operand<T> input);
+  Operand<T> call(Ops tf, Operand<T> input);
 }

tensorflow-framework/src/main/java/org/tensorflow/framework/activations/ELU.java

@@ -14,6 +14,8 @@
 =======================================================================*/
 package org.tensorflow.framework.activations;
 
+import static org.tensorflow.framework.utils.CastHelper.cast;
+
 import org.tensorflow.Operand;
 import org.tensorflow.op.Ops;
 import org.tensorflow.types.TBool;
@@ -44,53 +46,41 @@
  *     Operand<TFloat32> result = elu.call(input);
  * </pre>
  *
- * @param <T> the data type of the activation
  * @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>
  */
-public class ELU<T extends TFloating> extends Activation<T> {
+public class ELU<T extends TFloating> extends AbstractActivation<T> {
 
   private static final double ALPHA_DEFAULT = 1.0;
 
   /** A scalar, slope of negative section. */
   private final double alpha;
 
-  /**
-   * Creates a new ELU with alpha={@link #ALPHA_DEFAULT}.
-   *
-   * @param tf the TensorFlow Ops
-   */
-  public ELU(Ops tf) {
-    this(tf, ALPHA_DEFAULT);
+  /** Creates a new ELU with alpha={@link #ALPHA_DEFAULT}. */
+  public ELU() {
+    this(ALPHA_DEFAULT);
   }
 
   /**
    * Creates a new ELU
    *
-   * @param tf the TensorFlow Ops
    * @param alpha A scalar, slope of negative section. It controls the value to which an ELU
    *     saturates for negative net inputs.
    */
-  public ELU(Ops tf, double alpha) {
-    super(tf);
+  public ELU(double alpha) {
+    super();
     this.alpha = alpha;
   }
 
-  /**
-   * Gets the calculation operation for the activation.
-   *
-   * @param input the input tensor
-   * @return The operand for the activation
-   */
+  /** {@inheritDoc} */
   @Override
-  public Operand<T> call(Operand<T> input) {
-
+  public Operand<T> call(Ops tf, Operand<T> input) {
     Operand<T> 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));
-      Operand<TBool> cond = tf.math.greater(result, tf.dtypes.cast(tf.constant(0), inputType));
+      Operand<T> y = tf.math.mul(result, cast(tf, tf.constant(alpha), inputType));
+      Operand<TBool> cond = tf.math.greater(result, cast(tf, tf.constant(0), inputType));
       return tf.select(cond, result, y);
     }
   }

tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Exponential.java

@@ -30,28 +30,17 @@
  *   Operand<TFloat32> result = exp.call(input);
  *   // result is [0.04978707f,  0.36787945f,  1.f,  2.7182817f, 20.085537f]
  * </pre>
- *
- * @param <T> the data type of the activation
  */
-public class Exponential<T extends TFloating> extends Activation<T> {
+public class Exponential<T extends TFloating> extends AbstractActivation<T> {
 
-  /**
-   * Creates an Exponential activation.
-   *
-   * @param tf the TensorFlow Ops
-   */
-  public Exponential(Ops tf) {
-    super(tf);
+  /** Creates an Exponential activation. */
+  public Exponential() {
+    super();
   }
 
-  /**
-   * Calculates the Exponential activation.
-   *
-   * @param input the input tensor
-   * @return an Operand for the exponential activation: <code>exp(x)</code>.
-   */
+  /** {@inheritDoc} */
   @Override
-  public Operand<T> call(Operand<T> input) {
+  public Operand<T> call(Ops tf, Operand<T> input) {
     return tf.math.exp(input);
   }
 }

tensorflow-framework/src/main/java/org/tensorflow/framework/activations/HardSigmoid.java

@@ -14,6 +14,8 @@
 =======================================================================*/
 package org.tensorflow.framework.activations;
 
+import static org.tensorflow.framework.utils.CastHelper.cast;
+
 import org.tensorflow.Operand;
 import org.tensorflow.op.Ops;
 import org.tensorflow.types.family.TFloating;
@@ -40,34 +42,23 @@
  *     Operand<TFloat32> result = hardSigmoid.call(input);
  *     // result is [0.f , 0.3f, 0.5f, 0.7f, 1.f]
  * </pre>
- *
- * @param <T> the data type of the result
  */
-public class HardSigmoid<T extends TFloating> extends Activation<T> {
+public class HardSigmoid<T extends TFloating> extends AbstractActivation<T> {
 
-  /**
-   * Creates Hard sigmoid activation.
-   *
-   * @param tf the TensorFlow Ops
-   */
-  public HardSigmoid(Ops tf) {
-    super(tf);
+  /** Creates Hard sigmoid activation. */
+  public HardSigmoid() {
+    super();
   }
 
-  /**
-   * Gets the calculation operation for the activation.
-   *
-   * @param input the input tensor
-   * @return The operand for the activation
-   */
+  /** {@inheritDoc} */
   @Override
-  public Operand<T> call(Operand<T> input) {
+  public Operand<T> call(Ops tf, 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);
+    Operand<T> point2 = cast(tf, tf.constant(0.2), inputType);
+    Operand<T> point5 = cast(tf, tf.constant(0.5), inputType);
 
     Operand<T> 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));
+        x, cast(tf, tf.constant(0), inputType), cast(tf, tf.constant(1), inputType));
   }
 }

tensorflow-framework/src/main/java/org/tensorflow/framework/activations/Linear.java

@@ -19,9 +19,9 @@
 import org.tensorflow.types.family.TNumber;
 
 /**
- * Linear activation function  (pass-through).
+ * Linear activation function (pass-through).
  *
- * <p>The linear activation returns its input. It is also known as the Identity activation function.</p>
+ * <p>The linear activation returns its input. It is also known as the Identity activation function.
  *
  * <p>For example:
  *
@@ -33,20 +33,16 @@
  *    // result is [-3.0f,-1.0f, 0.0f,1.0f,3.0f]
  * </pre>
  */
-public class Linear<U extends TNumber> extends Activation<U> {
+public class Linear<U extends TNumber> extends AbstractActivation<U> {
 
-  /**
-   * Creates a linear activation.
-   *
-   * @param tf the TensorFlow Ops
-   */
-  public Linear(Ops tf) {
-    super(tf);
+  /** Creates a linear activation. */
+  public Linear() {
+    super();
   }
 
   /** {@inheritDoc} */
   @Override
-  public Operand<U> call(Operand<U> input) {
+  public Operand<U> call(Ops tf, Operand<U> input) {
     return input;
   }
 }