Add Regularizers 1

tensorflow-framework/src/main/java/org/tensorflow/framework/regularizers/L1.java

@@ -0,0 +1,50 @@
+/* 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.regularizers;
+
+import org.tensorflow.op.Ops;
+import org.tensorflow.types.family.TNumber;
+
+/**
+ * A regularizer that applies an L1 or Lasso(least absolute shrinkage and selection operator) Regression,
+ * regularization penalty.
+ *
+ * <p>The L1 regularization penalty is computed as: <code>loss = l1 * reduceSum(abs(x))</code>
+ *
+ * @param <R> the data type for the weights
+ */
+public class L1<R extends TNumber> extends L1L2<R> {
+
+  /**
+   * Create a regularizer that applies an L1 regularization penalty of {@link
+   * #DEFAULT_REGULARIZATION_PENALTY}
+   *
+   * @param tf the TensorFlow Ops
+   */
+  public L1(Ops tf, Class<R> type) {
+    this(tf, DEFAULT_REGULARIZATION_PENALTY, type);
+  }
+
+  /**
+   * Create a regularizer that applies an L1 regularization penalty
+   *
+   * @param tf the TensorFlow Ops
+   * @param l1 the L1 regularization penalty
+   * @throws IllegalArgumentException if the l1 regularization factor is NaN or is infinite.
+   */
+  public L1(Ops tf, float l1, Class<R> type) {
+    super(tf, l1, null, type);
+  }
+}

tensorflow-framework/src/main/java/org/tensorflow/framework/regularizers/L1L2.java

@@ -0,0 +1,132 @@
+/* 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.regularizers;
+
+import org.tensorflow.Operand;
+import org.tensorflow.framework.losses.impl.LossesHelper;
+import org.tensorflow.op.Ops;
+import org.tensorflow.types.family.TNumber;
+
+/**
+ * A regularizer that applies both L1 and L2 regularization penalties.
+ *
+ * <p>The L1 regularization penalty is computed as:
+ *
+ * <pre>loss = l1 * reduceSum(abs(x))</pre>
+ *
+ * <p>The L2 regularization penalty is computed as
+ *
+ * <pre>loss = l2 * reduceSum(square(x))</pre>
+ *
+ * <p>The difference between this class and the {@link L1_L2} is use of the default regularization
+ * penalty {@link #DEFAULT_REGULARIZATION_PENALTY}, whereas {@link L1L2} defaults to 0.
+ *
+ * @param <R> the data type for the weights
+ */
+public class L1L2<R extends TNumber> extends Regularizer<R> {
+
+  private final Float l1;
+  private final Float l2;
+
+  /**
+   * Creates an L1L2 regularizer with no l1 or l2 penalty with default penal
+   *
+   * @param tf the TensorFlow Ops
+   * @param type the data type for the weights
+   */
+  public L1L2(Ops tf, Class<R> type) {
+    this(tf, null, null, type);
+  }
+
+  /**
+   * Creates an L1L2 regularizer
+   *
+   * @param tf the TensorFlow Ops
+   * @param l1 L1 regularization factor, if null it is set to 0.
+   * @param l2 L2 regularization factor, if null it is set to 0.
+   * @param type the data type for the weights
+   * @throws IllegalArgumentException if the l1 or l2 regularization factor is {@link Float#isNaN}
+   *     of {@link Float#isInfinite}
+   */
+  public L1L2(Ops tf, Float l1, Float l2, Class<R> type) {
+    super(tf, type);
+    if (l1 != null) {
+      if (l1.isNaN() || l1.isInfinite()) {
+        throw new IllegalArgumentException(
+            String.format(
+                "L1 Value: %f is not a valid regularization penalty number, a positive/negative infinity or NaN is not a property value",
+                l1));
+      }
+      this.l1 = l1;
+    } else {
+      this.l1 = 0f;
+    }
+    if (l2 != null) {
+      if (l2.isNaN() || l2.isInfinite()) {
+        throw new IllegalArgumentException(
+            String.format(
+                "L2 Value: %f is not a valid regularization penalty number, a positive/negative infinity or NaN is not a property value",
+                l2));
+      }
+      this.l2 = l2;
+    } else {
+      this.l2 = 0f;
+    }
+  }
+
+  /** {@inheritDoc} */
+  @Override
+  public Operand<R> call(Operand<R> input) {
+    Ops tf = getTF();
+    if (this.getL1() == null && this.getL2() == null) {
+      return tf.dtypes.cast(tf.constant(0), input.type());
+    }
+    Operand<R> regularization = tf.dtypes.cast(tf.constant(0), input.type());
+
+    if (this.getL1() != null && this.getL1() != 0.f) {
+      Operand<R> l1Op = tf.dtypes.cast(tf.constant(this.getL1()), input.type());
+      Operand<R> abs = tf.math.abs(input);
+      Operand<R> reduceSum = tf.reduceSum(abs, LossesHelper.allAxes(tf, input));
+      regularization = tf.math.add(regularization, tf.math.mul(l1Op, reduceSum));
+    }
+
+    if (this.getL2() != null && this.getL2() != 0.f) {
+      Operand<R> l2Op = tf.dtypes.cast(tf.constant(this.getL2()), input.type());
+      Operand<R> sqr = tf.math.abs(input);
+      Operand<R> reduceSum = tf.reduceSum(sqr, LossesHelper.allAxes(tf, input));
+      regularization = tf.math.add(regularization, tf.math.mul(l2Op, reduceSum));
+    }
+
+    return regularization;
+  }
+
+  /**
+   * Gets the L1 regularization factor
+   *
+   * @return the L1 regularization factor
+   */
+  public Float getL1() {
+    return l1;
+  }
+
+  /**
+   * Gets the L2 regularization factor
+   *
+   * @return the L2 regularization factor
+   */
+  public Float getL2() {
+    return l2;
+  }
+}

tensorflow-framework/src/main/java/org/tensorflow/framework/regularizers/L1_L2.java

@@ -0,0 +1,62 @@
+/* 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.regularizers;
+
+import org.tensorflow.op.Ops;
+import org.tensorflow.types.family.TNumber;
+
+/**
+ * A regularizer that applies both L1 and L2 regularization penalties.
+ *
+ * <p>The L1 regularization penalty is computed as:
+ *
+ * <pre>loss = l1 * reduceSum(abs(x))</pre>
+ *
+ * <p>The L2 regularization penalty is computed as
+ *
+ * <pre>loss = l2 * reduceSum(square(x))</pre>
+ *
+ * <p>The difference between this class and the {@link L1L2} is use of the default regularization
+ * penalty {@link #DEFAULT_REGULARIZATION_PENALTY}, whereas {@link L1L2} defaults to 0.
+ *
+ * @param <R> the data type for the weights
+ */
+public class L1_L2<R extends TNumber> extends L1L2<R> {
+
+  /**
+   * Creates a regularizer that applies an L1 and l2 regularization penalty of {@link
+   * #DEFAULT_REGULARIZATION_PENALTY}
+   *
+   * @param tf the TensorFlow Ops
+   */
+  public L1_L2(Ops tf, Class<R> type) {
+    this(tf, DEFAULT_REGULARIZATION_PENALTY, DEFAULT_REGULARIZATION_PENALTY, type);
+  }
+
+  /**
+   * Creates a regularizer that applies an L1 and l2 regularization penalty
+   *
+   * @param tf the TensorFlow Ops
+   * @param l1 the L1 regularization penalty
+   * @param l2 the L2 regularization penalty
+   * @throws IllegalArgumentException if the l1 or l2 regularization factor is NaN or is infinite.
+   */
+  public L1_L2(Ops tf, Float l1, Float l2, Class<R> type) {
+    super(tf,
+            l1 == null ? DEFAULT_REGULARIZATION_PENALTY : l1,
+            l2 == null ? DEFAULT_REGULARIZATION_PENALTY : l2,
+            type);
+  }
+}

tensorflow-framework/src/main/java/org/tensorflow/framework/regularizers/L2.java

@@ -0,0 +1,49 @@
+/* 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.regularizers;
+
+import org.tensorflow.op.Ops;
+import org.tensorflow.types.family.TNumber;
+
+/**
+ * A regularizer that applies a L2 (Ridge Regression) regularization penalty.
+ *
+ * <p>The L2 regularization penalty is computed as: <code>loss = l2 * reduceSum(square(x))</code>
+ *
+ * @param <R> the data type for the operands and result
+ */
+public class L2<R extends TNumber> extends L1L2<R> {
+
+  /**
+   * Create a regularizer that applies an L2 regularization penalty of {@link
+   * #DEFAULT_REGULARIZATION_PENALTY}
+   *
+   * @param tf the TensorFlow Ops
+   */
+  public L2(Ops tf, Class<R> type) {
+    this(tf, DEFAULT_REGULARIZATION_PENALTY, type);
+  }
+
+  /**
+   * Create a regularizer that applies an L1 regularization penalty
+   *
+   * @param tf the TensorFlow Ops
+   * @param l2 the L2 regularization penalty
+   * @throws IllegalArgumentException if the l2 regularization factor is NaN or is infinite.
+   */
+  public L2(Ops tf, float l2, Class<R> type) {
+    super(tf, null, l2, type);
+  }
+}

tensorflow-framework/src/main/java/org/tensorflow/framework/regularizers/Regularizer.java

@@ -0,0 +1,92 @@
+/* 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.regularizers;
+
+import org.tensorflow.Operand;
+import org.tensorflow.framework.losses.Loss;
+import org.tensorflow.op.Ops;
+import org.tensorflow.types.family.TNumber;
+
+/**
+ * Base class for Regularizers
+ *
+ * <p>Regularizers allow you to apply penalties on layer parameters or layer activity during
+ * optimization. These penalties are summed into the loss function that the network optimizes.
+ *
+ * @param <R> the data type of the operands and result
+ */
+public abstract class Regularizer<R extends TNumber> {
+
+  public static final float DEFAULT_REGULARIZATION_PENALTY = 0.01f;
+
+  private final Ops tf;
+  private final String name;
+  protected Class<R> type;
+
+  /**
+   * Creates a Regularizer
+   *
+   * @param tf the TensorFlow ops.
+   */
+  protected Regularizer(Ops tf, Class<R> type) {
+    this(tf, null, type);
+  }
+  /**
+   * Creates a Regularizer
+   *
+   * @param tf the TensorFlow ops.
+   */
+  protected Regularizer(Ops tf, String name, Class<R> type) {
+    this.tf = tf;
+    this.type = type;
+    this.name = name == null ? this.getClass().getSimpleName() : name;
+  }
+
+  /**
+   * Returns this Regularizer as a Loss This is a convenience to use regularize a loss. Only
+   * sampleWeights are applied to the regularizer.
+   *
+   * @return this Regularizer as a Loss
+   */
+  public Loss asLoss() {
+    return new RegularizerLoss<>(this.tf, this);
+  }
+
+  /**
+   * Computes a regularization penalty from an input.
+   *
+   * @param input the weighted input
+   * @return the result of computing the regularization penalty
+   */
+  public abstract Operand<R> call(Operand<R> input);
+
+  /**
+   * Gets the TensorFlow Ops
+   *
+   * @return the TensorFlow Ops
+   */
+  public Ops getTF() {
+    return tf;
+  }
+
+  /**
+   * Gets the name for this regularizer
+   *
+   * @return the name for this regularizer
+   */
+  public String getName() {
+    return name;
+  }
+}