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JimClarke5rnett
JimClarke5
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rnett
authoredFeb 1, 2021
Metrics Phase 1 (#180)
* Initial checkin * Initial checkin and sync with master * Initial checkin and sync with master * JavaDoc cleanup * Javadoc fixes * Change LossInterface to LossMetric. Fix JavaDoc, modify one line code block to include braces. * Removed hashmap for variables, they are not needed as the variables only live within a single instance of a Metric. * reformat code * Add tests for assertBroadcastable * Change type to resultType * Added V data type for sampleWeights so that it is not forced to be the same type as the return or internal variables, * change 'type' to 'resultType' * clean up mean and fix assert assertBroadcastable * fix error message * Change sampleWeights to have its own generic type <S extends TNumber> * Add commment about invalid tests expecting IllegalArgumentExceptions * Add this exception instead of the more generic IllegalArgumentException when static shapes cannot boradcast. * change IllegalArgumentException to NotBroadcastableException. change hasValidNonscalarShape to canBroadcastNonscalarShapes change hasValidNonscalarShape to canBroadcastNonscalarShapes * reformat code * Fis=x Javadoc move the dynamic shapes and rank down to the dynamic section so they are created needlessly when static Fix if statement to check for unknown size and unknown dimensions * Fix Reduce to use boradcastWeights, renamed WeightBroadcastTest to AssertBroadcastableTest and added BroadcastWeightsTest * Added comment to count to indicate that it may be weighted. * Added SetsOps and fixed AssertBroadcastable to use SetsOps methods, * Fixed based on various PR comments. * Deleted, no longer needed after change to Variable handling in Metrics. * Nicer error messages for mode-forbidden ops (#169) * start fobbiden ops checks Signed-off-by: Ryan Nett <rnett@calpoly.edu> * fix style Signed-off-by: Ryan Nett <rnett@calpoly.edu> * move checks to builder method Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Initialization imprvements (#178) * No-op on initAdd in eager mode Signed-off-by: Ryan Nett <rnett@calpoly.edu> * runInit() method in session Signed-off-by: Ryan Nett <rnett@calpoly.edu> * add doInitialization() to Runner Signed-off-by: Ryan Nett <rnett@calpoly.edu> * fix javadoc Signed-off-by: Ryan Nett <rnett@calpoly.edu> * assume only graph or eager environments Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Remove doInit(), update javadocs Signed-off-by: Ryan Nett <rnett@calpoly.edu> * small fixes Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Clairify tensorOf lifetime requirements (#190) * Clairify tensorOf lifetime requirements Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Do codegen Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Remove extra generics from op generation (#193) * Successfully remove extra type params, but it broke javadoc generation Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Generate covariant types Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Do generation Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Update help text. Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Fixes Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Add Java 11 support - Initial Phase (#185) * Add profile for JDK11 and Automatic-Module-Name to jars * add maven.compiler.release=11 * Update manual ops for new codegen (#196) Signed-off-by: Ryan Nett <rnett@calpoly.edu> * Fix Losses to use CHANNELS_FIRST/LAST for CategoricalCrossentropy * Fix SetOps to properly convert sparse tensor to dense tensor using tf.sparse.sparseToDense with the output of tf.sparse.denseToDenseSetOperation * Initial checkin * Initial checkin and sync with master * Initial checkin and sync with master * JavaDoc cleanup * Javadoc fixes * Change LossInterface to LossMetric. Fix JavaDoc, modify one line code block to include braces. * Removed hashmap for variables, they are not needed as the variables only live within a single instance of a Metric. * reformat code * Add tests for assertBroadcastable * Change type to resultType * Added V data type for sampleWeights so that it is not forced to be the same type as the return or internal variables, * change 'type' to 'resultType' * clean up mean and fix assert assertBroadcastable * fix error message * Change sampleWeights to have its own generic type <S extends TNumber> * Add commment about invalid tests expecting IllegalArgumentExceptions * Add this exception instead of the more generic IllegalArgumentException when static shapes cannot boradcast. * change IllegalArgumentException to NotBroadcastableException. change hasValidNonscalarShape to canBroadcastNonscalarShapes change hasValidNonscalarShape to canBroadcastNonscalarShapes * reformat code * Fis=x Javadoc move the dynamic shapes and rank down to the dynamic section so they are created needlessly when static Fix if statement to check for unknown size and unknown dimensions * Fix Reduce to use boradcastWeights, renamed WeightBroadcastTest to AssertBroadcastableTest and added BroadcastWeightsTest * Added comment to count to indicate that it may be weighted. * Added SetsOps and fixed AssertBroadcastable to use SetsOps methods, * Fixed based on various PR comments. * Deleted, no longer needed after change to Variable handling in Metrics. * Fix Losses to use CHANNELS_FIRST/LAST for CategoricalCrossentropy * Fix SetOps to properly convert sparse tensor to dense tensor using tf.sparse.sparseToDense with the output of tf.sparse.denseToDenseSetOperation Co-authored-by: Ryan Nett <rnett@calpoly.edu>
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Diff for: ‎tensorflow-framework/src/main/java/org/tensorflow/framework/losses/CategoricalCrossentropy.java

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@@ -69,7 +69,7 @@
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public class CategoricalCrossentropy extends Loss {
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public static final boolean FROM_LOGITS_DEFAULT = false;
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public static final float LABEL_SMOOTHING_DEFAULT = 0.0f;
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public static final int DEFAULT_AXIS = -1;
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public static final int DEFAULT_AXIS = Losses.CHANNELS_LAST;
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private final boolean fromLogits;
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private final float labelSmoothing;
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* confidence on label values are relaxed. e.g. <code>labelSmoothing=0.2</code> means that we will use a
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* value of <code>0.1</code> for label <code>0</code> and <code>0.9</code> for label <code>1</code>
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* @param reduction Type of Reduction to apply to loss.
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* @param axis The channels axis. <code>axis=-1</code> corresponds to data format `Channels Last'
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* and <code>axis=1</code> corresponds to data format 'Channels First'.
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* @param axis The channels axis. <code>axis=-1</code> corresponds to data format "Channels Last"
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* and <code>axis=1</code> corresponds to data format "Channels First".
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* {@link Losses#CHANNELS_LAST} and {@link Losses#CHANNELS_FIRST}
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* @throws IllegalArgumentException if labelSmoothing is not in the inclusive range of 0. - 1.
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*/
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public CategoricalCrossentropy(

Diff for: ‎tensorflow-framework/src/main/java/org/tensorflow/framework/losses/Losses.java

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@@ -36,6 +36,9 @@ public class Losses {
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/** Default Fuzz factor. */
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public static final float EPSILON = 1e-7f;
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public static final int CHANNELS_LAST = -1;
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public static final int CHANNELS_FIRST = 1;
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/**
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* Calculates the mean absolute error between labels and predictions.
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*
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tLabels = smoothCategoricalLabels(tf, tLabels, labelSmoothing);
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}
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if (fromLogits) {
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return tf.nn.softmaxCrossEntropyWithLogits(tLabels, predictions, -1);
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return tf.nn.softmaxCrossEntropyWithLogits(tLabels, predictions, axis);
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}
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/* TODO
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if (!(predictions instanceof Variable) && (!tf.scope().env().isEager())) {

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