Update docs links to new website URLs [WIP] (#452)
* Update docs links to new website URLs Signed-off-by: Alex Black <blacka101@gmail.com> * One more link Signed-off-by: Alex Black <blacka101@gmail.com>master
Alex Black
GitHub
parent
e2cd461578
commit
872a511042
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@ -21,7 +21,7 @@ package org.deeplearning4j.nn.modelimport.keras.exceptions;
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* Indicates that user is attempting to import a Keras model configuration that
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* is malformed or invalid in some other way.
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*
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* See <a href="https://deeplearning4j.org/docs/latest/keras-import-overview">https://deeplearning4j.org/docs/latest/keras-import-overview</a> for more information.
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* See <a href="https://deeplearning4j.konduit.ai/keras-import/overview">https://deeplearning4j.konduit.ai/keras-import/overview</a> for more information.
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*
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* @author dave@skymind.io
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*/
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@ -40,6 +40,6 @@ public class InvalidKerasConfigurationException extends Exception {
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}
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private static String appendDocumentationURL(String message) {
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return message + ". For more information, see http://deeplearning4j.org/docs/latest/keras-import-overview";
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return message + ". For more information, see https://deeplearning4j.konduit.ai/keras-import/overview";
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}
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}
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@ -21,7 +21,7 @@ package org.deeplearning4j.nn.modelimport.keras.exceptions;
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* Indicates that user is attempting to import a Keras model configuration that
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* is not currently supported.
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*
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* See <a href="https://deeplearning4j.org/docs/latest/keras-import-overview">https://deeplearning4j.org/docs/latest/keras-import-overview</a>
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* See <a href="https://deeplearning4j.konduit.ai/keras-import/overview">https://deeplearning4j.konduit.ai/keras-import/overview</a>
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* for more information and file an issue at <a href="https://github.com/eclipse/deeplearning4j/issues">https://github.com/eclipse/deeplearning4j/issues</a>.
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*
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* @author dave@skymind.io
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@ -103,7 +103,7 @@ public class KerasEmbedding extends KerasLayer {
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"on Embedding layers. Zero Masking for the Embedding layer only works with unidirectional LSTM for now."
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+ " If you want to have this behaviour for your imported model " +
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"in DL4J, apply masking as a pre-processing step to your input." +
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"See http://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent#masking for more on this.");
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"See https://deeplearning4j.konduit.ai/models/recurrent#masking-one-to-many-many-to-one-and-sequence-classification for more on this.");
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IWeightInit init = getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_EMBEDDING_INIT(),
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enforceTrainingConfig, conf, kerasMajorVersion);
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@ -17,10 +17,10 @@
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package org.deeplearning4j.nn.conf;
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/**
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* Workspace mode to use. See <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces">https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces</a><br>
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* Workspace mode to use. See <a href="https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces">https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces</a><br>
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* <br>
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* NONE: No workspaces will be used for the network. Highest memory use, least performance.<br>
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* ENABLED: Use workspaces.<br>
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* ENABLED: Use workspaces. This is the default and should almost always be used<br>
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* SINGLE: Deprecated. Now equivalent to ENABLED, which should be used instead.<br>
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* SEPARATE: Deprecated. Now equivalent to ENABLED, which sohuld be used instead.<br>
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*
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@ -38,7 +38,7 @@ import java.util.Map;
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/**
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* LSTM recurrent neural network layer without peephole connections. Supports CuDNN acceleration - see <a
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* href="https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn">https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn</a> for details
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* href="https://deeplearning4j.konduit.ai/config/backends/config-cudnn">https://deeplearning4j.konduit.ai/config/backends/config-cudnn</a> for details
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*
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* @author Alex Black
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* @see GravesLSTM GravesLSTM class for an alternative LSTM (with peephole connections)
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@ -1540,8 +1540,8 @@ public class ComputationGraph implements Serializable, Model, NeuralNetwork {
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* (not) clearing the layer input arrays.<br>
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* Note: this method should NOT be used with clearInputs = true, unless you know what you are doing. Specifically:
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* when using clearInputs=false, in combination with workspaces, the layer input fields may leak outside of the
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* workspaces in which they were defined - potentially causing a crash. See <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces">
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* https://deeplearning4j.org/docs/latest/deeplearning4j-config-workspaces</a>
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* workspaces in which they were defined - potentially causing a crash. See <a href="https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces">
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* https://deeplearning4j.konduit.ai/config/config-memory/config-workspaces</a>
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* for more details
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*
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* @param input An array of ComputationGraph inputs
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@ -86,7 +86,7 @@ public class ConvolutionLayer extends BaseLayer<org.deeplearning4j.nn.conf.layer
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} else {
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OneTimeLogger.info(log, "cuDNN not found: "
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+ "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
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+ "For more information, please refer to: https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn", t);
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+ "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
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}
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}
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} else if("CPU".equalsIgnoreCase(backend)){
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@ -78,7 +78,7 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
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} else {
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OneTimeLogger.info(log, "cuDNN not found: "
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+ "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
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+ "For more information, please refer to: https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn", t);
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+ "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
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}
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}
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} else if("CPU".equalsIgnoreCase(backend) ){
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@ -86,7 +86,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
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} else {
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OneTimeLogger.info(log, "cuDNN not found: "
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+ "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
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+ "For more information, please refer to: https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn", t);
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+ "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
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}
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}
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} else if("CPU".equalsIgnoreCase(backend)){
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@ -96,7 +96,7 @@ public class LocalResponseNormalization
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} else {
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OneTimeLogger.info(log, "cuDNN not found: "
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+ "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
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+ "For more information, please refer to: https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn", t);
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+ "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
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}
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}
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}
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@ -32,7 +32,7 @@ import java.util.Map;
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/**
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*
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* RNN tutorial: https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent
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* RNN tutorial: https://deeplearning4j.konduit.ai/models/recurrent
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* READ THIS FIRST
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*
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* Bdirectional LSTM layer implementation.
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@ -71,7 +71,7 @@ public class LSTM extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.layers.L
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} else {
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OneTimeLogger.info(log, "cuDNN not found: "
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+ "use cuDNN for better GPU performance by including the deeplearning4j-cuda module. "
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+ "For more information, please refer to: https://deeplearning4j.org/docs/latest/deeplearning4j-config-cudnn", t);
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+ "For more information, please refer to: https://deeplearning4j.konduit.ai/config/backends/config-cudnn", t);
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}
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}
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}
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@ -52,8 +52,8 @@ import static org.nd4j.linalg.indexing.NDArrayIndex.*;
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/**
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*
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* RNN tutorial: <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent">https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent</a>
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* READ THIS FIRST if you want to understand what the heck is happening here.
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* RNN tutorial: <a href="https://deeplearning4j.konduit.ai/models/recurrent">https://deeplearning4j.konduit.ai/models/recurrent</a>
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* READ THIS FIRST if you want to understand this code.
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*
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* Shared code for the standard "forwards" LSTM RNN and the bidirectional LSTM RNN
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* This was extracted from GravesLSTM and refactored into static helper functions. The general reasoning for this was
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@ -826,7 +826,7 @@ public class ParallelWrapper implements AutoCloseable {
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/**
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* This method allows you to specify training mode for this instance of PW.<br>
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* 1) AVERAGING - stands for parameters averaging. Each X epochs weights and updaters state will be averaged across all models<br>
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* 2) SHARED_GRADIENTS - stands for gradients sharing - more details available here: <a href="https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-intro">https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-intro</a><br>
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* 2) SHARED_GRADIENTS - stands for gradients sharing - more details available here: <a href="https://deeplearning4j.konduit.ai/distributed-deep-learning/intro">https://deeplearning4j.konduit.ai/distributed-deep-learning/intro</a><br>
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* 3) CUSTOM - this method allows you to specify custom gradients accumulator, this giving you better control of configuration params for training.<br>
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*
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* @param mode
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@ -71,7 +71,7 @@ public class SparkUtils {
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+ "for ND4J INDArrays.\nWhen using Kryo, An appropriate Kryo registrator must be used to avoid"
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+ " serialization issues (NullPointerException) with off-heap data in INDArrays.\n"
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+ "Use nd4j-kryo_2.10 or _2.11 artifact, with sparkConf.set(\"spark.kryo.registrator\", \"org.nd4j.kryo.Nd4jRegistrator\");\n"
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+ "See https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-howto#kryo for more details";
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+ "See https://deeplearning4j.konduit.ai/distributed-deep-learning/howto#how-to-use-kryo-serialization-with-dl-4-j-and-nd-4-j for more details";
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private static String sparkExecutorId;
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@ -108,7 +108,7 @@ public class TestSparkDl4jMultiLayer extends BaseSparkTest {
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.activation(Activation.SOFTMAX).nIn(100).nOut(10).build())
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.build();
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//Configuration for Spark training: see https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-howto for explanation of these configuration options
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//Configuration for Spark training: see https://deeplearning4j.konduit.ai/distributed-deep-learning/howto for explanation of these configuration options
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TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(batchSizePerWorker)
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.averagingFrequency(2)
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@ -195,7 +195,7 @@ public class CharacterIterator implements DataSetIterator {
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// dimension 0 = number of examples in minibatch
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// dimension 1 = size of each vector (i.e., number of characters)
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// dimension 2 = length of each time series/example
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//Why 'f' order here? See https://deeplearning4j.org/docs/latest/deeplearning4j-nn-recurrent data section "Alternative: Implementing a custom DataSetIterator"
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//Why 'f' order here? See https://deeplearning4j.konduit.ai/models/recurrent data section "Alternative: Implementing a custom DataSetIterator"
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INDArray input = Nd4j.create(new int[]{currMinibatchSize, validCharacters.length, exampleLength}, 'f');
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INDArray labels = Nd4j.create(new int[]{currMinibatchSize, validCharacters.length, exampleLength}, 'f');
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@ -61,7 +61,7 @@ import java.util.*;
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@Slf4j
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public abstract class DefaultOpExecutioner implements OpExecutioner {
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private static final String SCOPE_PANIC_MSG = "For more details, see the ND4J User Guide: deeplearning4j.org/docs/latest/nd4j-overview#workspaces-panic";
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private static final String SCOPE_PANIC_MSG = "For more details, see the ND4J User Guide: https://deeplearning4j.konduit.ai/nd4j/overview#workspaces-scope-panic";
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protected ProfilingMode profilingMode = ProfilingMode.SCOPE_PANIC;
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