Repository: systemml Updated Branches: refs/heads/gh-pages b2e1e18f1 -> 9873b3f7e
[MINOR] [DOC] Updated the deep learning documentation - Scale the input features for training lenet using Keras. - Added table to highlight differences between three APIs. Project: http://git-wip-us.apache.org/repos/asf/systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/9873b3f7 Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/9873b3f7 Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/9873b3f7 Branch: refs/heads/gh-pages Commit: 9873b3f7e3d44297bcbc6fbff67e858ae1be10a0 Parents: b2e1e18 Author: Niketan Pansare <npan...@us.ibm.com> Authored: Wed Nov 29 15:08:18 2017 -0800 Committer: Niketan Pansare <npan...@us.ibm.com> Committed: Wed Nov 29 15:08:18 2017 -0800 ---------------------------------------------------------------------- deep-learning.md | 65 +++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 63 insertions(+), 2 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/systemml/blob/9873b3f7/deep-learning.md ---------------------------------------------------------------------- diff --git a/deep-learning.md b/deep-learning.md index 23084a5..64efe4c 100644 --- a/deep-learning.md +++ b/deep-learning.md @@ -27,11 +27,67 @@ limitations under the License. <br/> +# Introduction + There are three different ways to implement a Deep Learning model in SystemML: 1. Using the [DML-bodied NN library](https://github.com/apache/systemml/tree/master/scripts/nn): This library allows the user to exploit full flexibility of [DML language](http://apache.github.io/systemml/dml-language-reference) to implement your neural network. 2. Using the experimental [Caffe2DML API](http://apache.github.io/systemml/beginners-guide-caffe2dml.html): This API allows a model expressed in Caffe's proto format to be imported into SystemML. This API **doesnot** require Caffe to be installed on your SystemML. -3. Using the experimental [Keras2DML API](http://apache.github.io/systemml/beginners-guide-keras2dml.html): This API allows a model expressed in Keras to be imported into SystemML. However, this API requires Keras to be installed on your driver. +3. Using the experimental [Keras2DML API](http://apache.github.io/systemml/beginners-guide-keras2dml.html): This API allows a model expressed in Keras's functional API to be imported into SystemML. However, this API requires Keras to be installed on your driver. + +| | NN library | Caffe2DML | Keras2DML | +|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------| +| External dependency | None | None | Keras | +| Ability to add custom layers | Yes | No | No | +| The user needs to know | [DML](http://apache.github.io/systemml/dml-language-reference) | [Caffe's proto API](http://apache.github.io/systemml/reference-guide-caffe2dml#layers-supported-in-caffe2dml) | [Keras' functional API](https://keras.io/getting-started/functional-api-guide/) | +| Can be invoked using pyspark | Yes. Please see [Python MLContext API](http://apache.github.io/systemml/spark-mlcontext-programming-guide) | Yes. | Yes. | +| Can be invoked using spark-shell | Yes. Please see [Scala MLContext API](http://apache.github.io/systemml/spark-mlcontext-programming-guide) | Limited support | No | +| Can be invoked via command-line or JMLC API | Yes | No | No | +| GPU and [native BLAS](http://apache.github.io/systemml/native-backend.html) support | Yes | Yes | Yes | +| Part of SystemML's [mllearn](http://apache.github.io/systemml/python-reference.html#mllearn-api) API | No | Yes | Yes | + +## mllearn API + +Before we go any further, let us briefly discuss the training and prediction functions in the mllearn API (i.e. Caffe2DML and Keras2DML). +### Training functions + +<div class="codetabs"> +<div data-lang="sklearn way" markdown="1"> +{% highlight python %} +# Input: Two Python objects (X_train, y_train) of type numpy, pandas or scipy. +model.fit(X_train, y_train) +{% endhighlight %} +</div> +<div data-lang="mllib way" markdown="1"> +{% highlight python %} +# Input: One LabeledPoint DataFrame with atleast two columns: features (of type Vector) and labels. +model.fit(X_df) +{% endhighlight %} +</div> +</div> + +### Prediction functions + +<div class="codetabs"> +<div data-lang="sklearn way" markdown="1"> +{% highlight python %} +# Input: One Python object (X_test) of type numpy, pandas or scipy. +model.predict(X_test) +# OR model.score(X_test, y_test) +{% endhighlight %} +</div> +<div data-lang="mllib way" markdown="1"> +{% highlight python %} +# Input: One LabeledPoint DataFrame (df_test) with atleast one column: features (of type Vector). +model.transform(df_test) +{% endhighlight %} +</div> +</div> + +Please note that when training using mllearn API (i.e. `model.fit(X_df)`), SystemML +expects that labels have been converted to 1-based value. +This avoids unnecessary decoding overhead for large dataset if the label columns has already been decoded. +For scikit-learn API, there is no such requirement. # Training Lenet on the MNIST dataset @@ -144,6 +200,11 @@ x = Dense(10, activation='softmax')(x) keras_model = Model(input_img, x) keras_model.summary() +# Scale the input features +scale = 0.00390625 +X_train = X_train*scale +X_test = X_test*scale + from systemml.mllearn import Keras2DML sysml_model = Keras2DML(spark, keras_model, input_shape=(1,28,28), weights='weights_dir') # sysml_model.setConfigProperty("sysml.native.blas", "auto") @@ -183,7 +244,7 @@ from keras.applications.resnet50 import preprocess_input, decode_predictions, Re model = ResNet50(weights='imagenet',include_top=True,pooling='None',input_shape=(224,224,3)) model.compile(optimizer='sgd', loss= 'categorical_crossentropy') -resnet = Keras2DML(spark,model,input_shape=(3,224,224), weights='tmp', labels='https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/imagenet/labels.txt') +resnet = Keras2DML(spark,model,input_shape=(3,224,224), weights='weights_dir', labels='https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/imagenet/labels.txt') resnet.summary() urllib.urlretrieve('https://upload.wikimedia.org/wikipedia/commons/f/f4/Cougar_sitting.jpg', 'test.jpg') img_shape = (3, 224, 224)
