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| <h1 class="title">Beginner's Guide for Keras2DML users</h1> |
| |
| |
| <!-- |
| |
| --> |
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| <ul id="markdown-toc"> |
| <li><a href="#introduction" id="markdown-toc-introduction">Introduction</a> <ul> |
| <li><a href="#getting-started" id="markdown-toc-getting-started">Getting Started</a></li> |
| <li><a href="#model-conversion" id="markdown-toc-model-conversion">Model Conversion</a></li> |
| </ul> |
| </li> |
| <li><a href="#frequently-asked-questions" id="markdown-toc-frequently-asked-questions">Frequently asked questions</a> <ul> |
| <li><a href="#what-is-the-mapping-between-keras-parameters-and-caffes-solver-specification-" id="markdown-toc-what-is-the-mapping-between-keras-parameters-and-caffes-solver-specification-">What is the mapping between Keras’ parameters and Caffe’s solver specification ?</a></li> |
| <li><a href="#how-do-i-specify-the-batch-size-and-the-number-of-epochs-" id="markdown-toc-how-do-i-specify-the-batch-size-and-the-number-of-epochs-">How do I specify the batch size and the number of epochs ?</a></li> |
| <li><a href="#what-optimizer-and-loss-does-keras2dml-use-by-default-if-kerasmodel-is-not-compiled-" id="markdown-toc-what-optimizer-and-loss-does-keras2dml-use-by-default-if-kerasmodel-is-not-compiled-">What optimizer and loss does Keras2DML use by default if <code>keras_model</code> is not compiled ?</a></li> |
| <li><a href="#what-is-the-learning-rate-schedule-used-" id="markdown-toc-what-is-the-learning-rate-schedule-used-">What is the learning rate schedule used ?</a></li> |
| <li><a href="#how-to-set-the-size-of-the-validation-dataset-" id="markdown-toc-how-to-set-the-size-of-the-validation-dataset-">How to set the size of the validation dataset ?</a></li> |
| <li><a href="#how-to-monitor-loss-via-command-line-" id="markdown-toc-how-to-monitor-loss-via-command-line-">How to monitor loss via command-line ?</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| <p><br /></p> |
| |
| <h2 id="introduction">Introduction</h2> |
| |
| <p>Keras2DML is an <strong>experimental API</strong> that converts a Keras specification to DML through the intermediate Caffe2DML module. |
| It is designed to fit well into the mllearn framework and hence supports NumPy, Pandas as well as PySpark DataFrame.</p> |
| |
| <h3 id="getting-started">Getting Started</h3> |
| |
| <p>To create a Keras2DML object, one needs to create a Keras model through the Funcitonal API. please see the <a href="https://keras.io/models/model/">Functional API.</a> |
| This module utilizes the existing <a href="beginners-guide-caffe2dml">Caffe2DML</a> backend to convert Keras models into DML. Keras models are |
| parsed and translated into Caffe prototext and caffemodel files which are then piped into Caffe2DML. Thus one can follow the Caffe2DML |
| documentation for further information.</p> |
| |
| <h3 id="model-conversion">Model Conversion</h3> |
| |
| <p>Keras models are parsed based on their layer structure and corresponding weights and translated into the relative Caffe layer and weight |
| configuration. Be aware that currently this is a translation into Caffe and there will be loss of information from keras models such as |
| intializer information, and other layers which do not exist in Caffe.</p> |
| |
| <p>To create a Keras2DML object, simply pass the keras object to the Keras2DML constructor. It’s also important to note that your models |
| should be compiled so that the loss can be accessed for Caffe2DML</p> |
| |
| <p>```python |
| from systemml.mllearn import Keras2DML |
| import keras |
| from keras.applications.resnet50 import preprocess_input, decode_predictions, ResNet50</p> |
| |
| <p>keras_model = ResNet50(weights=’imagenet’,include_top=True,pooling=’None’,input_shape=(224,224,3)) |
| keras_model.compile(optimizer=’sgd’, loss= ‘categorical_crossentropy’)</p> |
| |
| <p>sysml_model = Keras2DML(spark, keras_model,input_shape=(3,224,224)) |
| sysml_model.summary() |
| ```</p> |
| |
| <h1 id="frequently-asked-questions">Frequently asked questions</h1> |
| |
| <h4 id="what-is-the-mapping-between-keras-parameters-and-caffes-solver-specification-">What is the mapping between Keras’ parameters and Caffe’s solver specification ?</h4> |
| |
| <table> |
| <thead> |
| <tr> |
| <th> </th> |
| <th>Specified via the given parameter in the Keras2DML constructor</th> |
| <th>From input Keras’ model</th> |
| <th>Corresponding parameter in the Caffe solver file</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td>Solver type</td> |
| <td> </td> |
| <td><code>type(keras_model.optimizer)</code>. Supported types: <code>keras.optimizers.{SGD, Adagrad, Adam}</code></td> |
| <td><code>type</code></td> |
| </tr> |
| <tr> |
| <td>Maximum number of iterations</td> |
| <td><code>max_iter</code></td> |
| <td>The <code>epoch</code> parameter in the <code>fit</code> method is not supported.</td> |
| <td><code>max_iter</code></td> |
| </tr> |
| <tr> |
| <td>Validation dataset</td> |
| <td><code>test_iter</code> (explained in the below section)</td> |
| <td>The <code>validation_data</code> parameter in the <code>fit</code> method is not supported.</td> |
| <td><code>test_iter</code></td> |
| </tr> |
| <tr> |
| <td>Monitoring the loss</td> |
| <td><code>display, test_interval</code> (explained in the below section)</td> |
| <td>The <code>LossHistory</code> callback in the <code>fit</code> method is not supported.</td> |
| <td><code>display, test_interval</code></td> |
| </tr> |
| <tr> |
| <td>Learning rate schedule</td> |
| <td><code>lr_policy</code></td> |
| <td>The <code>LearningRateScheduler</code> callback in the <code>fit</code> method is not supported.</td> |
| <td><code>lr_policy</code> (default: step)</td> |
| </tr> |
| <tr> |
| <td>Base learning rate</td> |
| <td> </td> |
| <td><code>keras_model.optimizer.lr</code></td> |
| <td><code>base_lr</code></td> |
| </tr> |
| <tr> |
| <td>Learning rate decay over each update</td> |
| <td> </td> |
| <td><code>keras_model.optimizer.decay</code></td> |
| <td><code>gamma</code></td> |
| </tr> |
| <tr> |
| <td>Global regularizer to use for all layers</td> |
| <td><code>regularization_type,weight_decay</code></td> |
| <td>The current version of Keras2DML doesnot support custom regularizers per layer.</td> |
| <td><code>regularization_type,weight_decay</code></td> |
| </tr> |
| <tr> |
| <td>If type of the optimizer is <code>keras.optimizers.SGD</code></td> |
| <td> </td> |
| <td><code>momentum, nesterov</code></td> |
| <td><code>momentum, type</code></td> |
| </tr> |
| <tr> |
| <td>If type of the optimizer is <code>keras.optimizers.Adam</code></td> |
| <td> </td> |
| <td><code>beta_1, beta_2, epsilon</code>. The parameter <code>amsgrad</code> is not supported.</td> |
| <td><code>momentum, momentum2, delta</code></td> |
| </tr> |
| <tr> |
| <td>If type of the optimizer is <code>keras.optimizers.Adagrad</code></td> |
| <td> </td> |
| <td><code>epsilon</code></td> |
| <td><code>delta</code></td> |
| </tr> |
| </tbody> |
| </table> |
| |
| <h4 id="how-do-i-specify-the-batch-size-and-the-number-of-epochs-">How do I specify the batch size and the number of epochs ?</h4> |
| |
| <p>Since Keras2DML is a mllearn API, it doesnot accept the batch size and number of epochs as the parameter in the <code>fit</code> method. |
| Instead, these parameters are passed via <code>batch_size</code> and <code>max_iter</code> parameters in the Keras2DML constructor. |
| For example, the equivalent Python code for <code>keras_model.fit(features, labels, epochs=10, batch_size=64)</code> is as follows:</p> |
| |
| <p><code>python |
| from systemml.mllearn import Keras2DML |
| epochs = 10 |
| batch_size = 64 |
| num_samples = features.shape[0] |
| max_iter = int(epochs*math.ceil(num_samples/batch_size)) |
| sysml_model = Keras2DML(spark, keras_model, batch_size=batch_size, max_iter=max_iter, ...) |
| sysml_model.fit(features, labels) |
| </code></p> |
| |
| <h4 id="what-optimizer-and-loss-does-keras2dml-use-by-default-if-kerasmodel-is-not-compiled-">What optimizer and loss does Keras2DML use by default if <code>keras_model</code> is not compiled ?</h4> |
| |
| <p>If the user does not <code>compile</code> the keras model, then we use cross entropy loss and SGD optimizer with nesterov momentum:</p> |
| |
| <p><code>python |
| keras_model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.SGD(lr=0.01, momentum=0.95, decay=5e-4, nesterov=True)) |
| </code></p> |
| |
| <h4 id="what-is-the-learning-rate-schedule-used-">What is the learning rate schedule used ?</h4> |
| |
| <p>Keras2DML does not support the <code>LearningRateScheduler</code> callback. |
| Instead one can set the custom learning rate schedule to one of the following schedules by using the <code>lr_policy</code> parameter of the constructor: |
| - <code>step</code>: return <code>base_lr * gamma ^ (floor(iter / step))</code> (default schedule) |
| - <code>fixed</code>: always return <code>base_lr</code>. |
| - <code>exp</code>: return <code>base_lr * gamma ^ iter</code> |
| - <code>inv</code>: return <code>base_lr * (1 + gamma * iter) ^ (- power)</code> |
| - <code>poly</code>: the effective learning rate follows a polynomial decay, to be zero by the max_iter. return <code>base_lr (1 - iter/max_iter) ^ (power)</code> |
| - <code>sigmoid</code>: the effective learning rate follows a sigmod decay return b<code>ase_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))</code></p> |
| |
| <h4 id="how-to-set-the-size-of-the-validation-dataset-">How to set the size of the validation dataset ?</h4> |
| |
| <p>The size of the validation dataset is determined by the parameters <code>test_iter</code> and the batch size. For example: If the batch size is 64 and |
| <code>test_iter</code> is set to 10 in the <code>Keras2DML</code>’s constructor, then the validation size is 640. This setting generates following DML code internally:</p> |
| |
| <p><code>python |
| num_images = nrow(y_full) |
| BATCH_SIZE = 64 |
| num_validation = 10 * BATCH_SIZE |
| X = X_full[(num_validation+1):num_images,]; y = y_full[(num_validation+1):num_images,] |
| X_val = X_full[1:num_validation,]; y_val = y_full[1:num_validation,] |
| num_images = nrow(y) |
| </code></p> |
| |
| <h4 id="how-to-monitor-loss-via-command-line-">How to monitor loss via command-line ?</h4> |
| |
| <p>To monitor loss, please set the parameters <code>display</code>, <code>test_iter</code> and <code>test_interval</code> in the <code>Keras2DML</code>’s constructor.<br /> |
| For example: for the expression <code>Keras2DML(..., display=100, test_iter=10, test_interval=500)</code>, we |
| - display the training loss and accuracy every 100 iterations and |
| - carry out validation every 500 training iterations and display validation loss and accuracy.</p> |
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