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<h1 class="title">Deep Learning with SystemML</h1>
<!--
-->
<ul id="markdown-toc">
<li><a href="#introduction" id="markdown-toc-introduction">Introduction</a> <ul>
<li><a href="#mllearn-api" id="markdown-toc-mllearn-api">mllearn API</a> <ul>
<li><a href="#training-functions" id="markdown-toc-training-functions">Training functions</a></li>
<li><a href="#prediction-functions" id="markdown-toc-prediction-functions">Prediction functions</a></li>
</ul>
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<li><a href="#training-lenet-on-the-mnist-dataset" id="markdown-toc-training-lenet-on-the-mnist-dataset">Training Lenet on the MNIST dataset</a></li>
<li><a href="#prediction-using-a-pretrained-resnet-50" id="markdown-toc-prediction-using-a-pretrained-resnet-50">Prediction using a pretrained ResNet-50</a></li>
</ul>
<p><br /></p>
<h1 id="introduction">Introduction</h1>
<p>There are three different ways to implement a Deep Learning model in SystemML:
1. Using the <a href="https://github.com/apache/systemml/tree/master/scripts/nn">DML-bodied NN library</a>: This library allows the user to exploit full flexibility of <a href="http://apache.github.io/systemml/dml-language-reference">DML language</a> to implement your neural network.
2. Using the experimental <a href="http://apache.github.io/systemml/beginners-guide-caffe2dml.html">Caffe2DML API</a>: This API allows a model expressed in Caffe&#8217;s proto format to be imported into SystemML. This API <strong>doesnot</strong> require Caffe to be installed on your SystemML.
3. Using the experimental <a href="http://apache.github.io/systemml/beginners-guide-keras2dml.html">Keras2DML API</a>: This API allows a model expressed in Keras&#8217;s API to be imported into SystemML. However, this API requires Keras to be installed on your driver.</p>
<table>
<thead>
<tr>
<th>&#160;</th>
<th>NN library</th>
<th>Caffe2DML</th>
<th>Keras2DML</th>
</tr>
</thead>
<tbody>
<tr>
<td>External dependency</td>
<td>None</td>
<td>None</td>
<td>Keras</td>
</tr>
<tr>
<td>Ability to add custom layers</td>
<td>Yes</td>
<td>No</td>
<td>No</td>
</tr>
<tr>
<td>The user needs to know</td>
<td><a href="http://apache.github.io/systemml/dml-language-reference">DML</a></td>
<td><a href="http://apache.github.io/systemml/reference-guide-caffe2dml#layers-supported-in-caffe2dml">Caffe&#8217;s proto API</a></td>
<td><a href="https://keras.io/models/about-keras-models/">Keras&#8217; API</a></td>
</tr>
<tr>
<td>Can be invoked using pyspark</td>
<td>Yes. Please see <a href="http://apache.github.io/systemml/spark-mlcontext-programming-guide">Python MLContext API</a></td>
<td>Yes.</td>
<td>Yes.</td>
</tr>
<tr>
<td>Can be invoked using spark-shell</td>
<td>Yes. Please see <a href="http://apache.github.io/systemml/spark-mlcontext-programming-guide">Scala MLContext API</a></td>
<td>Limited support</td>
<td>No</td>
</tr>
<tr>
<td>Can be invoked via command-line or JMLC API</td>
<td>Yes</td>
<td>No</td>
<td>No</td>
</tr>
<tr>
<td>GPU and <a href="http://apache.github.io/systemml/native-backend.html">native BLAS</a> support</td>
<td>Yes</td>
<td>Yes</td>
<td>Yes</td>
</tr>
<tr>
<td>Part of SystemML&#8217;s <a href="http://apache.github.io/systemml/python-reference.html#mllearn-api">mllearn</a> API</td>
<td>No</td>
<td>Yes</td>
<td>Yes</td>
</tr>
</tbody>
</table>
<h2 id="mllearn-api">mllearn API</h2>
<p>Before we go any further, let us briefly discuss the training and prediction functions in the mllearn API (i.e. Caffe2DML and Keras2DML).</p>
<h3 id="training-functions">Training functions</h3>
<div class="codetabs">
<div data-lang="sklearn way">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Input: Two Python objects (X_train, y_train) of type numpy, pandas or scipy.</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span></code></pre></div>
</div>
<div data-lang="mllib way">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Input: One LabeledPoint DataFrame with atleast two columns: features (of type Vector) and labels.</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_df</span><span class="p">)</span></code></pre></div>
</div>
</div>
<h3 id="prediction-functions">Prediction functions</h3>
<div class="codetabs">
<div data-lang="sklearn way">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Input: One Python object (X_test) of type numpy, pandas or scipy.</span>
<span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c"># OR model.score(X_test, y_test)</span></code></pre></div>
</div>
<div data-lang="mllib way">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="c"># Input: One LabeledPoint DataFrame (df_test) with atleast one column: features (of type Vector).</span>
<span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_test</span><span class="p">)</span></code></pre></div>
</div>
</div>
<p>Please note that when training using mllearn API (i.e. <code>model.fit(X_df)</code>), 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.</p>
<h1 id="training-lenet-on-the-mnist-dataset">Training Lenet on the MNIST dataset</h1>
<p>Download the MNIST dataset using <a href="https://pypi.python.org/pypi/mlxtend">mlxtend package</a>.</p>
<p><code>python
from mlxtend.data import mnist_data
import numpy as np
from sklearn.utils import shuffle
# Download the MNIST dataset
X, y = mnist_data()
X, y = shuffle(X, y)
# Split the data into training and test
n_samples = len(X)
X_train = X[:int(.9 * n_samples)]
y_train = y[:int(.9 * n_samples)]
X_test = X[int(.9 * n_samples):]
y_test = y[int(.9 * n_samples):]
</code></p>
<div class="codetabs">
<div data-lang="NN library">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">systemml</span> <span class="kn">import</span> <span class="n">MLContext</span><span class="p">,</span> <span class="n">dml</span>
<span class="n">ml</span> <span class="o">=</span> <span class="n">MLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="n">ml</span><span class="o">.</span><span class="n">setStatistics</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="c"># ml.setConfigProperty("sysml.native.blas", "auto")</span>
<span class="c"># ml.setGPU(True).setForceGPU(True)</span>
<span class="n">script</span> <span class="o">=</span> <span class="s">"""
source("nn/examples/mnist_lenet.dml") as mnist_lenet
# Scale images to [-1,1], and one-hot encode the labels
images = (images / 255) * 2 - 1
n = nrow(images)
labels = table(seq(1, n), labels+1, n, 10)
# Split into training (4000 examples) and validation (4000 examples)
X = images[501:nrow(images),]
X_val = images[1:500,]
y = labels[501:nrow(images),]
y_val = labels[1:500,]
# Train the model to produce weights &amp; biases.
[W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, y, X_val, y_val, C, Hin, Win, epochs)
"""</span>
<span class="n">out</span> <span class="o">=</span> <span class="p">(</span><span class="s">'W1'</span><span class="p">,</span> <span class="s">'b1'</span><span class="p">,</span> <span class="s">'W2'</span><span class="p">,</span> <span class="s">'b2'</span><span class="p">,</span> <span class="s">'W3'</span><span class="p">,</span> <span class="s">'b3'</span><span class="p">,</span> <span class="s">'W4'</span><span class="p">,</span> <span class="s">'b4'</span><span class="p">)</span>
<span class="n">prog</span> <span class="o">=</span> <span class="p">(</span><span class="n">dml</span><span class="p">(</span><span class="n">script</span><span class="p">)</span><span class="o">.</span><span class="nb">input</span><span class="p">(</span><span class="n">images</span><span class="o">=</span><span class="n">X_train</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y_train</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">Hin</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span> <span class="n">Win</span><span class="o">=</span><span class="mi">28</span><span class="p">)</span>
<span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="o">*</span><span class="n">out</span><span class="p">))</span>
<span class="n">W1</span><span class="p">,</span> <span class="n">b1</span><span class="p">,</span> <span class="n">W2</span><span class="p">,</span> <span class="n">b2</span><span class="p">,</span> <span class="n">W3</span><span class="p">,</span> <span class="n">b3</span><span class="p">,</span> <span class="n">W4</span><span class="p">,</span> <span class="n">b4</span> <span class="o">=</span> <span class="n">ml</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="n">prog</span><span class="p">)</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="o">*</span><span class="n">out</span><span class="p">)</span>
<span class="n">script_predict</span> <span class="o">=</span> <span class="s">"""
source("nn/examples/mnist_lenet.dml") as mnist_lenet
# Scale images to [-1,1]
X_test = (X_test / 255) * 2 - 1
# Predict
y_prob = mnist_lenet::predict(X_test, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)
y_pred = rowIndexMax(y_prob) - 1
"""</span>
<span class="n">prog</span> <span class="o">=</span> <span class="p">(</span><span class="n">dml</span><span class="p">(</span><span class="n">script_predict</span><span class="p">)</span><span class="o">.</span><span class="nb">input</span><span class="p">(</span><span class="n">X_test</span><span class="o">=</span><span class="n">X_test</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">Hin</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span> <span class="n">Win</span><span class="o">=</span><span class="mi">28</span><span class="p">,</span> <span class="n">W1</span><span class="o">=</span><span class="n">W1</span><span class="p">,</span> <span class="n">b1</span><span class="o">=</span><span class="n">b1</span><span class="p">,</span>
<span class="n">W2</span><span class="o">=</span><span class="n">W2</span><span class="p">,</span> <span class="n">b2</span><span class="o">=</span><span class="n">b2</span><span class="p">,</span> <span class="n">W3</span><span class="o">=</span><span class="n">W3</span><span class="p">,</span> <span class="n">b3</span><span class="o">=</span><span class="n">b3</span><span class="p">,</span> <span class="n">W4</span><span class="o">=</span><span class="n">W4</span><span class="p">,</span> <span class="n">b4</span><span class="o">=</span><span class="n">b4</span><span class="p">)</span>
<span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="s">"y_pred"</span><span class="p">))</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">ml</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="n">prog</span><span class="p">)</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">"y_pred"</span><span class="p">)</span><span class="o">.</span><span class="n">toNumPy</span><span class="p">()</span></code></pre></div>
</div>
<div data-lang="Caffe2DML">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">systemml.mllearn</span> <span class="kn">import</span> <span class="n">Caffe2DML</span>
<span class="kn">import</span> <span class="nn">urllib</span>
<span class="c"># Download the Lenet network</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="s">'https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/mnist_lenet/lenet.proto'</span><span class="p">,</span> <span class="s">'lenet.proto'</span><span class="p">)</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="s">'https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/mnist_lenet/lenet_solver.proto'</span><span class="p">,</span> <span class="s">'lenet_solver.proto'</span><span class="p">)</span>
<span class="c"># Train Lenet On MNIST using scikit-learn like API</span>
<span class="c"># MNIST dataset contains 28 X 28 gray-scale (number of channel=1).</span>
<span class="n">lenet</span> <span class="o">=</span> <span class="n">Caffe2DML</span><span class="p">(</span><span class="n">spark</span><span class="p">,</span> <span class="n">solver</span><span class="o">=</span><span class="s">'lenet_solver.proto'</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">))</span>
<span class="n">lenet</span><span class="o">.</span><span class="n">setStatistics</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span>
<span class="c"># lenet.setConfigProperty("sysml.native.blas", "auto")</span>
<span class="c"># lenet.setGPU(True).setForceGPU(True)</span>
<span class="c"># Since Caffe2DML is a mllearn API, it allows for scikit-learn like method for training.</span>
<span class="n">lenet</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c"># Either perform prediction: lenet.predict(X_test) or scoring:</span>
<span class="n">lenet</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span></code></pre></div>
</div>
<div data-lang="Keras2DML">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="kn">import</span> <span class="n">Input</span><span class="p">,</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span><span class="n">Flatten</span>
<span class="kn">from</span> <span class="nn">keras</span> <span class="kn">import</span> <span class="n">backend</span> <span class="k">as</span> <span class="n">K</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="kn">import</span> <span class="n">Model</span>
<span class="n">input_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">)</span> <span class="k">if</span> <span class="n">K</span><span class="o">.</span><span class="n">image_data_format</span><span class="p">()</span> <span class="o">==</span> <span class="s">'channels_first'</span> <span class="k">else</span> <span class="p">(</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">keras_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s">'relu'</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="n">input_shape</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s">'same'</span><span class="p">))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">activation</span><span class="o">=</span><span class="s">'relu'</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s">'same'</span><span class="p">))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Flatten</span><span class="p">())</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s">'relu'</span><span class="p">))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.5</span><span class="p">))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s">'softmax'</span><span class="p">))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="c"># Scale the input features</span>
<span class="n">scale</span> <span class="o">=</span> <span class="mf">0.00390625</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">*</span><span class="n">scale</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X_test</span><span class="o">*</span><span class="n">scale</span>
<span class="kn">from</span> <span class="nn">systemml.mllearn</span> <span class="kn">import</span> <span class="n">Keras2DML</span>
<span class="n">sysml_model</span> <span class="o">=</span> <span class="n">Keras2DML</span><span class="p">(</span><span class="n">spark</span><span class="p">,</span> <span class="n">keras_model</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">28</span><span class="p">,</span><span class="mi">28</span><span class="p">),</span> <span class="n">weights</span><span class="o">=</span><span class="s">'weights_dir'</span><span class="p">)</span>
<span class="c"># sysml_model.setConfigProperty("sysml.native.blas", "auto")</span>
<span class="c"># sysml_model.setGPU(True).setForceGPU(True)</span>
<span class="n">sysml_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">sysml_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">sysml_model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span></code></pre></div>
</div>
</div>
<h1 id="prediction-using-a-pretrained-resnet-50">Prediction using a pretrained ResNet-50</h1>
<div class="codetabs">
<div data-lang="NN library">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">Will</span> <span class="n">be</span> <span class="n">added</span> <span class="n">soon</span> <span class="o">...</span></code></pre></div>
</div>
<div data-lang="Caffe2DML">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="n">Will</span> <span class="n">be</span> <span class="n">added</span> <span class="n">soon</span> <span class="o">...</span></code></pre></div>
</div>
<div data-lang="Keras2DML">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">systemml.mllearn</span> <span class="kn">import</span> <span class="n">Keras2DML</span>
<span class="kn">import</span> <span class="nn">systemml</span> <span class="k">as</span> <span class="n">sml</span>
<span class="kn">import</span> <span class="nn">keras</span><span class="p">,</span> <span class="n">urllib</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">keras.applications.resnet50</span> <span class="kn">import</span> <span class="n">preprocess_input</span><span class="p">,</span> <span class="n">decode_predictions</span><span class="p">,</span> <span class="n">ResNet50</span>
<span class="n">keras_model</span> <span class="o">=</span> <span class="n">ResNet50</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s">'imagenet'</span><span class="p">,</span><span class="n">include_top</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span><span class="n">pooling</span><span class="o">=</span><span class="s">'None'</span><span class="p">,</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
<span class="n">keras_model</span><span class="o">.</span><span class="nb">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s">'sgd'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span> <span class="s">'categorical_crossentropy'</span><span class="p">)</span>
<span class="n">sysml_model</span> <span class="o">=</span> <span class="n">Keras2DML</span><span class="p">(</span><span class="n">spark</span><span class="p">,</span><span class="n">keras_model</span><span class="p">,</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">),</span> <span class="n">weights</span><span class="o">=</span><span class="s">'weights_dir'</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="s">'https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/imagenet/labels.txt'</span><span class="p">)</span>
<span class="n">sysml_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
<span class="n">urllib</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="s">'https://upload.wikimedia.org/wikipedia/commons/f/f4/Cougar_sitting.jpg'</span><span class="p">,</span> <span class="s">'test.jpg'</span><span class="p">)</span>
<span class="n">img_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
<span class="n">input_image</span> <span class="o">=</span> <span class="n">sml</span><span class="o">.</span><span class="n">convertImageToNumPyArr</span><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="nb">open</span><span class="p">(</span><span class="s">'test.jpg'</span><span class="p">),</span> <span class="n">img_shape</span><span class="o">=</span><span class="n">img_shape</span><span class="p">)</span>
<span class="n">sysml_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">input_image</span><span class="p">)</span></code></pre></div>
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