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| <li class="toctree-l4"><a class="reference internal" href="1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="4-train.html">Train the neural network</a></li> |
| <li class="toctree-l4 current"><a class="current reference internal" href="#">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/blocks/index.html">Blocks</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/learning_rates/index.html">Learning Rates</a><ul> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_finder.html">Learning Rate Finder</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules.html">Learning Rate Schedules</a></li> |
| <li class="toctree-l6"><a class="reference internal" href="../../packages/gluon/training/learning_rates/learning_rate_schedules_advanced.html">Advanced Learning Rate Schedules</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/normalization/index.html">Normalization Blocks</a></li> |
| </ul> |
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| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/kvstore/index.html">KVStore</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/kvstore/kvstore.html">Distributed Key-Value Store</a></li> |
| </ul> |
| </li> |
| <li class="toctree-l3"><a class="reference internal" href="../../packages/ndarray/index.html">NDArray</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/01-ndarray-intro.html">An Intro: Manipulate Data the MXNet Way with NDArray</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/02-ndarray-operations.html">NDArray Operations</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/03-ndarray-contexts.html">NDArray Contexts</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/ndarray/gotchas_numpy_in_mxnet.html">Gotchas using NumPy in Apache MXNet</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/csr.html">CSRNDArray - NDArray in Compressed Sparse Row Storage Format</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/row_sparse.html">RowSparseNDArray - NDArray for Sparse Gradient Updates</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train.html">Train a Linear Regression Model with Sparse Symbols</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/ndarray/sparse/train_gluon.html">Sparse NDArrays with Gluon</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/fine_tuning_gluon.html">Fine-tuning an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/inference_on_onnx_model.html">Running inference on MXNet/Gluon from an ONNX model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="../../packages/onnx/super_resolution.html">Importing an ONNX model into MXNet</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/deploy/export/onnx.html">Export ONNX Models</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../performance/compression/int8.html">Deploy with int-8</a></li> |
| <li class="toctree-l4"><a class="reference external" href="https://mxnet.apache.org/api/faq/float16">Float16</a></li> |
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| <li class="toctree-l1 current"><a class="reference internal" href="../../index.html">Python Tutorials</a><ul class="current"> |
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| <li class="toctree-l4"><a class="reference internal" href="1-ndarray.html">Manipulate data with <code class="docutils literal notranslate"><span class="pre">ndarray</span></code></a></li> |
| <li class="toctree-l4"><a class="reference internal" href="2-nn.html">Create a neural network</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="3-autograd.html">Automatic differentiation with <code class="docutils literal notranslate"><span class="pre">autograd</span></code></a></li> |
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| <li class="toctree-l4 current"><a class="current reference internal" href="#">Predict with a pre-trained model</a></li> |
| <li class="toctree-l4"><a class="reference internal" href="6-use_gpus.html">Use GPUs</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../to-mxnet/index.html">Moving to MXNet from Other Frameworks</a><ul> |
| <li class="toctree-l4"><a class="reference internal" href="../to-mxnet/pytorch.html">PyTorch vs Apache MXNet</a></li> |
| </ul> |
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| <li class="toctree-l3"><a class="reference internal" href="../gluon_from_experiment_to_deployment.html">Gluon: from experiment to deployment</a></li> |
| <li class="toctree-l3"><a class="reference internal" href="../logistic_regression_explained.html">Logistic regression explained</a></li> |
| <li class="toctree-l3"><a class="reference external" href="https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html">MNIST</a></li> |
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| <li class="toctree-l3"><a class="reference internal" href="../../packages/autograd/index.html">Automatic Differentiation</a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom-layer.html">Custom Layers</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/custom_layer_beginners.html">Customer Layers (Beginners)</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/hybridize.html">Hybridize</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/init.html">Initialization</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/naming.html">Parameter and Block Naming</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/nn.html">Layers and Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/parameters.html">Parameter Management</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/save_load_params.html">Saving and Loading Gluon Models</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/blocks/activations/activations.html">Activation Blocks</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/data/index.html">Data Tutorials</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Spatial-Augmentation">Spatial Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Color-Augmentation">Color Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/data_augmentation.html#Composed-Augmentations">Composed Augmentations</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html">Gluon <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s and <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-included-Datasets">Using own data with included <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Using-own-data-with-custom-Datasets">Using own data with custom <code class="docutils literal notranslate"><span class="pre">Dataset</span></code>s</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/data/datasets.html#Appendix:-Upgrading-from-Module-DataIter-to-Gluon-DataLoader">Appendix: Upgrading from Module <code class="docutils literal notranslate"><span class="pre">DataIter</span></code> to Gluon <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a></li> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/image-augmentation.html">Image Augmentation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/info_gan.html">Image similarity search with InfoGAN</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/mnist.html">Handwritten Digit Recognition</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/image/pretrained_models.html">Using pre-trained models in MXNet</a></li> |
| </ul> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/loss/index.html">Losses</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/custom-loss.html">Custom Loss Blocks</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/kl_divergence.html">Kullback-Leibler (KL) Divergence</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/loss/loss.html">Loss functions</a></li> |
| </ul> |
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| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/gnmt.html">Google Neural Machine Translation</a></li> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/text/transformer.html">Machine Translation with Transformer</a></li> |
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| <li class="toctree-l4"><a class="reference internal" href="../../packages/gluon/training/index.html">Training</a><ul> |
| <li class="toctree-l5"><a class="reference internal" href="../../packages/gluon/training/fit_api_tutorial.html">MXNet Gluon Fit API</a></li> |
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| <!--- Licensed to the Apache Software Foundation (ASF) under one --><!--- or more contributor license agreements. See the NOTICE file --><!--- distributed with this work for additional information --><!--- regarding copyright ownership. The ASF licenses this file --><!--- to you under the Apache License, Version 2.0 (the --><!--- "License"); you may not use this file except in compliance --><!--- with the License. You may obtain a copy of the License at --><!--- http://www.apache.org/licenses/LICENSE-2.0 --><!--- Unless required by applicable law or agreed to in writing, --><!--- software distributed under the License is distributed on an --><!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY --><!--- KIND, either express or implied. See the License for the --><!--- specific language governing permissions and limitations --><!--- under the License. --><div class="section" id="Predict-with-a-pre-trained-model"> |
| <h1>Predict with a pre-trained model<a class="headerlink" href="#Predict-with-a-pre-trained-model" title="Permalink to this headline">¶</a></h1> |
| <p>A saved model can be used in multiple places, such as to continue training, to fine tune the model, and for prediction. In this tutorial we will discuss how to predict new examples using a pretrained model.</p> |
| <div class="section" id="Prerequisites"> |
| <h2>Prerequisites<a class="headerlink" href="#Prerequisites" title="Permalink to this headline">¶</a></h2> |
| <p>Please run the <a class="reference external" href="4-train.html">previous tutorial</a> to train the network and save its parameters to file. You will need this file to run the following steps.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">nd</span> |
| <span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">gluon</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon</span> <span class="kn">import</span> <span class="n">nn</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.data.vision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span> |
| <span class="kn">from</span> <span class="nn">IPython</span> <span class="kn">import</span> <span class="n">display</span> |
| <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>To start, we will copy a simple model’s definition.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span> |
| <span class="n">net</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">channels</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">"relu"</span><span class="p">),</span> |
| <span class="n">nn</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>In the last section, we saved all parameters into a file, now let’s load it back.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">net</span><span class="o">.</span><span class="n">load_parameters</span><span class="p">(</span><span class="s1">'net.params'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Predict"> |
| <h2>Predict<a class="headerlink" href="#Predict" title="Permalink to this headline">¶</a></h2> |
| <p>Remember the data transformation we did for training? Now we need the same transformation for predicting.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">transformer</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> |
| <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="mf">0.13</span><span class="p">,</span> <span class="mf">0.31</span><span class="p">)])</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Now let’s try to predict the first six images in the validation dataset and store the predictions into <code class="docutils literal notranslate"><span class="pre">preds</span></code>.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">mnist_valid</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">FashionMNIST</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">mnist_valid</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span> |
| <span class="n">preds</span> <span class="o">=</span> <span class="p">[]</span> |
| <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">X</span><span class="p">:</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">transformer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">pred</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> |
| <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'int32'</span><span class="p">)</span><span class="o">.</span><span class="n">asscalar</span><span class="p">())</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Finally, we visualize the images and compare the prediction with the ground truth.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">_</span><span class="p">,</span> <span class="n">figs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span> |
| <span class="n">text_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'t-shirt'</span><span class="p">,</span> <span class="s1">'trouser'</span><span class="p">,</span> <span class="s1">'pullover'</span><span class="p">,</span> <span class="s1">'dress'</span><span class="p">,</span> <span class="s1">'coat'</span><span class="p">,</span> |
| <span class="s1">'sandal'</span><span class="p">,</span> <span class="s1">'shirt'</span><span class="p">,</span> <span class="s1">'sneaker'</span><span class="p">,</span> <span class="s1">'bag'</span><span class="p">,</span> <span class="s1">'ankle boot'</span><span class="p">]</span> |
| <span class="n">display</span><span class="o">.</span><span class="n">set_matplotlib_formats</span><span class="p">(</span><span class="s1">'svg'</span><span class="p">)</span> |
| <span class="k">for</span> <span class="n">f</span><span class="p">,</span><span class="n">x</span><span class="p">,</span><span class="n">yi</span><span class="p">,</span><span class="n">pyi</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">figs</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">preds</span><span class="p">):</span> |
| <span class="n">f</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">reshape</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="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="n">ax</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">axes</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">text_labels</span><span class="p">[</span><span class="n">yi</span><span class="p">]</span><span class="o">+</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="o">+</span><span class="n">text_labels</span><span class="p">[</span><span class="n">pyi</span><span class="p">])</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">set_fontsize</span><span class="p">(</span><span class="mi">14</span><span class="p">)</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">ax</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| </div> |
| <div class="section" id="Predict-with-models-from-Gluon-model-zoo"> |
| <h2>Predict with models from Gluon model zoo<a class="headerlink" href="#Predict-with-models-from-Gluon-model-zoo" title="Permalink to this headline">¶</a></h2> |
| <p>The LeNet trained on FashionMNIST is a good example to start with, but too simple to predict real-life pictures. Instead of training large-scale model from scratch, <a class="reference external" href="https://mxnet.apache.org/api/python/gluon/model_zoo.html">Gluon model zoo</a> provides multiple pre-trained powerful models. For example, we can download and load a pre-trained ResNet-50 V2 model that was trained on the ImageNet dataset.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="kn">from</span> <span class="nn">mxnet.gluon.model_zoo</span> <span class="kn">import</span> <span class="n">vision</span> <span class="k">as</span> <span class="n">models</span> |
| <span class="kn">from</span> <span class="nn">mxnet.gluon.utils</span> <span class="kn">import</span> <span class="n">download</span> |
| <span class="kn">from</span> <span class="nn">mxnet</span> <span class="kn">import</span> <span class="n">image</span> |
| |
| <span class="n">net</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet50_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>We also download and load the text labels for each class.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">url</span> <span class="o">=</span> <span class="s1">'http://data.mxnet.io/models/imagenet/synset.txt'</span> |
| <span class="n">fname</span> <span class="o">=</span> <span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">)</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">fname</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> |
| <span class="n">text_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">' '</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">split</span><span class="p">()[</span><span class="mi">1</span><span class="p">:])</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">f</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>We randomly pick a dog image from Wikipedia as a test image, download and read it.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">url</span> <span class="o">=</span> <span class="s1">'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/</span><span class="se">\</span> |
| <span class="s1">Golden_Retriever_medium-to-light-coat.jpg/</span><span class="se">\</span> |
| <span class="s1">365px-Golden_Retriever_medium-to-light-coat.jpg'</span> |
| <span class="n">fname</span> <span class="o">=</span> <span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">)</span> |
| <span class="n">x</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Following the conventional way of preprocessing ImageNet data:</p> |
| <ol class="arabic simple"> |
| <li><p>Resize the short edge into 256 pixes,</p></li> |
| <li><p>And then perform a center crop to obtain a 224-by-224 image.</p></li> |
| </ol> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">x</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">resize_short</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">256</span><span class="p">)</span> |
| <span class="n">x</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">center_crop</span><span class="p">(</span><span class="n">x</span><span class="p">,</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">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">())</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Now you may know it is a golden retriever (You can also infer it from the image URL).</p> |
| <p>The futher data transformation is similar to FashionMNIST except that we subtract the RGB means and divide by the corresponding variances to normalize each color channel.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="n">data</span><span class="p">):</span> |
| <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="n">rgb_mean</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span> |
| <span class="n">rgb_std</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span> |
| <span class="k">return</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span> <span class="o">/</span> <span class="mi">255</span> <span class="o">-</span> <span class="n">rgb_mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">rgb_std</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>Now we can recognize the object in the image now. We perform an additional softmax on the output to obtain probability scores. And then print the top-5 recognized objects.</p> |
| <div class="nbinput nblast docutils container"> |
| <div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]: |
| </pre></div> |
| </div> |
| <div class="input_area highlight-python notranslate"><div class="highlight"><pre> |
| <span></span><span class="n">prob</span> <span class="o">=</span> <span class="n">net</span><span class="p">(</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">softmax</span><span class="p">()</span> |
| <span class="n">idx</span> <span class="o">=</span> <span class="n">prob</span><span class="o">.</span><span class="n">topk</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">5</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">idx</span><span class="p">:</span> |
| <span class="n">i</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">asscalar</span><span class="p">())</span> |
| <span class="nb">print</span><span class="p">(</span><span class="s1">'With prob = </span><span class="si">%.5f</span><span class="s1">, it contains </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span> |
| <span class="n">prob</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">asscalar</span><span class="p">(),</span> <span class="n">text_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> |
| </pre></div> |
| </div> |
| </div> |
| <p>As can be seen, the model is fairly confident the image contains a golden retriever.</p> |
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