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"# Tune a CNN on MNIST\n",
"\n",
"This tutorial walks through using Ax to tune two hyperparameters (learning rate and momentum) for a PyTorch CNN on the MNIST dataset trained using SGD with momentum.\n"
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"[INFO 09-15 02:39:24] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
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"import torch\n",
"import numpy as np\n",
"\n",
"from ax.plot.contour import plot_contour\n",
"from ax.plot.trace import optimization_trace_single_method\n",
"from ax.service.managed_loop import optimize\n",
"from ax.utils.notebook.plotting import render, init_notebook_plotting\n",
"from ax.utils.tutorials.cnn_utils import load_mnist, train, evaluate, CNN\n",
"\n",
"init_notebook_plotting()"
]
},
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"torch.manual_seed(12345)\n",
"dtype = torch.float\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
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"## 1. Load MNIST data\n",
"First, we need to load the MNIST data and partition it into training, validation, and test sets.\n",
"\n",
"Note: this will download the dataset if necessary."
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"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
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"combined_train_valid_set = torch.utils.data.ConcatDataset([\n",
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" valid_loader.dataset.dataset,\n",
"])\n",
"combined_train_valid_loader = torch.utils.data.DataLoader(\n",
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" net=CNN(),\n",
" train_loader=combined_train_valid_loader, \n",
" parameters=best_arm.parameters,\n",
" dtype=dtype,\n",
" device=device,\n",
")\n",
"test_accuracy = evaluate(\n",
" net=net,\n",
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" dtype=dtype,\n",
" device=device,\n",
")"
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},
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"metadata": {},
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"name": "stdout",
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"text": [
"Classification Accuracy (test set): 97.7%\n"
]
}
],
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