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Fixed problems with MNIST1D

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udlbook
2024-07-19 15:55:44 -04:00
parent 54a020304e
commit 0d135f1ee7
1 changed files with 12 additions and 5 deletions
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17
Notebooks/Chap20/20_3_Lottery_Tickets.ipynb
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@@ -44,7 +44,8 @@
}, },
"source": [ "source": [
"# Run this if you're in a Colab to install MNIST 1D repository\n", "# Run this if you're in a Colab to install MNIST 1D repository\n",
"!pip install git+https://github.com/greydanus/mnist1d" "!pip install git+https://github.com/greydanus/mnist1d\n",
"!git clone https://github.com/greydanus/mnist1d"
], ],
"execution_count": null, "execution_count": null,
"outputs": [] "outputs": []
@@ -95,6 +96,12 @@
"id": "I-vm_gh5xTJs" "id": "I-vm_gh5xTJs"
}, },
"source": [ "source": [
"from mnist1d.data import get_dataset, get_dataset_args\n",
"from mnist1d.utils import set_seed, to_pickle, from_pickle\n",
"\n",
"import sys ; sys.path.append('./mnist1d/notebooks')\n",
"from train import get_model_args, train_model\n",
"\n",
"args = mnist1d.get_dataset_args()\n", "args = mnist1d.get_dataset_args()\n",
"data = mnist1d.get_dataset(args=args) # by default, this will download a pre-made dataset from the GitHub repo\n", "data = mnist1d.get_dataset(args=args) # by default, this will download a pre-made dataset from the GitHub repo\n",
"\n", "\n",
@@ -210,7 +217,7 @@
" # we would return [1,1,0,0,1]\n", " # we would return [1,1,0,0,1]\n",
" # Remember that these are torch tensors and not numpy arrays\n", " # Remember that these are torch tensors and not numpy arrays\n",
" # Replace this function:\n", " # Replace this function:\n",
" mask = torch.ones_like(scores)\n", " mask = torch.ones_like(absolute_weights)\n",
"\n", "\n",
"\n", "\n",
" return mask" " return mask"
@@ -237,7 +244,6 @@
"def find_lottery_ticket(model, dataset, args, sparsity_schedule, criteria_fn=None, **kwargs):\n", "def find_lottery_ticket(model, dataset, args, sparsity_schedule, criteria_fn=None, **kwargs):\n",
"\n", "\n",
" criteria_fn = lambda init_params, final_params: final_params.abs()\n", " criteria_fn = lambda init_params, final_params: final_params.abs()\n",
"\n",
" init_params = model.get_layer_vecs()\n", " init_params = model.get_layer_vecs()\n",
" stats = {'train_losses':[], 'test_losses':[], 'train_accs':[], 'test_accs':[]}\n", " stats = {'train_losses':[], 'test_losses':[], 'train_accs':[], 'test_accs':[]}\n",
" models = []\n", " models = []\n",
@@ -253,7 +259,7 @@
" model.set_layer_masks(masks)\n", " model.set_layer_masks(masks)\n",
"\n", "\n",
" # training process\n", " # training process\n",
" results = mnist1d.train_model(dataset, model, args)\n", " results = train_model(dataset, model, args)\n",
" model = results['checkpoints'][-1]\n", " model = results['checkpoints'][-1]\n",
"\n", "\n",
" # store stats\n", " # store stats\n",
@@ -291,7 +297,8 @@
}, },
"source": [ "source": [
"# train settings\n", "# train settings\n",
"model_args = mnist1d.get_model_args()\n", "from train import get_model_args, train_model\n",
"model_args = get_model_args()\n",
"model_args.total_steps = 1501\n", "model_args.total_steps = 1501\n",
"model_args.hidden_size = 500\n", "model_args.hidden_size = 500\n",
"model_args.print_every = 5000 # print never\n", "model_args.print_every = 5000 # print never\n",