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299 changes: 257 additions & 42 deletions MTGAI-MiniBatch-v2-Copy2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -568,81 +568,293 @@
},
{
"cell_type": "code",
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"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"torch.Size([100, 444, 1])\n",
"torch.Size([100, 444, 500])\n"
"torch.Size([100, 444, 50])\n",
"torch.Size([100, 444, 3398])\n",
"tensor([[[ nan],\n",
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"torch.Size([100, 444, 1])\n",
"torch.Size([100, 444, 50])\n",
"torch.Size([100, 444, 3398])\n",
"Trained on 100 cards.\n",
"Avg loss is: nan\n",
"tensor([[[ nan],\n",
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" [ nan],\n",
" ...,\n",
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" [ 0.0000]]], device='cuda:0', grad_fn=<EmbeddingBackward>)\n",
"torch.Size([100, 444, 1])\n",
"torch.Size([100, 444, 50])\n",
"torch.Size([100, 444, 3398])\n",
"Trained on 200 cards.\n",
"Avg loss is: nan\n",
"tensor([[[-1.9569],\n",
" [ nan],\n",
" [ nan],\n",
" ...,\n",
" [ 0.0000],\n",
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" [[-0.3117],\n",
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" ...,\n",
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" [[ nan],\n",
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"\n",
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" ...,\n",
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" [ 0.0000]]], device='cuda:0', grad_fn=<EmbeddingBackward>)\n",
"torch.Size([100, 444, 1])\n",
"torch.Size([100, 444, 50])\n",
"torch.Size([100, 444, 3398])\n",
"Trained on 300 cards.\n",
"Avg loss is: nan\n",
"tensor([[[ nan],\n",
" [ nan],\n",
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" [0.0000]]], device='cuda:0', grad_fn=<EmbeddingBackward>)\n",
"torch.Size([100, 444, 1])\n",
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"torch.Size([100, 444, 3398])\n"
]
},
{
"ename": "SystemExit",
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"An exception has occurred, use %tb to see the full traceback.\n",
"\u001b[0;31mSystemExit\u001b[0m\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bryce/anaconda3/envs/pt/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3275: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
" warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-a45c36a5a4c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLossF1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbloop\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbloop\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretain_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0mtotalloss\u001b[0m\u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \"\"\"\n\u001b[0;32m---> 93\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m 88\u001b[0m Variable._execution_engine.run_backward(\n\u001b[1;32m 89\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m allow_unreachable=True) # allow_unreachable flag\n\u001b[0m\u001b[1;32m 91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
Expand Down Expand Up @@ -718,15 +930,16 @@
" #sys.exit()\n",
" #print(len(predictions))\n",
" #loss = LossF1(torch.narrow(predictions.view(-1, len(Dict)), 0, 0, lengths[x]), torch.narrow(targets[x], 0, 0, lengths[x]))\n",
" loss = LossF1(predictions, targets)\n",
" for bloop in range(batch_size):\n",
" loss = LossF1(predictions[bloop], targets[bloop])\n",
"\n",
" loss.backward(retain_graph=True)\n",
" loss.backward(retain_graph=True)\n",
"\n",
" totalloss+= loss.item()\n",
" # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n",
" torch.nn.utils.clip_grad_norm_(AI.parameters(), .25)\n",
" totalloss+= loss.item()\n",
" # `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n",
" torch.nn.utils.clip_grad_norm_(AI.parameters(), .25)\n",
"\n",
" OptimF1.step()\n",
" OptimF1.step()\n",
" if i != 0:\n",
" print(\"Trained on \",i*batch_size, \" cards.\")\n",
" print(\"Avg loss is: \",totalloss/(i*100))\n",
Expand Down Expand Up @@ -790,7 +1003,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -799,10 +1012,12 @@
" def __init__(self, Dsize, input_size):\n",
" super(MTGAI, self).__init__()\n",
" self.Embed1 = nn.Embedding(Dsize, 1, padding_idx=0)\n",
" self.GRU = nn.GRU(1, 500, 3, batch_first=True) # input size, hidden size, num of layers to use\n",
" self.GRU = nn.GRU(1, 50, 3, batch_first=True) # input size, hidden size, num of layers to use\n",
" self.Drop1 = nn.Dropout(.5)\n",
" self.Drop2 = nn.Dropout(.5)\n",
" self.Lin1 = nn.Linear(500, Dsize)\n",
" self.dsize = Dsize\n",
" print(self.dsize)\n",
" self.Lin1 = nn.Linear(50, self.dsize)\n",
" #self.Lin1.bias.data.fill_(0)\n",
" #self.Lin1.weight.data.uniform_(-.1, .1)\n",
" self.input_size = input_size\n",
Expand All @@ -816,8 +1031,8 @@
" print(y.size())\n",
" y, newh = self.GRU(y, h)\n",
" print(y.size())\n",
" sys.exit()\n",
" y = self.Lin1(y)\n",
" print(y.size())\n",
" #for z in x:\n",
" #y = self.Embed1(z).view(1, 1, -1)\n",
" #y, newh = self.GRU(y, h)\n",
Expand All @@ -835,7 +1050,7 @@
" \n",
" def init_hidden(self, batch_size, input_size=1):\n",
" weight = next(self.parameters()).data\n",
" return Variable(weight.new(3, 100, 500).zero_())"
" return Variable(weight.new(3, 100, 50).zero_())"
]
},
{
Expand Down