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Computer Intelligence Project 3

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import torch\nimport numpy as np\nimport matplotlib.pyplot as plt","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def initialize_parameters():\n Wa = torch.rand(1, 2).requires_grad_(True)\n ba = torch.zeros(1, 1).requires_grad_(True)\n Wy = torch.rand(1).requires_grad_(True)\n by = torch.zeros(1,1).requires_grad_(True)\n a = torch.zeros(1, 1).float()\n return Wa, ba, Wy, by, a","execution_count":175,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def forward(x, a, Wa, ba, Wy, by):\n xa = torch.cat([x, a], dim = 0)\n a = (torch.matmul(Wa, xa) + ba)\n yhat = torch.matmul(Wy, a) + by\n yhat = yhat\n return yhat, a","execution_count":176,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def onestep(x, y, a, Wa, ba, Wy, by, costfn, alpha):\n Wa = Wa.clone().detach().requires_grad_(True)\n Wy = Wy.clone().detach().requires_grad_(True)\n ba = ba.clone().detach().requires_grad_(True)\n by = by.clone().detach().requires_grad_(True)\n yhat, a = forward(x, a, Wa, ba, Wy, by)\n cost = costfn(yhat, y)\n cost.backward()\n Wa.detach_()\n ba.detach_()\n Wy.detach_()\n by.detach_()\n yhat.detach_()\n a.detach_()\n Wa -= alpha*Wa.grad\n ba -= alpha*ba.grad\n Wy -= alpha*Wy.grad\n by -= alpha*by.grad\n\n return cost, yhat, a, Wa, ba, Wy, by\n ","execution_count":177,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Wa, ba, Wy, by, a = initialize_parameters()\ncostfn = torch.nn.MSELoss()\ncosts = []\ny = torch.zeros(1, 1)\nalpha = 0.1\nnum_iters = 10000000","execution_count":178,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(num_iters):\n x = torch.from_numpy((np.random.rand(1, 1) > 0.5).astype(int)).float().view(1, 1)\n y += x\n cost, yhat, a, Wa, ba, Wy, by = onestep(x, y, a, Wa, ba, Wy, by, costfn, alpha)\n costs.append(cost.item())\n if(i % 1000 == 0):\n print(i, x, y, yhat, cost)","execution_count":180,"outputs":[{"output_type":"stream","text":"0 tensor([[1.]]) tensor([[4843.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n1000 tensor([[1.]]) tensor([[5320.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n2000 tensor([[0.]]) tensor([[5821.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n3000 tensor([[0.]]) tensor([[6304.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n4000 tensor([[1.]]) tensor([[6802.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n5000 tensor([[1.]]) tensor([[7293.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n6000 tensor([[1.]]) tensor([[7806.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n7000 tensor([[0.]]) tensor([[8280.]]) tensor([[nan]]) tensor(nan, grad_fn=<MseLossBackward>)\n","name":"stdout"},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-180-c45c7bcd2e3e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myhat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mba\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0monestep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mba\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcostfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\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 5\u001b[0m \u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcost\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m1000\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\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<ipython-input-177-48ae863d3f5d>\u001b[0m in \u001b[0;36monestep\u001b[0;34m(x, y, a, Wa, ba, Wy, by, costfn, alpha)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0myhat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mba\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcostfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myhat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mcost\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[0m\u001b[1;32m 9\u001b[0m \u001b[0mWa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach_\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 10\u001b[0m \u001b[0mba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach_\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/opt/conda/lib/python3.7/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 193\u001b[0m \u001b[0mproducts\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mDefaults\u001b[0m \u001b[0mto\u001b[0m\u001b[0;31m 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\u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\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[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36m_make_grads\u001b[0;34m(outputs, grads)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"grad can be implicitly created only for scalar outputs\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mnew_grads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreserve_format\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[0m\u001b[1;32m 36\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mnew_grads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(costs)\nplt.show()","execution_count":169,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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0033/514/33514862.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

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0033/514/33514934.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

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0033/514/33514958.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

"{\"cells\":[{\"metadata\":{\"_uuid\":\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\",\"_cell_guid\":\"(...TRUNCATED)

0033/515/33515175.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED)

0033/515/33515468.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

"{\"cells\":[{\"metadata\":{\"_uuid\":\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\",\"_cell_guid\":\"(...TRUNCATED)

0033/516/33516002.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED)

0033/516/33516105.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

"{\"cells\":[{\"metadata\":{\"_uuid\":\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\",\"_cell_guid\":\"(...TRUNCATED)

0033/516/33516462.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED)

0033/516/33516528.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz

"{\"cells\":[{\"metadata\":{\"_uuid\":\"8f2839f25d086af736a60e9eeb907d3b93b6e0e5\",\"_cell_guid\":\"(...TRUNCATED)

0033/517/33517042.ipynb

s3://data-agents/kaggle-outputs/sharded/023_00033.jsonl.gz