implementing keepdim sum
This commit is contained in:
lucasdelimanogueira
parent
abcd17e679
commit
5dd3f3d202
15 changed files with 119 additions and 53 deletions
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@ -26,23 +26,28 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch [1/10], Loss: 1.7035\n",
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"Epoch [2/10], Loss: 0.7193\n",
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"Epoch [3/10], Loss: 0.3068\n",
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"Epoch [4/10], Loss: 0.1742\n",
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"Epoch [5/10], Loss: 0.1342\n",
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"Epoch [6/10], Loss: 0.1232\n",
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"Epoch [7/10], Loss: 0.1220\n",
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"Epoch [8/10], Loss: 0.1241\n",
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"Epoch [9/10], Loss: 0.1270\n",
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"Epoch [10/10], Loss: 0.1297\n"
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"Invalid axis"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "Matrix multiplication requires 2D tensors",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[7], line 56\u001b[0m\n\u001b[1;32m 53\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, target)\n\u001b[1;32m 55\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 56\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 57\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEpoch [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;250m \u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepochs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m], Loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mloss[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
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"File \u001b[0;32m~/Documentos/recreate_pytorch/PyNorch/norch/tensor.py:167\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;66;03m# Propagate gradients to inputs if not a leaf tensor\u001b[39;00m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tensor\u001b[38;5;241m.\u001b[39mgrad_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 167\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[43mtensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad_fn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 168\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tensor, grad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(tensor\u001b[38;5;241m.\u001b[39mgrad_fn\u001b[38;5;241m.\u001b[39minput, grads):\n\u001b[1;32m 169\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(tensor, Tensor) \u001b[38;5;129;01mand\u001b[39;00m tensor \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m visited:\n",
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"File \u001b[0;32m~/Documentos/recreate_pytorch/PyNorch/norch/autograd/functions.py:90\u001b[0m, in \u001b[0;36mMatmulBackward.backward\u001b[0;34m(self, gradient)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [aux_sum, x\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m@\u001b[39m gradient]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[43mgradient\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m@\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtranspose\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m, x\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m@\u001b[39m gradient]\n",
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"File \u001b[0;32m~/Documentos/recreate_pytorch/PyNorch/norch/tensor.py:487\u001b[0m, in \u001b[0;36mTensor.__matmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 485\u001b[0m \u001b[38;5;66;03m#2D matmul\u001b[39;00m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m other\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 487\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMatrix multiplication requires 2D tensors\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 489\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m other\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m 490\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncompatible shapes for matrix multiplication\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"\u001b[0;31mValueError\u001b[0m: Matrix multiplication requires 2D tensors"
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]
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}
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],
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@ -87,11 +92,13 @@
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"for x in x_values:\n",
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" y_true.append(math.pow(math.sin(x), 2))\n",
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"\n",
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"batch_size = 5\n",
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"\n",
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"\n",
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"for epoch in range(epochs):\n",
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" for x, target in zip(x_values, y_true):\n",
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" x = norch.Tensor([[x]]).T\n",
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" target = norch.Tensor([[target]]).T\n",
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" x = norch.Tensor([[x] for _ in range(batch_size)]).T\n",
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" target = norch.Tensor([[target] for _ in range(batch_size)]).T\n",
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"\n",
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" x = x.to(device)\n",
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" target = target.to(device)\n",
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