NMR_1.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"machine_shape":"hm","gpuType":"V100","collapsed_sections":["tIla_CY_wEmr","si0F6rbw2kbs"],"toc_visible":true,"authorship_tag":"ABX9TyO6lKQOzLUZrJBcGOFFwam5"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["# Создание и обучение сиамской сети"],"metadata":{"id":"WgzpRDnljli-"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"h0WVsFxGX0qa","executionInfo":{"status":"ok","timestamp":1686550776552,"user_tz":-180,"elapsed":5409,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"outputs":[],"source":["import numpy as np\n","import random\n","import torch\n","import matplotlib.pyplot as plt\n","\n","\n","def set_random_seed(seed):\n","    torch.backends.cudnn.deterministic = True\n","    torch.manual_seed(seed)\n","    torch.cuda.manual_seed(seed)\n","    np.random.seed(seed)\n","    random.seed(seed)\n","\n","\n","set_random_seed(42)\n","\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"]},{"cell_type":"code","source":["!pip install unrar\n","!unrar x /content/dataset_red_2.rar\n","x_train = np.load('/content/x_train_red.npy')\n","y_train = np.load('/content/y_train_red.npy')\n","x_val = np.load('/content/x_val_red.npy')\n","y_val = np.load('/content/y_val_red.npy')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"icVukEU_X2hX","executionInfo":{"status":"ok","timestamp":1686550820653,"user_tz":-180,"elapsed":7594,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"26ab7699-5967-4ee8-ae6a-acb3558b3a64"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting unrar\n","  Downloading unrar-0.4-py3-none-any.whl (25 kB)\n","Installing collected packages: unrar\n","Successfully installed unrar-0.4\n","\n","UNRAR 5.61 beta 1 freeware      Copyright (c) 1993-2018 Alexander Roshal\n","\n","\n","Extracting from /content/dataset_red_2.rar\n","\n","Extracting  x_train_red.npy                                              \b\b\b\b 65%\b\b\b\b 78%\b\b\b\b\b  OK \n","Extracting  x_val_red.npy                                                \b\b\b\b 97%\b\b\b\b\b  OK \n","Extracting  y_test_red.npy                                               \b\b\b\b 97%\b\b\b\b\b  OK \n","Extracting  y_train_red.npy                                              \b\b\b\b 97%\b\b\b\b\b  OK \n","Extracting  y_val_red.npy                                                \b\b\b\b 97%\b\b\b\b\b  OK \n","Extracting  x_test_red.npy                                               \b\b\b\b 99%\b\b\b\b\b  OK \n","All OK\n"]}]},{"cell_type":"code","source":["plt.plot(x_train[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"pX4OdgFLX8wm","executionInfo":{"status":"ok","timestamp":1686403891790,"user_tz":-180,"elapsed":431,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"aadef889-86a2-4e54-8f5d-ccaa7bc32a03"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fece4b8e440>]"]},"metadata":{},"execution_count":3},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAiwAAAGdCAYAAAAxCSikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA96UlEQVR4nO3df3SU5Z3//9ckIQmoCSqayC+xFQXkl4DQsFbarzkNLV3F3bUsx1M56Fe/7soWlx7aQhV2v3ZPXHfhA1VW1t1j/fbsWiy7Sm2lbGkU+sMoJUCVatFaFRZMAiqEnyFkru8fIZO5k8mPyfy63jPPx2lOk5l7Zq77luua131d133dIeecEwAAgMfyMl0AAACA3hBYAACA9wgsAADAewQWAADgPQILAADwHoEFAAB4j8ACAAC8R2ABAADeK8h0AZIhHA7r0KFDuuiiixQKhTJdHAAA0AfOOR0/flxDhw5VXl7PfShZEVgOHTqkESNGZLoYAACgHw4cOKDhw4f3uE1WBJaLLrpIUtsOl5SUZLg0AACgL5qamjRixIjI93hPsiKwtA8DlZSUEFgAADCmL9M5mHQLAAC8R2ABAADeI7AAAADvEVgAAID3CCwAAMB7BBYAAOA9AgsAAPAegQUAAHiPwAIAALxHYAEAAN4jsAAAAO8RWAAAgPcILABg1Gt//Eg/2LE/08UA0iIr7tYMALlo3pOvSpKuvvxC3TDqkgyXBkgtelgAwLgDH5/KdBGAlCOwAAAA7xFYAMA45zJdAiD1CCwAAMB7BBYAAOA9AgsAAPAegQUAAHiPwAIAxjHnFrmAwAIAxoUyXQAgDQgsAGAcPSzIBQQWAADgPQILABjkWC0OOYbAAgAAvEdgAQCD6GBBriGwAIBB5BXkGgILABjHfBbkAgILABhESEGuIbAAgHFEF+QCAgsAGERIQa4hsACAQdEjQizNj1xAYAEA4+htQS4gsACAQY6YghxDYAEAg7hICLmGwAIA1hFekAMILAAAwHsEFgAwiCEh5BoCCwAA8B6BBQAMsniV0LuHT+j4mZZMFwNGEVgAwDgL4eV3h47p5lXbddOjL2e6KDCKwAIABlmbw1LzVqMk6ZNT9LCgf/oVWNatW6dRo0apuLhYM2bM0I4dO3rcfuPGjRozZoyKi4s1YcIEbd68OfD8iRMntGjRIg0fPlwDBw7UuHHjtH79+v4UDQBygrG8AiQs7sDy7LPPasmSJVq5cqV27dqlSZMmqaqqSo2NjTG3f+WVVzR//nzdfffd2r17t+bOnau5c+dq7969kW2WLFmiLVu26D/+4z/01ltv6YEHHtCiRYv0wgsv9H/PAABA1og7sKxevVr33HOPFi5cGOkJGTRokJ566qmY269du1azZ8/W0qVLNXbsWD388MOaMmWKHn/88cg2r7zyihYsWKDPfe5zGjVqlO69915NmjSp154bAMhVztqYEJCguALL2bNnVVdXp8rKyo43yMtTZWWlamtrY76mtrY2sL0kVVVVBbafOXOmXnjhBR08eFDOOb388st6++239YUvfCGe4gFAziCuINcUxLPxkSNH1NraqrKyssDjZWVl+v3vfx/zNfX19TG3r6+vj/z92GOP6d5779Xw4cNVUFCgvLw8/du//ZtuuummmO/Z3Nys5ubmyN9NTU3x7AYAZBU6W5ALvLhK6LHHHtOrr76qF154QXV1dVq1apXuv/9+/fznP4+5fXV1tUpLSyM/I0aMSHOJASCzCCnINXH1sAwZMkT5+flqaGgIPN7Q0KDy8vKYrykvL+9x+9OnT2v58uV6/vnnNWfOHEnSxIkTtWfPHv3zP/9zl+EkSVq2bJmWLFkS+bupqYnQAiC3EFiQY+LqYSksLNTUqVNVU1MTeSwcDqumpkYVFRUxX1NRURHYXpK2bt0a2b6lpUUtLS3KywsWJT8/X+FwOOZ7FhUVqaSkJPADAPBXKNMFgHlx9bBIbZcgL1iwQNOmTdP06dO1Zs0anTx5UgsXLpQk3XnnnRo2bJiqq6slSYsXL9asWbO0atUqzZkzRxs2bNDOnTv15JNPSpJKSko0a9YsLV26VAMHDtSVV16p7du36/vf/75Wr16dxF0FgOxhYXVbIJniDizz5s3T4cOHtWLFCtXX12vy5MnasmVLZGLt/v37A70lM2fO1DPPPKMHH3xQy5cv1+jRo7Vp0yaNHz8+ss2GDRu0bNky3XHHHfr444915ZVX6h/+4R903333JWEXASC7EV2QC0IuCy7mb2pqUmlpqY4dO8bwEICc8MnJs7r+4a2SpOo/m6D500dmuEQ9e6zmHa3a+rYk6f1H5mS4NPBFPN/fXlwlBACIj/kzTSBOBBYAMM5+PznQOwILABiUBaP5QFwILABgkLW4EuK6ZiSIwAIAxnGJM3IBgQUADIoeEWJ0CLmAwAIABkX3qljIKyHGhJAgAgsAWEcXC3IAgQUALCKjIMcQWADAOLILcgGBBQAMig4pjAghFxBYAMCg4FVCJBZkPwILABhHXEEuILAAgEGBy5oNJBauakaiCCwAYJCFkNIdhrDQHwQWADDO2tc/eQX9QWABAIOCVwnZSgC2SgtfEFgAAGllLWDBDwQWADAo+kvf2ve/seLCEwQWADAosA6LgQgQUsdlQtYCFvxAYDHktweO6t3DJzJdDABIiIWABf8UZLoA6JuGpjO6dd2vJUnvPzInw6UB4BNrPRbWygs/0MNixPtHTma6CAA8EhwSArIfgQUAjLPQY8FKt0gUgcWIUFRtD4cNtE4AUsryPBALAQv+IbAYdI7AAuQ8a1cJRbNWXviBwGJQmNMTAFEsNAnRI0IWygv/EFgMamkNZ7oIADLM8ne+5bIjcwgsBjEiBMAyluZHfxBYjAjMsKeuAzkvuDS//41CdBvmf2nhIwKLQUxYA2C5FTCQr+AhAotBVHYA0cy1CdbKCy8QWAyirgOwvNItvcToDwKLQRbGqwGkmt12gCYM/UFgMYI5twC6QwBALiCwGBHdHtE4AbC90i0QPwKLEZYbJwDJZ60VCEX1EzOsjf4gsFhEXQcQxcL3f/SJloHiwkMEFiMCi0RlsBwA/GD6KiFrBYYXCCxGMIcFgGUMayNRBBYjqOwAogXaAQNnMa7bP4C+IbAYERj/pbIDOc/0kFCmCwCTCCwGUdkBWONsdQjBQwQWKwKVndoO5DprASB4lZCBAsM7BBYjmHQLIJq1AGAtYME/BBYjqOAAgFxGYDGCSbcAolnusTBWXHiCwGKQhe5fAOljoUUILH5pLWHBCwQWIyyfTQFANNow9AeBxQjXze8AcpO1kxgLZYTfCCxG0J0KoDsWhon9LyF8R2AxiIoPwEJI6Q7nXOgPAosRrMMCIJozNk7M/dCQKAKLFdw5DEA3LLQIhBQkisBiBOuwAIhmuRmgDUN/EFiMiK7gYSo7kPOsTcS3fHdp+IHAYhBdqwCiGcgrnebhGSgwvENgMcLamgsAUstcM0DDhQQRWIzgKiEA0SwPsVgrL/xAYDEiMF5NdQcQxcJJDCddSBSBxSAqOwBr/RS0W0gUgcUI6jqA7tjrdbVWXviAwGIEk24BRLPWJrCWFBJFYDGDOSwAOlhrBSxPEoYfCCxGWDubAgAgmfoVWNatW6dRo0apuLhYM2bM0I4dO3rcfuPGjRozZoyKi4s1YcIEbd68ucs2b731lm655RaVlpbqggsu0A033KD9+/f3p3hZj7wCIHgS43+rwFVCSFTcgeXZZ5/VkiVLtHLlSu3atUuTJk1SVVWVGhsbY27/yiuvaP78+br77ru1e/duzZ07V3PnztXevXsj27z77ru68cYbNWbMGG3btk2vv/66HnroIRUXF/d/z7IMq0QCiGatHeBuzUhU3IFl9erVuueee7Rw4UKNGzdO69ev16BBg/TUU0/F3H7t2rWaPXu2li5dqrFjx+rhhx/WlClT9Pjjj0e2+fa3v60vfelLevTRR3X99dfr05/+tG655RZdfvnl/d+zLMP4L4DuWGgTmHSLRMUVWM6ePau6ujpVVlZ2vEFeniorK1VbWxvzNbW1tYHtJamqqiqyfTgc1osvvqhrrrlGVVVVuvzyyzVjxgxt2rSp23I0Nzerqakp8JPtqOwAojHEglwTV2A5cuSIWltbVVZWFni8rKxM9fX1MV9TX1/f4/aNjY06ceKEHnnkEc2ePVs/+9nPdNttt+nP/uzPtH379pjvWV1drdLS0sjPiBEj4tkNk4INEq0TkOvMhRQuHECCMn6VUDgcliTdeuut+tu//VtNnjxZ3/rWt/TlL39Z69evj/maZcuW6dixY5GfAwcOpLPIGUdlBxDNwpyQQI+QgfLCPwXxbDxkyBDl5+eroaEh8HhDQ4PKy8tjvqa8vLzH7YcMGaKCggKNGzcusM3YsWP1q1/9KuZ7FhUVqaioKJ6im+e6+R1AbrI8TGytvPBDXD0shYWFmjp1qmpqaiKPhcNh1dTUqKKiIuZrKioqAttL0tatWyPbFxYW6oYbbtC+ffsC27z99tu68sor4yleVgvc/JDKDsAYa1c1wT9x9bBI0pIlS7RgwQJNmzZN06dP15o1a3Ty5EktXLhQknTnnXdq2LBhqq6uliQtXrxYs2bN0qpVqzRnzhxt2LBBO3fu1JNPPhl5z6VLl2revHm66aab9PnPf15btmzRj3/8Y23bti05e5llqPgAZOzKQZotJCruwDJv3jwdPnxYK1asUH19vSZPnqwtW7ZEJtbu379feXkdHTczZ87UM888owcffFDLly/X6NGjtWnTJo0fPz6yzW233ab169erurpaX/va13Tttdfqv//7v3XjjTcmYRezD/UegOWrhKyVF36IO7BI0qJFi7Ro0aKYz8XqFbn99tt1++239/ied911l+66667+FCcnUMEBWMakWyQq41cJoW8sT7ADkHzWljqg3UKiCCxGsKw1gGiWT2KslRd+ILAY4biuGYBhgYCVwXLALgKLQVR2AMG7NWeuHH1l7e7S8A+BxQjLVwQASD4msSLXEFiM4IwEQHesNQ/GigtPEFiM4GwKQDRrJzGs1o1EEVgMorIDiGahSXA9/AX0BYHFCmPLcANILea1IdcQWIwIrrlA6wTkPGPNgLWrmuAfAosRjh4WAN2wMK+NdViQKAKLEcZW4QaQYo5xYuQYAgsAGGRtWIUhISSKwGIE9xIC0B0LLUJwkrCFEsM3BBYjLN/oDEDyWVvqnnl4SBSBxQi6UwFEoxlAriGwGMHNmgF0x0abQC8xEkNgMchC9y+A1LK21D3z8JAoAosVFlokAOgLmjP0A4HFCIaEAESz1iZwzoVEEViMYNItgGjmrhJipVskiMBiEtUdgF0G8hU8RGAxwtoEOwCpZqvHgkm3SBSBxQhr49UAUssZaxQMFBGeI7AYQa8KgGxBe4b+ILAYEbwPR8aKAcATwQ4W/xsFluZHoggsBllonACklrUrB4P3QzNQYHiHwGIEk24BmEa7hQQRWAyi3gOwfAd3Y8WFJwgsRlhbJApA+lgYJnbd/gH0DYEFAAyydt4SGNYmsaAfCCxGUMEBROPKQeQaAosR1q4IAJA+FpoEAhYSRWAxwtqaCwBSy9pcNk66kCgCixFUdgDdsdYmGCsuPEFgMcha4xSLc07/Z+vbeuG3hzJdFCAL+N8o+F9C+K4g0wVA32TbMNDODz7R2pp3JEm3TBqa4dIA9lg7cQkufmms8PACPSxGZNt9OI6easl0EYCsYeH739jNpeEhAotB2XB2UpAXynQRANMCK91msBz9kQVNGDKAwGJEcNEl+/IJLEBCzH3pWysvvENgsSgLKn50D0trOAt2CMggC72ujkEhJIjAYkRwDkt2VXYLjS3gG8vz2qjy6A8CixFZV7+jRoToYAHiZ63aEFKQKAKLEdm8cFy29RgB6WahTbDcIwQ/EFgMyrbKbqGxBXxjeSI+dR79QWAxInAJY5ZV9mzbHyAdrFWb4GXY1koPHxBYjMi2SbehqEksYRILkBALE9cNFBGeI7AYkc23ZiewAP1grNpkcxuG9CCwWJHFNTx79wxALNR59AeBxaBsq+wunOkSAPZYm9cWvNLRQIHhHQKLEYHqnWWVPRvm5ADpZm9em4UywmcEFiOybQ2DEAvHAQDiQGAxwlr3bzzoHgbiZ20SazYvfon0ILAYlA1f8NG7QA8LED9rASB460MDBYZ3CCxGWGiQ4hFcpTPLdg4AkHQEFiOy7cbs1rqzAd9YWzk2cJLif3HhIQKLEda6f3uTbfsDpJu1OsRJChJFYDEieDZlX/Tqtqx0C+QWajz6g8BiUFZMuo36ncACxM/aMDHVHIkisFiRZZWd8WwgiQzUoeCQkIECwzsEFiOyrXpbG38HvGPsSrvglYFA/AgsRmRbj4S1KxwA35iuNaYLj0whsBhh774hPWPhOCB5suEkBugNgcWgbGicwoEhoSzYISDNrN1fLNtOupB+/Qos69at06hRo1RcXKwZM2Zox44dPW6/ceNGjRkzRsXFxZowYYI2b97c7bb33XefQqGQ1qxZ05+iZS1rVwT0xgUua85gQQCjLAd9w0VHBsUdWJ599lktWbJEK1eu1K5duzRp0iRVVVWpsbEx5vavvPKK5s+fr7vvvlu7d+/W3LlzNXfuXO3du7fLts8//7xeffVVDR06NP49yXLZNknV9fAXgPhYCC/ZtpYU0i/uwLJ69Wrdc889WrhwocaNG6f169dr0KBBeuqpp2Juv3btWs2ePVtLly7V2LFj9fDDD2vKlCl6/PHHA9sdPHhQf/M3f6P//M//1IABA/q3N1ks27pQ6WEBEmOt19VApoLn4gosZ8+eVV1dnSorKzveIC9PlZWVqq2tjfma2trawPaSVFVVFdg+HA7rq1/9qpYuXarrrruu13I0Nzerqakp8JNLsiG8ZFuPEZBuluuN5bIjc+IKLEeOHFFra6vKysoCj5eVlam+vj7ma+rr63vd/h//8R9VUFCgr33ta30qR3V1tUpLSyM/I0aMiGc3TMq2L3hWugWSx0IVYtItEpXxq4Tq6uq0du1aPf300wqFQn16zbJly3Ts2LHIz4EDB1JcSiQb9xICEmNuSMhEKeGzuALLkCFDlJ+fr4aGhsDjDQ0NKi8vj/ma8vLyHrf/5S9/qcbGRo0cOVIFBQUqKCjQBx98oK9//esaNWpUzPcsKipSSUlJ4CfbBReOs1/xs63HCEDPqPNIVFyBpbCwUFOnTlVNTU3ksXA4rJqaGlVUVMR8TUVFRWB7Sdq6dWtk+69+9at6/fXXtWfPnsjP0KFDtXTpUv3P//xPvPuTtbLt1uxZsAtARjnDCcBWaeGLgnhfsGTJEi1YsEDTpk3T9OnTtWbNGp08eVILFy6UJN15550aNmyYqqurJUmLFy/WrFmztGrVKs2ZM0cbNmzQzp079eSTT0qSLr30Ul166aWBzxgwYIDKy8t17bXXJrp/8JRjSAhIGgs1KFBG6jz6Ie7AMm/ePB0+fFgrVqxQfX29Jk+erC1btkQm1u7fv195eR0dNzNnztQzzzyjBx98UMuXL9fo0aO1adMmjR8/Pnl7kQOi6/d3X3pH7x05qVVfmdTneT++MXxyCHjBXL2xVl54J+7AIkmLFi3SokWLYj63bdu2Lo/dfvvtuv322/v8/u+//35/ipXVoiestbQ6Pbf7oO74zEhNvfKSDJaq/1hECkgea+HFWHHhiYxfJYS+idUgnWkJp78gSRLsYaH5AuJl7Y7ngfL6X1x4iMCCjIhe3ZaVboH4WfvS5yQFiSKwGBGreluu88EGy/COABmSbVcOAr0hsBiRbQ0SjS2QPBbqEKcoSBSBxYzsquKBhfAyWA7AKgshJVpw8csMFgRmEViMyLYKHr0/YSaxAHGzfKWdtfLCDwQWwyxcGdAduoeBxFibxOp/CeE7AosRBtqjuITpHgZyirWABf8QWIyI1Ztiuc5zq3kgt1DLkSgCixGxwonlBoCrhIDEWJ7Eaq288AOBxTDL3aqWG1vAB+bqjbkCwzcEFiOyb+G4qN9N9xUBmWehDgUn2vtfXviHwGJErHASNpxY6GEBEmNtWJU7tCNRBBYjYp2RWF6+JHgvIcM7AmSItWpjed0Y+IHAYkW29bB08zuA+FGHkAsILIZly6RbWlsgfoEeCwNtAUNCSBSBxYhY9dvykBCTboHEWPvSp84jUQQWI2KdQdkeEuooezicwYIARlkbVrU2SRj+IbAYkd09LAASYqASWRi2gt8ILEbEXOnWcAMQDoxn290PIGOoN8gxBBbDsmVIyO5eAJljbUgoGicp6A8CixExh4QMz/3gzq1AYqzVIa4SQqIILEZk3aRbVroFcgpXBiFRBBYjsvteQgDiZW1YlTqPRBFYrIg16dZwtecSRyAx1oZYrNT5ug8+1gMbdqux6Uymi4JOCjJdAPRNtt1LKLrBsjy0BWQKtSY1/vyJWknS0dMtenrh9AyXBtHoYTGs1XBiiQ4pdvcC8IOF3tbAvDUD5X2n4USmi4BOCCxGxFyHJf3FSJpg97DlPQEygyGh1Go+Z/gyzCxFYDEi2xaOM9FiAR6z0EsRYGzSLUPV/iGwGBGrcbJcn8LMYQESY7iHBegPAosRsRoky1/0gUsy7e4GgP4wUOlN92BnKQKLEVm9Dovh/QAyxVq1cUy0R4IILEZkWw9L2Nh4NuCb4GrR/tcia5NuDRQx5xBYzMiu6hM9JGQ5eAGZYm3lWKo5EkVgMSLbelhM32oWQNyCtxKg0iN+BBYjYt6t2XCdDxtbRArwjbUhlmgWymuhjLmGwGJErDFqyxWKSbdAYoJDQv5XImtDWPAPgcUwy0NC0SW33FMEoG8MN1fwBIHFiGyr6wwJAYmxvJaRhfJauPIq1xBYjIg56dZw1wRDQkBirA2xWLv5IfxDYDEi5sJxaS9FamTLfgDoIwOV3kARcw6BxYhY3ZOW57CEjS16BfjMQhUyUER4jsBimOERIYaEgAQFg77/lcjaEBb8Q2AxIuaXuuFv+uCEQbv7AWSKtXVYqPNIFIHFiFiT1Cz3sHAvISAx1r7z6VVFoggsRsSq4JZn2kfvj+XgBfjAQhWyUEb4jcBiROx7CaW/HMlD9zCQCMtDLCZKa6KQuYXAYkTsISG7NSocznQJANusVX9rQ0IGiphzCCyWGa5RllfpBHxg6xohKdCraqDEoUwXAF0QWIyIPSTkf6XvTnAOi939AHxgoQpZKCP8RmAxIsuuajZ4dgj4xVr9t3YZNvxDYLEiyybdBle6zWBBALPsTroF+oPAYkSsMV8L48DdCqzDYng/gAyxtnIsoQqJIrAYEXMdFsP1n+5hILcE67yBSs+sW+8QWIyIVb0tT1bl5odAYozdSshcjxD8Q2AxItaXuuXveWtrMgC+CSwNkMFy9JVj3hoSRGAxItt6WIydHALeMVz9gX4hsBgR+15CdkWHLcvBC/CBhWHV4EmK/+WFfwgsRsReh8VwpWdICEiIuV5K6jwSRGAxzHKltzb+DvjGWv03F7DgHQKLFTFaJ8tDKYGbHxreD8AHFqqQ6R5heIHAYkTsSbdpL0bSRPewWN4PIFOCvZS2KhHZBf1BYDEi6xaOY6VbIDHGqo3r4S+gLwgsRsRcmt9wYgkzAQ9IiLXVoll7CYnqV2BZt26dRo0apeLiYs2YMUM7duzocfuNGzdqzJgxKi4u1oQJE7R58+bIcy0tLfrmN7+pCRMm6IILLtDQoUN155136tChQ/0pWtbKtsuaxaRbIGks1CF6UpGouAPLs88+qyVLlmjlypXatWuXJk2apKqqKjU2Nsbc/pVXXtH8+fN19913a/fu3Zo7d67mzp2rvXv3SpJOnTqlXbt26aGHHtKuXbv03HPPad++fbrlllsS27MsEyuwWJ50G110y/sBZIq1HlZ6WJCouAPL6tWrdc8992jhwoUaN26c1q9fr0GDBumpp56Kuf3atWs1e/ZsLV26VGPHjtXDDz+sKVOm6PHHH5cklZaWauvWrfrKV76ia6+9Vp/5zGf0+OOPq66uTvv3709s77JI7HVY0l6MpAkHJ7EAiJPr9g8/sXAcEhVXYDl79qzq6upUWVnZ8QZ5eaqsrFRtbW3M19TW1ga2l6Sqqqput5ekY8eOKRQKafDgwTGfb25uVlNTU+An28U6m7LcM8GaDEBiLE9ct9B0cbNm/8QVWI4cOaLW1laVlZUFHi8rK1N9fX3M19TX18e1/ZkzZ/TNb35T8+fPV0lJScxtqqurVVpaGvkZMWJEPLuRNSxU+u4Eu4cN7wiAvjFQzWmL/ObVVUItLS36yle+IuecnnjiiW63W7ZsmY4dOxb5OXDgQBpLmRmxJ93arVzBewllsCCAUeauEjIw0Z62yG8F8Ww8ZMgQ5efnq6GhIfB4Q0ODysvLY76mvLy8T9u3h5UPPvhAL730Ure9K5JUVFSkoqKieIqelQKrxRpmobEFfBPdG2ChClmYdGt5mD0XxNXDUlhYqKlTp6qmpibyWDgcVk1NjSoqKmK+pqKiIrC9JG3dujWwfXtYeeedd/Tzn/9cl156aTzFygkx12Ex0UzFZnn8HfCBtVpjYdItgcVvcfWwSNKSJUu0YMECTZs2TdOnT9eaNWt08uRJLVy4UJJ05513atiwYaqurpYkLV68WLNmzdKqVas0Z84cbdiwQTt37tSTTz4pqS2s/MVf/IV27dqln/zkJ2ptbY3Mb7nkkktUWFiYrH01LfZlzcn9jKOnzqrxeLOuKbsouW8cQ6B7mDYCSAhzL5KDw+i3uAPLvHnzdPjwYa1YsUL19fWaPHmytmzZEplYu3//fuXldXTczJw5U88884wefPBBLV++XKNHj9amTZs0fvx4SdLBgwf1wgsvSJImT54c+KyXX35Zn/vc5/q5a9klHZc1f+6ft+noqRb9dPFnNfaK7ofkkiF6OIvGFugHYysDOANLGdDD4re4A4skLVq0SIsWLYr53LZt27o8dvvtt+v222+Puf2oUaP4wuqDWMco2cft6KkWSdK2fYdTHlgsTMADfObrsEp3LCxlEN1rHQpxYbNvvLpKCN2LfbdmX6t97yxMwAN8Zq0OWVjKwHKbmgsILFak8V5C6TixYNItAN+4LLnyMlsRWIyI3cOSos9KQ36IDimsfQDEz3JngK9Fp4fFbwQWI9IxhyXyvmloTqx1ZwO+6VxPfR1mkbqWzdeiRgcWn49nriKwGJHOmx+mo54Gz2RoGIBE+fz92rlsvhY1HBiqhm8ILEbEXocleVUqHE7vmYW1ZcUB31iqN1aKauHS61xGYDEsmQ1Wa6ArNHnv253oz2DcGIhf51rjcy3qOiTkZ2nDtEteI7AYEWteSTIrVGt0D0vS3rV7Ls0BCcg2XYZZDFUkX0vKTVn9RmAxIvbdmpMnnO4elm5+B5B9rNTxYGCxUurcQWAxImZgSVkPC1cJAf5zPfzlly513NPC0i75jcBiWFLnsITT28PC5YNAYroOCWWmHH3R5RJsTxMLPSx+I7AYEetL3fIcFi4fBBJjqd5YCVdMuvUbgcWIVK9025rmvlBHDwuQVL72WljCpFu/EViMSPmk26h7aJxLQ01luQMgMVZWj43F17Jaufw6VxFYjIh19pTUSbdR79WahkrKmQyQGEvVputKt36WvnNbRNvkFwKLEbGvEkre+0evdBtORw9L9O+cxQBxs1Rtut73KEMF6UXneSvMY/ELgcWI2HNYUjPpNh1DQoGrhFL+aUD26XKlsMcVyeeyRYseGpcILL4hsBiWqqX509LDwspxQEIs9UwaWYalS0AxdIhzAoHFiHTe/DAdc1hcYA4LrQIQLyvzQmLxtcpbufw6VxFYzIg16TZ57x6YdBvuYcMkCazDQqMAxM3KvBApVm+Qn4VlDovfCCxGxL6sOTVzWFo7D+SmQHAOC40CEK80VNOksVLDCSx+I7AYkeqF46Ibv3T0sHDPDiAxXZe795eVoRYua/YbgcWIWBPskjnp7lxUYknHWUVwDkvKPw7IOl1DgL8VqcuCbBkqR29YOM5vBBYjYlWbpK7DEpjDko7LmqP/olEA4tV10q2/OjcpvgYBelj8RmAxItVL87cGhoTSvA4LjQIQN0tzv3wNKJ0xh8VvBBYj0nm35nQEFpZhARLTtdciM+Xoiy5lzUwxekVg8RuBxYhUr3QbGBJiHRbAe11OYjyuRlYuwe585ZWv5cxVBBYjUn0vodY030uIdViAxFiaX2ElW9HD4jcCixGxKk6qFo5Lx72Eos8OaRKA+HVdis3fmmTli79rYMlQQRATgcWI2D0sqVmaPx2NS7CHhVYBiFfXS3AzVJA+sHIJdudipaO3GX1HYDEiVohIZl1K96RbrhICEmOp3ljtYTFS7JxBYDEi5Tc/TPM6LNG91z53ZQO+6vLlmqFy9IWVlW47t320TX4hsBgRcw5LEt+fdVgAW6wMs0h26jhzWPxGYDEi9qTbJK7DkubLmrlKCEiMlWEWKVZvkJ9l77rSrZ/lzFUEFiNSvTR/9B2a0zHRLLrBolEAEudzb0CXK5o8LWuXISFfC5qjCCwGOOdSPoclMCSU7h6WlH8akH4//M0B/WjPwZS9v6UhISsnJQwJ+a0g0wVA77qr68msS9G9Kuda07sOC4kF2ebY6RZ9479flyRVji3TBUXJb2otfblamXTLwnF+o4fFgO4qTTKHbqJ7VdK+DguJBVnmTEtr5PdPTp1NyWd0rjXp6Bntr869P74GgdZOS/N3XqofmUVgMaC7XJKqpfnTcvPDQEBK+ccBaXX2XMc33ZmW1HzrdekN8LgiWblRIz0sfiOwGNBdD0RSh4TSvA4LK90imzUHAktrD1smwNAVLZ3bMF97gzqHPk+LmbMILAZ0V2mS2UBFz1tJdWPSZUnxlH4akH7N51pj/p5Mnet/WhZ87KfOQyu+lrVz25fpEHjo6Gl9fDI1Q4oWMenWgO4qTTLrUvRnpHrc1soEPKC/ooeEmlM0JNS52niaASR17WHxtVfVp3VYjp9p0cxHXpIkvVf9JYVCoYyVxRf0sBjQXUOU1B6WNM5h6Xq/Dj8bL6C/AkNCaephyXRvQE86F83KkFAmQ+DBo6cjvzedOZe5gniEwGJAd1/oyV2aP+qy5pQHluDffjZdQP9F97BE/55MXe4s7GkIkGINX2WoIL3waeG46I8+fTZF86CMIbAY0P1VQslcOC59lzV37R5O6ccBaRfdw9KcpsDi67wQKUa48rSsPt1QMnqy9qmz9LBIBBYTugsmyazz6RwS6jKHhT4WZJn09LB0vqw5JR+TFF16WDw9S/HpUvHoy+FP0cMiicBiQjp6WKIrZqoraddGIaUfB6Rd9JVBZ1M0/tF10q2fIUCyU9auk24zUw4pOPfpdKoujTeGwGJAtyvdpqiHJdVzWLr2sADZJR09LFZ6LaRYvUF+ltWnOSzNUSElVVeaWUNgMaCnOpOsChVYOC7FlZSrhJDt0jGHpXPHjc/1qHM+8TVc+XSVUFoWHzSGwGJATw1Rsup99MJxqR8SSunbAxmXjh6W1k5jqb5eeSPFCAKeltWndViiQ0qqQq81BBYDevqCT1Z1SmcPS+cA5vPVDUB/BOawpCywtNWbvPPrifk6L0Tq2qb4Wud9Wuk2etItPSxtCCwG9FRpklWhzkWd8jiX2l6WznNkUj1nBki3QA9Liro+2qtNQX5bM+7rvBCpa0DxNVz5dC8heli6IrAY0NPZSLIqfuc2NZW9LJ33p8XnvmygH5rTMCTUfpJReD6w+DovROqo8wPy27qDvA0s9LB4jcBiQHsPxAWF+V2eS1Z96joenr4eFl+7h4H+Ssek2/Yq2xECUvIxSdERWPICf/um65BQhgqi4GXN9LC0IbAY0B4mCgvytOLL43T71OGR55I1nNK5kyOVZxatrZ17WPxsvID+SkcPS/uX6wADQ0Lt7VRhgd+BpetVQn5MuqWHpQ13azagvbLn5+Xprhuv0rnWsDbW/W/bc0kaTuncw5LKeSXnunwWZw/ILqmew+Kc69Jr4eswi9QRBDrKmsnSdK/Lfc48GRKih6UNPSwGtF9yXHD+coD8vJDa7zSerMawcydHKs/WOp9dnaOHBVkmeJVQ8s+Oo6uQ770WUlQPi+fhqvMxzOT0umZ6WLogsBjQGulhaUspoVAocqaSrOGUTM5hoYcF2eZsiuewRNdP3yeySh1l8z1cdb4AIJMXBDCHpSsCiwHtX/AF5xsmqeNMpSVJ/5A7j7On4yqh9l4ieliQbVI9hyU6nBTk+T3MInXUcd97WDoHlFTNP+qLwJAQPSySCCwmdO5hkTrOqpJ1BtA5waejh6W4ID/yt8/LigPxir5ZXSq+9KJ7KQd43mshRV0lVBAK/O2bzj3WzSkYzusr1mHpisBiQPuQSUEgsLT9p0vWHJYuPSwpncPS9llFA/KiHvOzAQP6I/rLJhWTbqPrS6GBIaEuVzQ5P+991Pm/VSaDwqmzzGHpjMBiQHt3an5ex3+uZM9h6VxRUzlM0/7e7T0sEqvdIrucSXEPSyCwnO9h8Xlo9Vynq4SkzK4i253OQ+ypWqW4L06fpYelMwKLAe2NU3QPS3sjlawhoc6N6smz55LyvrG0V74LijoCC6vdIpukekioPRANyA/pgsKCLp/pm/YgcGFRx0oayfgSfu/ISf39j3+ng0dPJ/xeUkc71N6+ZnIOS3QbTA9LGwKLAe3/WIsHRPewnJ/DkqJJt9HdkcnW3rBeWDwg8pjPZ4dAvE6fTe1VQpE2oSBfg86vgH06hXU2Ue11/pILCiOPJeOk6Ls17+h7v35fi3+wO+H3kjp6rC86H6wyGVjoYemqX4Fl3bp1GjVqlIqLizVjxgzt2LGjx+03btyoMWPGqLi4WBMmTNDmzZsDzzvntGLFCl1xxRUaOHCgKisr9c477/SnaFmpPTwMLOw4O0n2HJbOAeVkc+p6WNob2wuL8iNXCtHDgmzSnOI5LO1XkBQNyNeg81+uqewVTVR7nb+gsCNgnWpOPGA9v/ugJGnnB58k/F6SdOr8MSwd2HYylcmgQA9LV3EHlmeffVZLlizRypUrtWvXLk2aNElVVVVqbGyMuf0rr7yi+fPn6+6779bu3bs1d+5czZ07V3v37o1s8+ijj+q73/2u1q9fr9dee00XXHCBqqqqdObMmf7vWRZpr0SDBnQMoRSd77JM1lnV8TMtkqQhF7adAZ1MQmPSnfYyDxyQr5LzvSxHT7ek7POAdGoNu8CXzfEzLUmfYNq+RkfxgLxIu+B1D8v5shUX5uuC8wHrRIInRZ17P5Kx6vfxM21luvR8O5ipHpbWsAtc1uzzcF86xR1YVq9erXvuuUcLFy7UuHHjtH79eg0aNEhPPfVUzO3Xrl2r2bNna+nSpRo7dqwefvhhTZkyRY8//riktt6VNWvW6MEHH9Stt96qiRMn6vvf/74OHTqkTZs2JbRz2aK992NQ1M0PL7uoSJJ0+ERzwu8fDjsdP994lJcWS0ptD0t7ozCosCDSMHx04mzKPg9Ip09OnQ2siXKmJaymM8mtT+29E4MKbfSwnGrpOElpv4nrqQTL++7hE4G/3//oZELvJ3W0TcMvHiRJ+uhk4u1rf3zUqV1vPN7s9b2i0iWuewmdPXtWdXV1WrZsWeSxvLw8VVZWqra2NuZramtrtWTJksBjVVVVkTDy3nvvqb6+XpWVlZHnS0tLNWPGDNXW1uov//Ivu7xnc3Ozmps7/oM2NTXFsxt9dq41rO+8+Fbk7/azJBf5+/z/n3+k4+/g85LreK6Pr2l/Xk567ny352UlRZGylJW0BYvHXvqD3m08IXf+tU7u/CWDbS8Oh9sec+78pYRyOv8/hV3b44ePN8u5tkWdxpaXaO/BJq35+dt688MmOecC2zp1vHfbewYfb/+s9td1POciz29+o16SdEVpsT48dlp/PHxSD//kTU2/6pK+/YfppKez196qeE8nvq6XV/f82v5/bm+v7vFze/ngnvap99em5nN7O1g9f24vx6rH1/b/c3v67A8+OiWp7aSiuaVVTWfO6YENuzXykkFd60mkHO31Jvhc2HXU1+g6tf3tw5La2oGS4rZmfNPuQ5FF5BJpqwLPu95fE91WBZ/vKMOP9hySJJWXFOvC8+X9zotvaeLw0o525Pxr2tus9nbMORd5vv1yaOekPQeOBo778uf2atzQksjf3bVd0W1RdLvY0upU39TWq3/9yMF6fvdBbX6jXn/3wu+67E/0vkcfl97a/FjbtrfJHY87vRf1b+ijE806ey6sv/3hnsAcoEwoyAvp23PGZe7z49n4yJEjam1tVVlZWeDxsrIy/f73v4/5mvr6+pjb19fXR55vf6y7bTqrrq7W3//938dT9H5pdU5Pv/J+yj+nr64fcXHH7yMH6/u1H+jw8Wb9f7UfJOX9p1w5WJ+79nJtrPtfHTp2JuX7fv3Itv35zfuf6M0Pm/Tmh6kJnkAmzPz0pTrX6vTiGx/q5X2HU/IZk0cM1g2j2oL+ieZzXrVXsUwcPlgzGk9o78Em7TlwtEvo6I8hFxbpyIlm7Xj/Y+14/+OE32/4xQNVObZM/++P39S5cGa/A2Zdc5kOfnJatX/8KBL6MqmwIM9OYPHFsmXLAr02TU1NGjFiRNI/Jz8U0v2f/7QkKaT2+/jo/N/nhUKR3zue67pt5PdQx6XJ3b0m1vuVlRar6rqOUDd38jDl5+Xp7frjHa8JhZQXats+FFLb7+ffJC8UimzX9lzHfYnyQlJBfp6qxpXpsouK9H/mTdK7jSc7ynN+H0NR7x2SlJcXXeaoz1QosL8hdZSl/bWXXdS2P//XmMs1dPBAHT7ec9drp8PW9fmen+71DXp7fU8vD/Xy6kTL3uvre9sggffP+L4l+Pk9vzZ1/yYKC/L05YlDNSA/pInDS3X8zLlAPY1Vn9rrmrp7rlO9urAoX7dOHqbiAfn69zunafeBT3psp6L/7qmtCvzdh/aqL+8rSaPLLtK4oSUaNeQaXVFarKOnWgLtQl57W5EXbLOi25T29qr9uWGDB+qmay7Tc7v+V//7yelIz0nw2IW6HM/u2q68UCjSJv3wvgq9/PtGOdd7u99d+x3r+HW7badjVjJwgP504lCdPRfWf7z2QcJDaMkQvRZYJsQVWIYMGaL8/Hw1NDQEHm9oaFB5eXnM15SXl/e4ffv/NzQ06IorrghsM3ny5JjvWVRUpKKiopjPJVNBfp6WVo1J+ef0RygU0i2ThkqTkv/et10/PPlv2o3CgpAWzByVts8D0u3/mfXplH9G5bgyVY4r631DDwwqLND//dlPJfU9590wMqnvJ0lTRl6sKSMv7n3DFBtYmK/7P391povhhbjiUmFhoaZOnaqamprIY+FwWDU1NaqoqIj5moqKisD2krR169bI9ldddZXKy8sD2zQ1Nem1117r9j0BAEBuiXtIaMmSJVqwYIGmTZum6dOna82aNTp58qQWLlwoSbrzzjs1bNgwVVdXS5IWL16sWbNmadWqVZozZ442bNignTt36sknn5TU1lPwwAMP6Dvf+Y5Gjx6tq666Sg899JCGDh2quXPnJm9PAQCAWXEHlnnz5unw4cNasWKF6uvrNXnyZG3ZsiUyaXb//v3Kixrnmjlzpp555hk9+OCDWr58uUaPHq1NmzZp/PjxkW2+8Y1v6OTJk7r33nt19OhR3XjjjdqyZYuKi4uTsIsAAMC6kPPxlplxampqUmlpqY4dO6aSkpLeXwAAADIunu9v7iUEAAC8R2ABAADeI7AAAADvEVgAAID3CCwAAMB7BBYAAOA9AgsAAPAegQUAAHiPwAIAALwX99L8PmpfrLepqSnDJQEAAH3V/r3dl0X3syKwHD9+XJI0YsSIDJcEAADE6/jx4yotLe1xm6y4l1A4HNahQ4d00UUXKRQKJfW9m5qaNGLECB04cID7FKUQxzk9OM7pw7FOD45zeqTqODvndPz4cQ0dOjRw4+RYsqKHJS8vT8OHD0/pZ5SUlFAZ0oDjnB4c5/ThWKcHxzk9UnGce+tZacekWwAA4D0CCwAA8B6BpRdFRUVauXKlioqKMl2UrMZxTg+Oc/pwrNOD45wePhznrJh0CwAAshs9LAAAwHsEFgAA4D0CCwAA8B6BBQAAeI/A0ot169Zp1KhRKi4u1owZM7Rjx45MF8lbv/jFL/Snf/qnGjp0qEKhkDZt2hR43jmnFStW6IorrtDAgQNVWVmpd955J7DNxx9/rDvuuEMlJSUaPHiw7r77bp04cSKwzeuvv67PfvazKi4u1ogRI/Too4+mete8Ul1drRtuuEEXXXSRLr/8cs2dO1f79u0LbHPmzBndf//9uvTSS3XhhRfqz//8z9XQ0BDYZv/+/ZozZ44GDRqkyy+/XEuXLtW5c+cC22zbtk1TpkxRUVGRrr76aj399NOp3j1vPPHEE5o4cWJkoayKigr99Kc/jTzPMU6NRx55RKFQSA888EDkMY51cvzd3/2dQqFQ4GfMmDGR570/zg7d2rBhgyssLHRPPfWU+93vfufuueceN3jwYNfQ0JDponlp8+bN7tvf/rZ77rnnnCT3/PPPB55/5JFHXGlpqdu0aZP77W9/62655RZ31VVXudOnT0e2mT17tps0aZJ79dVX3S9/+Ut39dVXu/nz50eeP3bsmCsrK3N33HGH27t3r/vBD37gBg4c6P71X/81XbuZcVVVVe573/ue27t3r9uzZ4/70pe+5EaOHOlOnDgR2ea+++5zI0aMcDU1NW7nzp3uM5/5jJs5c2bk+XPnzrnx48e7yspKt3v3brd582Y3ZMgQt2zZssg2f/zjH92gQYPckiVL3Jtvvukee+wxl5+f77Zs2ZLW/c2UF154wb344ovu7bffdvv27XPLly93AwYMcHv37nXOcYxTYceOHW7UqFFu4sSJbvHixZHHOdbJsXLlSnfddde5Dz/8MPJz+PDhyPO+H2cCSw+mT5/u7r///sjfra2tbujQoa66ujqDpbKhc2AJh8OuvLzc/dM//VPksaNHj7qioiL3gx/8wDnn3Jtvvukkud/85jeRbX7605+6UCjkDh486Jxz7l/+5V/cxRdf7JqbmyPbfPOb33TXXnttivfIX42NjU6S2759u3Ou7bgOGDDAbdy4MbLNW2+95SS52tpa51xbuMzLy3P19fWRbZ544glXUlISObbf+MY33HXXXRf4rHnz5rmqqqpU75K3Lr74Yvfv//7vHOMUOH78uBs9erTbunWrmzVrViSwcKyTZ+XKlW7SpEkxn7NwnBkS6sbZs2dVV1enysrKyGN5eXmqrKxUbW1tBktm03vvvaf6+vrA8SwtLdWMGTMix7O2tlaDBw/WtGnTIttUVlYqLy9Pr732WmSbm266SYWFhZFtqqqqtG/fPn3yySdp2hu/HDt2TJJ0ySWXSJLq6urU0tISONZjxozRyJEjA8d6woQJKisri2xTVVWlpqYm/e53v4tsE/0e7dvk4r//1tZWbdiwQSdPnlRFRQXHOAXuv/9+zZkzp8vx4Fgn1zvvvKOhQ4fqU5/6lO644w7t379fko3jTGDpxpEjR9Ta2hr4DyNJZWVlqq+vz1Cp7Go/Zj0dz/r6el1++eWB5wsKCnTJJZcEton1HtGfkUvC4bAeeOAB/cmf/InGjx8vqe04FBYWavDgwYFtOx/r3o5jd9s0NTXp9OnTqdgd77zxxhu68MILVVRUpPvuu0/PP/+8xo0bxzFOsg0bNmjXrl2qrq7u8hzHOnlmzJihp59+Wlu2bNETTzyh9957T5/97Gd1/PhxE8c5K+7WDOSq+++/X3v37tWvfvWrTBclK1177bXas2ePjh07pv/6r//SggULtH379kwXK6scOHBAixcv1tatW1VcXJzp4mS1L37xi5HfJ06cqBkzZujKK6/UD3/4Qw0cODCDJesbeli6MWTIEOXn53eZId3Q0KDy8vIMlcqu9mPW0/EsLy9XY2Nj4Plz587p448/DmwT6z2iPyNXLFq0SD/5yU/08ssva/jw4ZHHy8vLdfbsWR09ejSwfedj3dtx7G6bkpISE41bMhQWFurqq6/W1KlTVV1drUmTJmnt2rUc4ySqq6tTY2OjpkyZooKCAhUUFGj79u367ne/q4KCApWVlXGsU2Tw4MG65ppr9Ic//MHEv2kCSzcKCws1depU1dTURB4Lh8OqqalRRUVFBktm01VXXaXy8vLA8WxqatJrr70WOZ4VFRU6evSo6urqItu89NJLCofDmjFjRmSbX/ziF2ppaYlss3XrVl177bW6+OKL07Q3meWc06JFi/T888/rpZde0lVXXRV4furUqRowYEDgWO/bt0/79+8PHOs33ngjEBC3bt2qkpISjRs3LrJN9Hu0b5PL//7D4bCam5s5xkl0880364033tCePXsiP9OmTdMdd9wR+Z1jnRonTpzQu+++qyuuuMLGv+mEp+1msQ0bNriioiL39NNPuzfffNPde++9bvDgwYEZ0uhw/Phxt3v3brd7924nya1evdrt3r3bffDBB865tsuaBw8e7H70ox+5119/3d16660xL2u+/vrr3WuvveZ+9atfudGjRwcuaz569KgrKytzX/3qV93evXvdhg0b3KBBg3Lqsua/+qu/cqWlpW7btm2ByxNPnToV2ea+++5zI0eOdC+99JLbuXOnq6iocBUVFZHn2y9P/MIXvuD27NnjtmzZ4i677LKYlycuXbrUvfXWW27dunU5dRnot771Lbd9+3b33nvvuddff91961vfcqFQyP3sZz9zznGMUyn6KiHnONbJ8vWvf91t27bNvffee+7Xv/61q6ysdEOGDHGNjY3OOf+PM4GlF4899pgbOXKkKywsdNOnT3evvvpqpovkrZdfftlJ6vKzYMEC51zbpc0PPfSQKysrc0VFRe7mm292+/btC7zHRx995ObPn+8uvPBCV1JS4hYuXOiOHz8e2Oa3v/2tu/HGG11RUZEbNmyYe+SRR9K1i16IdYwlue9973uRbU6fPu3++q//2l188cVu0KBB7rbbbnMffvhh4H3ef/9998UvftENHDjQDRkyxH396193LS0tgW1efvllN3nyZFdYWOg+9alPBT4j2911113uyiuvdIWFhe6yyy5zN998cySsOMcxTqXOgYVjnRzz5s1zV1xxhSssLHTDhg1z8+bNc3/4wx8iz/t+nEPOOZd4Pw0AAEDqMIcFAAB4j8ACAAC8R2ABAADeI7AAAADvEVgAAID3CCwAAMB7BBYAAOA9AgsAAPAegQUAAHiPwAIAALxHYAEAAN4jsAAAAO/9/0/OFFt3NlniAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["from torch.utils.data import Dataset\n","\n","class SiameseDataset(Dataset):\n","  \n","  def __init__(self, x, y, transform = None):\n","    self.x = x\n","    self.transform = transform \n","    self.y = y.reshape(-1)\n","  \n","  def __getitem__(self, idx):\n","    anchor = self.x[idx]\n","    same_class_idx = np.arange(self.x.shape[0])[idx//20 * 20 : (idx//20 + 1) * 20]\n","    other_class_idx = np.concatenate((np.arange(self.x.shape[0])[0 : idx//20 * 20],\n","                                      np.arange(self.x.shape[0])[(idx//20 + 1) * 20 :])) \n","\n","\n","    positive = self.x[np.random.choice(same_class_idx)]\n","    positive = positive + self.x[np.random.choice(other_class_idx)] + self.x[np.random.choice(other_class_idx)]\n","    \n","    negative = self.x[np.random.choice(other_class_idx)]\n","    negative = negative + self.x[np.random.choice(other_class_idx)] + self.x[np.random.choice(other_class_idx)]\n","    if self.transform is not None:\n","      anchor = self.transform(anchor)\n","      positive = self.transform(positive)\n","      negative = self.transform(negative)\n","    return anchor, positive, negative\n","  \n","  \n","  def __len__(self):\n","    return len(self.x)"],"metadata":{"id":"r9fWNZ1BYDnf","executionInfo":{"status":"ok","timestamp":1686550820653,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["from torch.utils.data import Dataset\n","\n","class ValidationSiameseDataset(Dataset):\n","  \n","  def __init__(self, x, y, transform):\n","    self.x = x\n","    self.transform = transform \n","    self.y = y.reshape(-1)\n","  \n","  def __getitem__(self, idx):\n","    anchor = self.x[idx]\n","    same_class_idx = np.arange(self.x.shape[0])[idx//5 * 5 : (idx//5 + 1) * 5]\n","    other_class_idx = np.concatenate((np.arange(self.x.shape[0])[0 : idx//5 * 5],\n","                                      np.arange(self.x.shape[0])[(idx//5 + 1) * 5 :]))\n","    \n","    positive = self.x[np.random.choice(same_class_idx)]\n","    positive = positive + self.x[np.random.choice(other_class_idx)] + self.x[np.random.choice(other_class_idx)] \n","    \n","    negative = self.x[np.random.choice(other_class_idx)]\n","    negative = negative + self.x[np.random.choice(other_class_idx)] + self.x[np.random.choice(other_class_idx)] \n","\n","    if self.transform is not None:\n","      anchor = self.transform(anchor)\n","      positive = self.transform(positive)\n","      negative = self.transform(negative)\n","    return anchor, positive, negative\n","  \n","  \n","  \n","  def __len__(self):\n","    return len(self.x)"],"metadata":{"id":"f0QOoUsPYF6F","executionInfo":{"status":"ok","timestamp":1686550820653,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["def transformation(x):\n","  x = x/np.max(x)\n","  x = x + np.random.normal(0,0.005,x.shape[0])\n","  x = torch.Tensor(x)\n","  return x"],"metadata":{"id":"zKn5yY3CYHtN","executionInfo":{"status":"ok","timestamp":1686550820654,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["from torch.utils.data import  DataLoader\n","\n","train_dataset = SiameseDataset(x_train[2000:], y_train[2000:], transform = transformation)\n","val_dataset = ValidationSiameseDataset(x_val[500:], y_val[500:], transform=transformation)"],"metadata":{"id":"DFU0KrNEaEym","executionInfo":{"status":"ok","timestamp":1686550820654,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["val_data = list(val_dataset)\n","plt.plot(val_data[0][2].numpy()) #example of processed data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"wamZSJv-aJkF","executionInfo":{"status":"ok","timestamp":1686473912234,"user_tz":-180,"elapsed":2150,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"70a108f6-0cc0-4345-b262-8c3dd975848d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f583dd1d030>]"]},"metadata":{},"execution_count":7},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)\n","val_loader = DataLoader(val_dataset, batch_size=128, shuffle=False)"],"metadata":{"id":"ANR0hOxeaYoG","executionInfo":{"status":"ok","timestamp":1686550821467,"user_tz":-180,"elapsed":437,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["from torch import nn\n","class SiameseNet(nn.Module):\n","\n","    def __init__(self, latent_dim):\n","      super().__init__()\n","      self.latent_dim = latent_dim\n","      self.model = nn.Sequential(nn.Conv1d(in_channels=1, out_channels=16,kernel_size=21, padding = 'same'),\n","                                  nn.MaxPool1d(kernel_size=2, stride=2),\n","                                  nn.LeakyReLU(),\n","                                  nn.BatchNorm1d(16),\n","                                  nn.Conv1d(16, 32, 11, padding = 'same'),\n","                                  nn.MaxPool1d(2, 2),\n","                                  nn.LeakyReLU(),\n","                                  nn.BatchNorm1d(32),\n","                                  nn.Conv1d(32, 64, 5, padding = 'same'),\n","                                  nn.MaxPool1d(2, 2),\n","                                  nn.LeakyReLU(),\n","                                  nn.BatchNorm1d(64),\n","                                 \n","                                 nn.Flatten(),\n","                                 nn.Linear(64*625, self.latent_dim))\n","\n","    \n","    def _forward(self, x):\n","      out = x.view(-1, 1, 5000)\n","      out = self.model(out)\n","      # normalize embedding to unit vector\n","      out = torch.nn.functional.normalize(out) \n","      return out\n","    \n","\n","    def predict(self, x):\n","      out = x.view(-1, 1, 5000)\n","      out = self.model(out)\n","      out = torch.nn.functional.normalize(out)\n","      return out\n","\n","    def forward(self, anchor, positive, negative, latent_dim):\n","        output1 = self._forward(anchor)\n","        output2 = self._forward(positive)\n","        output3 = self._forward(negative)\n","        \n","        return output1, output2, output3"],"metadata":{"id":"zqfsUsn4ajMW","executionInfo":{"status":"ok","timestamp":1686550822352,"user_tz":-180,"elapsed":2,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["def train(num_epochs, model, criterion_train, criterion_val, optimizer, train_loader, val_loader, latent_dim):\n","    loss_history = []\n","    l = []\n","    l_val = []\n","    n_p_train = []\n","    n_p_val = []\n","    \n","    for epoch in range(0, num_epochs):\n","      model.train()\n","      for i, batch in enumerate(train_loader, 0):\n","          anc, pos, neg = batch\n","          output_anc, output_pos, output_neg = model(anc.to(device), pos.to(device), neg.to(device), latent_dim)\n","          loss, number_pos = criterion_train(output_anc, output_pos, output_neg)\n","          loss.backward()\n","          optimizer.step()\n","          optimizer.zero_grad()\n","          l.append(loss.item())\n","          n_p_train.append(number_pos.item())\n","      \n","      model.eval()\n","      for i, batch in enumerate(val_loader, 0):\n","          anc, pos, neg = batch\n","          output_anc, output_pos, output_neg = model(anc.to(device), pos.to(device), neg.to(device), latent_dim)\n","          loss_val, number_pos_val = criterion_val(output_anc, output_pos, output_neg)\n","          l_val.append(loss_val.item())\n","          n_p_val.append(number_pos_val.item())\n","      #scheduler.step()\n","      last_epoch_loss =  torch.tensor(l[-len(train_loader):-1]).mean()\n","      last_epoch_val_loss = torch.tensor(l_val[-len(val_loader):-1]).mean()\n","      print(\"Epoch {} with {:.4f} loss and {:.4f} val_loss\".format(epoch, last_epoch_loss, last_epoch_val_loss))\n","        \n","    return l, last_epoch_loss, l_val, last_epoch_val_loss, n_p_train, n_p_val"],"metadata":{"id":"saBy4NoHanUW","executionInfo":{"status":"ok","timestamp":1686550824312,"user_tz":-180,"elapsed":452,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":["Будем расчитывать loss только по триплетам, которые дают достаточно большую ошибку (hard triplets и \"сложная\" часть semi-hard triplets)."],"metadata":{"id":"-3X7gUFNcBad"}},{"cell_type":"markdown","source":["![Diagram-a-demonstrates-a-triplet-violating-the-triplet-loss-condition-i.ppm](data:application/octet-stream;base64,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)"],"metadata":{"id":"s6V9rxmMbXJO"}},{"cell_type":"code","source":["import torch.nn.functional as F\n","class TripletLoss(nn.Module):\n","    \"\"\"\n","    Triplet loss\n","    Takes embeddings of an anchor sample, a positive sample and a negative sample\n","    \"\"\"\n","\n","    def __init__(self, margin, semi_hard):\n","        super(TripletLoss, self).__init__()\n","        self.margin = margin\n","        self.semi_hard = semi_hard\n","\n","    def forward(self, anchor, positive, negative, size_average=True):\n","        distance_positive = 1.0 - F.cosine_similarity(anchor, positive)\n","        distance_negative = 1.0 - F.cosine_similarity(anchor, negative)\n","        losses = F.relu(distance_positive - distance_negative + self.margin)\n","        losses = torch.where(losses > self.semi_hard, losses, torch.zeros(losses.shape).to(device))\n","        return losses.sum()/torch.count_nonzero(losses) if size_average else losses.sum(), torch.count_nonzero(losses)"],"metadata":{"id":"YSmXaJpwaqXt","executionInfo":{"status":"ok","timestamp":1686550826591,"user_tz":-180,"elapsed":433,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["!pip install --quiet optuna"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3hpJHkZOFA_O","executionInfo":{"status":"ok","timestamp":1686547745774,"user_tz":-180,"elapsed":7563,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"25116ff7-6478-4203-f3da-bf21bec8d61d"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m390.6/390.6 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.7/78.7 kB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}]},{"cell_type":"code","source":["import optuna\n","from optuna.samplers import RandomSampler\n","import torch.optim as optim\n","\n","# define function which will optimized\n","def objective(trial):\n","    # boundaries for the optimizer's \n","    lr = trial.suggest_float(\"lr\", 1e-5, 1e-2)\n","    latent_dim = trial.suggest_int(\"latent_dim\", 8, 64, step=8)\n","    semi_hard = 0.5\n","\n","    # create new model(and all parameters) every iteration\n","    model = SiameseNet(latent_dim).to(device)\n","    criterion_train = TripletLoss(margin = 1, semi_hard = semi_hard)\n","    criterion_val = TripletLoss(margin = 1, semi_hard = semi_hard)\n","    optimizer = optim.AdamW(\n","        model.parameters(), lr=lr\n","    )  # learning step regulates by optuna\n","    \n","    # To save time, we will take only 4 epochs\n","    train_loader = DataLoader(train_dataset, num_workers=2, batch_size=128, shuffle=True)\n","    val_loader = DataLoader(val_dataset,  num_workers=2, batch_size=128, shuffle=False)\n","    l, _, l_val, __, n_p_train, n_p_val = train(4, model, criterion_train, criterion_val, optimizer, train_loader, val_loader, latent_dim)\n","    return np.array(n_p_val).reshape(4, 16)[-1].mean()\n","\n","# Create \"exploration\"\n","study = optuna.create_study(direction=\"minimize\", study_name=\"Optimizer\", sampler=RandomSampler(42))\n","\n","study.optimize(\n","    objective, n_trials=20\n",")  # The more iterations, the higher the chances of catching the most optimal hyperparameters"],"metadata":{"id":"FXREq8tcE41f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch.optim as optim\n","\n","\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","latent_dim = 64\n","model = SiameseNet(latent_dim).to(device)\n","criterion_train = TripletLoss(margin = 1, semi_hard = 0.5)\n","criterion_val = TripletLoss(margin = 1, semi_hard = 0.5)\n","\n","optimizer = optim.AdamW(model.parameters(), lr=5e-3)\n","scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[4,8,12,16], gamma=0.5)\n","num_epochs = 20\n","l, _, l_val, __, n_p_train, n_p_val = train(num_epochs, model, criterion_train, criterion_val, optimizer, train_loader, val_loader, latent_dim)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H-omNiY4dPYm","executionInfo":{"status":"ok","timestamp":1686552010243,"user_tz":-180,"elapsed":209300,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"b9d1feda-0c68-438b-b750-8077635d33f6"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 0 with 0.7349 loss and 0.7084 val_loss\n","Epoch 1 with 0.7072 loss and 0.7068 val_loss\n","Epoch 2 with 0.7045 loss and 0.6939 val_loss\n","Epoch 3 with 0.7006 loss and 0.6980 val_loss\n","Epoch 4 with 0.6970 loss and 0.6936 val_loss\n","Epoch 5 with 0.6996 loss and 0.6967 val_loss\n","Epoch 6 with 0.6965 loss and 0.7012 val_loss\n","Epoch 7 with 0.6939 loss and 0.6887 val_loss\n","Epoch 8 with 0.6946 loss and 0.6976 val_loss\n","Epoch 9 with 0.6896 loss and 0.6971 val_loss\n","Epoch 10 with 0.6940 loss and 0.6908 val_loss\n","Epoch 11 with 0.6918 loss and 0.7033 val_loss\n","Epoch 12 with 0.6914 loss and 0.6888 val_loss\n","Epoch 13 with 0.6955 loss and 0.6912 val_loss\n","Epoch 14 with 0.6940 loss and 0.6922 val_loss\n","Epoch 15 with 0.6923 loss and 0.6849 val_loss\n","Epoch 16 with 0.6932 loss and 0.6948 val_loss\n","Epoch 17 with 0.6892 loss and 0.6969 val_loss\n","Epoch 18 with 0.6932 loss and 0.6891 val_loss\n","Epoch 19 with 0.6926 loss and 0.6884 val_loss\n"]}]},{"cell_type":"code","source":["n_p_plot_val = np.array(n_p_val).reshape(20, 16).mean(1)\n","n_p_plot_train = np.array(n_p_train).reshape(20, 63).mean(1)\n","plt.plot([i for i in range(len(n_p_plot_val))], n_p_plot_train, n_p_plot_val)\n","plt.ylabel('Number of positive triplet')\n","plt.xlabel('num of epochs')\n","x = range(0, len(n_p_plot_val), len(n_p_plot_val) // num_epochs)\n","labels = range(0, num_epochs)\n","plt.xticks(x, labels)\n","plt.grid()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":449},"id":"qa_8_1PlfH6z","executionInfo":{"status":"ok","timestamp":1686487469307,"user_tz":-180,"elapsed":558,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"57dac2fa-0394-4540-9691-1577c7745d2c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["import torch.nn.functional as F\n","def validate(model, val_loader, latent_dim):\n","    distances_pos = []\n","    distances_neg = []\n","    model.eval()\n","\n","    for i, batch in enumerate(val_loader, 0):\n","        anc, pos, neg = batch\n","        output_anc, output_pos, output_neg = model(anc.to(device), pos.to(device), neg.to(device), latent_dim)\n","        \n","        distance_pos = F.pairwise_distance(output_anc, output_pos).detach().cpu()\n","        distance_neg = F.pairwise_distance(output_anc, output_neg).detach().cpu()\n","        \n","\n","\n","        distances_pos.append(distance_pos)\n","        distances_neg.append(distance_neg)\n","        \n","    return distances_pos, distances_neg"],"metadata":{"id":"g5iy1PHLfjtP","executionInfo":{"status":"ok","timestamp":1686552329809,"user_tz":-180,"elapsed":475,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["d_pos, d_neg = validate(model, val_loader, latent_dim = 64)"],"metadata":{"id":"e9Jz1fwCitmM","executionInfo":{"status":"ok","timestamp":1686552332570,"user_tz":-180,"elapsed":1919,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","execution_count":27,"metadata":{"id":"h2E8HH41xhND","executionInfo":{"status":"ok","timestamp":1686552332572,"user_tz":-180,"elapsed":10,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9b84869e-a22e-4f6e-c884-4fc01c06b9f5"},"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-27-5922c58f970f>:10: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n","  neg_dist = np.concatenate(np.array(neg_dist), axis=0, dtype = object)\n","<ipython-input-27-5922c58f970f>:11: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n","  pos_dist = np.concatenate(np.array(pos_dist), axis=0, dtype = object )\n"]}],"source":["pos_dist = []\n","neg_dist = []\n","for d in d_pos:\n","  d = d.detach().cpu().numpy()\n","  pos_dist.append(d)\n","for d in d_neg:\n","  d = d.detach().cpu().numpy()\n","  neg_dist.append(d)\n","\n","neg_dist = np.concatenate(np.array(neg_dist), axis=0, dtype = object)\n","pos_dist = np.concatenate(np.array(pos_dist), axis=0, dtype = object )"]},{"cell_type":"code","source":["import seaborn as sns\n","\n","distances = {\"substance is present\": pos_dist, \"substance is not present\": neg_dist}\n","\n","ax = sns.displot(distances, kde=True, stat=\"density\")\n","ax.set(xlabel=\"Pairwise distance\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":526},"id":"-nlUWPnei5f4","executionInfo":{"status":"ok","timestamp":1686514628494,"user_tz":-180,"elapsed":2081,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"052c81e7-41d4-49cc-af22-d8e3e9c8a1ad"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720.5x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Тест (знакомые классы)"],"metadata":{"id":"7XWAckL3jjCV"}},{"cell_type":"code","source":["def accuracy_(pred, cor):\n","  accuracy = np.zeros((4))\n","  total = 0\n","  for i in range(cor.shape[0]):\n","    intersect = np.intersect1d(cor[i], pred[i])\n","    error = len(cor[i]) - len(intersect)\n","    for j in range(accuracy.shape[0]-error):\n","      accuracy[j] = accuracy[j] +1\n","    total = total + 1\n","  return accuracy/total"],"metadata":{"id":"RBVFjImekW7l","executionInfo":{"status":"ok","timestamp":1686552387815,"user_tz":-180,"elapsed":469,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":29,"outputs":[]},{"cell_type":"code","source":["x_test = np.load('/content/x_test_red.npy')\n","y_test = np.load('/content/y_test_red.npy')"],"metadata":{"id":"ZzXSy8lFja_t","executionInfo":{"status":"ok","timestamp":1686552388166,"user_tz":-180,"elapsed":2,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["x_test = x_test[2000:]\n","y_test = y_test[2000:]"],"metadata":{"id":"U0rCIz9ekG_8","executionInfo":{"status":"ok","timestamp":1686552389469,"user_tz":-180,"elapsed":6,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":31,"outputs":[]},{"cell_type":"code","source":["molecules = []\n","molecules_embedding = []\n","label = []\n","for i in range(400, 500):\n","  molecules = []\n","  embedding = np.zeros((20, latent_dim))\n","  for j in range(0, 20):\n","    molecule = x_train[20*i + j]/np.max(x_train[20*i + j]) + np.random.normal(0,0.005,5000)\n","    molecules.append(molecule)\n","  molecules = np.array(molecules)\n","  molecules = torch.Tensor(molecules).to(device)\n","  embedding = embedding + model.predict(molecules).detach().cpu().numpy()\n","  embedding = np.mean(embedding, axis = 0)\n","  molecules_embedding.append(embedding/np.linalg.norm(embedding))\n","  label.append(i)\n","label = np.array(label)"],"metadata":{"id":"r8oNqMNzkJIl","executionInfo":{"status":"ok","timestamp":1686552391147,"user_tz":-180,"elapsed":332,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":32,"outputs":[]},{"cell_type":"code","source":["label_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)*(len(label)-3)//2//3//4, 4))\n","mixture_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)*(len(label)-3)//2//3//4, 64))\n","l = 0\n","for i in range(len(molecules_embedding)):\n","  for j in range(i+1, len(molecules_embedding)):\n","    for k in range(j+1, len(molecules_embedding)):\n","      for m in range(k+1, len(molecules_embedding)):\n","        emb = molecules_embedding[i] + molecules_embedding[j] + molecules_embedding[k] + molecules_embedding[m]\n","        mixture_train[l] = emb/np.linalg.norm(emb)\n","        label_train[l] = np.array([label[i], label[j], label[k], label[m]])\n","        l = l + 1"],"metadata":{"id":"ONZmGthCkNG0","executionInfo":{"status":"ok","timestamp":1686552435793,"user_tz":-180,"elapsed":43546,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":["mixtures_test = []\n","correct_test = []\n","for i in range(0, 500):\n","  a = x_test[i]\n","  idxs = np.concatenate((np.arange(x_test[:].shape[0])[0 : i//5 * 5],\n","                                      np.arange(x_test[:].shape[0])[(i//5 + 1) * 5 :]))\n","  b_idx = np.random.choice(idxs)\n","  b = x_test[b_idx]\n","  c_idx = np.random.choice(idxs)\n","  c = x_test[c_idx]\n","  d_idx = np.random.choice(idxs)\n","  d = x_test[d_idx]\n","  mixtures_test.append((a+b+c+d)/np.max(a+b+c+d) + np.random.normal(0,0.005,a.shape[0]))\n","  correct_test.append([y_test[i], y_test[b_idx], y_test[c_idx], y_test[d_idx]])\n","mixtures_test = torch.Tensor(mixtures_test).to(device)\n","classification_data_test = model.predict(mixtures_test).detach().cpu().numpy()\n","correct_test = np.array(correct_test).reshape(-1, 4)"],"metadata":{"id":"EPx1FVmxkPat","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1686552435794,"user_tz":-180,"elapsed":20,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"ce2b53fb-3374-4277-9709-e9f8d8ed3810"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-34-18c6c86903d2>:15: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:245.)\n","  mixtures_test = torch.Tensor(mixtures_test).to(device)\n"]}]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","neigh = KNeighborsClassifier(n_neighbors=1)\n","neigh.fit(mixture_train, label_train)\n","preds = neigh.predict(classification_data_test)"],"metadata":{"id":"TTAs5LlukRPt","executionInfo":{"status":"ok","timestamp":1686552468644,"user_tz":-180,"elapsed":32864,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":35,"outputs":[]},{"cell_type":"code","source":["accuracy = accuracy_(preds, correct_test)"],"metadata":{"id":"0Niq4ln-kX8N","executionInfo":{"status":"ok","timestamp":1686552468644,"user_tz":-180,"elapsed":18,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":36,"outputs":[]},{"cell_type":"code","source":["print('.....Accuracy.....')\n","print('1 of 4 ......', accuracy[0])\n","print('2 of 4 ......', accuracy[1])\n","print('3 of 4 ......', accuracy[2])\n","print('4 of 4 ......', accuracy[3])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uWpFZzRHW1GF","executionInfo":{"status":"ok","timestamp":1686552468645,"user_tz":-180,"elapsed":18,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"38539577-ff8e-400b-c77d-31226386e9f2"},"execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":[".....Accuracy.....\n","1 of 4 ...... 1.0\n","2 of 4 ...... 0.998\n","3 of 4 ...... 0.962\n","4 of 4 ...... 0.822\n"]}]},{"cell_type":"markdown","source":["Каждый класс представлен примерно одинаковое количество раз при формировании выборки используемым способом:"],"metadata":{"id":"-nqN5R2vvL3R"}},{"cell_type":"code","source":["from collections import Counter\n","cnt = Counter(correct_test.reshape(-1))\n","vals = np.fromiter(cnt.values(), dtype = float)\n","keys = np.fromiter(cnt.keys(), dtype = float)"],"metadata":{"id":"Rs8bfN2BmlRc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["coefficient_of_variation = np.std(vals)/np.mean(vals)\n","coefficient_of_variation"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9zEv8POFrHwM","executionInfo":{"status":"ok","timestamp":1686407776672,"user_tz":-180,"elapsed":2,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"7595c51d-e89c-49ff-c3ed-4b666262cbf1"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.200124960961895"]},"metadata":{},"execution_count":66}]},{"cell_type":"code","source":["plt.bar(keys, vals)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"LNhkbv0AnHAU","executionInfo":{"status":"ok","timestamp":1686406552340,"user_tz":-180,"elapsed":404,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"694b38b5-c078-4fa7-f09e-7f42d590a82c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<BarContainer object of 100 artists>"]},"metadata":{},"execution_count":63},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["# Тест (новые классы)"],"metadata":{"id":"tIla_CY_wEmr"}},{"cell_type":"code","source":["def accuracy_(pred, cor):\n","  accuracy = np.zeros((4))\n","  total = 0\n","  for i in range(cor.shape[0]):\n","    intersect = np.intersect1d(cor[i], pred[i])\n","    error = len(cor[i]) - len(intersect)\n","    for j in range(accuracy.shape[0]-error):\n","      accuracy[j] = accuracy[j] +1\n","    total = total + 1\n","  return accuracy/total"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552498123,"user_tz":-180,"elapsed":526,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"ReXIvVm9wEmw"},"execution_count":38,"outputs":[]},{"cell_type":"code","source":["x_test = np.load('/content/x_test_red.npy')\n","y_test = np.load('/content/y_test_red.npy')"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552498468,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"9dV4-x4UwEmx"},"execution_count":39,"outputs":[]},{"cell_type":"code","source":["x_test = x_test[:500]\n","y_test = y_test[:500]"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552500190,"user_tz":-180,"elapsed":1,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"NrKO_eAawEmx"},"execution_count":40,"outputs":[]},{"cell_type":"code","source":["molecules = []\n","molecules_embedding = []\n","label = []\n","for i in range(0, 100):\n","  molecules = []\n","  embedding = np.zeros((20, latent_dim))\n","  for j in range(0, 20):\n","    molecule = x_train[20*i + j]/np.max(x_train[20*i + j]) + np.random.normal(0,0.005,5000)\n","    molecules.append(molecule)\n","  molecules = np.array(molecules)\n","  molecules = torch.Tensor(molecules).to(device)\n","  embedding = embedding + model.predict(molecules).detach().cpu().numpy()\n","  embedding = np.mean(embedding, axis = 0)\n","  molecules_embedding.append(embedding/np.linalg.norm(embedding))\n","  label.append(i)\n","label = np.array(label)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552500981,"user_tz":-180,"elapsed":370,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"db6PHHMhwEmx"},"execution_count":41,"outputs":[]},{"cell_type":"code","source":["label_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)*(len(label)-3)//2//3//4, 4))\n","mixture_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)*(len(label)-3)//2//3//4, 64))\n","l = 0\n","for i in range(len(molecules_embedding)):\n","  for j in range(i+1, len(molecules_embedding)):\n","    for k in range(j+1, len(molecules_embedding)):\n","      for m in range(k+1, len(molecules_embedding)):\n","        emb = molecules_embedding[i] + molecules_embedding[j] + molecules_embedding[k] + molecules_embedding[m]\n","        mixture_train[l] = emb/np.linalg.norm(emb)\n","        label_train[l] = np.array([label[i], label[j], label[k], label[m]])\n","        l = l + 1"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552545957,"user_tz":-180,"elapsed":43778,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"iGuAxEJzwEmx"},"execution_count":42,"outputs":[]},{"cell_type":"code","source":["mixtures_test = []\n","correct_test = []\n","for i in range(0, 500):\n","  a = x_test[i]\n","  idxs = np.concatenate((np.arange(x_test[:].shape[0])[0 : i//5 * 5],\n","                                      np.arange(x_test[:].shape[0])[(i//5 + 1) * 5 :]))\n","  b_idx = np.random.choice(idxs)\n","  b = x_test[b_idx]\n","  c_idx = np.random.choice(idxs)\n","  c = x_test[c_idx]\n","  d_idx = np.random.choice(idxs)\n","  d = x_test[d_idx]\n","  mixtures_test.append((a+b+c+d)/np.max(a+b+c+d) + np.random.normal(0,0.005,a.shape[0]))\n","  correct_test.append([y_test[i], y_test[b_idx], y_test[c_idx], y_test[d_idx]])\n","mixtures_test = torch.Tensor(mixtures_test).to(device)\n","classification_data_test = model.predict(mixtures_test).detach().cpu().numpy()\n","correct_test = np.array(correct_test).reshape(-1, 4)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552545958,"user_tz":-180,"elapsed":26,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"UyHr5ZeZwEmx"},"execution_count":43,"outputs":[]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","neigh = KNeighborsClassifier(n_neighbors=1)\n","neigh.fit(mixture_train, label_train)\n","preds = neigh.predict(classification_data_test)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552578368,"user_tz":-180,"elapsed":32435,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"D_Sn7tYVwEmy"},"execution_count":44,"outputs":[]},{"cell_type":"code","source":["accuracy = accuracy_(preds, correct_test)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552578369,"user_tz":-180,"elapsed":32,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"rSZKLNJ8wEmy"},"execution_count":45,"outputs":[]},{"cell_type":"code","source":["print('.....Accuracy.....')\n","print('1 of 4 ......', accuracy[0])\n","print('2 of 4 ......', accuracy[1])\n","print('3 of 4 ......', accuracy[2])\n","print('4 of 4 ......', accuracy[3])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gLaoGqxBEBd4","executionInfo":{"status":"ok","timestamp":1686463642668,"user_tz":-180,"elapsed":36,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"70ca3ef0-f149-4b5a-9eb0-0f2b60bce0b3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[".....Accuracy.....\n","1 of 4 ...... 1.0\n","2 of 4 ...... 0.986\n","3 of 4 ...... 0.928\n","4 of 4 ...... 0.722\n"]}]},{"cell_type":"markdown","source":["# Тест (неравные количества компонентов в смесях)"],"metadata":{"id":"RdSxlaIqLzpL"}},{"cell_type":"code","source":["def accuracy_(pred, cor):\n","  accuracy = np.zeros((4))\n","  total = 0\n","  for i in range(cor.shape[0]):\n","    intersect = np.intersect1d(cor[i], pred[i])\n","    error = len(cor[i]) - len(intersect)\n","    for j in range(accuracy.shape[0]-error):\n","      accuracy[j] = accuracy[j] +1\n","    total = total + 1\n","  return accuracy/total"],"metadata":{"id":"CPtanLooLzpR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x_test = np.load('/content/x_test_red.npy')\n","y_test = np.load('/content/y_test_red.npy')"],"metadata":{"id":"iN2h6rn8LzpS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x_test = x_test[:500]\n","y_test = y_test[:500]"],"metadata":{"id":"CNWnHVoTLzpS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["molecules = []\n","molecules_embedding = []\n","label = []\n","for i in range(0, 100):\n","  molecules = []\n","  embedding = np.zeros((20, latent_dim))\n","  for j in range(0, 20):\n","    molecule = x_train[20*i + j]/np.max(x_train[20*i + j])\n","    molecules.append(molecule)\n","  molecules = np.array(molecules)\n","  molecules = torch.Tensor(molecules).to(device)\n","  embedding = embedding + model.predict(molecules).detach().cpu().numpy()\n","  embedding = np.mean(embedding, axis = 0)\n","  molecules_embedding.append(embedding/np.linalg.norm(embedding))\n","  label.append(i)\n","label = np.array(label)"],"metadata":{"id":"AnKUO67bLzpS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["label_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)*(len(label)-3)//2//3//4, 4))\n","mixture_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)*(len(label)-3)//2//3//4, 64))\n","l = 0\n","for i in range(len(molecules_embedding)):\n","  for j in range(i+1, len(molecules_embedding)):\n","    for k in range(j+1, len(molecules_embedding)):\n","      for m in range(k+1, len(molecules_embedding)):\n","        emb = molecules_embedding[i] + molecules_embedding[j] + molecules_embedding[k] + molecules_embedding[m]\n","        mixture_train[l] = emb/np.linalg.norm(emb)\n","        label_train[l] = np.array([label[i], label[j], label[k], label[m]])\n","        l = l + 1"],"metadata":{"id":"28_oekIXLzpS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["mixtures_test = []\n","correct_test = []\n","for i in range(0, 500):\n","  a = 0.15*x_test[i]\n","  idxs = np.concatenate((np.arange(x_test[:].shape[0])[0 : i//5 * 5],\n","                                      np.arange(x_test[:].shape[0])[(i//5 + 1) * 5 :]))\n","  b_idx = np.random.choice(idxs)\n","  b = 0.22*x_test[b_idx]\n","  c_idx = np.random.choice(idxs)\n","  c = 0.23*x_test[c_idx]\n","  d_idx = np.random.choice(idxs)\n","  d = 0.4*x_test[d_idx]\n","  mixtures_test.append((a+b+c+d)/np.max(a+b+c+d) + np.random.normal(0,0.005,a.shape[0]))\n","  correct_test.append([y_test[i], y_test[b_idx], y_test[c_idx], y_test[d_idx]])\n","mixtures_test = torch.Tensor(mixtures_test).to(device)\n","classification_data_test = model.predict(mixtures_test).detach().cpu().numpy()\n","correct_test = np.array(correct_test).reshape(-1, 4)"],"metadata":{"id":"apVM2i9VLzpS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","neigh = KNeighborsClassifier(n_neighbors=1)\n","neigh.fit(mixture_train, label_train)\n","preds = neigh.predict(classification_data_test)"],"metadata":{"id":"77DME5lHLzpS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["accuracy = accuracy_(preds, correct_test)"],"metadata":{"id":"5o-YZlmPLzpT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print('.....Accuracy.....')\n","print('1 of 4 ......', accuracy[0])\n","print('2 of 4 ......', accuracy[1])\n","print('3 of 4 ......', accuracy[2])\n","print('4 of 4 ......', accuracy[3])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WF_SZSlUhC3D","executionInfo":{"status":"ok","timestamp":1686493925721,"user_tz":-180,"elapsed":4,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"f081869a-37c2-49ef-ea9e-e953b072e9ff"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[".....Accuracy.....\n","1 of 4 ...... 0.996\n","2 of 4 ...... 0.942\n","3 of 4 ...... 0.726\n","4 of 4 ...... 0.398\n"]}]},{"cell_type":"markdown","source":["# Бинарные смеси"],"metadata":{"id":"-W3RrV-pIjw-"}},{"cell_type":"code","source":["def accuracy_(pred, cor):\n","  accuracy = np.zeros((2))\n","  total = 0\n","  for i in range(cor.shape[0]):\n","    intersect = np.intersect1d(cor[i], pred[i])\n","    error = len(cor[i]) - len(intersect)\n","    for j in range(accuracy.shape[0]-error):\n","      accuracy[j] = accuracy[j] +1\n","    total = total + 1\n","  return accuracy/total"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552605601,"user_tz":-180,"elapsed":593,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"j1EUz0vBIjxD"},"execution_count":47,"outputs":[]},{"cell_type":"code","source":["x_test = np.load('/content/x_test_red.npy')\n","y_test = np.load('/content/y_test_red.npy')"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552606659,"user_tz":-180,"elapsed":620,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"sbiJ9uyLIjxE"},"execution_count":48,"outputs":[]},{"cell_type":"code","source":["x_test = x_test[:500]\n","y_test = y_test[:500]"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552606660,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"H7MFUx5KIjxE"},"execution_count":49,"outputs":[]},{"cell_type":"code","source":["molecules = []\n","molecules_embedding = []\n","label = []\n","for i in range(0, 100):\n","  molecules = []\n","  embedding = np.zeros((20, latent_dim))\n","  for j in range(0, 20):\n","    molecule = x_train[20*i + j]/np.max(x_train[20*i + j]) + np.random.normal(0,0.005,5000)\n","    molecules.append(molecule)\n","  molecules = np.array(molecules)\n","  molecules = torch.Tensor(molecules).to(device)\n","  embedding = embedding + model.predict(molecules).detach().cpu().numpy()\n","  embedding = np.mean(embedding, axis = 0)\n","  molecules_embedding.append(embedding/np.linalg.norm(embedding))\n","  label.append(i)\n","label = np.array(label)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552608792,"user_tz":-180,"elapsed":420,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"giQgwQYMIjxE"},"execution_count":50,"outputs":[]},{"cell_type":"code","source":["label_train = np.zeros(shape = (len(label)*(len(label)-1)//2, 2))\n","mixture_train = np.zeros(shape = (len(label)*(len(label)-1)//2, 64))\n","l = 0\n","for i in range(len(molecules_embedding)):\n","  for j in range(i+1, len(molecules_embedding)):\n","    emb = molecules_embedding[i] + molecules_embedding[j] \n","    mixture_train[l] = emb/np.linalg.norm(emb)\n","    label_train[l] = np.array([label[i], label[j]])\n","    l = l + 1"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552609954,"user_tz":-180,"elapsed":334,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"nDHGssM6IjxE"},"execution_count":51,"outputs":[]},{"cell_type":"code","source":["mixtures_test = []\n","correct_test = []\n","for i in range(0, 500):\n","  a = x_test[i]\n","  idxs = np.concatenate((np.arange(x_test[:].shape[0])[0 : i//5 * 5],\n","                                      np.arange(x_test[:].shape[0])[(i//5 + 1) * 5 :]))\n","  b_idx = np.random.choice(idxs)\n","  b = x_test[b_idx]\n","  mixtures_test.append((a+b)/np.max(a+b) + np.random.normal(0,0.005,a.shape[0]))\n","  correct_test.append([y_test[i], y_test[b_idx]])\n","mixtures_test = torch.Tensor(mixtures_test).to(device)\n","classification_data_test = model.predict(mixtures_test).detach().cpu().numpy()\n","correct_test = np.array(correct_test).reshape(-1, 2)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552610270,"user_tz":-180,"elapsed":322,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"N7XPCM8QIjxE"},"execution_count":52,"outputs":[]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","neigh = KNeighborsClassifier(n_neighbors=1)\n","neigh.fit(mixture_train, label_train)\n","preds = neigh.predict(classification_data_test)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552611511,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"ObDu4j0MIjxE"},"execution_count":53,"outputs":[]},{"cell_type":"code","source":["accuracy = accuracy_(preds, correct_test)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552612696,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"rIkt-9krIjxE"},"execution_count":54,"outputs":[]},{"cell_type":"code","source":["print('.....Accuracy.....')\n","print('1 of 2 ......', accuracy[0])\n","print('2 of 2 ......', accuracy[1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5Ulz6zYhXqTW","executionInfo":{"status":"ok","timestamp":1686552619770,"user_tz":-180,"elapsed":415,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"469ab404-1c61-4ace-9ca7-d86515c12baf"},"execution_count":55,"outputs":[{"output_type":"stream","name":"stdout","text":[".....Accuracy.....\n","1 of 2 ...... 0.998\n","2 of 2 ...... 0.966\n"]}]},{"cell_type":"markdown","source":["# Тройные смеси"],"metadata":{"id":"XxWMhSO8JXcV"}},{"cell_type":"code","source":["def accuracy_(pred, cor):\n","  accuracy = np.zeros((3))\n","  total = 0\n","  for i in range(cor.shape[0]):\n","    intersect = np.intersect1d(cor[i], pred[i])\n","    error = len(cor[i]) - len(intersect)\n","    for j in range(accuracy.shape[0]-error):\n","      accuracy[j] = accuracy[j] +1\n","    total = total + 1\n","  return accuracy/total"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552625903,"user_tz":-180,"elapsed":436,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"Jpyl0P13JXcb"},"execution_count":56,"outputs":[]},{"cell_type":"code","source":["x_test = np.load('/content/x_test_red.npy')\n","y_test = np.load('/content/y_test_red.npy')"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552626301,"user_tz":-180,"elapsed":2,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"kIqOvk3qJXcb"},"execution_count":57,"outputs":[]},{"cell_type":"code","source":["x_test = x_test[:500]\n","y_test = y_test[:500]"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552627923,"user_tz":-180,"elapsed":426,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"tm8IFzV7JXcb"},"execution_count":58,"outputs":[]},{"cell_type":"code","source":["molecules = []\n","molecules_embedding = []\n","label = []\n","for i in range(0, 100):\n","  molecules = []\n","  embedding = np.zeros((20, latent_dim))\n","  for j in range(0, 20):\n","    molecule = x_train[20*i + j]/np.max(x_train[20*i + j]) + np.random.normal(0,0.005,5000)\n","    molecules.append(molecule)\n","  molecules = np.array(molecules)\n","  molecules = torch.Tensor(molecules).to(device)\n","  embedding = embedding + model.predict(molecules).detach().cpu().numpy()\n","  embedding = np.mean(embedding, axis = 0)\n","  molecules_embedding.append(embedding/np.linalg.norm(embedding))\n","  label.append(i)\n","label = np.array(label)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552628866,"user_tz":-180,"elapsed":407,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"VrUKzvr-JXcb"},"execution_count":59,"outputs":[]},{"cell_type":"code","source":["label_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)//2//3, 3))\n","mixture_train = np.zeros(shape = (len(label)*(len(label)-1)*(len(label) - 2)//2//3, 64))\n","l = 0\n","for i in range(len(molecules_embedding)):\n","  for j in range(i+1, len(molecules_embedding)):\n","    for k in range(j+1, len(molecules_embedding)):\n","      emb = molecules_embedding[i] + molecules_embedding[j] + molecules_embedding[k]\n","      mixture_train[l] = emb/np.linalg.norm(emb)\n","      label_train[l] = np.array([label[i], label[j], label[k]])\n","      l = l + 1"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552631182,"user_tz":-180,"elapsed":1846,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"qRkE_KquJXcc"},"execution_count":60,"outputs":[]},{"cell_type":"code","source":["mixtures_test = []\n","correct_test = []\n","for i in range(0, 500):\n","  a = x_test[i]\n","  idxs = np.concatenate((np.arange(x_test[:].shape[0])[0 : i//5 * 5],\n","                                      np.arange(x_test[:].shape[0])[(i//5 + 1) * 5 :]))\n","  b_idx = np.random.choice(idxs)\n","  b = x_test[b_idx]\n","  c_idx = np.random.choice(idxs)\n","  c = x_test[c_idx]\n","  mixtures_test.append((a+b+c)/np.max(a+b+c) + np.random.normal(0,0.005,a.shape[0]))\n","  correct_test.append([y_test[i], y_test[b_idx], y_test[c_idx]])\n","mixtures_test = torch.Tensor(mixtures_test).to(device)\n","classification_data_test = model.predict(mixtures_test).detach().cpu().numpy()\n","correct_test = np.array(correct_test).reshape(-1, 3)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552631561,"user_tz":-180,"elapsed":382,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"tYJH-QGEJXcc"},"execution_count":61,"outputs":[]},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","neigh = KNeighborsClassifier(n_neighbors=1)\n","neigh.fit(mixture_train, label_train)\n","preds = neigh.predict(classification_data_test)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552633620,"user_tz":-180,"elapsed":1202,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"YCgXR2e-JXcc"},"execution_count":62,"outputs":[]},{"cell_type":"code","source":["accuracy = accuracy_(preds, correct_test)"],"metadata":{"executionInfo":{"status":"ok","timestamp":1686552633620,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"id":"M0xrbaZKJXcc"},"execution_count":63,"outputs":[]},{"cell_type":"code","source":["print('.....Accuracy.....')\n","print('1 of 3 ......', accuracy[0])\n","print('2 of 3 ......', accuracy[1])\n","print('3 of 3 ......', accuracy[2])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TvdYAY7QXuxu","executionInfo":{"status":"ok","timestamp":1686552638099,"user_tz":-180,"elapsed":321,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"5a44a453-aef1-4267-d0cd-6f5337f63583"},"execution_count":64,"outputs":[{"output_type":"stream","name":"stdout","text":[".....Accuracy.....\n","1 of 3 ...... 1.0\n","2 of 3 ...... 0.994\n","3 of 3 ...... 0.868\n"]}]},{"cell_type":"markdown","source":["# Explainability\n"],"metadata":{"id":"si0F6rbw2kbs"}},{"cell_type":"markdown","source":["Рассмотрим feature map на последнем сверточном слое модели."],"metadata":{"id":"Jii9xkHn0ZhP"}},{"cell_type":"code","source":["print(model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k5c7h8TiCczT","executionInfo":{"status":"ok","timestamp":1686552651053,"user_tz":-180,"elapsed":591,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"15d41cf4-3b7a-4741-d239-7ee600c0c4ac"},"execution_count":65,"outputs":[{"output_type":"stream","name":"stdout","text":["SiameseNet(\n","  (model): Sequential(\n","    (0): Conv1d(1, 16, kernel_size=(21,), stride=(1,), padding=same)\n","    (1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (2): LeakyReLU(negative_slope=0.01)\n","    (3): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (4): Conv1d(16, 32, kernel_size=(11,), stride=(1,), padding=same)\n","    (5): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (6): LeakyReLU(negative_slope=0.01)\n","    (7): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (8): Conv1d(32, 64, kernel_size=(5,), stride=(1,), padding=same)\n","    (9): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (10): LeakyReLU(negative_slope=0.01)\n","    (11): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (12): Flatten(start_dim=1, end_dim=-1)\n","    (13): Linear(in_features=40000, out_features=64, bias=True)\n","  )\n",")\n"]}]},{"cell_type":"code","source":["class ForwardHook():\n","    def __init__(self, layer):\n","        self.hook = layer.register_forward_hook(self.hook_func)\n","\n","    def hook_func(self, layer, input_, output): \n","        self.activation = output.detach().clone()\n","\n","    def __enter__(self, *args): \n","        return self\n","\n","    def __exit__(self, *args): \n","      self.hook.remove()"],"metadata":{"id":"1eq6fZRd5uiQ","executionInfo":{"status":"ok","timestamp":1686552652501,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":66,"outputs":[]},{"cell_type":"code","source":["anchor, positive, negative  = next(iter(train_loader))"],"metadata":{"id":"Fmm6aIp6DAKz","executionInfo":{"status":"ok","timestamp":1686552654545,"user_tz":-180,"elapsed":404,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":67,"outputs":[]},{"cell_type":"code","source":["anchor, positive, negative = anchor[0], positive[0], negative[0]"],"metadata":{"id":"8Nzwy22jDQOX","executionInfo":{"status":"ok","timestamp":1686552654940,"user_tz":-180,"elapsed":3,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":68,"outputs":[]},{"cell_type":"code","source":["plt.plot(positive.detach().cpu().numpy())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"4fRTcezoGlt4","executionInfo":{"status":"ok","timestamp":1686552656605,"user_tz":-180,"elapsed":440,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"71df76d5-0dc1-45d2-a21a-947aba9cbbc5"},"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fdd68c108e0>]"]},"metadata":{},"execution_count":69},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["with ForwardHook(model.model[8]) as forward_hook:\n","    output = model._forward(positive.to(device))\n","    activation_positive_trained = forward_hook.activation"],"metadata":{"id":"uauEvAOsGfp6","executionInfo":{"status":"ok","timestamp":1686552659778,"user_tz":-180,"elapsed":515,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}}},"execution_count":70,"outputs":[]},{"cell_type":"code","source":["plt.plot(activation_positive_trained.detach().cpu().numpy()[0][0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"id":"ksY5b19esfCn","executionInfo":{"status":"ok","timestamp":1686552670615,"user_tz":-180,"elapsed":529,"user":{"displayName":"neuralnetworkss neuralnetworkss","userId":"15948508034815209973"}},"outputId":"6375d6dc-0534-4247-9f2c-926fecbb8249"},"execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7fdd68ae88e0>]"]},"metadata":{},"execution_count":72},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"MUNlhqewX200"},"execution_count":null,"outputs":[]}]}