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0000000000000000000000000000000000000000..e5f58e48c7cf542112e048f662e4553d6c6d641b --- /dev/null +++ b/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints/analysis_cosine_similarity.ipynb @@ -0,0 +1,1015 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"/datadrive2/samyak/parity-nn/\")\n", + "from utils_competition import *\n", + "sys.path.append(\"/datadrive2/samyak/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints\")\n", + "from torch.utils.data import TensorDataset, DataLoader" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "width=20\n", + "n=20\n", + "k=3\n", + "device='cuda'\n", + "model_path = \"/datadrive2/samyak/parity-nn/results_new_optim_train_nowd/n_20/k_3/N_1000/lr_0.1/wd_0.0/width_20/loss_hinge/optim_sgd/momentum_0.0/scheduler_False/seed0_checkpoints\"" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def parity(n, k, n_samples, seed=42):\n", + " 'Data generation'\n", + "\n", + " random.seed(seed)\n", + " samples = torch.Tensor([[random.choice([-1, 1]) for j in range(n)] for i in range(n_samples)])\n", + " targets = torch.prod(samples[:, :k], dim=1) # parity hidden in first k bits\n", + " return samples, targets\n", + "\n", + "\n", + "\n", + "def data_preparation(seed_id=42, test_samples=100, test_batchsize=100, return_raw=False):\n", + " # Data generation - different seed each time\n", + " N=1000\n", + " _data = parity(n, k, N, seed=seed_id*17)\n", + " train_dataset = TensorDataset(_data[0], _data[1])\n", + " train_dataloader = DataLoader(train_dataset, batch_size=100, shuffle=True)\n", + "\n", + " data = parity(n, k, test_samples, seed=2001) # constant test samples\n", + " test_dataset = TensorDataset(data[0], data[1])\n", + " test_dataloader = DataLoader(test_dataset, batch_size=test_batchsize, shuffle=True)\n", + "\n", + " if (return_raw):\n", + " return train_dataloader, test_dataloader, _data[0][:100], data[0]\n", + " return train_dataloader, test_dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def acc_calc(dataloader, model, device='cuda'):\n", + " model.eval()\n", + " acc, total = 0, 0\n", + " iter_counter=0\n", + " for id, (x_batch, y_batch) in enumerate(dataloader):\n", + " iter_counter+=1\n", + " x_batch, y_batch = x_batch.to(device), y_batch.to(device)\n", + " pred = model(x_batch)\n", + " acc += (torch.sign(torch.squeeze(pred)) == y_batch).sum().item()\n", + " total += x_batch.shape[0]\n", + " \n", + " return acc / total" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def get_svd(arr):\n", + " U, S, V = np.linalg.svd(arr)\n", + " return U, S, V" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/4008625498.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_299.pt'))\n" + ] + }, + { + "data": { + "text/plain": [ + "FF1(\n", + " (linear1): Linear(in_features=20, out_features=20, bias=True)\n", + " (activation): ReLU()\n", + " (linear2): Linear(in_features=20, out_features=1, bias=False)\n", + ")" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = FF1(input_dim=n, width=width)\n", + "model = model.to(device)\n", + "model.load_state_dict(torch.load(model_path+'/model_299.pt'))\n", + "model.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "singular_values_final [3.6981118 2.057817 1.3466041 1.2434828 1.0755663 0.95081556\n", + " 0.9194288 0.7707997 0.6697056 0.5568011 0.5009025 0.37642404\n", + " 0.34557137 0.28535122 0.2435944 0.16020165 0.11774121 0.06673991\n", + " 0.02046109 0.0126453 ]\n" + ] + } + ], + "source": [ + "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n", + "bias = model.linear1.bias.clone().detach().cpu().numpy()\n", + "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n", + "\n", + "\n", + "left_svd_final, singular_values_final, right_svd_final = get_svd(weight1)\n", + "print(\"singular_values_final\",singular_values_final)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/2135357872.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "/tmp/ipykernel_2812256/2135357872.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "lst = []\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "model.eval()\n", + "weight1_last = model.linear1.weight.clone().detach().cpu().numpy()\n", + "lst_proj_all = []\n", + "loss_fn = MyHingeLoss()\n", + "lst_loss = []\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "for epoch in range(300):\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + " model.eval()\n", + " weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n", + " bias = model.linear1.bias.clone().detach().cpu().numpy()\n", + " weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n", + " weight1_imp = []\n", + " bias1_imp = []\n", + " weight2_imp = []\n", + " lst_proj = []\n", + " for i in range(len(weight1)):\n", + " proj = np.dot(weight1[i], weight1_last[i])/(np.linalg.norm(weight1[i])*np.linalg.norm(weight1_last[i]))\n", + " lst_proj.append(proj)\n", + " lst_proj_all.append(lst_proj)\n", + " loss = loss_calc(test_dataloader, model, loss_fn)\n", + " lst_loss.append(loss)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(300, 20)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst_proj_all = np.array(lst_proj_all)\n", + "lst_proj_all.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LtMupUvL0Pob9zGo6SFarB3sggCUcwhwKYQypWIIqppCKQQsDStfYh4IGioIGaB0/a2FQDAponeMmSp/HSoJaEJPSv1+b+Mn29Wy1k7KRjyZ1ZSONapIdFm1LVrLRyMaw7dlTZ+EPMegjI32iDQCgPfPMM6fd5+KLL9buueeebtueeOIJzel09rkdt9utAZrb7e7xWSAQ0J599lktEAj0+XgnZxsnTtQ00LSiooizEbf7mc9oGmj77/uO9vP/ukb7+X9doxU/Vqipfr925N//0T6cPkPbO3mqdvizP9YK7/u5dtMrD2vZb+7QJha/rX32oV9rP/rcp7Xf3nGTdmjbVu2ZZ57p9zkP5HpJVrLxlI1l25KVbDSysWw7GtnT/f0+WdRHRhYuXMjLH7u9UVxczMKFC6Pd9Jm1teE6ckT/vq8L3g1Ex6jOoerjAEy9+Aou/dRN1N53P77nnsMMKIu+SHnOOI6Pfp8NlpsA+OQ7Oxi57zVMVhufffAXJGeOYHd5ZfT7K4QQQgyBiB/tbW1tZefOnezcuRPQH93duXMnZR3rt6xZs4Zbb721a/8777yTI0eO8M1vfpP9+/fz29/+ln/84x/ce++9g3MGA6B88AGGcBgtL29wnpY5k463sAY6JufmZudQ9vlbcT/3HADu827EPuJ8Dox4k4/Gj0NTDMxrPsjI/frtnMtvu5203Lzo91MIIYQYQhGPjHzwwQdcfvnlXT93zu247bbbePLJJ6mqquoqTADGjRvHSy+9xL333ssvf/lLRo0axeOPPx4Xj/UqmzcDoC1ciKIMwRMmHSMjfjWAKRTG+pvfE6iphcwRNF1wB6NNYyixHiRr+iZ2sAKAOe94QGsjyZXG9EuvjH4fhRBCiCEWcTFy2WWXoZ3maeBTvV31sssuY8eOHZE2FXUnFyNDomNkRA0FmVpZj9LYgik3F98132B0fTIhJUxpfjGlxvMJKmYyWxtIr9oEwJxl12A0De2SzkIIIcRQSOyF8jpePqYtWjQ07XWMjDiaPeQ3toCikPk/PyatXn8z61ujGsnO3cu7XALAxLJaCNWgKQqzlvT9RXNCCCHEcJLQxUjozTd56a9/RZs9e2gadDrxWa2cU14LgPW6T+Ir1QenthtLORbcSLvdwj5lBgAzD+j7JeeOxuF0DU0fhRBCiCGW0MUIQDA5GUxD87oVX1sbZaPyMYU16pPtJH/qi4Tq2lEJ8pqlnXOyd/E+CwCY5DlARlMVAJPnzB2S/gkhhBCxkPDFyFBp37mTY7/6NSGTCY/Nwvax2WgH9KdqDhlqaAirZGSUsxV9/sqYhlLCwQoARs+YEbN+CyGEENEmxcgQUGtrKV/1P4S9Xqy+dt6bMBKbNRP1gL42z+tKG+dmfUirMZkDTAVgVLkbLdwEwMjJ02LWdyGEECLapBiJMi0YpGL1akL19VjHjcXVWE/IZOKC7GshpFFra2GLZmHsiBLeZwGaYmCOZz/OurB+gJR07MkpsT0JIYQQIoqkGImyxv/3V9o/2IYhKYlRP/85AZOJOelXkGHJRbEZeTJ8nMm2CtLTK9nPdACWNbyDyRsCIGPMuFh2XwghhIg6KUaiKHD8OHW/+hUAI779LcxTpxK86C7Occ0HwHBlNlvUFBaN3ozBEKaMMQBMbzmIKaCv5Js/ZXJsOi+EEEIMkbhctbc3qqqiqmqPbSd/jfR40cqGW1up+Mo9aO3t2M87j6TrrqP1ozqUaVejaRq72jZS3rKUydZKRo7cTwAzVYwEILXFA6FmAPInTRo25yxZyQ51NpZtS1ay0cjGsu1oZPt6LEU73etUY6ywsJDCwkJCoRAlJSUUFRXhcDhi3a0zC4UY9ccncBw+TDApifKVdxNMzWD6hy6sfiN7mzdzJLyHV3Mu5YZz/snIkSUcCk5irfknpKmt/HbDA2w/ZANg7Kc/h8lmj/EJCSGEEJHzer0UFBTgdrtxdrz481TiemRk5cqVrFy5Eo/Hg8vlYunSpT1ORlVViouLWbJkCWZzZK9Lj1a24TeFNB0+jOJwMPaPjzNl+nTaNlTQ6i8n5G9mZ2gfrbnjWJ5XzMiRJQBUmEYBMLX1CFX+HKCZsMHMtZ/6dI91c+LxnCUr2VhkY9m2ZCUbjWws245G1uPx9Ckf18XIx5nN5l4v0Ok+G8hxI816t++g6bHHABj5wx+QMmcOAL5d9QCUNW2kbXQ+Nlsb4ye8r2eacyhL1eeLzGjbT7PPBTQTtCdhsViGpN+SlexwzsaybclKNhrZWLY9mNm+HkcmsA6icCBA1Xe/C+Ewzk9ei33upTS/eAT/MQ9qrZfjxkY25ZpQDAYsWdUoBo1G9wgsdjflHZNXp7Yewee3AhBKSYrl6QghhBBDYliNjMS7hscfJ1BaijEzk+w136H+yf0Ea7y0vV/NTuNRtpmPAFZaw2byR5TzDX5FnWsEozlGhZIPwLS2w1T6RwBgzk6N3ckIIYQQQ0RGRgaJWltLw6O/ByB7zbfxHw0QrPEC4PG3stN0FIDWdiM7LGm0pKRRpeQRVMwcUSbix4olrDKutQol2ApA+riRMTkXIYQQYijJyMggaXm9GC0QwDZzJilLrqLmke0AGNNtfNCyB0w+RuTU4zME+e+c51jHcgDmBLdzqXEdvhF3cu6bRQQCqWihRgAmTj0nZucjhBBCDBUpRgZJS3ExAM7ly3G/XEqo0YchxYxneTJHny1j1uzXSU5uovMVZnu1GaDAecbNLFC2c+64idga3uFIYAQQRMPA+DFTY3Y+QgghxFCR2zSDINjUhPf99zHlLyQcmkXbe1UAaEtH8K9n/8P06W+RnNyEJ5DMgZLJ7F8/jsNMAmAqe0hPu4CD1UcxKfUc86Xrx7Q4yHHmxuychBBCiKEixUg/BQMh/I1GwiGN1jffxJQ7D/u8FfgPewkRpthZwmMv/T+ysj/ClVpLu2rjib33YNycxIHWaYQNBlLDTYyghszMxZSX7sKMl1q//oKzdrsdq9Ea47MUQgghok+KkX4IhcK8XLibui0O/vW9tyh/+PdYp98AQHkgzHNaCccC5ZjN7eSP2QXAPw9ex9TR82jX4MOp5wEwTdmFAmRmXUlbpf7ys5aA/oIzb3Lv7xcRQgghziZSjPTDlueOEDjWwhy7gTkVH5Ey7WYMjnS8oRDvhWpotFcAoOSVYTH7KfPksfH4QqZPTOdPn/gCBybOBGAh72IyTcdqycbQUAlAQNUXyPNlyjtGhBBCJAaZwBqhuvIW9heXcVmKCYtBgTELANDQOGQ1o6YeBxVKcHHrqB0A/KPkegwjHDzobsSblYfN5+VO42+Ya9pGqut+qtuqSfe10x40QUgvRgwjU2N1ikIIIcSQkpGRCO17o5zzkoxYDArBlgoCpesxj2gk9xvnMePL+XhVD0HNQEbuEcyGAMnJUym46FNknZeNN6wxqrKUe3f8kPNMW2hvTyYz8wo+qv8IJwrVPhcAisFFxqiMGJ+pEEIIMTSkGImA58M68vfXk2YyoGkqvk2/JtzyHiP+52pCLgu/evo1kpMbGDv1Xa6ZsB6A0flfYs7MbI4Gg5gVuO71IiaeUwpAZeVkUlJcfFT/ETZF5Xi7/uZVzZTJ2BHZMTpLIYQQYmgNq9s0qqqiqmqPbSd/jfR4fc2GWgI0//MAZkWhNayibPxftPZG0lY/SBAo3nkMW2sl42ZtJzWtGoD3TdfyUPkUbIZyAC6zGRmRUY8tPUAwaKKmeiIWi4UPaz/kKqWVYz59ReKA1cE4V06v/Rqqc5asZOM9G8u2JSvZaGRj2XY0sn09lqJpmhZxq0OksLCQwsJCQqEQJSUlFBUV4XA4YtKXcSVJpDdYaQ6Gad31K1xle2mdMoXKFV8A4MUDHvJ9+1m46B8YDGGO+r/IA9blqMqJwaeVx3cxPfgLXGNaqayYzLHSC5g2awY/dP+QZyqDvLlvDI1+DXfWTEZfMYEsY1ZMzlUIIYQYDF6vl4KCAtxuN06ns9f94npkZOXKlaxcuRKPx4PL5WLp0qU9TkZVVYqLi1myZEnESx73NatWtNK4eTdhTaOk/jhTy/aCyUjdJ65hyZIlmEwmnt31JC5XLQZDGLMlj387P4Pa0s5Eu4Vyn8o5DivL017F7dDXnamsnEJqUhKTF01GeQnSgvW0BscBIVocQW686kYc5lMXXkNxzpKV7HDIxrJtyUo2GtlYth2NrMfj6VM+rouRjzObzb1eoNN9NpDjArTtbQKgStUY0fIBAMlLlqBmZWE2mylt9JGsNpOWpj+eW+e8ju0N7SQZDTw9ZyJpZhOlR/6P6tYtADTuyaG93Ul2mom9zXvJDOeQFDxCIBQCoMUewOVwDbjfkpVsomRj2bZkJRuNbCzbHsxsX48zrIqRWNA0jfZd9QBUBEJMKd8KQMry5dCuP4a77qPjpCte0tL1YmRPaAwA55rKCNW+T23YT3X5bwE4/m42bWUTIA+SFYUdtTtJskylRd3W0aIJX1JoCM9QCCGEiC15muYMWkuaCDX7CWoa/oZDKJ5GDCkpOC68ENCLlQ079mG1tuFw6MNR7zXpIyljfG9w8NCPOXzkFwA4ShTqd6cTNtkASNI03qt6D4Mln+aAvk0xuFCSh/oshRBCiNiRkZHT0DSNvX/bTx5Qo2qMbtsJQMrixSgW/XXtbx9qIOSuwZlbB0AYIyXKNAAuz53BiFAIr/cIzuoavHv1+SJhs5590+Wi1LSIEWE7zao+P0QxOrE5ZU0aIYQQiUOKkdM4ur2WTF8QDArp89MJvfUeGuD65LVd+zz51kdMNtbhTNGLkfaMW2htTMJhNHDV5C9hMigQCsL/jmFrOBWAsNlMoyOFf4yZA8whtaaYer/+GYYUXJkpQ3maQgghRExJMdILTdM49vwRJhoUghYjad6t1LW3Y500CccFFxAMBtlWV0lKTSsmQ5jUtCoASs0XATA/xYFp+5NQuhFm3giBVlRDLgBhk4ljGTkdDQVJD3poDOj3ZvwWM9nOnCE/XyGEECJWpBjpxfH9jWR7VTAqOC/KpeGh7wGQftuthEJt/GPdH/CUBRhh8KEZgiQledCAN73ZQJAFR/4DH/1IP9ihNwAIJo0EwoSMJsrS9TesJjf9jf9pDfGiqt+aabfBOSnyfhEhhBCJQyaw9qLi7UpSjAohg4LStJ1gVRWGtFTqppXw7qbL2b6ljVSDj6DByE2fOQ/Q2Gq8ii0tQWxakM8c/COYO1be9esTW1VHNs0padQlp1LtSgcgWztGatCHN6j/T9HiUMl3jYjFKQshhBAxISMjpxAOa7QcagYDGLLt1Bf+CgDvVQYaq//I8ZrxpGghVM3A5279EkmGV2gjib9qBQDcc/RJxviq4dbn4c0fwvGtVFsy+NU5n2DrRWO62jH7K7lh3EWoR95DDYcBaEr2Mtop69IIIYRIHDIycgpVB5uxqfq7PkzeMoK1tWhZdhovqMZszqSiahkAlYYsJuZl0ezeyRN8mUYtiQm+Ku4ufwrO/28YfylccCcA/zvudrZmjOnWjrV9F5+a+Cla2tz6BsWG2+EmKylz6E5WCCGEiDEZGTmFw9trSTUqALRuehGA5qtbwGwgI+MBAp4tBDWFpAz9dsrLTQrvKRdh1ML8eu8DWFOy4cr79YNNvQ7O/zK77fp7SZatfwZTejaVrgzSml5lnOvb7G9vB8woBhftpkYcptisvyOEEELEgoyMfIwW1jiy6xhOk75+YLi6lGC6hnLJaKZP+zmHj+i3U46F05ieacLvr+a14PkArDpexNyW/bD8f8Ha8eYyowlt+U/Zjx2AnIZq8ptqmX90N1/eo19+T7u+qqFicBE0tKIoylCeshBCCBFTUox08PtrOVL6a95++2LGX3gfJoxo4SDh1lqct3yKCxa9wYgR17J79x4APLZccu3Q6N7JESYC8Onq1yF/AUy5ptuxq30+VCyghXG26ZNZk7w+Ljmif94Y6BigMjjB0L8lp4UQQojhaljdplFVFVVVe2w7+WskKir+gcX6Jh999BzN7nfRtCAAya3nAhBuqUJJtpPzuW8TDAY5evQoAZ8Xv2Zk1OgxKEoVOxoO41fySQq3MclbRujilYSDwW7tvFC2A3CQ0taMSdNQgSRvO1pbGwFfO80B/bFe1WLGRtIZz2Ug5yxZyZ5N2Vi2LVnJRiMby7ajke3rsRRN07SIWx0ihYWFFBYWEgqFKCkpoaioCIdj8OZT2B3/h9FY3vVzMDiO+g+vILvyPKZYbahlm6m27KP2U9cDcPToUZqamigJZpIzajSXZwfY4tjE45ZbWdj6Af/Z9nVem/Er/ObuK+4WaofZ5bqC0RUHueGtZ2nPn0Te8eP817p1bP7JDyh7oYgW1UBb1oVsH7eFFRNXDNo5CiGEELHi9XopKCjA7XbjdDp73S+uR0ZWrlzJypUr8Xg8uFwuli5d2uNkVFWluLiYJUuWRLzk8bFjlRw89D6TJp1PRvqFeOvzePY/OzknSb97FfIcZ/p9dzJ/7lz27dvHjh070ICSUBZXXziLjXuf4SPXdADOc+9GG3sxV153c9fxN1dt5vHdj3MsNBOArFY3msEIgM3nI8Vs5rJzZ/C7ZzreMWIPke0axdVXX33afg/knCUr2bMpG8u2JSvZaGRj2XY0sh6Pp0/5uC5GPs5sNvd6gU73WW/GjLmdPXtGMmb01ZjNZo5sOQpAhikIGFFM7aScdx7NbjcvvfQSAB8Fc3EbnDxaV86hEZd3HWtBwx4Ml38Zw0l9+M2Hv2Ff4z7aMy8FIKu5Ac2oX3Kr34/i8+GrqSDcMTbV4vCTlpzd5/PozzlLVrJnYzaWbUtWstHIxrLtwcz29TgygfUkFQeaMABm9IuXtHA6YU3j3//+N36/H0faCHYERzJyUhqHlO6L2c27/icw68aun8s8Zexr3IdRMZKSPBWA1IYaNOOJkRHa23FXlukBQwqtVg+Zrtzon6gQQggRR6QY6RAKhqk67CY91IqiGAj7W3Bdt5yNGzdy/PhxrFYr3pHz0TDQlqsXKzO1ndiVEItSk0kdM6/b8V4/9jo+xyLaRv2CZk2f5+JqqOl2m4b2djy11YD+WG+bxU12Rv4QnrUQQggRe1KMdKg56iEYCDPG3zFSoXlgzGg2b94MwCc+8Qn2N4UIpVs5bjGiaCHuCD3K1vMm8fTsCT2O969jO2nJuJ0WJYOQBk6jgZQ2D3SOjPj9+shIYx0ABoOLNksz+ZmjhuaEhRBCiDghxUiHigNNoIXJDLYAYB2bwbZt2wgEAmRlZTFjxgxKPT7UWWkAXMEbjPSqpFrstASaeO3oa6gh/RGmdeWb2GW5FhQzF7lsfGNsDr8am4mC1m1kRAmHcTfrr4JXDC7arM2MTpV1aYQQQiSWYTWBNZqqDrtJbT6EKSkDANv8SWzZ8m8AFi1ahC8YpnysHaxGssNl3KI8yQY1nfM1jW9s+AZbq7eyePRiVs9bzapdWwk7LiNF8fL4zBmkmk24a2vYA2DSb/HYfD4AGlvaAVCMafiMHvJS0ob83IUQQohYkmKkg6e+nYymfRhylwJwKFBNS0sLycnJzJgxgwdLKghn2CAU5l7l51gJ8IqvmZq3v87W6q0AvFH2Bq9UHaI550EAfjP9HFLN+iUOBvx6Qx3FiNXvRwOavfpoSsBiw6KGsJqNQ3jWQgghROzJbRpA0zTamvxktB3HYHOiEWbLvu0A+M67kNlb9vN4bSMAkyq3kK9UEAracIcMvHX8LQDmjpiLSTHhdV0PioGlaWaWZZ1YfVf168VI520acyhEu9VCIKQ/19tmB6sqtaEQQojEI3/9AL83CN5WXEoIgKoUD7V1tVgsFp63OGn0qZg0Da20lUWWYgDyRn2SRY2NbPJvwma08fNLf45HDXDFjjrQ4NsTx3drI/ixYsQSCtGYkgSAYnDSZm3FFuz/8+hCCCHEcNWvkZHCwkLGjh2LzWZjwYIFbN269bT7P/LII0yePBm73U5+fj733nsvvo45E/GgrclPavNBDA59JONDg/5Ezai553HUp2JS4PyjuzEf9DAl5TgATue5XGW7itXnrubhyx4my5HFITUZvwajbGamJtm6taEG9Nsy4Y4VeU1Ao1Nf2VcxpNNmcWMLWYfmhIUQQog4EnEx8vTTT7N69WrWrl3L9u3bmT17NsuWLaO2tvaU+xcVFfHtb3+btWvXsm/fPv74xz/y9NNP853vfGfAnR8sbc0B0poPYHCkU2lopMJfh9FopHXCZADOcyVxpLYBoxIkL7kJAGfKHAyKgc9N/RwXj7oYgFfr9Sdjrsp0oXQUHZ2Cfj8oBujYbtG0E8WIMZ02SzM27ENyvkIIIUQ8ibgYefjhh7njjjtYsWIF06ZN49FHH8XhcPDEE0+ccv9NmzZx4YUXUlBQwNixY1m6dCk333zzGUdThlJr6XFyq7egONL5wHQEgHnz5vFeu7767gybH097EqNTKjAawpjNadjtY7sdI6RpvF6vv4N/WUb3hfKgY2Sk4x0jiqJgUBQaU08UI832GuyGpGidohBCCBG3IipGAoEA27ZtY/HixScOYDCwePHirpeDfdyiRYvYtm1bV/Fx5MgRXn755TMuBjdUgsA/92ziuUsuZf2kXGoNbkxGE+dfeBHvNLcC8N7BQsJqOhNSSwFwOuf0GPl4q7GFBjWI02Tggo4io1s7fn/XujR2u52w1UqjU3+lvMGQRpO9hiRTSo+cEEIIcbaLaAJrfX09oVCI7OzuL+bKzs5m//79p8wUFBRQX1/PRRddhKZpBINB7rzzztPepvH7/fg7JnzCiVX/VFVFVdVu+3b+/PHtfeFta+OvjX42X3QxoN9qyW/M4fpQE5/eWoTXdAGGUDOVdTsg/AnGu44CkJI8u0e7vz6qv9b9s9lpEAqihrq35Wv3onU81puUlETAasFj7ZhXYkyl2V5LkjK1T+cxkHOWrGTPpmws25asZKORjWXb0cj29ViKpmlaXxurrKwkLy+PTZs2sXDhwq7t3/zmN9mwYQNbtmzpkVm/fj2f/exn+eEPf8iCBQs4dOgQ99xzD3fccQf33XffKdt54IEHePDBB3tsLyoqwuFw9LW7p6UFAvy70k3xrPmYVZWRTY2UZ2YRNnQfLEpueBxr/RHajq7k+xc8RJ6zinbvFwmFZgCwz2ijzGjhP7Y0jJrGj1orSNNCPdpr3LODmqOl+EaOIyUlhWv+/U9eys8ExQpZn+HJ+T/mipqLuWLyskE5PyGEECLWvF4vBQUFuN1unE5nr/tFNDKSmZmJ0Wikpqam2/aamhpycnJOmbnvvvv4/Oc/z+233w7AzJkzaWtr48tf/jLf/e53MRh63ilas2YNq1ev7vrZ4/GQn5/P0qVLe5yMqqoUFxezZMmSiJY89gZD/OGlDRhCIT6/5SCWUAkeq4Pa0RfxRq4NFAOfSle4b843effP/8s3gAy7Pnn1oouuw2IZz8/eeoffOUZ0HfPGnDRumTTjlO1tbmuiurwcgPz8fJrt+pMziiENj0Of/Dt54rQ+3b7q7zlLVrJnWzaWbUtWstHIxrLtaGQ772ycSUTFiMViYd68eaxbt47rr78egHA4zLp161i1atUpM16vt0fBYeyYyNnboIzVasVq7fmYq9ls7vUCne6zU3GZzfx96UL+8MKraG0teG0wy5vE1P1VXDE/hRFp57M8y4Vx/U+orfdgM7ZjM+uPIycnj6HWr/D/bPqr42cl2xlpM/OtCSN77UM4qBLuuE2TkpKC22YBOiav2vTiLjuj9/xgnLNkJXu2ZmPZtmQlG41sLNsezGxfjxPxS89Wr17Nbbfdxvz58zn//PN55JFHaGtrY8WKFQDceuut5OXl8dBDDwFw7bXX8vDDD3Puued23aa57777uPbaa7uKklhx2O2MNRrZY9WLgenBfJQsA1+cvPTETrv/zXHt0q5REZMpFcWYzFdLDtFqMDLVYeWFeZOwnmKE52Sq3981ZyQ5OZlqi/69wZhOraMEgJysvME+RSGEECLuRVyM3HTTTdTV1XH//fdTXV3NnDlzePXVV7smtZaVlXUbCfne976Hoih873vfo6KigqysLK699lp+9KMfDd5ZDIC7sRWUMPaQnRwtFSX3pCdavI3QcJAy7UYy7Q0A2O15PHSkio3NbZi1ML+cPOqMhQiA6vOhGU9MYPUY9YxiSKc6uQqAUdlSjAghhEg8/Xod/KpVq3q9LbN+/fruDZhMrF27lrVr1/anqahrbdMf300LO1FQSBmRfuLD4x8AUKaMZJLtIwCCplx+U6bP8bitvYGpSTP71I7f24Zm0i+3w+GgpaN+UYzpNNn142W7MnuLCyGEEGethF8or93XBkCmpr8bxJR20mvcj29F1YxUhlLJtOsL5VVpWQBcnJrEeUFvn9vRi5GOWzMhlZACYMBmgqAxgFkFm8l22mMIIYQQZ6OELkb8fj/+kF5QjAmnAWDKPOmV7OVbqdIyCGFghEMvRg4G9ZGTRa7I3pbq83q7XnoW9OivjVcMLswmfaaxI5DQ/1MIIYRIYAn9F7CiogIAY9BKNvor3M2dxUg4BBXbKNP0R3dzkpsB2O7T97sgwmKkvd0HioKiKLQ11gP6LRqjSZ8YawvEdjKvEEIIESsJXYwcP66vwOsI6gWGlmTA4Oh4DKl2HwRaKTOOBiDdqk9gLQ1mYjcYmJUc2S0VX0B/o6zdYuLge+8A+jtGNJM+Z8UetAzsZIQQQohhql8TWM8W5R0vIXMFXWAC64iT1pSp3AFAmWMaNrUdq1EvGurJ4jyXA0sfnqDpFA6HCARDEA5jOribqrYWFIwYLVNQTdsBsGs936sihBBCJIKELkbCioYSNjEirL/V1TzipFfNV+0EoNw4umvyql9JoR0HC0+xEN7pBLztaCYzZncDWlsLdqcTl3E+zYzAb9LnrDiQyatCCCESU8LepvGqXtaF3iWjdiEZRr0QMGWeVIxU7gSgLJTO5PRDAFQo4wA41xnZ+jh+bythowlLo76Y3vxPfgbFlKv3o6MYSTJEVuAIIYQQZ4uELUYcZgeXuZagoJBp6ShGRnRMXg0FoWY3AGVeEzMz9wKwJTwHgGlJ9h7HOx1fWxtKKIgh4MdosTL9ssX4w/oxWs367Z8koxQjQgghEtOwuk2jqmqvyxP3Z8nj6aZz2cFx7GH9ZyXNrB+nZg/moI9mSzbtrT6mpB0EYAdzSTcZSVU0VDXY53a9Hg9Gnz4CMmLGuWAw4gvroytNncWIKanP5xBvS0RLVrKy3LpkJTs42Vi2HY1sX4+laL2tVhcHCgsLKSwsJBQKUVJSQlFREQ5HZLdITqdxlw2qzCxxmgkrGjsWNIECoxs2cm7Z47xtu5yfmRZxz9zf066lc7vyByaH/Kz21pz54CfxlB6kZvN6FMB54RIyc8dSuU5/7fx7U+5nZ5qba8rPZ+HMTw7auQkhhBCx5vV6KSgowO1243Q6e90vrkdGVq5cycqVK/F4PLhcLpYuXdrjZAay5PHzJTtROp6SsWQncfU1CwEwvLoBysAz8iJmhvXXwDcmXwRehQvzR3L1hHkRtbvuj7+lFghZbCy6cjGp9myeXbcLq9KCxxwA4Jwx53D11Vf3qd/xtkS0ZCUry61LVrKDk41l29HIejyePuXjuhj5uNMta9yfJY+dGXYctfrr4G3jU/V8OAxH3gTguHUiE63PA/CKfxoA051JPZZHPlO7NYf12zzBlDSSk5MJePT7Qg5jEy3GEAAZzqyI+x8vS0RLVrKxzsaybclKNhrZWLY9mNm+HidhJ7ACXPb5yWR3vEjVOjFV/+bQG9BUCjYXB5RcRibrT8B8FBwDwNSkyB7BVf0+Giv095kEnWmYjVZaG/UXoCUZGmk16XNPMp2ySJ4QQojENKxGRgZbqMmHzWcEA1jH629hZcuj+tdzP09p2QFMmSFaw0nUG/QF8iZHWIzUlB4mHAoRNpnRzMm88n8l+Nr0AsRhaKLdrI+SZKfmDM5JCSGEEMNMQo+M+A/r97LMo5Ix2EzQcBgOrwMUqud8EUf4MAAewwRQFKYm2UgyRbaGTN3RIwCEbQ7MalpXIQJgNLZ0fZ+dkT3AsxFCCCGGp4QeGQkc1lfPtXSOiuz+t/51whW8pjoZbS0DYMaIOfxj5ATybZGvH1N7tBSAkNWBNaCvDGy2gBoAm1VfG8fm13C4UgdwJkIIIcTwlbDFiKZpBCs6nqSZ0FGM7HlW/zrj0xQfb+LCFL1YGJE+k3PTU/rVTt2xEyMjlo5i5LJLveR/eCf7rPqKwEk+MNgje5GaEEIIcbZI2Ns0iqKQcc9s9k/3YB6VDHUlULsHDGZCk6/m/eom8lMqAHA5p/erjVAwSH35MQA0swtD0A4K5DiqsBtaaFb0+SdJPlAssmqvEEKIxJSwxQiAYjTQ5gyimAyw91l94/jL+Chow+6vxG7yEwybcTjG9+v4jZXHCakqKAomTZ8TkpWfgs1XBUAT+kq9jkBC/88ghBAiwclfQQBNg13/0L+ffj1vN7UwFn2uR0toNAZD/5637py8qpnMWFT9Fs2oKWnQUgdAs6LfJbP7I5sUK4QQQpxNpBgBlLJ3oeEgWJJh2nW80eBhjOEoAAFlQr+PW9v5JI3RhNmfCsCoyWkoLfp7R5oMHcVIQIoRIYQQiUuKEcCw/Un9m5k3UhqysMXdxhiz/iSN0TK538ftfNmZZnJiDNtA0cidmIqh5ZD+OfqIi12VYkQIIUTiStinaTqltx5AOfyS/sP8FfyjuhGAsXa9GHEkTen3sZtr9LkhBqM+XyQpS8FsCKD59ImxjYpeC9rVhP+fQQghRAJL3JER1YfxxXu4+OCPUMIqjLmQcM4s/lHdiFNrxmV2E9YUMlL79yRNOBTCXauv7ms0jAYgdZQFGg6hECasJeMx6ksr24NSjAghhEhcw+qvoKqqqKraY9vJX/tMM2Bo1kc/gjML8F6xlm/sOUqFX+UCwzEIQ403i/OSUk557DO1666tJhwKYTAaMaIXI+mjbQSr92ACVG00rcZ2AJICpoj63+9zlqxkz7JsLNuWrGSjkY1l29HI9vVYiqZpWsStDpHCwkIKCwsJhUKUlJRQVFSEw+EYtOMn+aqxBFtoSp7EH+2ZbDUnYdA0vhv4G1Msz7C1ai7j7Z/D1o8pHd6q41S+9QpGWzJm+5fRlBDOC6pZ4H2dyTXP0xpcxnXZx6l1tnDHKxmMufneQTsvIYQQIh54vV4KCgpwu904nc5e94vrkZGVK1eycuVKPB4PLpeLpUuX9jgZVVUpLi5myZIlES953Jm94PIr+J/tR0DT+POMsSSXteH1QJU3n6//19WnzfbW7q43XqUSsFgy0QC/tYHLLr+UUe++AjUQ1EbTbtaftsnAytVXn7qd/rQtWckmSjaWbUtWstHIxrLtaGQ9Hk+f8nFdjHyc2Wzu9QKd7rPebGpu4zf2LFo87QQ0jYkOK4uzUnlr/34A2sLjz3jM3tptqdPniwSDaRiN4LfVkpKSglJXAoCq5eMz6cNXLp+hX7+w/TlnyUr2bMzGsm3JSjYa2Vi2PZjZvh5nWBUjg8kbCrPyQDn1ZgffPaw/9XJNViqhkJdwsBwFCBon9vv4nU/SaFoaYUUlYG3CbjFCkz4a0qLkoJrDALjawwM7GSGEEGIYS9inaRxGAw9PygPAH9anzXwiy0Vr234UNJp9TpxJ2f0+fnO1XowoxlT8tnoURcPaVoGihQhrdhqstq5909qDAzgTIYQQYnhL2GIE4PL0FK7yuwEYa7cwI9lOa8s+AMpaRjEixdqv42rhcNfIiGJIZYZ1IxcZd6A06qMiQS2PBlsLACleDYc/MNBTEUIIIYathL1N0+mT/maumD6VuanJKIpCS8seQC9Gpub2rxhpaWzQF8jDgGJwssi0juRQPdRcBEBQG0m9VS+CUtvA7vMOyrkIIYQQw1HCFyNG4OactK5JNi2tnSMjeVyaYjtNsneNx/X3lygGF0bFR4qxDgXggP6m16A2kjqzPsM4tVXD7m0Z0DkIIYQQw1lC36b5uHBYpa3tAABlnlHkpdn7dZz68mMAKMZMnMbjKErHB5U7AAiG86g36iMjLi9YvW0D67gQQggxjEkxchKv9wjhcID2oI369gzy0/r3grX6cn1kxGDMJMdysMfnQW0kdQZ9NCS1FQzt7f3vtBBCCDHMSTFykjavPsG0sjUHs9HU7wmsJ4+MjLKU9Phc1UbSqOjFiKtNw9DWBvH7IlwhhBAiqqQYOUm7Vy8iarxZ5KXZMRiUMyR60sJhGo6fKEayzEcIcuJ98iHNiUYKbjrmjLSBIRgEv38QzkAIIYQYfqQYOUl7u15E1HozGdXP+SLu2hqCgQBgxGRMItVYyX4moOlTWAlqIwFo7RgZSW0DQzgMXnmiRgghRGKSYuQk3m7FSH/ni3SOimQwzvoBBiXMASYQSMkH9GJEsRrxdxQjKW2KXqa0ySRWIYQQiUmKkZN0jYy0Z/V7ZKRu33ZAL0amONbxdmguu5lMMGMqAMHwKEgzoRr0SauO9o5bOFKMCCGESFDD6j0jqqqiqmqPbSd/jfR4nV9DoXb8/moAar1ZjHRaTnvM3to99t67ANhMSdS5xrCubRSg4D3/fzCFsmk7uBR3qg8UUMIayT79/SaqxwN9PIfBOmfJSna4Z2PZtmQlG41sLNuORravx1I0LX4f4ygsLKSwsJBQKERJSQlFRUU4HP27fXImBkMVjqSf4VXt/M9bP+HeGSHGpkR2DEULUfbPv+MPtpMx9jLcDTtoGjUJgBkzZjDuuIvsahtbRx1ibcrDuFo1fvVbM3P37uTtH/2IxunTo3BmQgghRGx4vV4KCgpwu904nc5e94vrkZGVK1eycuVKPB4PLpeLpUuX9jgZVVUpLi5myZIlES95fHLW7d7A3n36kzSgcOPVV5J1mkd7T9VuuOwDfvN3HwBzllxO8RPvdO1/7bXX4i46SKC6CftEJ9Tok1dTLHp24axZaMuWRdzvgZyzZCU73LOxbFuyko1GNpZtRyPr8Xj6lI/rYuTjzGZzrxfodJ/15bgBtQLQJ69aTQZy05JQlDM/2ntyu5UfbgA0wII9OYxm1C+vzWbDarUSbtYf3623tAKQ2qbhMuvTdkyBAETY/4Ges2Qle7ZkY9m2ZCUbjWws2x7MbF+PIxNYO3ROXq1rzyQvzd6nQuTjju3S37ZqsqTTVHG4qxhxOBxomkaoUR81qaYZAFcbpDg61r+RCaxCCCESlBQjHdraDgP6yEhWcj/evBoOUVWlj3gkp2ZTdbikWzESblHR1DAoUOmvB/RXwZtTUjo7MPCTEEIIIYYhKUaAUKgdj0dfxO6IewyZ/SlGavfS0K4XH+mjJ1B9qATNdKIYCTbqj/IaU63UufVbQintRoydc2DkpWdCCCESlBQjgNv9AeFwgICWSVVbDhnJlsgPUr2bNjUIQOaoXLzuZjDp98rsdjvBBv0WjSndRmO7/gixI5SC0jky0tIy4PMQQgghhiMpRoCmJv2pl7rAHEAhIynykZH2Yx8RCuu3WlIy9HkgVlcq0DEyUquPfJjS7bhDTQDYTZmQlaUfoLa2/ycghBBCDGPD6mmaaOksRkpbZwL0a2SkZLc+D0Qx2FH9zQCYk1JADUNJKy0Vx/XPMyy0ejqKFnsuZGfrB6ipGcgpCCGEEMNWwo+MKEoj3vbDKIqRfQ3nAJB5UjHSWNnGzjfKCKqh0x7n4HF9NCXZNYLGinIAVKN+m8ZWFQYD2Gdn4ZthJmzQUMIarowxUowIIYRIeAk/MmI2bwbA5ZpHVYsZUMnomMCqaRqvPb6bxso2Whv9XPRfk/C1qfz7p9tIzbaj5ejHCLlrqPImA5AzcQKNFeVoKLT5AwBkaU6SFuSSdv1EdtfvBiC9FZJz86QYEUIIkfASemQkGGzBbNHXkhmdv4KGVr14yEjSR0YqS5pprNRvqXz4VjnVpW5KP6yjucbL0V0NNOywEw6FOf7+HoJB/S1zo2dNpbGinLDNTljTsGImRbNjn5EJQHWbPnk1vQWSR8ltGiGEEKJfxUhhYSFjx47FZrOxYMECtm7detr9m5ubWblyJbm5uVitVs455xxefvnlfnV4MFVVPY2i+HDYJ5DsupxWv/40TOfIyK71+jwPk8UAGmz8ewnHdjd05X11Jna9VcH+992EQ/r2lIwMfG2thOxJAGSFUjAmmbGOc+lttlbpbXg00kaPPFGMeDzg80X/pIUQQog4E3Ex8vTTT7N69WrWrl3L9u3bmT17NsuWLaO2l6dBAoEAS5Ys4ejRo/zrX//iwIEDPPbYY+Tl5Q248wMRDqtUVPwZgFH5t9Pk1QsRs1HBaTPR2uSndGcdAJ9YNRuT2UBdWQtHduoTVacs0u/R7Hi1nIOlVtBaux3fmKaPhGRqTuzTMlGM+htdj9brb3pNb4HMsXngcoGlY46KjI4IIYRIQBEXIw8//DB33HEHK1asYNq0aTz66KM4HA6eeOKJU+7/xBNP0NjYyLPPPsuFF17I2LFjufTSS5k9e/aAOz8QBoOZGTN+jxpYxIisT5x0i8aKoihUHGhE02DEmBTyzklj8gV68aGFNaxJJi68cQKmpBCqP4QW7hgVSXPRUq8XMGFbx8hI2IltWnpXuxX1Zfq+bSasyUmgKHKrRgghREKLaAJrIBBg27ZtrFmzpmubwWBg8eLFbN68+ZSZ559/noULF7Jy5Uqee+45srKyKCgo4Fvf+hZGo/GUGb/fj9/v7/q5c9U/VVVRVbXbvp0/f3x7X1itk/D7P0MoBDVufW5IepIZVVWpPNwMwIhxKaiqyrSLc9jzdiUAo6akEdZCOM8J0LjDjhLquPUyeiJ15cfQFAM+TW8jy+DCODqpq381bfq+1oCja5sxKwtDeTnBigq0PpzHQM5ZspI9m7KxbFuyko1GNpZtRyPb12MpmqZpfW2ssrKSvLw8Nm3axMKFC7u2f/Ob32TDhg1s2bKlR2bKlCkcPXqUW265hbvvvptDhw5x991385WvfIW1a9eesp0HHniABx98sMf2oqIiHA5HX7sbka21Cn87bGSKK8xd08LUbHKguo2kz2nHkavfwqn/wI6vzkTGue3Yc4IooQDjtv6b0oZ6DnpspE6dhbeyjHZ/AO+4adg1C5+wXcChaSfervrTmh/jsXq5/eWRjC24G4AFP/gBOdu2sWPlSsqWLInK+QkhhBBDzev1UlBQgNvtxtm5/MkpRP3R3nA4zIgRI/jDH/6A0Whk3rx5VFRU8LOf/azXYmTNmjWsXr2662ePx0N+fj5Lly7tcTKqqlJcXMySJUsiXvL45GzFluNw+CCTx+axdPE0/vTaZkBj2fWXdL1RNXB5kIaKNnImOAkGg3zwzG+ZaXuFPeF5AEybNZtN+3ahperzRVyag7xFE6kZc4hUayrTM6az9m/3A2AhtavPxmeegW3bmJWdzYyrr46o3wM5Z8lKdrhnY9m2ZCUbjWws245GtvPOxplEVIxkZmZiNBqp+djchpqaGnJyck6Zyc3N1f/gnnRLZurUqVRXVxMIBLBYer7t1Gq1YrX2fCW72Wzu9QKd7rMzMZvNNLfrLzXLSrHRXO1HC2vYU8ykZSejKErXfklOOwCKomBXG9A0qGvXt7W7mwFwjRpHe0jDGbbjHhvgq+u/iobGHTPvIGQIo2gaZkvGiT7n5gJgrK/HGME5DPScJSvZsyUby7YlK9loZGPZ9mBm+3qciCawWiwW5s2bx7p167q2hcNh1q1b1+22zckuvPBCDh06RDgc7tpWUlJCbm7uKQuRWKlv1eeoZCRbqSl1A5A9ztVViJyKI9CAW7URCCkYzWYqDuwFwJbSMTJiTeGAdgQN/U7YYx89BsBFezS0lNQTB5IJrEIIIRJYxE/TrF69mscee4w///nP7Nu3j7vuuou2tjZWrFgBwK233tptgutdd91FY2Mj99xzDyUlJbz00kv8+Mc/ZuXKlYN3FoOgsa3jaZpkCzVH9WGl7HG9398CsKuN1Pn0p2bScvOoLT2Mohjwh0FDIy01jYPNB7tl5h20c9dLYbRU14mNUowIIYRIYBHPGbnpppuoq6vj/vvvp7q6mjlz5vDqq6+S3fEHtaysDIPhRI2Tn5/Pa6+9xr333susWbPIy8vjnnvu4Vvf+tbgncUgqGhqByDbaePYEX1tmTMWI4FGjvj118A7XHpxkTlmLEd9dbw66lWOWQ+TVK8/1psTvJHf3/A5ai6/CVMYjOkpJw4kxYgQQogE1q8JrKtWrWLVqlWn/Gz9+vU9ti1cuJD33nuvP00NiXBY41ijF4CRVjO7G32gQPaYMxUjDdR2jIwYjPqlzMgfy0vBLXjNXraFP8RapT/9M37TQcyfzMDZrj9CbE6TkREhhBACEnxtmk5VHh+BYBizUUFp1G/XpOcmYbGfvlazqw1dt2mCHe9FMWVlciTlSNc+/rBe5Hxx+wds2LALAxqqYsSRetIjyllZ+tfGRjhpbo0QQgiRCKQYAY416AVDfrqD+mP6O0Gyx55+VISgj5DPR0tQf+y3882rHxqP4jN1X2MmrUXD2Q77nnkVgDpnFmbTSZc+OfnE917vQE5FCCGEGHakGAGOdhQj4zKSqC7t2+RVPJW8XTsWgIz8Mbjraggawrzk1Z80mtU8uWvXMbX60zSzj+4EwDB2XPdj2e36a+EB2toGcCZCCCHE8CPFCCdGRsZmOKg91lmMuE4X4ciWDex25wAac5bqLyrbP0OlUWvEHrTzyWPzGNmoX97k8EgApjWUApA/Z2r3gykKdL5ZtrX7gntCCCHE2U6KEU6MjIwKG1F9IUxWI+kjk3rdP+BrZ/1zxQDMG6+gKAbaLSF25OkrF89umE1eo4+5B/MJB1K5xKa/odXY8b6R9Gnn9Dxo560aGRkRQgiRYKQYQS9GksKgvq3P+xg3KxODofeXnW155h+0trbjMrez6Nw86o6VUpPuI6SEcQaczGg9hxHHj2KoGI+vdA2XzbyoW94yfnzPgyZ1FD9SjAghhEgwCV+MhDUob/JyZbsFtVUlLcfBpQWTe93fU1/LthefAeCy7CMY00dRdfAAdan6UziZvkxm+1IJVXxARXIW80ankTblpJEQRcE8dmzPA3cWI3KbRgghRIJJ+GKkyQ/BoMY4Vb8UV942DetpHund+dpLhIJBRrn8TEhuRHXkUnvsCA1Z+kjKSF82udueAy1MZXImV0wdgWXM6K4JquZRozDYbD0PLCMjQgghElTUV+0dTKqqoqpqj20nf430eHU+hREhBQsKFruJ1JG2Xo+l+v3sWvcaAPNTDhE02jkWzCdMmLok/ZHg81vHEq54G4CKpCwumZBOyGjENHIkwYoKzOPGnbLPxqQkDEDQ7UY7w7kM9JwlK9mzJRvLtiUr2WhkY9l2NLJ9PZaiaZoWcatDpLCwkMLCQkKhECUlJRQVFeFwOM4cjMDGKoWj+y1c4bNgywqSOb+91309B/dQ+/4mUiwqt49/j0M51/J2zWhKanfx8oJjmENmnnxlMeaPXqDJmszd163lgbkhFAXy/vgESSUlNF56CfVXX93j2Of/+Mfkbt3Kzrvu4tiyZYN6jkIIIUQseL1eCgoKcLvdOJ29vzIjrkdGVq5cycqVK/F4PLhcLpYuXdrjZFRVpbi4mCVLlkS85LGqqvzz9+vIDxkBmL1oErMXjzrlvpqmUfTSkwDMTS1HMds4nLUM84F3qRmpt5vny8FeX04QqEjO4qLJuVxzzSwAWkJh6h95hFl33IFx2rQefTY+9RRs3crMceOYfopiZTDPWbKSPVuysWxbspKNRjaWbUcj6/F4+pSP62Lk48xmc68X6HSfnU6NF2YH9fkio6ak93qM8j27aHCrmJQQM2aOI3TBD/Ad1Kj0+alL0yevzmqfRKj5AwAqkzI5f3xG1/HSP/0p0j/9KeDEsFW3PncUWUafD2Mfz6O/5yxZyZ5t2Vi2LVnJRiMby7YHM9vX4yT8BFa11YBDUzCYDWSNTul1v+0v/huA6a4abJ//G9rUT9J6sARf9miaLE0AnBeejilbLyqOJ2cxf0x63zsiE1iFEEIkqIQtRjRN46XDxYwy7QMgZ6ILo/HUl6P26BEO79gOwLljDWBPBcBTVUnQEKLVrE9enZI+Bf9XvsGm3BlsnngBk3N6L256kGJECCFEgkrYYkRRFH730W+Z1JYNwOT52afcz+tu5tmf/QBN05iYXE/GhOldn7UCbosbTYG0oJNwUiZPtmXwgwVfYNzk0RhP8+K0HuQ9I0IIIRLUsJozMtgmhy4h05tHWAkzfk5Wj88bjpfz/MM/pqW+jrRkI8tGHoTc/wLAU1+H35FEs6USgAm+fL57sIwdhAAiu0UD8jp4IYQQCSuhi5ERFTMAqHAdxGi7tNtnBza/zWu/+yWq30dyWjrXjd2PzRdEzZrOv/7+dwI1VYStdpqtzQCMsk7gLUKkOsxcOCGTmxfkR9YZuU0jhBAiQSV0MWKrcABhDmVs4/3q2SzKW0QoGGTjX59g+yvPA5A/fRafuPMuHL/VC5fdDUYOHDjQdYwWi/7Yki/jHDgEV0wZwcP/NSfyzkgxIoQQIkElbDGihTX2JWukK5UcTfuIN8vf5FzXTJ796Q+oPLAXgPM+eQMXffZWDAdf1UOu0WzdfbjrGGHCNFmaAXD7cgGViSOS+9chmTMihBAiQSXuBFaDwrfuOpe2GVX4zV42H3mbf/3ge1Qe2Iti0C/LiHETMBiNsP3/AdA6ZjFVVVUogMHfTrWjmrASJkVJ5nidvt7MxKx+FiMyZ0QIIUSCSthiBGBcZhKXpY3BoVqY/ZZG7dHD2FJScBicTHLO5+iefaiNZbQeKsZtMPBKbSYAVncjlvJ9lKTqIyjXZVzDkTq9iBjwyIgUI0IIIRJMwt6m6WT0hfjEtlE4PCEMSTamX7oY9+4Qe211+CqqePNvn+OVMSMBGNm2kYsMizA0VrN/VCMNVjcZqovrx93G797ehcVoYHR6P9fOkds0QgghElRCFyMNFeUcf/05HG0h2i0hvJ/M49j7xym1n1g7cIe9gc4BpMqkSlKO16L629g3Xh/B+EL9dXzUqu8/NtOBqZcXp52R3KYRQgiRoBL2No3q8/GfH91HsK0VW2Yar1xQzRtNmzliCgMw1u/iOjWXarMBRdOY0jYagA8MH1I+op1Wa5DUYAr5zefy9f98BAzgFg2cGBkJBCAYHNC5CSGEEMPJsBoZUVW1a5G5k7ed/LXPjEYWffZW3n3mn8y95Vb+cOB/CCvttFhamO2bSHqwgRLnBgDGBYzcVv8p1iT9kgPj2nB5fAAsbr6AYyfVc+MyHH3qxyn7bLHQuZyQ2twMLldk+T6SrGTPpmws25asZKORjWXb0cj29ViKpmnamXeLjcLCQgoLCwmFQpSUlFBUVITD0c85Gb3w+XwcOHCAjVkbqXHUcF7LDL5T+UXeCr9CXdar/D3TyCfco7i7cg1fHv99jltrurKPH3qAF9QUdqX72deksGp6iLERLEfTjaZx7Q03YAiHee2Pf8SXkTE4JyiEEELEiNfrpaCgALfbjbNjdfpTietipJPH48HlclFfX9/jZFRVpbi4mCVLlkS85LGqqjz++OM0NjZizDbwb9u/CRnCfCf5K0xWzPy69kd8YLdxb+UnucT2KQ61vkNR+rsctpWzqGUOK6sLeHp6Eqs/Ows1FMbcx/kivfXZlJWF4naj7t4N55wTcX4gbUtWssMxG8u2JSvZaGRj2XY0sh6Ph8zMzDMWI8PqNo3ZbO71Ap3us97U1dXR2NgIwKLgWGz11/CXES/wd+UFHku6kj1WCwCT22eTc1km4dXPs/bCb2I16fM71jt8fPGT8zvaHoTzSUoCtxtzIEBfDtifc5asZM/GbCzblqxko5GNZduDme3rcRJ2AivAxo0bAThn/CTyj2fyiaZLAChtKeXZw5toNxhwBDXc03Lxbn2OUFMV79e9hKZp4DTimuEl1dH/X7Qe5F0jQgghElDCFiPBYJBAIADArMpsHEoKil9F8eur7b6h6OvPjPYYSfYfoOkv/4+gQaHCV8orFX/EtWIKYeMgd0reNSKEECIBDavbNIPJiIGrAnOo8Y8g1WelLejh3w2vEfSPxWhtZL8jBEBOvZ0jB98lq62N0PixAPhNXmyZ/Z2pehryrhEhhBAJKGFHRhSTAcVsIBUHlbYa3qj8C/vMLhaNOrfbfq4aJw0NdbSbjWhTpwCQnBalJ13kNo0QQogElLDFCEDyVWN4Z6qblw49gy/URvL4Kdy18PKuz9M1A9NyZwFQl5JEMDdbz6VLMSKEEEIMloQuRkzpNv51tAWn6kZD4aG7rmNW1jQsHZdlgT2XcbPnAVCXYifg0h9Lilox0nmbRuaMCCGESCAJXYysL6nDW1MFQPrYiaSlOjEbzcwI65dlUfZ55GXqoyENKQ7a0V8VLyMjQgghxOBJ2GLEHwzxw5cOMKq9AoBzzp3b8UEra6sq+HZDI9fOuYPkhmbMwRBBo4FDH2wBpBgRQgghBlPCFiNmg4G7Lx3HGP9xAEbP0OeGULmd8QE/t5BKsEGl5YUXyGxtB6CtuQmD0cToGbOj0yl5tFcIIUQCSthHew0Ghctz4HjQi9FsJvcc/UkZyvXRj1DqVEo/fQOa309megpVqfp8josLbiMjL7/fCyCdljzaK4QQIgElbDECUL73IwByJ03GbLF2bNwKQGupD83vxzZ9OueuuI3j614kZ/wk5l19XfQ6JLdphBBCJKCELkaO790FwKhpHbdowmE4/j4ATRsPA5D93e/gmDuXL37i2uh3SIoRIYQQCWhYFSOqqva4PdL5c39umzQcLwMgd/I0PV+3H3N7E2HNRHuthmXyFEwzZpzy2ANpt7esYrViAsItLYROc9xotC1ZyQ7HbCzblqxko5GNZdvRyPb1WIqmaVrErQ6RwsJCCgsLCYVClJSUUFRUhMPhGLTja5pGwN2ExZmKYjBw7ke/YnTwA1orrZRvzKD6hhvwnH/eoLV3Jlk7drDowQdpHjeODf/3f0PWrhBCCBENXq+XgoIC3G43Tqez1/3iuhjp5PF4cLlc1NfX9zgZVVUpLi5myZIlES95rKoq6195lmWevxN25GPcXYTBqFF59Dwsy76E84YbUBSl1+xA2j1VVnn3XUyXX442cSLBvXsjzg+kbclKdjhmY9m2ZCUbjWws245G1uPxkJmZecZiZFjdpjGbzb1eoNN9djqjmt7FeHwjRgAj+Nud5Pz+JQxW64D7FHHW5QJA8Xr7dMxBbVuykh3G2Vi2LVnJRiMby7YHM9vX4yTse0Y6jWzSJ6x2jg8pS77X50Jk0Mnr4IUQQiSgxC5GWqrJaCsBoPS1LMp3TMd85Zdj15+Tn6aJ/7tnQgghxKBI6GLEcOBlFDQCWg7+ZjPmect7nSMyJDqLkVAIAoHY9UMIIYQYQgldjCj7nwPAfdgIQPKll8SyOyeKEZB3jQghhEgYiVuMaBpaxiQChiTc+1QUqxXHggWx7ZPJBJ3zVWTeiBBCiASRuMWIohBe/nPea/ov1DYTjgsWYLDZYt0reQurEEKIhJO4xUiHpAP6BNbkS2J8i6aTFCNCCCESTEIXI6GWFuxHjwFxWIzIbRohhBAJol/FSGFhIWPHjsVms7FgwQK2bt3ap9xTTz2Foihcf/31/Wl20LVv2owSDmMeNw5Lfn6su6PrfNeIjIwIIYRIEBEXI08//TSrV69m7dq1bN++ndmzZ7Ns2TJqa2tPmzt69Chf//rXufjii/vd2cHW9vbbACRdEj99kts0QgghEk3ExcjDDz/MHXfcwYoVK5g2bRqPPvooDoeDJ554otdMKBTilltu4cEHH2T8+PED6vBg0TQN7+bNADjiqECSYkQIIUSiiagYCQQCbNu2jcWLF584gMHA4sWL2dzxh/1Uvv/97zNixAi+9KUv9b+ng0xRFEb/5z9UFtyMfe7cWHfnBHklvBBCiAQT0UJ59fX1hEIhsrOzu23Pzs5m//79p8y88847/PGPf2Tnzp19bsfv9+P3+7t+9ng8gL4qoKqq3fbt/Pnj2/si7LDTOns2QUCJMD+Qdk+XNdrtGICQx0O4l2NHq23JSna4ZWPZtmQlG41sLNuORravx1I0re+LoFRWVpKXl8emTZtYuHBh1/ZvfvObbNiwgS1btnTbv6WlhVmzZvHb3/6W5cuXA/CFL3yB5uZmnn322V7beeCBB3jwwQd7bC8qKsLhcPS1u8PSzD/8gfEvv8yBG29k/y23xLo7QgghRL95vV4KCgpwu904nc5e94toZCQzMxOj0UhNTU237TU1NeTk5PTY//Dhwxw9epRrr722a1s4HNYbNpk4cOAAEyZM6JFbs2YNq1ev7vrZ4/GQn5/P0qVLe5yMqqoUFxezZMmSiJc8jses4Z134OWXmThyJOOvvnrY9Fuyko1FNpZtS1ay0cjGsu1oZDvvbJxJRMWIxWJh3rx5rFu3ruvx3HA4zLp161i1alWP/adMmcJHH33Ubdv3vvc9Wlpa+OUvf0l+L4/TWq1WrJ2vRT+J2Wzu9QKd7rMziausywWA0evFeIbjxlW/JSvZGGZj2bZkJRuNbCzbHsxsX48TUTECsHr1am677Tbmz5/P+eefzyOPPEJbWxsrVqwA4NZbbyUvL4+HHnoIm83GjBkzuuVTU1MBemwXHeRpGiGEEAkm4mLkpptuoq6ujvvvv5/q6mrmzJnDq6++2jWptaysDIMhoV/sOjBSjAghhEgwERcjAKtWrTrlbRmA9evXnzb75JNP9qfJxCHFiBBCiAQjQxjxRt4zIoQQIsFIMRJvZGRECCFEgpFiJN5IMSKEECLBSDESb+Q2jRBCiAQjxUi8kZERIYQQCUaKkXjTWYx4vdDxtlohhBDibCbFSLzpfN29psnoiBBCiIQgxUi8sdnA1PH6lz6+018IIYQYzvr10rNYUVW11+WJ42W55MHImlwulIYG1Pp6GDFiSNuWrGSHUzaWbUtWstHIxrLtaGT7eixF0zQt4laHSGFhIYWFhYRCIUpKSigqKsLhcMS6W1G3+L//m6SaGjb+5Cc0TZkS6+4IIYQQ/eL1eikoKMDtduPsnIZwCnFdjHTyeDy4XC7q6+t7nEy8LZc8GFnTeeehfPghwRdfRFu6dNj0W7KSleXWJStZ+Z0+OevxeMjMzDxjMTKsbtOcblnjeFkueVCyHSsbm9ra4DTHjrt+S1aysty6ZCU7KNlYtj2Y2b4eRyawxqPO6tHtjm0/hBBCiCEgxUg8crn0r/I0jRBCiAQgxUg86ixGZGRECCFEApBiJB7JbRohhBAJRIqReCS3aYQQQiQQKUbikdymEUIIkUCkGIlHcptGCCFEApFiJB7JbRohhBAJRIqReCS3aYQQQiQQKUbikdymEUIIkUCkGIlHcptGCCFEApFiJB51FiM+HwQCse2LEEIIEWXDaqE8VVVRVbXHtpO/Rnq8uMzabHQuLaTW10NW1tC1LVnJDqNsLNuWrGSjkY1l29HI9vVYiqZpWsStDpHCwkIKCwsJhUKUlJRQVFSEw+GIdbeGxDWf/Swmn4/i3/0Ob25urLsjhBBCRMzr9VJQUIDb7cbZOR/yFOK6GOnk8XhwuVzU19f3OBlVVSkuLmbJkiURL3kcz1nT2LEolZWoW7bAuecOm35LVrJDmY1l25KVbDSysWw7GlmPx0NmZuYZi5FhdZvGbDb3eoFO99lAjhuzrMsFlZWY29ogUc5ZspLtZzaWbUtWstHIxrLtwcz29TgygTVeyRM1QgghEoQUI/FK3jUihBAiQUgxEq/kLaxCCCEShBQj8Upu0wghhEgQUozEq85ipLk5pt0QQgghok2KkXiVmqp/lds0QgghznJSjMSrzmJERkaEEEKc5aQYiVedxUhTU0y7IYQQQkSbFCPxSkZGhBBCJAgpRuJVWpr+VYoRIYQQZzkpRuKVjIwIIYRIEFKMxKuTi5H4X8tQCCGE6LdhtVCeqqqoqtpj28lfIz1e3GaTkjADBIOobjckJQ1d25KV7DDJxrJtyUo2GtlYth2NbF+PpWha/P5nd2FhIYWFhYRCIUpKSigqKsLhcMS6W0ND07j2M5/BEArx2h//iC8jI9Y9EkIIISLi9XopKCjA7Xbj7Fxz7RTiuhjp5PF4cLlc1NfX9zgZVVUpLi5myZIlES95HO9Z08iRKPX1qDt2wPTpw6bfkpXsUGVj2bZkJRuNbCzbjkbW4/GQmZl5xmJkWN2mMZvNvV6g0302kOPGNJuaCvX1mFtb4RT7xW2/JSvZIc7Gsm3JSjYa2Vi2PZjZvh5HJrDGM3miRgghRAKQYiSeSTEihBAiAUgxEs+kGBFCCJEApBiJZ1KMCCGESABSjMQzeSW8EEKIBCDFSDyTkREhhBAJQIqReCbFiBBCiAQgxUg8k2JECCFEApBiJJ51FiNNTTHthhBCCBFN/SpGCgsLGTt2LDabjQULFrB169Ze933ssce4+OKLSUtLIy0tjcWLF592f3ESGRkRQgiRACIuRp5++mlWr17N2rVr2b59O7Nnz2bZsmXU1taecv/169dz880389Zbb7F582by8/NZunQpFRUVA+78WU+KESGEEAkg4mLk4Ycf5o477mDFihVMmzaNRx99FIfDwRNPPHHK/f/2t79x9913M2fOHKZMmcLjjz9OOBxm3bp1A+78We/kYiT+1zMUQggh+iWihfICgQDbtm1jzZo1XdsMBgOLFy9m8+bNfTqG1+tFVVXS09N73cfv9+P3+7t+9ng8gL4qoKqq3fbt/Pnj2/si7rN2O2aAcBjV7YakpKFrW7KSHQbZWLYtWclGIxvLtqOR7euxFE3r+39yV1ZWkpeXx6ZNm1i4cGHX9m9+85ts2LCBLVu2nPEYd999N6+99hp79uzBZrOdcp8HHniABx98sMf2oqIiHA5HX7s7/IVCXHfDDQC88pe/EDjN8stCCCFEvPF6vRQUFOB2u3Ge5m9YRCMjA/WTn/yEp556ivXr1/daiACsWbOG1atXd/3s8Xi65pp8/GRUVaW4uJglS5ZEvOTxcMhqFgtKIMDiRYtg9Ohh02/JSnYosrFsW7KSjUY2lm1HI9t5Z+NMIipGMjMzMRqN1NTUdNteU1NDTk7OabM///nP+clPfsIbb7zBrFmzTruv1WrFarX22G42m3u9QKf77EziOutwQCCAORiEj+0b1/2WrGSHMBvLtiUr2WhkY9n2YGb7epyIJrBaLBbmzZvXbfJp52TUk2/bfNxPf/pTfvCDH/Dqq68yf/78SJoUdrv+1euNbT+EEEKIKIn4Ns3q1au57bbbmD9/Pueffz6PPPIIbW1trFixAoBbb72VvLw8HnroIQD+93//l/vvv5+ioiLGjh1LdXU1AMnJySQnJw/iqZylOufItLfHth9CCCFElERcjNx0003U1dVx//33U11dzZw5c3j11VfJzs4GoKysDIPhxIDL7373OwKBAJ/5zGe6HWft2rU88MADA+t9IugsRmRkRAghxFmqXxNYV61axapVq0752fr167v9fPTo0f40ITp13qaRkREhhBBnKVmbJt7JyIgQQoiznBQj8U4msAohhDjLSTES72QCqxBCiLOcFCPxTkZGhBBCnOWkGIl3MjIihBDiLCfFSLyTkREhhBBnOSlG4p2MjAghhDjLSTES7+TRXiGEEGe5IV21d6BUVUVV1R7bTv4a6fHiPWuwWDAC4bY2Qh/LxXO/JSvZocjGsm3JSjYa2Vi2HY1sX4+laJqmRdzqECksLKSwsJBQKERJSQlFRUU4OkcKEsTYV15h9u9/T+UFF/D+t78d6+4IIYQQfeb1eikoKMDtduN0OnvdL66LkU4ejweXy0V9fX2Pk1FVleLiYpYsWRLxksfDIav85S+Ybr+d8FVXEXr++WHTb8lKdiiysWxbspKNRjaWbUcj6/F4yMzMPGMxMqxu05jN5l4v0Ok+G8hxY55NSQHA0N6O4WP7xnW/JSvZIczGsm3JSjYa2Vi2PZjZvh5HJrDGO1koTwghxFlOipF4J0/TCCGEOMtJMRLvpBgRQghxlpNiJN7JbRohhBBnOSlG4p2MjAghhDjLSTES72RkRAghxFlOipF41zkyEghAKBTbvgghhBBRIMVIvOscGQEZHRFCCHFWkmIk3tlsJ76XeSNCCCHOQlKMxDuD4URBIiMjQgghzkJSjAwH8kSNEEKIs5gUI8NBZzEiIyNCCCHOQsNqoTxVVVFVtce2k79GerzhkDXZbChA0ONBO+kaxHu/JSvZaGdj2bZkJRuNbCzbjka2r8dSNE3TIm51iBQWFlJYWEgoFKKkpISioiIcnaMECeSyr34V19GjbFq7lrpzz411d4QQQog+8Xq9FBQU4Ha7cTqdve4X18VIJ4/Hg8vlor6+vsfJqKpKcXExS5YsiXjJ4+GSNV58MYYtWwj+619on/zksOm3ZCUb7Wws25asZKORjWXb0ch6PB4yMzPPWIwMq9s0ZrO51wt0us8Gcty4yCYlAWAKBOCk/eO+35KV7BBlY9m2ZCUbjWws2x7MbF+PIxNYhwN5JbwQQoizmBQjw4E82iuEEOIsJsXIcCAjI0IIIc5iUowMBzIyIoQQ4iwmxchwkJqqf926NabdEEIIIaJBipHh4NZbwWSCl1+GF16IdW+EEEKIQSXFyHAwdSp87Wv69/fcA6FQbPsjhBBCDCIpRoaL++7T546UlsLBg7HujRBCCDFopBgZLpKSYPZsAJSdO2PbFyGEEGIQSTEynMydC0gxIoQQ4uwixchw0rFInrJzJ4RCKJs2wWOPQXNzTLslhBBCDMSwWpsm4XWOjGzfzsVr1mAqKdG3HzwIP/1pDDsmhBBC9N+wKkZUVUVV1R7bTv4a6fGGVfacczCZzSjNzaSfNBoS3rKFUB+PNezOWbKSjdO2JSvZaGRj2XY0sn09lqJpmhZxq0OksLCQwsJCQqEQJSUlFBUV4eh8G2mCunT1alKPHAGgdPlyxr3yCoGUFF75y19AUWLcOyGEEOIEr9dLQUEBbrcbp9PZ635xXYx08ng8uFwu6uvre5yMqqoUFxezZMmSiJc8Ho5Z45e/jOHJJ2kZNQrj9u3Yc3NRQiHUo0dh5Mi47bdkJRuNbCzblqxko5GNZdvRyHo8HjIzM89YjAyr2zRms7nXC3S6zwZy3LjL/s//EC4tZcfy5SxMTUWZNAn278e8fz+MGRPdtiUr2TjNxrJtyUo2GtlYtj2Y2b4eR56mGW7mziVUXEzTlCn6zzNn6l8/+ih2fRJCCCEGQIqR4U6KESGEEMOcFCPDXWcxsmtXbPshhBBC9NOwmsB6qgkwqqry8ssvc/XVV/drws1wy/bIl5fDhAn6qr4FBfoieu3tvWbD4TDV1dXk5ORgMERWi0pWsvGWjWXbkpVsNLKxbFv9/vd5+dChQf27drq/3ycbVhNYxSmMHQvnnAMlJfCXv5xxdwNw5mduJCvZ4ZGNZduSlWw0srFsW7n33n4mB06KkeHOYIDt22H9ev2rw6H/08s7R0KhELt372bGjBkYjcaImpKsZOMtG8u2JSvZaGRj2bY2diw0NESUGSxSjJwNkpLgmmv0f84grKocffllpl19NcYIh+EkK9l4y8aybclKNhrZmLbdzzfGDgaZwCqEEEKImOpXMVJYWMjYsWOx2WwsWLCArVu3nnb/f/7zn0yZMgWbzcbMmTN5+eWX+9VZIYQQQpx9Ii5Gnn76aVavXs3atWvZvn07s2fPZtmyZdTW1p5y/02bNnHzzTfzpS99iR07dnD99ddz/fXXs3v37gF3XgghhBDDX8TFyMMPP8wdd9zBihUrmDZtGo8++igOh4MnnnjilPv/8pe/5KqrruIb3/gGU6dO5Qc/+AFz587lN7/5zYA7L4QQQojhL6IJrIFAgG3btrFmzZqubQaDgcWLF7N58+ZTZjZv3szq1au7bVu2bBnPPvtsr+34/X78fn/Xzx6PB9CfY+5teeJ4WS452tlYti1ZycZbNpZtS1ay0cjGsu1oZPt6rIheelZZWUleXh6bNm1i4cKFXdu/+c1vsmHDBrZs2dIjY7FY+POf/8zNN9/cte23v/0tDz74IDU1Nads54EHHuDBBx/ssb2oqAiHw9HX7gohhBAihrxeLwUFBcPzpWdr1qzpNpri8XjIz89n6dKlp3wDazwtlxztbCzblqxk4y0by7YlK9loZGPZdjSynXc2ziSiYiQzMxOj0dhjRKOmpoacnJxTZnJyciLaH8BqtWK1WntsP92yxvGyXPJQZWPZtmQlG2/ZWLYtWclGIxvLtgcz29fjRDSB1WKxMG/ePNatW9e1LRwOs27dum63bU62cOHCbvsDFBcX97q/EEIIIRJLxLdpVq9ezW233cb8+fM5//zzeeSRR2hra2PFihUA3HrrreTl5fHQQw8BcM8993DppZfyi1/8gmuuuYannnqKDz74gD/84Q+DeyZCCCGEGJYiLkZuuukm6urquP/++6murmbOnDm8+uqrZGdnA1BWVtZtpcBFixZRVFTE9773Pb7zne8wadIknn32WWbMmDF4ZyGEEEKIYatfE1hXrVrFqlWrTvnZ+vXre2y78cYbufHGG/vTlBBCCCHOcrI2jRBCCCFiKi4f7f24zlehnOoRIVVV8Xq9eDyefj2KNNyysWxbspKNt2ws25asZKORjWXb0ch2/t0+0yvNhkUx0tLSAkB+fn6MeyKEEEKISLW0tOByuXr9PKI3sMZKOBymsrKSlJQUFEXp8fl5553H+++/H/FxO1+mVl5efto3w/Wmv+0ONBurtofr9ZJrFd/ZgV6rgbQ9HLPyu9V3w/VaxartaFwvTdNoaWlh5MiR3R5u+bhhMTJiMBgYNWpUr58bjcZ+/58YgNPp7Fd+IO0OtM+xbHu4XS+5VvGfhf5fq4G2PRyzIL9bkRhu1yrWbQ/29TrdiEins2IC68qVK4dduwPtcyzbjkW7wzE7EMPxfGN1rQba9nDMDsRwPN9Eu1axbjsW7Q6L2zTR4vF4cLlcZ1zAR+jkevWdXKu+k2sVGblefSfXKjKxvF5nxchIf1mtVtauXXvKdXBET3K9+k6uVd/JtYqMXK++k2sVmVher4QeGRFCCCFE7CX0yIgQQgghYk+KESGEEELElBQjQgghhIgpKUaEEEIIEVMJXYwUFhYyduxYbDYbCxYsYOvWrbHuUsw98MADKIrS7Z8pU6Z0fe7z+Vi5ciUZGRkkJydzww03UFNTE8MeD52NGzdy7bXXMnLkSBRF4dlnn+32uaZp3H///eTm5mK321m8eDEHDx7stk9jYyO33HILTqeT1NRUvvSlL9Ha2jqEZzF0znS9vvCFL/T4Xbvqqqu67ZMo1+uhhx7ivPPOIyUlhREjRnD99ddz4MCBbvv05d+9srIyrrnmGhwOByNGjOAb3/gGwWBwKE8l6vpyrS677LIev1t33nlnt30S4VoB/O53v2PWrFldLzJbuHAhr7zyStfn8fJ7lbDFyNNPP83q1atZu3Yt27dvZ/bs2Sxbtoza2tpYdy3mpk+fTlVVVdc/77zzTtdn9957Ly+88AL//Oc/2bBhA5WVlXz605+OYW+HTltbG7Nnz6awsPCUn//0pz/lV7/6FY8++ihbtmwhKSmJZcuW4fP5uva55ZZb2LNnD8XFxbz44ots3LiRL3/5y0N1CkPqTNcL4Kqrrur2u/b3v/+92+eJcr02bNjAypUree+99yguLkZVVZYuXUpbW1vXPmf6dy8UCnHNNdcQCATYtGkTf/7zn3nyySe5//77Y3FKUdOXawVwxx13dPvd+ulPf9r1WaJcK4BRo0bxk5/8hG3btvHBBx9wxRVXcN1117Fnzx4gjn6vtAR1/vnnaytXruz6ORQKaSNHjtQeeuihGPYq9tauXavNnj37lJ81NzdrZrNZ++c//9m1bd++fRqgbd68eYh6GB8A7Zlnnun6ORwOazk5OdrPfvazrm3Nzc2a1WrV/v73v2uapml79+7VAO3999/v2ueVV17RFEXRKioqhqzvsfDx66Vpmnbbbbdp1113Xa+ZRL5etbW1GqBt2LBB07S+/bv38ssvawaDQauuru7a53e/+53mdDo1v98/tCcwhD5+rTRN0y699FLtnnvu6TWTqNeqU1pamvb444/H1e9VQo6MBAIBtm3bxuLFi7u2GQwGFi9ezObNm2PYs/hw8OBBRo4cyfjx47nlllsoKysDYNu2baiq2u26TZkyhdGjRyf8dSstLaW6urrbtXG5XCxYsKDr2mzevJnU1FTmz5/ftc/ixYsxGAxs2bJlyPscD9avX8+IESOYPHkyd911Fw0NDV2fJfL1crvdAKSnpwN9+3dv8+bNzJw5k+zs7K59li1bhsfj6fqv4LPRx69Vp7/97W9kZmYyY8YM1qxZg9fr7fosUa9VKBTiqaeeoq2tjYULF8bV79WwWChvsNXX1xMKhbpdXIDs7Gz2798fo17FhwULFvDkk08yefJkqqqqePDBB7n44ovZvXs31dXVWCwWUlNTu2Wys7Oprq6OTYfjROf5n+p3qvOz6upqRowY0e1zk8lEenp6Ql6/q666ik9/+tOMGzeOw4cP853vfIfly5ezefNmjEZjwl6vcDjMV7/6VS688EJmzJgB0Kd/96qrq0/5+9f52dnoVNcKoKCggDFjxjBy5Eh27drFt771LQ4cOMB//vMfIPGu1UcffcTChQvx+XwkJyfzzDPPMG3aNHbu3Bk3v1cJWYyI3i1fvrzr+1mzZrFgwQLGjBnDP/7xD+x2ewx7Js42n/3sZ7u+nzlzJrNmzWLChAmsX7+eK6+8MoY9i62VK1eye/fubnO1xKn1dq1Onlc0c+ZMcnNzufLKKzl8+DATJkwY6m7G3OTJk9m5cydut5t//etf3HbbbWzYsCHW3eomIW/TZGZmYjQae8wYrqmpIScnJ0a9ik+pqamcc845HDp0iJycHAKBAM3Nzd32ketG1/mf7ncqJyenxwTpYDBIY2Njwl8/gPHjx5OZmcmhQ4eAxLxeq1at4sUXX+Stt95i1KhRXdv78u9eTk7OKX//Oj872/R2rU5lwYIFAN1+txLpWlksFiZOnMi8efN46KGHmD17Nr/85S/j6vcqIYsRi8XCvHnzWLduXde2cDjMunXrWLhwYQx7Fn9aW1s5fPgwubm5zJs3D7PZ3O26HThwgLKysoS/buPGjSMnJ6fbtfF4PGzZsqXr2ixcuJDm5ma2bdvWtc+bb75JOBzu+j/LRHb8+HEaGhrIzc0FEut6aZrGqlWreOaZZ3jzzTcZN25ct8/78u/ewoUL+eijj7oVcMXFxTidTqZNmzY0JzIEznStTmXnzp0A3X63EuFa9SYcDuP3++Pr92rQpsIOM0899ZRmtVq1J598Utu7d6/25S9/WUtNTe02YzgRfe1rX9PWr1+vlZaWau+++662ePFiLTMzU6utrdU0TdPuvPNObfTo0dqbb76pffDBB9rChQu1hQsXxrjXQ6OlpUXbsWOHtmPHDg3QHn74YW3Hjh3asWPHNE3TtJ/85Cdaamqq9txzz2m7du3SrrvuOm3cuHFae3t71zGuuuoq7dxzz9W2bNmivfPOO9qkSZO0m2++OVanFFWnu14tLS3a17/+dW3z5s1aaWmp9sYbb2hz587VJk2apPl8vq5jJMr1uuuuuzSXy6WtX79eq6qq6vrH6/V27XOmf/eCwaA2Y8YMbenSpdrOnTu1V199VcvKytLWrFkTi1OKmjNdq0OHDmnf//73tQ8++EArLS3VnnvuOW38+PHaJZdc0nWMRLlWmqZp3/72t7UNGzZopaWl2q5du7Rvf/vbmqIo2uuvv65pWvz8XiVsMaJpmvbrX/9aGz16tGaxWLTzzz9fe++992LdpZi76aabtNzcXM1isWh5eXnaTTfdpB06dKjr8/b2du3uu+/W0tLSNIfDoX3qU5/SqqqqYtjjofPWW29pQI9/brvtNk3T9Md777vvPi07O1uzWq3alVdeqR04cKDbMRoaGrSbb75ZS05O1pxOp7ZixQqtpaUlBmcTfae7Xl6vV1u6dKmWlZWlmc1mbcyYMdodd9zR4z8GEuV6neo6Adqf/vSnrn368u/e0aNHteXLl2t2u13LzMzUvva1r2mqqg7x2UTXma5VWVmZdskll2jp6ema1WrVJk6cqH3jG9/Q3G53t+MkwrXSNE374he/qI0ZM0azWCxaVlaWduWVV3YVIpoWP79XiqZp2uCNswghhBBCRCYh54wIIYQQIn5IMSKEEEKImJJiRAghhBAxJcWIEEIIIWJKihEhhBBCxJQUI0IIIYSIKSlGhBBCCBFTUowIIYQQIqakGBFCCCFETEkxIoQQQoiYkmJECCGEEDElxYgQQgghYur/A+PoM5QWB+72AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n", + "start_epoch = 0\n", + "end_epoch = 300\n", + "lst_indices = []\n", + "for i in range(20):\n", + " lst_indices.append(i)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot([i for i in range(end_epoch-start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n", + "for i in lst_indices:\n", + " plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all[start_epoch:end_epoch, i])\n", + "plt.grid(True, which = 'minor')\n", + "\n", + "plt.minorticks_on()\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/2156236853.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "/tmp/ipykernel_2812256/2156236853.py:10: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "lst = []\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "model.eval()\n", + "weight1_last = model.linear1.weight.clone().detach().cpu().numpy().transpose()\n", + "lst_proj_all_column = []\n", + "loss_fn = MyHingeLoss()\n", + "lst_loss = []\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "for epoch in range(300):\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + " model.eval()\n", + " weight1 = model.linear1.weight.clone().detach().cpu().numpy().transpose()\n", + " bias = model.linear1.bias.clone().detach().cpu().numpy().transpose()\n", + " weight2 = model.linear2.weight.clone().detach().cpu().numpy().transpose()\n", + " weight1_imp = []\n", + " bias1_imp = []\n", + " weight2_imp = []\n", + " lst_proj = []\n", + " for i in range(len(weight1)):\n", + " proj = np.dot(weight1[i], weight1_last[i])/(np.linalg.norm(weight1[i])*np.linalg.norm(weight1_last[i]))\n", + " lst_proj.append(proj)\n", + " lst_proj_all_column.append(lst_proj)\n", + " loss = loss_calc(test_dataloader, model, loss_fn)\n", + " lst_loss.append(loss)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/929039255.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "/tmp/ipykernel_2812256/929039255.py:11: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[17 13 4 15 19 8 6 0 16 2 9 10 1 14 18 11 12 3 7 5]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/929039255.py:43: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "lst = []\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "model.eval()\n", + "weight1_last = model.linear1.weight.clone().detach().cpu().numpy().transpose()\n", + "lst_proj_top_column = []\n", + "lst_proj_norm = []\n", + "loss_fn = MyHingeLoss()\n", + "lst_loss = []\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "for epoch in range(300):\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + " model.eval()\n", + " weight1 = model.linear1.weight.clone().detach().cpu().numpy().transpose()\n", + " bias = model.linear1.bias.clone().detach().cpu().numpy().transpose()\n", + " weight2 = model.linear2.weight.clone().detach().cpu().numpy().transpose()\n", + " weight1_imp = []\n", + " bias1_imp = []\n", + " weight2_imp = []\n", + " lst_proj = []\n", + " lst_norm = []\n", + " weight1_orig = weight1.transpose()\n", + " for i in range(len(weight1_orig)):\n", + " norm = np.linalg.norm(weight1_orig[i])\n", + " lst_norm.append(norm)\n", + " # for i in range(len(weight1)):\n", + " # proj = np.dot(weight1[i], weight1_last[i])/(np.linalg.norm(weight1[i])*np.linalg.norm(weight1_last[i]))\n", + " # lst_proj.append(proj)\n", + " # lst_proj_all_column.append(lst_proj)\n", + " lst_proj_norm.append(lst_norm)\n", + " loss = loss_calc(test_dataloader, model, loss_fn)\n", + " lst_loss.append(loss)\n", + "\n", + "lst_proj_norm = np.array(lst_proj_norm)\n", + "# lst_proj_all_column = np.array(lst_proj_all_column)\n", + "indices_top_norm = np.argsort(lst_proj_norm[-1])\n", + "print(indices_top_norm)\n", + "top_indices = indices_top_norm[-6:]\n", + "# lst_proj_top_column = lst_proj_all_column[:,top_indices]\n", + "# print(lst_proj_top_column.shape)\n", + "\n", + "weight1_last = weight1_last[:, top_indices]\n", + "for epoch in range(300):\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + " model.eval()\n", + " weight1 = model.linear1.weight.clone().detach().cpu().numpy().transpose()\n", + " bias = model.linear1.bias.clone().detach().cpu().numpy().transpose()\n", + " weight2 = model.linear2.weight.clone().detach().cpu().numpy().transpose()\n", + " weight1_imp = []\n", + " bias1_imp = []\n", + " weight2_imp = []\n", + " lst_proj = []\n", + " lst_norm = []\n", + " weight1 = weight1[:, top_indices]\n", + " for i in range(len(weight1)):\n", + " proj = np.dot(weight1[i], weight1_last[i])/(np.linalg.norm(weight1[i])*np.linalg.norm(weight1_last[i]))\n", + " lst_proj.append(proj)\n", + " lst_proj_top_column.append(lst_proj)\n", + " loss = loss_calc(test_dataloader, model, loss_fn)\n", + " lst_loss.append(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/2000974629.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "/tmp/ipykernel_2812256/2000974629.py:9: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "lst = []\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(299)))\n", + "model.eval()\n", + "weight1_last = model.linear1.weight.clone().detach().cpu().numpy().transpose()\n", + "lst_proj_all_column_sign_preserve = []\n", + "loss_fn = MyHingeLoss()\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "for epoch in range(300):\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + " model.eval()\n", + " weight1 = model.linear1.weight.clone().detach().cpu().numpy().transpose()\n", + " bias = model.linear1.bias.clone().detach().cpu().numpy().transpose()\n", + " weight2 = model.linear2.weight.clone().detach().cpu().numpy().transpose()\n", + " weight1_imp = []\n", + " bias1_imp = []\n", + " weight2_imp = []\n", + " lst_proj = []\n", + " for i in range(len(weight1)):\n", + " proj = np.dot(np.sign(weight1[i]), np.sign(weight1_last[i]))/(np.linalg.norm(np.sign(weight1[i]))*np.linalg.norm(np.sign(weight1_last[i])))\n", + " lst_proj.append(proj)\n", + " lst_proj_all_column_sign_preserve.append(lst_proj)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "lst_proj_all_column = np.array(lst_proj_all_column)\n", + "lst_proj_all_column_sign_preserve = np.array(lst_proj_all_column_sign_preserve)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n", + "start_epoch = 0\n", + "end_epoch = 300\n", + "lst_indices = []\n", + "for i in range(20):\n", + " lst_indices.append(i)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot([i for i in range(end_epoch-start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n", + "for i in lst_indices:\n", + " if i<3:\n", + " plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all_column[start_epoch:end_epoch, i], label=i)\n", + " else:\n", + " plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all_column[start_epoch:end_epoch, i])\n", + "plt.grid(True, which = 'minor')\n", + "\n", + "plt.minorticks_on()\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "lst_proj_top_column = np.array(lst_proj_top_column)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n", + "start_epoch = 0\n", + "end_epoch = 300\n", + "lst_indices = []\n", + "for i in range(20):\n", + " lst_indices.append(i)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot([i for i in range(end_epoch-start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n", + "for i in lst_indices:\n", + " if i<3:\n", + " plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_top_column[start_epoch:end_epoch, i], label=i, color=color_lst[i])\n", + " plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all_column[start_epoch:end_epoch, i], linestyle='--', color=color_lst[i])\n", + " # else:\n", + " # plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_top_column[start_epoch:end_epoch, i])\n", + "plt.grid(True, which = 'minor')\n", + "\n", + "plt.minorticks_on()\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "color_lst = ['black', 'brown', 'blue', 'green', 'purple', 'yellow']\n", + "start_epoch = 0\n", + "end_epoch = 300\n", + "lst_indices = []\n", + "for i in range(20):\n", + " lst_indices.append(i)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot([i for i in range(end_epoch-start_epoch)], lst_loss[start_epoch:end_epoch], color='red', label='loss')\n", + "for i in lst_indices:\n", + " if i<3:\n", + " plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all_column_sign_preserve[start_epoch:end_epoch, i], label=i)\n", + " else:\n", + " plt.plot([j for j in range(end_epoch- start_epoch)], lst_proj_all_column_sign_preserve[start_epoch:end_epoch, i])\n", + "plt.grid(True, which = 'minor')\n", + "\n", + "plt.minorticks_on()\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w0 -0.0016741039 0.11995243 -0.18403849\n", + "w1 0.08121782 0.18568039 -0.046018317\n", + "w2 0.011759831 -0.11463951 0.037830845\n", + "w3 0.101176046 0.089925796 -0.13245405\n", + "w4 -0.20839128 0.19867295 0.17002161\n", + "w5 0.21035793 0.15067026 -0.097498186\n", + "w6 0.08774963 0.013394084 -0.10910419\n", + "w7 0.21181068 0.14200282 0.21229532\n", + "w8 -0.04307008 -0.043903563 -0.20065355\n", + "w9 0.023994112 -0.046816267 0.15968044\n", + "w10 0.009406718 0.14414209 -0.16902886\n", + "w11 -0.016056353 0.22120051 0.08077506\n", + "w12 0.0738332 0.1677561 -0.07198258\n", + "w13 -0.1288202 0.12856407 0.11841627\n", + "w14 0.007123738 -0.15046093 -0.18074623\n", + "w15 -0.107883126 0.040169857 -0.11615411\n", + "w16 0.01867821 0.027920755 -0.17578797\n", + "w17 -0.05126393 -0.07847294 0.1835921\n", + "w18 -0.15129054 -0.2205965 -0.17956872\n", + "w19 0.19882815 -0.057257425 0.09838746\n", + "#############################\n", + "b0 0.14299408\n", + "b1 0.016621593\n", + "b2 -0.10557266\n", + "b3 0.20550534\n", + "b4 0.091441914\n", + "b5 -0.16975006\n", + "b6 0.21401024\n", + "b7 0.16980113\n", + "b8 -0.08150263\n", + "b9 0.12570158\n", + "b10 -0.12705092\n", + "b11 -0.035042387\n", + "b12 0.1898648\n", + "b13 0.009239239\n", + "b14 -0.15813878\n", + "b15 -0.07473675\n", + "b16 -0.06069698\n", + "b17 -0.043140188\n", + "b18 0.02139931\n", + "b19 0.20679818\n", + "#############################\n", + "w0 0.011971986\n", + "w1 -0.13806377\n", + "w2 0.011460803\n", + "w3 0.10721659\n", + "w4 0.11091796\n", + "w5 -0.20436287\n", + "w6 -0.040012747\n", + "w7 -0.16617216\n", + "w8 -0.095409796\n", + "w9 0.08056405\n", + "w10 -0.15878995\n", + "w11 0.08312065\n", + "w12 0.18979256\n", + "w13 0.014666002\n", + "w14 -0.14903118\n", + "w15 -0.080114946\n", + "w16 0.0488277\n", + "w17 -0.1704595\n", + "w18 0.11109016\n", + "w19 -0.2030058\n", + "################# Loss ############# \n", + " 0.9785285949707031\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/3867525577.py:16: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "lst = []\n", + "for i in range(width):\n", + " neuron = weight1[i]\n", + " # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n", + " proj = np.linalg.norm(neuron)\n", + " lst.append(proj)\n", + "indices = np.argsort((np.array(lst)))\n", + "# print(indices)\n", + "# print(np.sort((np.array(lst))))\n", + "important_indices_set = indices[:]\n", + "lst_proj_6 = []\n", + "loss_fn = MyHingeLoss()\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "# for epoch in range(300):\n", + "epoch = 0\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + "model.eval()\n", + "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n", + "bias = model.linear1.bias.clone().detach().cpu().numpy()\n", + "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n", + "weight1_imp = []\n", + "bias1_imp = []\n", + "weight2_imp = []\n", + "for i in range(width):\n", + " if i in important_indices_set:\n", + " weight1_imp.append(weight1[i])\n", + " bias1_imp.append(bias[i])\n", + " weight2_imp.append(weight2[0][i])\n", + "for i in range(len(weight1_imp)):\n", + " print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n", + "print(\"#############################\")\n", + "for i in range(len(bias1_imp)):\n", + " print(\"b{}\".format(i),bias1_imp[i])\n", + "print(\"#############################\")\n", + "for i in range(len(weight2_imp)):\n", + " print(\"w{}\".format(i),weight2_imp[i])\n", + "loss = loss_calc(test_dataloader, model, loss_fn)\n", + "print(\"################# Loss ############# \\n\", loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w0 0.59348345 0.4699195 -0.2736816\n", + "w1 -0.2764275 0.09530643 -0.33410606\n", + "w2 -0.13971551 -0.26737675 0.38807774\n", + "w3 1.0061741 0.95482004 -0.45426163\n", + "w4 -0.23844586 -0.10693528 0.37807927\n", + "w5 1.4641773 1.4704639 -1.4941789\n", + "w6 0.60019016 0.37528753 -0.2121918\n", + "w7 0.935344 0.94653237 0.9586796\n", + "w8 -0.21233243 -0.08899325 -0.45256308\n", + "w9 -0.05824005 -0.23724952 0.44288102\n", + "w10 -0.25162622 0.10441463 -0.44112182\n", + "w11 0.83078367 0.6868357 -0.22606844\n", + "w12 0.8800349 0.70537746 -0.50904787\n", + "w13 0.08548605 0.14597915 0.0693136\n", + "w14 0.47695023 -0.74833834 -0.56568223\n", + "w15 -0.14546578 -0.22376855 -0.6339036\n", + "w16 0.045255914 -0.09446194 -0.027584856\n", + "w17 0.0853401 -0.16724034 0.12309753\n", + "w18 -0.41551253 -0.33124772 0.02181336\n", + "w19 0.33106035 -0.2731311 -0.1632673\n", + "#############################\n", + "b0 0.28144687\n", + "b1 -0.108413234\n", + "b2 -0.27605534\n", + "b3 0.46015227\n", + "b4 0.07253106\n", + "b5 -1.530311\n", + "b6 0.21860224\n", + "b7 0.9009224\n", + "b8 -0.0049577104\n", + "b9 0.31877872\n", + "b10 -0.45604855\n", + "b11 0.17700297\n", + "b12 0.51167285\n", + "b13 -0.04841186\n", + "b14 0.20883371\n", + "b15 -0.05862399\n", + "b16 -0.0039949925\n", + "b17 -0.1362047\n", + "b18 0.034514215\n", + "b19 0.06417704\n", + "#############################\n", + "w0 0.6306787\n", + "w1 -0.52441424\n", + "w2 0.5134147\n", + "w3 1.2700663\n", + "w4 0.29851052\n", + "w5 -2.730364\n", + "w6 0.60445035\n", + "w7 -1.5298215\n", + "w8 -0.4429593\n", + "w9 0.6221878\n", + "w10 -0.5241894\n", + "w11 0.8574937\n", + "w12 1.0062855\n", + "w13 0.23271199\n", + "w14 -0.7579778\n", + "w15 -0.60590136\n", + "w16 0.36392713\n", + "w17 -0.1627666\n", + "w18 0.45464066\n", + "w19 -0.37646684\n", + "################# Loss ############# \n", + " 0.02547661304473877\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/1213103838.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + "/tmp/ipykernel_2812256/1213103838.py:22: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "epoch = 299\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + "model.eval()\n", + "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n", + "bias = model.linear1.bias.clone().detach().cpu().numpy()\n", + "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n", + "lst = []\n", + "for i in range(width):\n", + " neuron = weight1[i]\n", + " # proj = np.dot(neuron, right_svd_final[0])/np.linalg.norm(neuron)\n", + " proj = np.linalg.norm(neuron)\n", + " lst.append(proj)\n", + "indices = np.argsort((np.array(lst)))\n", + "# print(indices)\n", + "# print(np.sort((np.array(lst))))\n", + "important_indices_set = indices[:]\n", + "lst_proj_6 = []\n", + "loss_fn = MyHingeLoss()\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "# for epoch in range(300):\n", + "epoch = 299\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + "model.eval()\n", + "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n", + "bias = model.linear1.bias.clone().detach().cpu().numpy()\n", + "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n", + "weight1_imp = []\n", + "bias1_imp = []\n", + "weight2_imp = []\n", + "for i in range(width):\n", + " if i in important_indices_set:\n", + " weight1_imp.append(weight1[i])\n", + " bias1_imp.append(bias[i])\n", + " weight2_imp.append(weight2[0][i])\n", + "for i in range(len(weight1_imp)):\n", + " print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n", + "print(\"#############################\")\n", + "for i in range(len(bias1_imp)):\n", + " print(\"b{}\".format(i),bias1_imp[i])\n", + "print(\"#############################\")\n", + "for i in range(len(weight2_imp)):\n", + " print(\"w{}\".format(i),weight2_imp[i])\n", + "loss = loss_calc(test_dataloader, model, loss_fn)\n", + "print(\"################# Loss ############# \\n\", loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "w0 0.101176046 0.089925796 -0.13245405\n", + "w1 0.21035793 0.15067026 -0.097498186\n", + "w2 0.21181068 0.14200282 0.21229532\n", + "w3 -0.016056353 0.22120051 0.08077506\n", + "w4 0.0738332 0.1677561 -0.07198258\n", + "w5 -0.15129054 -0.2205965 -0.17956872\n", + "#############################\n", + "b0 0.20550534\n", + "b1 -0.16975006\n", + "b2 0.16980113\n", + "b3 -0.035042387\n", + "b4 0.1898648\n", + "b5 0.02139931\n", + "#############################\n", + "w0 0.10721659\n", + "w1 -0.20436287\n", + "w2 -0.16617216\n", + "w3 0.08312065\n", + "w4 0.18979256\n", + "w5 0.11109016\n", + "################# Loss ############# \n", + " 0.9785285949707031\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/1477691893.py:6: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "\n", + "lst_proj_6 = []\n", + "loss_fn = MyHingeLoss()\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "# for epoch in range(300):\n", + "epoch = 0\n", + "model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + "model.eval()\n", + "weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n", + "bias = model.linear1.bias.clone().detach().cpu().numpy()\n", + "weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n", + "weight1_imp = []\n", + "bias1_imp = []\n", + "weight2_imp = []\n", + "for i in range(width):\n", + " if i in important_indices_set:\n", + " weight1_imp.append(weight1[i])\n", + " bias1_imp.append(bias[i])\n", + " weight2_imp.append(weight2[0][i])\n", + "for i in range(len(weight1_imp)):\n", + " print(\"w{}\".format(i),weight1_imp[i][0], weight1_imp[i][1], weight1_imp[i][2])\n", + "print(\"#############################\")\n", + "for i in range(len(bias1_imp)):\n", + " print(\"b{}\".format(i),bias1_imp[i])\n", + "print(\"#############################\")\n", + "for i in range(len(weight2_imp)):\n", + " print(\"w{}\".format(i),weight2_imp[i])\n", + "loss = loss_calc(test_dataloader, model, loss_fn)\n", + "print(\"################# Loss ############# \\n\", loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "class n_neuron(torch.nn.Module):\n", + " def __init__(self, input_dim=20, width=1,act='relu'):\n", + " super(n_neuron, self).__init__()\n", + " self.linear1 = torch.nn.Linear(n, width)\n", + " self.activation = torch.nn.ReLU()\n", + " self.linear2 = torch.nn.Linear(width, 1, bias=False)\n", + "\n", + " def forward(self, x,return_first=False):\n", + " x = self.linear1(x)\n", + " x = self.activation(x)\n", + " x = self.linear2(x)\n", + " return x\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[((0, 1, 2, 3, 4, 5), 0.83)]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2812256/3502063656.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n" + ] + } + ], + "source": [ + "for epoch in range(30):\n", + " model.load_state_dict(torch.load(model_path+'/model_{}.pt'.format(epoch)))\n", + " model.eval()\n", + " weight1 = model.linear1.weight.clone().detach().cpu().numpy()\n", + " bias = model.linear1.bias.clone().detach().cpu().numpy()\n", + " weight2 = model.linear2.weight.clone().detach().cpu().numpy()\n", + " weight1_imp = []\n", + " bias1_imp = []\n", + " weight2_imp = []\n", + " for i in range(width):\n", + " if i in important_indices_set:\n", + " weight1_imp.append(weight1[i])\n", + " bias1_imp.append(bias[i])\n", + " weight2_imp.append(weight2[0][i])\n", + "\n", + "\n", + "train_dataloader, test_dataloader = data_preparation()\n", + "dataloader = test_dataloader\n", + "width_value = 6\n", + "model_test = n_neuron(width=width_value)\n", + "idx_lst = [0,1,2,5]\n", + "indices_init = [0,1,2,3,4,5]\n", + "planting_sparsity = 6\n", + "lst_value = []\n", + "for comb in itertools.combinations(list(indices_init), planting_sparsity):\n", + " with torch.no_grad():\n", + " counter=-1\n", + " for idx in comb:\n", + " counter+=1\n", + " model_test.linear1.weight[counter] = torch.nn.Parameter(torch.Tensor(weight1_imp[idx]).clone())\n", + " model_test.linear1.bias[counter] = torch.nn.Parameter(torch.Tensor(bias1_imp).clone()[idx])\n", + " model_test.linear2.weight[0][counter] = torch.nn.Parameter(torch.Tensor(weight2_imp).clone()[idx])\n", + " model_test.cuda()\n", + " # print(model_test)\n", + " acc = acc_calc(dataloader, model_test, device='cuda')\n", + " value = (comb, acc)\n", + " lst_value.append(value)\n", + "\n", + "print(lst_value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + 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