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marty1885/Esperanto-Ptyorch-RNN.ipynb

Created November 27, 2018 09:26
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Esperanto-Ptyorch-RNN.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train_data = np.load('dataset.npy')\n",
"test_data = np.load('test.npy')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"esperanto_low_alphabet = ['.', 'a', 'b', 'c', 'ĉ', 'd', 'e', 'f', 'g', 'ĝ', 'h', 'ĥ', 'i', 'j',\n",
" 'ĵ', 'k', 'l', 'm', 'n', 'o', 'p', 'r', 's', 'ŝ', 't', 'u', 'ŭ', 'v', 'z', ' ']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def letter_to_tensor(index):\n",
" tensor = torch.zeros(1, 30)\n",
" tensor[0][index] = 1\n",
" return tensor"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def sequence_to_tensor(seq):\n",
" tensor = torch.zeros(1, len(seq), 30)\n",
" for ni, letter in enumerate(seq):\n",
" tensor[0][ni][letter] = 1\n",
" return tensor"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[[0., 0., 0., ..., 0., 0., 1.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [1., 0., 0., ..., 0., 0., 0.]]])\n"
]
}
],
"source": [
"X = sequence_to_tensor(train_data)\n",
"print(X)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"class Net(nn.Module):\n",
" def __init__(self):\n",
" super(Net, self).__init__()\n",
" self.lstm1 = nn.LSTM(30, 256)\n",
" self.lstm2 = nn.LSTM(256, 256)\n",
" self.fc0 = nn.Linear(256, 30)\n",
"\n",
" def forward(self, input, future = 0):\n",
" outputs = []\n",
" hidden = (torch.zeros(1, 1, 256),\n",
" torch.zeros(1, 1, 256))\n",
" hidden2 = (torch.zeros(1, 1, 256),\n",
" torch.zeros(1, 1, 256))\n",
" \n",
" def run_model(input_t):\n",
" nonlocal hidden, hidden2\n",
" out, hidden = self.lstm1(input_t, hidden)\n",
" out, hidden2 = self.lstm2(out, hidden2)\n",
" out = self.fc0(out)\n",
" out = F.softmax(out, dim=2)\n",
" return out\n",
"\n",
" for i, input_t in enumerate(input.chunk(input.size(1), dim=1)):\n",
" out = run_model(input_t)\n",
" outputs += [out]\n",
" for i in range(future):# if we should predict the future\n",
" out = run_model(out)\n",
" outputs += [out]\n",
" outputs = torch.stack(outputs, 1).squeeze(2)\n",
" return outputs"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 1991, 30])\n",
"torch.Size([1, 1991, 30])\n",
"torch.Size([1, 1992, 30])\n"
]
}
],
"source": [
"train_in = X[:,:-1]\n",
"train_out = X[:,1:]\n",
"\n",
"print(train_in.size())\n",
"print(train_out.size())\n",
"print(X.size())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"model = Net()\n",
"criterion = nn.MSELoss()\n",
"optimizer = optim.LBFGS(model.parameters(), lr=3.0, history_size=30)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0\n",
"loss: 0.031842470169067384\n",
"Epoch: 1\n",
"loss: 0.031813692301511765\n",
"Epoch: 2\n",
"loss: 0.031783837825059896\n",
"Epoch: 3\n",
"loss: 0.031752880662679675\n",
"Epoch: 4\n",
"loss: 0.031720802187919625\n",
"Epoch: 5\n",
"loss: 0.031687613576650624\n",
"Epoch: 6\n",
"loss: 0.031653355807065964\n",
"Epoch: 7\n",
"loss: 0.031618133187294006\n",
"Epoch: 8\n",
"loss: 0.031582109630107884\n",
"Epoch: 9\n",
"loss: 0.031545531004667284\n",
"Epoch: 10\n",
"loss: 0.055516492575407036\n",
"Epoch: 11\n",
"loss: 0.05551649257540703\n",
"Epoch: 12\n",
"loss: 0.05551649257540703\n",
"Epoch: 13\n",
"loss: 0.05551649257540703\n",
"Epoch: 14\n",
"loss: 0.05551649257540703\n",
"Epoch: 15\n",
"loss: 0.05551649257540703\n",
"Epoch: 16\n",
"loss: 0.05551649257540703\n",
"Epoch: 17\n",
"loss: 0.05551649257540703\n",
"Epoch: 18\n",
"loss: 0.05551649257540703\n",
"Epoch: 19\n",
"loss: 0.05551649257540703\n",
"Epoch: 20\n",
"loss: 0.05551649257540703\n",
"Epoch: 21\n",
"loss: 0.05551649257540703\n",
"Epoch: 22\n",
"loss: 0.05551649257540703\n",
"Epoch: 23\n",
"loss: 0.05551649257540703\n",
"Epoch: 24\n",
"loss: 0.05551649257540703\n",
"Epoch: 25\n",
"loss: 0.05551649257540703\n",
"Epoch: 26\n",
"loss: 0.05551649257540703\n",
"Epoch: 27\n",
"loss: 0.05551649257540703\n",
"Epoch: 28\n",
"loss: 0.05551649257540703\n",
"Epoch: 29\n",
"loss: 0.05551649257540703\n",
"Epoch: 30\n",
"loss: 0.05551649257540703\n",
"Epoch: 31\n",
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"Epoch: 32\n",
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"Epoch: 33\n",
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"loss: 0.05551649257540703\n",
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"Epoch: 39\n",
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"Epoch: 40\n",
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"Epoch: 147\n",
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"Epoch: 151\n",
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"Epoch: 215\n",
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"Epoch: 216\n",
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"Epoch: 217\n",
"loss: 0.05551649257540703\n",
"Epoch: 218\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss: 0.05551649257540703\n",
"Epoch: 219\n",
"loss: 0.05551649257540703\n",
"Epoch: 220\n",
"loss: 0.05551649257540703\n",
"Epoch: 221\n",
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"Epoch: 222\n",
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"Epoch: 223\n",
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]
},
{
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"output_type": "stream",
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"Epoch: 800\n",
"loss: 0.05551649257540703\n",
"Epoch: 801\n",
"loss: 0.05551649257540703\n",
"Epoch: 802\n",
"loss: 0.05551649257540703\n",
"Epoch: 803\n",
"loss: 0.05551649257540703\n",
"Epoch: 804\n",
"loss: 0.05551649257540703\n",
"Epoch: 805\n",
"loss: 0.05551649257540703\n",
"Epoch: 806\n",
"loss: 0.05551649257540703\n",
"Epoch: 807\n",
"loss: 0.05551649257540703\n",
"Epoch: 808\n",
"loss: 0.05551649257540703\n",
"Epoch: 809\n",
"loss: 0.05551649257540703\n",
"Epoch: 810\n",
"loss: 0.05551649257540703\n",
"Epoch: 811\n",
"loss: 0.05551649257540703\n",
"Epoch: 812\n",
"loss: 0.05551649257540703\n",
"Epoch: 813\n",
"loss: 0.05551649257540703\n",
"Epoch: 814\n",
"loss: 0.05551649257540703\n",
"Epoch: 815\n",
"loss: 0.05551649257540703\n",
"Epoch: 816\n",
"loss: 0.05551649257540703\n",
"Epoch: 817\n",
"loss: 0.05551649257540703\n",
"Epoch: 818\n",
"loss: 0.05551649257540703\n",
"Epoch: 819\n",
"loss: 0.05551649257540703\n",
"Epoch: 820\n",
"loss: 0.05551649257540703\n",
"Epoch: 821\n",
"loss: 0.05551649257540703\n",
"Epoch: 822\n",
"loss: 0.05551649257540703\n",
"Epoch: 823\n",
"loss: 0.05551649257540703\n",
"Epoch: 824\n",
"loss: 0.05551649257540703\n",
"Epoch: 825\n",
"loss: 0.05551649257540703\n",
"Epoch: 826\n",
"loss: 0.05551649257540703\n",
"Epoch: 827\n",
"loss: 0.05551649257540703\n",
"Epoch: 828\n",
"loss: 0.05551649257540703\n",
"Epoch: 829\n",
"loss: 0.05551649257540703\n",
"Epoch: 830\n",
"loss: 0.05551649257540703\n",
"Epoch: 831\n",
"loss: 0.05551649257540703\n",
"Epoch: 832\n",
"loss: 0.05551649257540703\n",
"Epoch: 833\n",
"loss: 0.05551649257540703\n",
"Epoch: 834\n",
"loss: 0.05551649257540703\n",
"Epoch: 835\n",
"loss: 0.05551649257540703\n",
"Epoch: 836\n",
"loss: 0.05551649257540703\n",
"Epoch: 837\n",
"loss: 0.05551649257540703\n",
"Epoch: 838\n",
"loss: 0.05551649257540703\n",
"Epoch: 839\n",
"loss: 0.05551649257540703\n",
"Epoch: 840\n",
"loss: 0.05551649257540703\n",
"Epoch: 841\n",
"loss: 0.05551649257540703\n",
"Epoch: 842\n",
"loss: 0.05551649257540703\n",
"Epoch: 843\n",
"loss: 0.05551649257540703\n",
"Epoch: 844\n",
"loss: 0.05551649257540703\n",
"Epoch: 845\n",
"loss: 0.05551649257540703\n",
"Epoch: 846\n",
"loss: 0.05551649257540703\n",
"Epoch: 847\n",
"loss: 0.05551649257540703\n",
"Epoch: 848\n",
"loss: 0.05551649257540703\n",
"Epoch: 849\n",
"loss: 0.05551649257540703\n",
"Epoch: 850\n",
"loss: 0.05551649257540703\n",
"Epoch: 851\n",
"loss: 0.05551649257540703\n",
"Epoch: 852\n",
"loss: 0.05551649257540703\n",
"Epoch: 853\n",
"loss: 0.05551649257540703\n",
"Epoch: 854\n",
"loss: 0.05551649257540703\n",
"Epoch: 855\n",
"loss: 0.05551649257540703\n",
"Epoch: 856\n",
"loss: 0.05551649257540703\n",
"Epoch: 857\n",
"loss: 0.05551649257540703\n",
"Epoch: 858\n",
"loss: 0.05551649257540703\n",
"Epoch: 859\n",
"loss: 0.05551649257540703\n",
"Epoch: 860\n",
"loss: 0.05551649257540703\n",
"Epoch: 861\n",
"loss: 0.05551649257540703\n",
"Epoch: 862\n",
"loss: 0.05551649257540703\n",
"Epoch: 863\n",
"loss: 0.05551649257540703\n",
"Epoch: 864\n",
"loss: 0.05551649257540703\n",
"Epoch: 865\n",
"loss: 0.05551649257540703\n",
"Epoch: 866\n",
"loss: 0.05551649257540703\n",
"Epoch: 867\n",
"loss: 0.05551649257540703\n",
"Epoch: 868\n",
"loss: 0.05551649257540703\n",
"Epoch: 869\n",
"loss: 0.05551649257540703\n",
"Epoch: 870\n",
"loss: 0.05551649257540703\n",
"Epoch: 871\n",
"loss: 0.05551649257540703\n",
"Epoch: 872\n",
"loss: 0.05551649257540703\n",
"Epoch: 873\n",
"loss: 0.05551649257540703\n",
"Epoch: 874\n",
"loss: 0.05551649257540703\n",
"Epoch: 875\n",
"loss: 0.05551649257540703\n",
"Epoch: 876\n",
"loss: 0.05551649257540703\n",
"Epoch: 877\n",
"loss: 0.05551649257540703\n",
"Epoch: 878\n",
"loss: 0.05551649257540703\n",
"Epoch: 879\n",
"loss: 0.05551649257540703\n",
"Epoch: 880\n",
"loss: 0.05551649257540703\n",
"Epoch: 881\n",
"loss: 0.05551649257540703\n",
"Epoch: 882\n",
"loss: 0.05551649257540703\n",
"Epoch: 883\n",
"loss: 0.05551649257540703\n",
"Epoch: 884\n",
"loss: 0.05551649257540703\n",
"Epoch: 885\n",
"loss: 0.05551649257540703\n",
"Epoch: 886\n",
"loss: 0.05551649257540703\n",
"Epoch: 887\n",
"loss: 0.05551649257540703\n",
"Epoch: 888\n",
"loss: 0.05551649257540703\n",
"Epoch: 889\n",
"loss: 0.05551649257540703\n",
"Epoch: 890\n",
"loss: 0.05551649257540703\n",
"Epoch: 891\n",
"loss: 0.05551649257540703\n",
"Epoch: 892\n",
"loss: 0.05551649257540703\n",
"Epoch: 893\n",
"loss: 0.05551649257540703\n",
"Epoch: 894\n",
"loss: 0.05551649257540703\n",
"Epoch: 895\n",
"loss: 0.05551649257540703\n",
"Epoch: 896\n",
"loss: 0.05551649257540703\n",
"Epoch: 897\n",
"loss: 0.05551649257540703\n",
"Epoch: 898\n",
"loss: 0.05551649257540703\n",
"Epoch: 899\n",
"loss: 0.05551649257540703\n",
"Epoch: 900\n",
"loss: 0.05551649257540703\n",
"Epoch: 901\n",
"loss: 0.05551649257540703\n",
"Epoch: 902\n",
"loss: 0.05551649257540703\n",
"Epoch: 903\n",
"loss: 0.05551649257540703\n",
"Epoch: 904\n",
"loss: 0.05551649257540703\n",
"Epoch: 905\n",
"loss: 0.05551649257540703\n",
"Epoch: 906\n",
"loss: 0.05551649257540703\n",
"Epoch: 907\n",
"loss: 0.05551649257540703\n",
"Epoch: 908\n",
"loss: 0.05551649257540703\n",
"Epoch: 909\n",
"loss: 0.05551649257540703\n",
"Epoch: 910\n",
"loss: 0.05551649257540703\n",
"Epoch: 911\n",
"loss: 0.05551649257540703\n",
"Epoch: 912\n",
"loss: 0.05551649257540703\n",
"Epoch: 913\n",
"loss: 0.05551649257540703\n",
"Epoch: 914\n",
"loss: 0.05551649257540703\n",
"Epoch: 915\n",
"loss: 0.05551649257540703\n",
"Epoch: 916\n",
"loss: 0.05551649257540703\n",
"Epoch: 917\n",
"loss: 0.05551649257540703\n",
"Epoch: 918\n",
"loss: 0.05551649257540703\n",
"Epoch: 919\n",
"loss: 0.05551649257540703\n",
"Epoch: 920\n",
"loss: 0.05551649257540703\n",
"Epoch: 921\n",
"loss: 0.05551649257540703\n",
"Epoch: 922\n",
"loss: 0.05551649257540703\n",
"Epoch: 923\n",
"loss: 0.05551649257540703\n",
"Epoch: 924\n",
"loss: 0.05551649257540703\n",
"Epoch: 925\n",
"loss: 0.05551649257540703\n",
"Epoch: 926\n",
"loss: 0.05551649257540703\n",
"Epoch: 927\n",
"loss: 0.05551649257540703\n",
"Epoch: 928\n",
"loss: 0.05551649257540703\n",
"Epoch: 929\n",
"loss: 0.05551649257540703\n",
"Epoch: 930\n",
"loss: 0.05551649257540703\n",
"Epoch: 931\n",
"loss: 0.05551649257540703\n",
"Epoch: 932\n",
"loss: 0.05551649257540703\n",
"Epoch: 933\n",
"loss: 0.05551649257540703\n",
"Epoch: 934\n",
"loss: 0.05551649257540703\n",
"Epoch: 935\n",
"loss: 0.05551649257540703\n",
"Epoch: 936\n",
"loss: 0.05551649257540703\n",
"Epoch: 937\n",
"loss: 0.05551649257540703\n",
"Epoch: 938\n",
"loss: 0.05551649257540703\n",
"Epoch: 939\n",
"loss: 0.05551649257540703\n",
"Epoch: 940\n",
"loss: 0.05551649257540703\n",
"Epoch: 941\n",
"loss: 0.05551649257540703\n",
"Epoch: 942\n",
"loss: 0.05551649257540703\n",
"Epoch: 943\n",
"loss: 0.05551649257540703\n",
"Epoch: 944\n",
"loss: 0.05551649257540703\n",
"Epoch: 945\n",
"loss: 0.05551649257540703\n",
"Epoch: 946\n",
"loss: 0.05551649257540703\n",
"Epoch: 947\n",
"loss: 0.05551649257540703\n",
"Epoch: 948\n",
"loss: 0.05551649257540703\n",
"Epoch: 949\n",
"loss: 0.05551649257540703\n",
"Epoch: 950\n",
"loss: 0.05551649257540703\n",
"Epoch: 951\n",
"loss: 0.05551649257540703\n",
"Epoch: 952\n",
"loss: 0.05551649257540703\n",
"Epoch: 953\n",
"loss: 0.05551649257540703\n",
"Epoch: 954\n",
"loss: 0.05551649257540703\n",
"Epoch: 955\n",
"loss: 0.05551649257540703\n",
"Epoch: 956\n",
"loss: 0.05551649257540703\n",
"Epoch: 957\n",
"loss: 0.05551649257540703\n",
"Epoch: 958\n",
"loss: 0.05551649257540703\n",
"Epoch: 959\n",
"loss: 0.05551649257540703\n",
"Epoch: 960\n",
"loss: 0.05551649257540703\n",
"Epoch: 961\n",
"loss: 0.05551649257540703\n",
"Epoch: 962\n",
"loss: 0.05551649257540703\n",
"Epoch: 963\n",
"loss: 0.05551649257540703\n",
"Epoch: 964\n",
"loss: 0.05551649257540703\n",
"Epoch: 965\n",
"loss: 0.05551649257540703\n",
"Epoch: 966\n",
"loss: 0.05551649257540703\n",
"Epoch: 967\n",
"loss: 0.05551649257540703\n",
"Epoch: 968\n",
"loss: 0.05551649257540703\n",
"Epoch: 969\n",
"loss: 0.05551649257540703\n",
"Epoch: 970\n",
"loss: 0.05551649257540703\n",
"Epoch: 971\n",
"loss: 0.05551649257540703\n",
"Epoch: 972\n",
"loss: 0.05551649257540703\n",
"Epoch: 973\n",
"loss: 0.05551649257540703\n",
"Epoch: 974\n",
"loss: 0.05551649257540703\n",
"Epoch: 975\n",
"loss: 0.05551649257540703\n",
"Epoch: 976\n",
"loss: 0.05551649257540703\n",
"Epoch: 977\n",
"loss: 0.05551649257540703\n",
"Epoch: 978\n",
"loss: 0.05551649257540703\n",
"Epoch: 979\n",
"loss: 0.05551649257540703\n",
"Epoch: 980\n",
"loss: 0.05551649257540703\n",
"Epoch: 981\n",
"loss: 0.05551649257540703\n",
"Epoch: 982\n",
"loss: 0.05551649257540703\n",
"Epoch: 983\n",
"loss: 0.05551649257540703\n",
"Epoch: 984\n",
"loss: 0.05551649257540703\n",
"Epoch: 985\n",
"loss: 0.05551649257540703\n",
"Epoch: 986\n",
"loss: 0.05551649257540703\n",
"Epoch: 987\n",
"loss: 0.05551649257540703\n",
"Epoch: 988\n",
"loss: 0.05551649257540703\n",
"Epoch: 989\n",
"loss: 0.05551649257540703\n",
"Epoch: 990\n",
"loss: 0.05551649257540703\n",
"Epoch: 991\n",
"loss: 0.05551649257540703\n",
"Epoch: 992\n",
"loss: 0.05551649257540703\n",
"Epoch: 993\n",
"loss: 0.05551649257540703\n",
"Epoch: 994\n",
"loss: 0.05551649257540703\n",
"Epoch: 995\n",
"loss: 0.05551649257540703\n",
"Epoch: 996\n",
"loss: 0.05551649257540703\n",
"Epoch: 997\n",
"loss: 0.05551649257540703\n",
"Epoch: 998\n",
"loss: 0.05551649257540703\n",
"Epoch: 999\n",
"loss: 0.05551649257540703\n"
]
}
],
"source": [
"for i in range(1000):\n",
" print(\"Epoch: \", i)\n",
" def closure():\n",
" optimizer.zero_grad()\n",
" out = model(train_in)\n",
" loss = criterion(out, train_out)\n",
" print('loss:', loss.item(), end='\\r')\n",
" loss.backward()\n",
" return loss\n",
" optimizer.step(closure)\n",
" print()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" "
]
}
],
"source": [
"with torch.no_grad():\n",
" out = model(sequence_to_tensor([0]), 400)\n",
" out = out[0]\n",
" for i in range(400):\n",
" o = out[i]\n",
" m = o.argmax()\n",
" print(esperanto_low_alphabet[m], end='')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunatelly the network is predicting ' ' (spaces)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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