236 lines
6.4 KiB
Plaintext
236 lines
6.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"from tqdm import tqdm\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\PROGRAMY\\Anaconda3\\envs\\ium\\lib\\site-packages\\ipykernel_launcher.py:2: MatplotlibDeprecationWarning: Support for setting an rcParam that expects a str value to a non-str value is deprecated since 3.5 and support will be removed two minor releases later.\n",
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" \n"
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]
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}
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],
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"source": [
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"matplotlib.rc('text', usetex=True)\n",
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"matplotlib.rcParams['text.latex.preamble']=[r\"\\usepackage{amsmath}\"]\n",
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"sns.set_style(\"darkgrid\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dataset = pd.read_csv('../train_dataset.csv')\n",
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"test_dataset = pd.read_csv('../test_dataset.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train = train_dataset.drop(columns=['No-show']).to_numpy()\n",
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"X_test = test_dataset.drop(columns=['No-show']).to_numpy()\n",
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"y_train = train_dataset['No-show'].to_numpy()\n",
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"y_test = test_dataset['No-show'].to_numpy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"class LogisticRegression(torch.nn.Module):\n",
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" def __init__(self, input_dim, output_dim):\n",
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" super(LogisticRegression, self).__init__()\n",
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" self.linear = torch.nn.Linear(input_dim, output_dim) \n",
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" def forward(self, x):\n",
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" outputs = torch.sigmoid(self.linear(x))\n",
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" return outputs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"epochs = 50_000\n",
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"input_dim = 9\n",
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"output_dim = 1\n",
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"learning_rate = 0.01"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LogisticRegression(input_dim, output_dim)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"criterion = torch.nn.BCELoss()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, X_test = torch.Tensor(X_train),torch.Tensor(X_test)\n",
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"y_train, y_test = torch.Tensor(y_train),torch.Tensor(y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Training Epochs: 100%|██████████| 50000/50000 [02:01<00:00, 411.29it/s]\n"
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]
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}
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],
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"source": [
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"losses = []\n",
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"losses_test = []\n",
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"Iterations = []\n",
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"iter = 0\n",
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"for epoch in tqdm(range(int(epochs)), desc='Training Epochs'):\n",
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" x = X_train\n",
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" labels = y_train\n",
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" optimizer.zero_grad() # Setting our stored gradients equal to zero\n",
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" outputs = model(X_train)\n",
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" loss = criterion(torch.squeeze(outputs), labels) \n",
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" \n",
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" loss.backward() # Computes the gradient of the given tensor w.r.t. the weights/bias\n",
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" \n",
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" optimizer.step() # Updates weights and biases with the optimizer (SGD)\n",
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" \n",
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" iter+=1\n",
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" if iter%10000==0:\n",
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" with torch.no_grad():\n",
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" # Calculating the loss and accuracy for the test dataset\n",
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" correct_test = 0\n",
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" total_test = 0\n",
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" outputs_test = torch.squeeze(model(X_test))\n",
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" loss_test = criterion(outputs_test, y_test)\n",
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" \n",
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" predicted_test = outputs_test.round().detach().numpy()\n",
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" total_test += y_test.size(0)\n",
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" correct_test += np.sum(predicted_test == y_test.detach().numpy())\n",
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" accuracy_test = 100 * correct_test/total_test\n",
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" losses_test.append(loss_test.item())\n",
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" \n",
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" # Calculating the loss and accuracy for the train dataset\n",
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" total = 0\n",
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" correct = 0\n",
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" total += y_train.size(0)\n",
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" correct += np.sum(torch.squeeze(outputs).round().detach().numpy() == y_train.detach().numpy())\n",
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" accuracy = 100 * correct/total\n",
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" losses.append(loss.item())\n",
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" Iterations.append(iter)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Iteration: 50000. \n",
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"Test - Loss: 0.480914831161499. Accuracy: 79.76567447751742\n",
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"Train - Loss: 0.48352959752082825. Accuracy: 79.37570685365301\n",
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"\n"
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]
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}
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],
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"source": [
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"print(f\"Iteration: {iter}. \\nTest - Loss: {loss_test.item()}. Accuracy: {accuracy_test}\")\n",
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"print(f\"Train - Loss: {loss.item()}. Accuracy: {accuracy}\\n\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"logs.txt\", \"a\") as myfile:\n",
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" myfile.write(f\"loss={loss.item()}, accuracy={accuracy}\\n\")"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "3c12dc341c1078754dffca0e61bfc548ab04f96cfe0a82a85a936b702c4881ab"
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},
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"kernelspec": {
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"display_name": "Python 3.7.11 ('ium')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.11"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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