feat: idek anymore
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215cde2d19
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d294ac0e38
@ -16,6 +16,7 @@ from torch import nn
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import numpy as np
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import torch
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import os
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from torchvision.transforms.functional import equalize
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class CNN3D(nn.Module):
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def __init__(self, hidden_size=32, dropout=0.0):
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@ -27,7 +28,7 @@ class CNN3D(nn.Module):
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self.maxpool = nn.MaxPool3d(kernel_size=2, stride=2)
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self.fc1 = nn.Linear(hidden_size*32, 256) # Calculate input size based on output from conv3
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self.fc2 = nn.Linear(256, 6)
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self.dropout = nn.Dropout(dropout)
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# self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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x = self.conv1(x)
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@ -37,7 +38,7 @@ class CNN3D(nn.Module):
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x = self.conv2(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.dropout(x)
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# x = self.dropout(x)
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x = x.view(x.size(0), -1) # Flatten features for fully connected layers
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x = self.fc1(x)
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@ -56,17 +57,16 @@ def train(model, criterion, optimizer, loader, epochs=5):
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print(f'Epoch {epoch}, Loss: {loss.item()}')
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return model
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class Model():
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def __init__(self, batch_size=8,lr=0.001,epochs=10, dropout=0.0, hidden_size=32):
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def __init__(self, batch_size=64,lr=0.001,epochs=5, dropout=0.0, hidden_size=32, n_samples=900):
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print(batch_size, epochs, lr, dropout, hidden_size, n_samples)
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self.batch_size = batch_size
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self.lr = lr
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self.epochs = epochs
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self.model = CNN3D(dropout=dropout, hidden_size=hidden_size)
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self.criterion = nn.CrossEntropyLoss()
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self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr)
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self.n_samples = n_samples
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def fit(self, X, y):
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X, y = self.process_data(X, y)
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@ -81,31 +81,25 @@ class Model():
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tensor_videos = torch.tensor(X, dtype=torch.float32)
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# Clip values to 0 and 255
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tensor_videos = np.clip(tensor_videos, 0, 255)
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# TEMP
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threshold = 180
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tensor_videos[tensor_videos > threshold] = 255
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tensor_videos[tensor_videos < threshold] = 0
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# END TEMP
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# Replace NaNs in each frame, with the average of the frame. This was generated with GPT
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for i in range(tensor_videos.shape[0]):
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for j in range(tensor_videos.shape[1]):
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tensor_videos[i][j][torch.isnan(tensor_videos[i][j])] = torch.mean(
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tensor_videos[i][j][~torch.isnan(tensor_videos[i][j])])
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X = torch.Tensor(tensor_videos.unsqueeze(1))
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result = self.model(X)
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# tensor_videos = torch.Tensor(tensor_videos).to(torch.uint8).reshape(-1, 1, 16, 16)
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# tensor_videos = equalize(tensor_videos).float().reshape(-1, 1, 6, 16, 16)
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tensor_videos = torch.Tensor(tensor_videos).reshape(-1, 1, 6, 16, 16)
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# some funky code to make the features more prominent
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result = self.model(tensor_videos)
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return torch.max(result, dim=1)[1].numpy()
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def process_data(self, X, y, n_samples=600):
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def process_data(self, X, y):
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y = np.array(y)
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X = np.array([video[:6] for video in X])
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tensor_videos = torch.tensor(X, dtype=torch.float32)
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# Clip values to 0 and 255
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tensor_videos = np.clip(tensor_videos, 0, 255)
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# TEMP
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threshold = 180
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tensor_videos[tensor_videos > threshold] = 255
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tensor_videos[tensor_videos < threshold] = 0
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# END TEMP
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# Replace NaNs in each frame, with the average of the frame. This was generated with GPT
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for i in range(tensor_videos.shape[0]):
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@ -118,13 +112,19 @@ class Model():
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indices = [np.argwhere(y == i).squeeze(1) for i in range(6)]
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# Get the number of samples to take for each class
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# Get the indices of the samples to take
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indices_to_take = [np.random.choice(indices[i], n_samples, replace=True) for i in range(6)]
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indices_to_take = [np.random.choice(indices[i], self.n_samples, replace=True) for i in range(6)]
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# Concatenate the indices
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indices_to_take = np.concatenate(indices_to_take)
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# Select the samples
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tensor_videos = tensor_videos[indices_to_take].unsqueeze(1)
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tensor_videos = tensor_videos[indices_to_take]
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tensor_videos = torch.Tensor(tensor_videos).reshape(-1, 1, 6, 16, 16)
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# Reshape the tensor to int for image processing
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# tensor_videos = torch.Tensor(tensor_videos).to(torch.uint8).reshape(-1, 1, 16, 16)
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# tensor_videos = equalize(tensor_videos).float().reshape(-1, 1, 6, 16, 16)
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y = y[indices_to_take]
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return torch.Tensor(tensor_videos), torch.Tensor(y).long()
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return tensor_videos, torch.Tensor(y).long()
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)
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@ -315,12 +315,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"execution_count": 10,
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"id": "a44b7aa4",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-28T12:00:17.228662Z",
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"start_time": "2024-04-28T12:00:17.209494Z"
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"end_time": "2024-04-28T12:27:25.926991Z",
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"start_time": "2024-04-28T12:27:25.917322Z"
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}
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},
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"outputs": [],
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@ -406,8 +406,8 @@
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" def fit(self, X, y):\n",
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" X, y = process_data(X, y)\n",
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" train_dataset = torch.utils.data.TensorDataset(X, y)\n",
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" train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True)\n",
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" train(self.model, self.criterion, self.optimizer, train_loader, 10)\n",
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" train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
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" train(self.model, self.criterion, self.optimizer, train_loader, 20)\n",
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"\n",
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" def predict(self, X):\n",
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" self.model.eval()\n",
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@ -438,12 +438,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 73,
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"execution_count": 2,
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"id": "4f4dd489",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-28T12:00:19.363096Z",
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"start_time": "2024-04-28T12:00:19.352424Z"
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"end_time": "2024-04-28T12:09:46.115322Z",
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"start_time": "2024-04-28T12:09:45.631452Z"
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}
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},
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"outputs": [],
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@ -458,12 +458,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"execution_count": 3,
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"id": "3064e0ff",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-28T12:00:20.265060Z",
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"start_time": "2024-04-28T12:00:20.234748Z"
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"end_time": "2024-04-28T12:09:47.340881Z",
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"start_time": "2024-04-28T12:09:47.317719Z"
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}
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},
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"outputs": [],
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@ -477,12 +477,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 75,
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"execution_count": 12,
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"id": "27c9fd10",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-28T12:00:37.185569Z",
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"start_time": "2024-04-28T12:00:22.239036Z"
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"end_time": "2024-04-28T12:28:29.269402Z",
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"start_time": "2024-04-28T12:28:02.494602Z"
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}
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},
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"outputs": [
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@ -490,19 +490,29 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 0, Loss: 0.7495917081832886\n",
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"Epoch 1, Loss: 0.42713749408721924\n",
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"Epoch 2, Loss: 0.21424821019172668\n",
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"Epoch 3, Loss: 0.02086367830634117\n",
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"Epoch 4, Loss: 0.005386564414948225\n",
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"Epoch 5, Loss: 0.00319607718847692\n",
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"Epoch 6, Loss: 0.007663913071155548\n",
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"Epoch 7, Loss: 0.003004509722813964\n",
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"Epoch 8, Loss: 0.0044013322331011295\n",
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"Epoch 9, Loss: 0.0016760551370680332\n",
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"F1 Score (macro): 0.75\n",
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"CPU times: user 57.8 s, sys: 1min 12s, total: 2min 10s\n",
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"Wall time: 14.9 s\n"
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"Epoch 0, Loss: 0.5610745549201965\n",
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"Epoch 1, Loss: 0.22023160755634308\n",
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"Epoch 2, Loss: 0.03679683431982994\n",
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"Epoch 3, Loss: 0.009054183959960938\n",
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"Epoch 4, Loss: 0.0021134500857442617\n",
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"Epoch 5, Loss: 0.002705463906750083\n",
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"Epoch 6, Loss: 0.0045105633325874805\n",
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"Epoch 7, Loss: 0.001958428416401148\n",
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"Epoch 8, Loss: 0.0010891605634242296\n",
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"Epoch 9, Loss: 0.0010821395553648472\n",
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"Epoch 10, Loss: 0.0007317279814742506\n",
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"Epoch 11, Loss: 0.0006673489115200937\n",
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"Epoch 12, Loss: 0.00047141974209807813\n",
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"Epoch 13, Loss: 0.00024128056247718632\n",
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"Epoch 14, Loss: 0.0003150832490064204\n",
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"Epoch 15, Loss: 0.0004005862574558705\n",
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"Epoch 16, Loss: 0.00024190203112084419\n",
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"Epoch 17, Loss: 0.0004451812419574708\n",
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"Epoch 18, Loss: 0.000376795680494979\n",
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"Epoch 19, Loss: 0.0003616203321143985\n",
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"F1 Score (macro): 0.65\n",
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"CPU times: user 2min 33s, sys: 255 ms, total: 2min 34s\n",
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"Wall time: 26.8 s\n"
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]
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}
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],
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