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- {
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "9f087356",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\saeed\\Desktop\\Master\\bci\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "import torch\n",
- "import torch.nn as nn\n",
- "import torch.nn.functional as F\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.model_selection import KFold, StratifiedKFold\n",
- "import librosa\n",
- "import librosa.display\n",
- "import IPython.display as ipd\n",
- "import matplotlib.pyplot as plt\n",
- "import numpy as np\n",
- "import scipy.io\n",
- "from tqdm import tqdm\n",
- "import glob\n",
- "import os\n",
- "import json\n",
- "import pickle\n",
- "from einops import rearrange\n",
- "from captum.attr import DeepLift\n",
- "from captum.attr import visualization as viz"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "ba4bf52c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1913, 62, 20, 11)\n"
- ]
- }
- ],
- "source": [
- "with open(\"data/normal_all_data.pkl\", \"rb\") as f:\n",
- " all_data = pickle.load(f)\n",
- "with open(\"data/all_label.pkl\", \"rb\") as f:\n",
- " labels = pickle.load(f)\n",
- "with open(\"data/vowel_label.pkl\", \"rb\") as f:\n",
- " vowel_label = pickle.load(f)\n",
- "with open(\"data/bilab_label.pkl\", \"rb\") as f:\n",
- " bilab_label = pickle.load(f)\n",
- "with open(\"data/nasal_label.pkl\", \"rb\") as f:\n",
- " nasal_label = pickle.load(f)\n",
- "with open(\"data/iy_label.pkl\", \"rb\") as f:\n",
- " iy_label = pickle.load(f)\n",
- "with open(\"data/uw_label.pkl\", \"rb\") as f:\n",
- " uw_label = pickle.load(f)\n",
- "\n",
- "print(all_data.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 220,
- "id": "17f8364a",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 0%| | 0/1913 [00:00<?, ?it/s]C:\\Users\\saeed\\Desktop\\Master\\bci\\lib\\site-packages\\librosa\\util\\decorators.py:88: UserWarning: n_fft=250 is too small for input signal of length=11\n",
- " return f(*args, **kwargs)\n",
- " 5%|███▉ | 95/1913 [00:23<07:36, 3.98it/s]\n",
- "\n",
- "KeyboardInterrupt\n",
- "\n"
- ]
- }
- ],
- "source": [
- "#calculate MFCCs with windowing\n",
- "n_mfcc = 20\n",
- "framesize = 1 * 250\n",
- "hop_size = int(framesize/2)\n",
- "\n",
- "trials = []\n",
- "for i, trial in enumerate(tqdm(data)):\n",
- " channels = []\n",
- " for j, channel in enumerate(trial):\n",
- " mfccs = librosa.feature.mfcc(y=channel, n_mfcc=n_mfcc, n_fft=framesize, hop_length=hop_size, sr=250)\n",
- " channels.append(np.array(mfccs))\n",
- " trials.append(np.array(channels)) \n",
- "mfc_data = np.array(trials)\n",
- "\n",
- "print(mfc_data.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "2830a90b",
- "metadata": {},
- "outputs": [],
- "source": [
- "#save as (windows MFCCs)\n",
- "with open('data/11_20mfc.pkl', 'wb') as f:\n",
- " pickle.dump(mfc_data, f)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "86e2469b",
- "metadata": {},
- "outputs": [],
- "source": [
- "class Dataset():\n",
- " def __init__(self, data, label, oversample=True):\n",
- " self.data = data\n",
- " self.label = label\n",
- " self.over = oversample\n",
- " self.train = None\n",
- " self.val = None\n",
- " self.test = None\n",
- " \n",
- " def picturize(self):\n",
- " trials = []\n",
- " depth = self.data.shape[2]\n",
- " for trial in self.data:\n",
- " pic = np.zeros((7,9,depth,11))\n",
- " pic[0,2] = trial[3]\n",
- " pic[0,3] = trial[0]\n",
- " pic[0,4] = trial[1]\n",
- " pic[0,5] = trial[2]\n",
- " pic[0,6] = trial[4]\n",
- " pic[1,:] = trial[5:14]\n",
- " pic[2,:] = trial[14:23]\n",
- " pic[3,:] = trial[23:32]\n",
- " pic[4,:] = trial[32:41]\n",
- " pic[5,:] = trial[41:50]\n",
- " pic[6,0] = trial[50]\n",
- " pic[6,1] = trial[51]\n",
- " pic[6,2] = trial[52]\n",
- " pic[6,3] = trial[58]\n",
- " pic[6,4] = trial[53]\n",
- " pic[6,5] = trial[60]\n",
- " pic[6,6] = trial[54]\n",
- " pic[6,7] = trial[55]\n",
- " pic[6,8] = trial[56]\n",
- " trials.append(pic)\n",
- " self.data = np.array(trials)\n",
- " return self.data\n",
- " \n",
- " def split(self, train_idx, test_idx, val_size=0.1, norm=False):\n",
- " train_val_data = np.stack([self.data[index] for index in train_idx])\n",
- " train_val_label = [self.label[index] for index in train_idx]\n",
- " test_data = np.stack([self.data[index] for index in test_idx])\n",
- " test_label = [self.label[index] for index in test_idx]\n",
- " \n",
- " if norm:\n",
- " Max = np.max(train_val_data, axis=(0,1,2,4), keepdims=True)\n",
- " Min = np.min(train_val_data, axis=(0,1,2,4), keepdims=True)\n",
- " train_val_data = (train_val_data-Min)/(Max-Min)\n",
- "\n",
- " Max_test = np.max(test_data, axis=(0,1,2,4), keepdims=True)\n",
- " Min_test = np.min(test_data, axis=(0,1,2,4), keepdims=True)\n",
- " test_data = (test_data-Min)/(Max-Min)\n",
- " \n",
- " train_val = [[train_val_data[i], train_val_label[i]] for i in range(len(train_val_data))]\n",
- " self.test = [[test_data[i], test_label[i]] for i in range(len(test_data))]\n",
- " \n",
- " num_train_val = len(train_val)\n",
- " indices = list(range(num_train_val))\n",
- " np.random.shuffle(indices)\n",
- " split = int(np.floor(val_size*num_train_val))\n",
- " train, val = [train_val[i] for i in indices[split:]] ,[train_val[i] for i in indices[:split]]\n",
- " \n",
- " if self.over:\n",
- " train_labels = [data[1] for data in train]\n",
- " _, counts = np.unique(train_labels, return_counts=True)\n",
- " print(counts)\n",
- " if counts[1]>counts[0]:\n",
- " label0 = [data for data in train if data[1]==0]\n",
- " coef = int(counts[1]/counts[0])\n",
- " for i in range(coef):\n",
- " train = train + label0\n",
- " elif counts[1]<counts[0]:\n",
- " label1 = [data for data in train if data[1]==1]\n",
- " coef = int(counts[0]/counts[1])\n",
- " for i in range(coef):\n",
- " train = train + label1\n",
- " self.train = train\n",
- " self.val = val\n",
- " \n",
- " return self.train, self.val, self.test\n",
- " \n",
- " \n",
- " def show(self):\n",
- " print('data shape = ', self.data.shape)\n",
- " \n",
- " if self.train is None:\n",
- " print('train not creaeted!')\n",
- " else:\n",
- " print('train shape = ', len(self.train))\n",
- " \n",
- " if self.val is None:\n",
- " print('validation not creaeted!')\n",
- " else:\n",
- " print('validation shape = ', len(self.val))\n",
- " \n",
- " if self.test is None:\n",
- " print('test not creaeted!')\n",
- " else:\n",
- " print('test shape = ', len(self.test))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "4fb6541e",
- "metadata": {},
- "outputs": [],
- "source": [
- "def train_model(train_loader, val_loader, epochs, lr, fold, steps):\n",
- " print('creating model...')\n",
- " model = cnn3d().float()\n",
- " optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
- " criterion = nn.BCELoss()\n",
- "\n",
- " scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, total_steps=steps, max_lr=lr*10)\n",
- " scheduler1 = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)\n",
- " l1_lambda = 0.0001\n",
- " \n",
- " min_val_loss = np.inf\n",
- " max_val_acc = 0\n",
- " for epoch in range(epochs):\n",
- " print('epoch: ', epoch+1)\n",
- " train_loss = 0\n",
- " train_correct = 0\n",
- " model.train()\n",
- " for iteration, (data,label) in enumerate(train_loader):\n",
- " optimizer.zero_grad()\n",
- " output = model(data.float())\n",
- " label = torch.reshape(label, (-1,1))\n",
- " label = label.float()\n",
- " loss = criterion(output, label)\n",
- " for W in model.parameters():\n",
- " loss = loss + l1_lambda*W.norm(1)\n",
- " loss.backward()\n",
- " optimizer.step()\n",
- " scheduler.step()\n",
- " targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
- " #print([output[i].round().item() for i in range(len(label))])\n",
- " train_correct += sum(targets)\n",
- " train_loss += loss.item()*data.shape[0]\n",
- " #scheduler1.step() \n",
- " train_acc = train_correct/len(train_loader.sampler) \n",
- " train_loss = train_loss/len(train_loader.sampler)\n",
- " \n",
- " val_loss = 0\n",
- " val_correct = 0\n",
- " model.eval()\n",
- " for data, label in val_loader:\n",
- " output = model(data.float())\n",
- " label = torch.reshape(label, (-1,1))\n",
- " label = label.float()\n",
- " loss = criterion(output, label) \n",
- " val_loss += loss.item()*data.shape[0]\n",
- " targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
- " val_correct += sum(targets)\n",
- " \n",
- " val_loss = val_loss/len(val_loader.sampler)\n",
- " val_acc = val_correct/len(val_loader.sampler)\n",
- " if val_loss <= min_val_loss:\n",
- " print(\"validation loss decreased ({:.6f} ---> {:.6f}), val_acc = {}\".format(min_val_loss, val_loss, val_acc))\n",
- " torch.save(model.state_dict(), 'train/model'+str(fold)+'.pt')\n",
- " min_val_loss = val_loss\n",
- " torch.save(model.state_dict(), 'train/last_model'+str(fold)+'.pt') \n",
- " print('epoch {}: train loss = {}, train acc = {},\\nval_loss = {}, val_acc = {}\\n'\n",
- " .format(epoch+1, train_loss, train_acc, val_loss, val_acc))\n",
- " \n",
- " if int(train_acc)==1:\n",
- " print('!!! overfitted !!!')\n",
- " break\n",
- " model.train()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "7d61fc48",
- "metadata": {},
- "outputs": [],
- "source": [
- "def evaluate_model(test_loader, fold):\n",
- " model =cnn3d().float()\n",
- " model.load_state_dict(torch.load('train/model'+str(fold)+'.pt'))\n",
- " \n",
- " n_correct = 0\n",
- " model.eval()\n",
- " for data, label in test_loader:\n",
- " output = model(data.float())\n",
- " targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
- " print(targets)\n",
- " n_correct += sum(targets) \n",
- " test_accs = n_correct/len(test_loader.sampler)\n",
- " print('early stoping results:\\n\\t', test_accs)\n",
- " \n",
- " n_correct = 0\n",
- " model.eval()\n",
- " for data, label in train_loader:\n",
- " output = model(data.float())\n",
- " targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
- " n_correct += sum(targets)\n",
- " \n",
- " train_accs = n_correct/len(train_loader.sampler)\n",
- " print('\\t', train_accs)\n",
- " \n",
- " model = cnn3d().float()\n",
- " model.load_state_dict(torch.load('train/last_model'+str(fold)+'.pt'))\n",
- " \n",
- " n_correct = 0\n",
- " model.eval()\n",
- " for data, label in test_loader:\n",
- " output = model(data.float())\n",
- " targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
- " print(targets)\n",
- " n_correct += sum(targets)\n",
- " test_accs_over = n_correct/len(test_loader.sampler)\n",
- " print('full train results:\\n\\t', test_accs_over)\n",
- " \n",
- " n_correct = 0\n",
- " model.eval()\n",
- " for data, label in train_loader:\n",
- " output = model(data.float())\n",
- " targets = [1 if output[i].round()==label[i] else 0 for i in range(len(label))]\n",
- " n_correct += sum(targets)\n",
- " train_accs_over = n_correct/len(train_loader.sampler)\n",
- " print('\\t', train_accs_over)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "402f6c76",
- "metadata": {},
- "outputs": [],
- "source": [
- "def calculate_steps(train_loader, epochs):\n",
- " steps = 0\n",
- " for epoch in range(epochs):\n",
- " for data, label in train_loader:\n",
- " steps += 1\n",
- " return steps"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "076c9c78",
- "metadata": {},
- "outputs": [],
- "source": [
- "class cnn3d(nn.Module):\n",
- " def __init__(self):\n",
- " super().__init__()\n",
- " self.conv1 = nn.Conv3d(20, 16, kernel_size=(3, 3, 3), padding=1)\n",
- " self.conv2 = nn.Conv3d(16, 32, kernel_size=(3, 3, 3), padding=0)\n",
- " self.pool = nn.MaxPool3d((2, 2, 2), stride=2)\n",
- " self.fc1 = nn.Linear(192, 128)\n",
- " self.fc2 = nn.Linear(128, 1)\n",
- " self.drop = nn.Dropout(0.25)\n",
- " self.batch1 = nn.BatchNorm3d(16)\n",
- " self.batch2 = nn.BatchNorm3d(32)\n",
- " self.batch3 = nn.BatchNorm1d(128)\n",
- " \n",
- " def forward(self, x):\n",
- " x = rearrange(x, 'n h w m t -> n m t h w')\n",
- " out = self.pool(F.relu(self.batch1(self.conv1(x))))\n",
- " out = F.relu(self.batch2(self.conv2(out)))\n",
- " out = out.view(out.size(0), -1)\n",
- " out = self.drop(F.relu(self.batch3(self.fc1(out))))\n",
- " out = F.sigmoid(self.fc2(out))\n",
- " return out"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "1b859362",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "tensor([[0.6052],\n",
- " [0.5052],\n",
- " [0.2035],\n",
- " [0.6347]], grad_fn=<SigmoidBackward0>)"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#test model\n",
- "model = cnn3d()\n",
- "sample = torch.rand((4,7,9,20,11))\n",
- "model(sample)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "7c72830d",
- "metadata": {},
- "outputs": [],
- "source": [
- "#congig\n",
- "\n",
- "val_size = 0.25\n",
- "n_epochs = 100\n",
- "batch_size = 128\n",
- "print_every = 10\n",
- "lr = 0.00001\n",
- "k = 10\n",
- "skf=StratifiedKFold(n_splits=k, shuffle=True, random_state=32)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "96df8fcb",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(1913, 62, 17, 11)\n"
- ]
- }
- ],
- "source": [
- "print(all_data[:,:,3:,:].shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "9eb46d39",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "------------fold 0-----------\n",
- "[ 291 1258]\n",
- "0.4636933284187247\n",
- "data shape = (1913, 7, 9, 20, 11)\n",
- "train shape = 2713\n",
- "validation shape = 172\n",
- "test shape = 192\n",
- "calculating total steps...\n",
- "creating model...\n",
- "epoch: 1\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\saeed\\Desktop\\Master\\bci\\lib\\site-packages\\torch\\nn\\functional.py:1960: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
- " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "validation loss decreased (inf ---> 0.679123), val_acc = 0.7093023255813954\n",
- "epoch 1: train loss = 0.8683970651853318, train acc = 0.5208256542572798,\n",
- "val_loss = 0.6791226988614991, val_acc = 0.7093023255813954\n",
- "\n",
- "epoch: 2\n",
- "epoch 2: train loss = 0.8702899437086019, train acc = 0.524511610762993,\n",
- "val_loss = 0.7166987934777903, val_acc = 0.4186046511627907\n",
- "\n",
- "epoch: 3\n",
- "epoch 3: train loss = 0.8651134706141103, train acc = 0.5344636933284187,\n",
- "val_loss = 0.7987130630848019, val_acc = 0.3430232558139535\n",
- "\n",
- "epoch: 4\n",
- "epoch 4: train loss = 0.8593147574778724, train acc = 0.5407298193881312,\n",
- "val_loss = 0.8141745814057284, val_acc = 0.3023255813953488\n",
- "\n",
- "epoch: 5\n",
- "epoch 5: train loss = 0.8594019789780104, train acc = 0.539255436785846,\n",
- "val_loss = 0.81581727987112, val_acc = 0.3081395348837209\n",
- "\n",
- "epoch: 6\n",
- "epoch 6: train loss = 0.855004380604371, train acc = 0.5477331367489864,\n",
- "val_loss = 0.8083413853201755, val_acc = 0.3372093023255814\n",
- "\n",
- "epoch: 7\n",
- "epoch 7: train loss = 0.8482353424744724, train acc = 0.5547364541098415,\n",
- "val_loss = 0.7954940158267354, val_acc = 0.3546511627906977\n",
- "\n",
- "epoch: 8\n",
- "epoch 8: train loss = 0.8403365777464226, train acc = 0.5772207887946922,\n",
- "val_loss = 0.7943125145379887, val_acc = 0.3430232558139535\n",
- "\n",
- "epoch: 9\n",
- "epoch 9: train loss = 0.8323755666772862, train acc = 0.5794323626981202,\n",
- "val_loss = 0.7893233784409457, val_acc = 0.3546511627906977\n",
- "\n",
- "epoch: 10\n",
- "epoch 10: train loss = 0.8240972711202451, train acc = 0.5927018061186878,\n",
- "val_loss = 0.7756274495013925, val_acc = 0.37209302325581395\n",
- "\n",
- "epoch: 11\n",
- "epoch 11: train loss = 0.8123821243341673, train acc = 0.618134906008109,\n",
- "val_loss = 0.7595333221346833, val_acc = 0.38953488372093026\n",
- "\n",
- "epoch: 12\n",
- "epoch 12: train loss = 0.8052803598511847, train acc = 0.6376704754883893,\n",
- "val_loss = 0.7610093549240468, val_acc = 0.38372093023255816\n",
- "\n",
- "epoch: 13\n",
- "epoch 13: train loss = 0.794128361483847, train acc = 0.6439366015481017,\n",
- "val_loss = 0.746018763198409, val_acc = 0.43023255813953487\n",
- "\n",
- "epoch: 14\n",
- "epoch 14: train loss = 0.7812480943869982, train acc = 0.6804275709546628,\n",
- "val_loss = 0.7397682084593662, val_acc = 0.42441860465116277\n",
- "\n",
- "epoch: 15\n",
- "epoch 15: train loss = 0.770653328441229, train acc = 0.6859565057132326,\n",
- "val_loss = 0.7616525181504183, val_acc = 0.38953488372093026\n",
- "\n",
- "epoch: 16\n",
- "epoch 16: train loss = 0.7537735868897598, train acc = 0.7150755621083671,\n",
- "val_loss = 0.692143508168154, val_acc = 0.5348837209302325\n",
- "\n",
- "epoch: 17\n",
- "epoch 17: train loss = 0.7378950803604154, train acc = 0.7412458532989311,\n",
- "val_loss = 0.7284444473510565, val_acc = 0.43023255813953487\n",
- "\n",
- "epoch: 18\n",
- "validation loss decreased (0.679123 ---> 0.677328), val_acc = 0.5872093023255814\n",
- "epoch 18: train loss = 0.7233544636642718, train acc = 0.7640987836343531,\n",
- "val_loss = 0.6773282483566639, val_acc = 0.5872093023255814\n",
- "\n",
- "epoch: 19\n",
- "epoch 19: train loss = 0.699734561411692, train acc = 0.7876889052709178,\n",
- "val_loss = 0.6925027619960696, val_acc = 0.5523255813953488\n",
- "\n",
- "epoch: 20\n",
- "validation loss decreased (0.677328 ---> 0.633773), val_acc = 0.6686046511627907\n",
- "epoch 20: train loss = 0.6822101758539655, train acc = 0.8112790269074824,\n",
- "val_loss = 0.6337725229041521, val_acc = 0.6686046511627907\n",
- "\n",
- "epoch: 21\n",
- "epoch 21: train loss = 0.6548226526905767, train acc = 0.832288978990048,\n",
- "val_loss = 0.7115770703138307, val_acc = 0.5174418604651163\n",
- "\n",
- "epoch: 22\n",
- "epoch 22: train loss = 0.6249409928736632, train acc = 0.8698857353483229,\n",
- "val_loss = 0.643924496894659, val_acc = 0.5930232558139535\n",
- "\n",
- "epoch: 23\n",
- "epoch 23: train loss = 0.5943754564516227, train acc = 0.8772576483597494,\n",
- "val_loss = 0.6660033076308495, val_acc = 0.5697674418604651\n",
- "\n",
- "epoch: 24\n",
- "validation loss decreased (0.633773 ---> 0.579030), val_acc = 0.7151162790697675\n",
- "epoch 24: train loss = 0.5627755398817094, train acc = 0.9012163656468853,\n",
- "val_loss = 0.5790298899938894, val_acc = 0.7151162790697675\n",
- "\n",
- "epoch: 25\n",
- "epoch 25: train loss = 0.5276576605090961, train acc = 0.92480648728345,\n",
- "val_loss = 0.5794833940128947, val_acc = 0.7151162790697675\n",
- "\n",
- "epoch: 26\n",
- "epoch 26: train loss = 0.4913847099425298, train acc = 0.9343899741983045,\n",
- "val_loss = 0.6454916582551113, val_acc = 0.622093023255814\n",
- "\n",
- "epoch: 27\n",
- "validation loss decreased (0.579030 ---> 0.547536), val_acc = 0.7441860465116279\n",
- "epoch 27: train loss = 0.45694537973254534, train acc = 0.9502395871728714,\n",
- "val_loss = 0.547536434129227, val_acc = 0.7441860465116279\n",
- "\n",
- "epoch: 28\n",
- "validation loss decreased (0.547536 ---> 0.514732), val_acc = 0.8023255813953488\n",
- "epoch 28: train loss = 0.42175784723546905, train acc = 0.9638776262440103,\n",
- "val_loss = 0.5147316435048747, val_acc = 0.8023255813953488\n",
- "\n",
- "epoch: 29\n",
- "epoch 29: train loss = 0.39061159156935704, train acc = 0.9712495392554368,\n",
- "val_loss = 0.535475094651067, val_acc = 0.7616279069767442\n",
- "\n",
- "epoch: 30\n",
- "validation loss decreased (0.514732 ---> 0.508574), val_acc = 0.8081395348837209\n",
- "epoch 30: train loss = 0.36516626786139894, train acc = 0.9764098783634353,\n",
- "val_loss = 0.5085743651833645, val_acc = 0.8081395348837209\n",
- "\n",
- "epoch: 31\n",
- "epoch 31: train loss = 0.33421938774377447, train acc = 0.9826760044231478,\n",
- "val_loss = 0.5448793408482574, val_acc = 0.7616279069767442\n",
- "\n",
- "epoch: 32\n",
- "epoch 32: train loss = 0.3104572842646647, train acc = 0.9900479174345743,\n",
- "val_loss = 0.5426593824874523, val_acc = 0.7965116279069767\n",
- "\n",
- "epoch: 33\n",
- "epoch 33: train loss = 0.28988734955415046, train acc = 0.9904165130851456,\n",
- "val_loss = 0.5494307279586792, val_acc = 0.7848837209302325\n",
- "\n",
- "epoch: 34\n",
- "epoch 34: train loss = 0.26993356656641526, train acc = 0.9941024695908588,\n",
- "val_loss = 0.5200380724529887, val_acc = 0.813953488372093\n",
- "\n",
- "epoch: 35\n",
- "epoch 35: train loss = 0.2577803114266401, train acc = 0.9959454478437154,\n",
- "val_loss = 0.5192096496737281, val_acc = 0.8197674418604651\n",
- "\n",
- "epoch: 36\n",
- "epoch 36: train loss = 0.24427844815222574, train acc = 0.997788426096572,\n",
- "val_loss = 0.5306130952613298, val_acc = 0.8197674418604651\n",
- "\n",
- "epoch: 37\n",
- "validation loss decreased (0.508574 ---> 0.504719), val_acc = 0.8372093023255814\n",
- "epoch 37: train loss = 0.23392101167160517, train acc = 0.9981570217471434,\n",
- "val_loss = 0.504719233097032, val_acc = 0.8372093023255814\n",
- "\n",
- "epoch: 38\n",
- "epoch 38: train loss = 0.22321982001230103, train acc = 0.9992628086988573,\n",
- "val_loss = 0.5188674448534499, val_acc = 0.8313953488372093\n",
- "\n",
- "epoch: 39\n",
- "epoch 39: train loss = 0.2165969933942372, train acc = 0.9996314043494287,\n",
- "val_loss = 0.5341297630653825, val_acc = 0.8372093023255814\n",
- "\n",
- "epoch: 40\n",
- "epoch 40: train loss = 0.2110399665799125, train acc = 1.0,\n",
- "val_loss = 0.5399310172990311, val_acc = 0.8197674418604651\n",
- "\n",
- "!!! overfitted !!!\n",
- "[0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1]\n",
- "[1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0]\n",
- "early stoping results:\n",
- "\t 0.7552083333333334\n",
- "\t 0.9996314043494287\n",
- "[1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1]\n",
- "[1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1]\n",
- "full train results:\n",
- "\t 0.7395833333333334\n",
- "\t 1.0\n",
- "------------fold 1-----------\n",
- "[ 283 1266]\n",
- "0.47221186124580383\n",
- "data shape = (1913, 7, 9, 20, 11)\n",
- "train shape = 2681\n",
- "validation shape = 172\n",
- "test shape = 192\n",
- "calculating total steps...\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "creating model...\n",
- "epoch: 1\n",
- "validation loss decreased (inf ---> 0.646471), val_acc = 0.8255813953488372\n",
- "epoch 1: train loss = 0.86992137868295, train acc = 0.5330100708690787,\n",
- "val_loss = 0.6464709279149078, val_acc = 0.8255813953488372\n",
- "\n",
- "epoch: 2\n",
- "epoch 2: train loss = 0.8734584440939553, train acc = 0.505408429690414,\n",
- "val_loss = 0.6706514441689779, val_acc = 0.6046511627906976\n",
- "\n",
- "epoch: 3\n",
- "epoch 3: train loss = 0.8688026436315193, train acc = 0.5210742260350616,\n",
- "val_loss = 0.6759761059006979, val_acc = 0.5697674418604651\n",
- "\n",
- "epoch: 4\n",
- "epoch 4: train loss = 0.8661403424790527, train acc = 0.5203282357329355,\n",
- "val_loss = 0.6691561743270519, val_acc = 0.5872093023255814\n",
- "\n",
- "epoch: 5\n",
- "epoch 5: train loss = 0.8571998774227212, train acc = 0.5389779932860873,\n",
- "val_loss = 0.6676994409672049, val_acc = 0.5872093023255814\n",
- "\n",
- "epoch: 6\n",
- "epoch 6: train loss = 0.8471142522704463, train acc = 0.5479298769116001,\n",
- "val_loss = 0.6665740276491919, val_acc = 0.5872093023255814\n",
- "\n",
- "epoch: 7\n",
- "epoch 7: train loss = 0.841995612025839, train acc = 0.5557627750839239,\n",
- "val_loss = 0.6650435148283492, val_acc = 0.6162790697674418\n",
- "\n",
- "epoch: 8\n",
- "epoch 8: train loss = 0.8332126938065056, train acc = 0.5747855277881387,\n",
- "val_loss = 0.6654224465059679, val_acc = 0.6162790697674418\n",
- "\n",
- "epoch: 9\n",
- "epoch 9: train loss = 0.828857630791392, train acc = 0.5863483774710929,\n",
- "val_loss = 0.6667825177658436, val_acc = 0.6046511627906976\n",
- "\n",
- "epoch: 10\n",
- "epoch 10: train loss = 0.8170930889727953, train acc = 0.6094740768370012,\n",
- "val_loss = 0.662314846072086, val_acc = 0.6104651162790697\n",
- "\n",
- "epoch: 11\n",
- "epoch 11: train loss = 0.8065087129401876, train acc = 0.6258858634837747,\n",
- "val_loss = 0.6654108665710272, val_acc = 0.5755813953488372\n",
- "\n",
- "epoch: 12\n",
- "epoch 12: train loss = 0.7962307475787942, train acc = 0.6478925773964939,\n",
- "val_loss = 0.6616917931756308, val_acc = 0.5813953488372093\n",
- "\n",
- "epoch: 13\n",
- "epoch 13: train loss = 0.776722994525679, train acc = 0.6833271167474823,\n",
- "val_loss = 0.6591206082077914, val_acc = 0.5872093023255814\n",
- "\n",
- "epoch: 14\n",
- "epoch 14: train loss = 0.7678748263836085, train acc = 0.701603879149571,\n",
- "val_loss = 0.6578961167224618, val_acc = 0.6104651162790697\n",
- "\n",
- "epoch: 15\n",
- "epoch 15: train loss = 0.7510278017295914, train acc = 0.7135397239835882,\n",
- "val_loss = 0.6544989874196607, val_acc = 0.5988372093023255\n",
- "\n",
- "epoch: 16\n",
- "validation loss decreased (0.646471 ---> 0.642755), val_acc = 0.6104651162790697\n",
- "epoch 16: train loss = 0.7301953062776397, train acc = 0.7601641178664678,\n",
- "val_loss = 0.6427546046500983, val_acc = 0.6104651162790697\n",
- "\n",
- "epoch: 17\n",
- "validation loss decreased (0.642755 ---> 0.635279), val_acc = 0.6627906976744186\n",
- "epoch 17: train loss = 0.7135588408227911, train acc = 0.7859007832898173,\n",
- "val_loss = 0.6352790053500685, val_acc = 0.6627906976744186\n",
- "\n",
- "epoch: 18\n",
- "validation loss decreased (0.635279 ---> 0.631912), val_acc = 0.6395348837209303\n",
- "epoch 18: train loss = 0.6874780969493471, train acc = 0.8157403953748601,\n",
- "val_loss = 0.6319117740143178, val_acc = 0.6395348837209303\n",
- "\n",
- "epoch: 19\n",
- "validation loss decreased (0.631912 ---> 0.623324), val_acc = 0.6627906976744186\n",
- "epoch 19: train loss = 0.6639259783290563, train acc = 0.8489369638194704,\n",
- "val_loss = 0.6233235237210296, val_acc = 0.6627906976744186\n",
- "\n",
- "epoch: 20\n",
- "validation loss decreased (0.623324 ---> 0.621211), val_acc = 0.6686046511627907\n",
- "epoch 20: train loss = 0.6353608048793674, train acc = 0.8776575904513242,\n",
- "val_loss = 0.6212111226347989, val_acc = 0.6686046511627907\n",
- "\n",
- "epoch: 21\n",
- "validation loss decreased (0.621211 ---> 0.620210), val_acc = 0.6627906976744186\n",
- "epoch 21: train loss = 0.6062298955778629, train acc = 0.8955613577023499,\n",
- "val_loss = 0.6202103772828745, val_acc = 0.6627906976744186\n",
- "\n",
- "epoch: 22\n",
- "validation loss decreased (0.620210 ---> 0.578789), val_acc = 0.7267441860465116\n",
- "epoch 22: train loss = 0.5769774110314563, train acc = 0.9089891831406192,\n",
- "val_loss = 0.5787890664366788, val_acc = 0.7267441860465116\n",
- "\n",
- "epoch: 23\n",
- "epoch 23: train loss = 0.5446096906151535, train acc = 0.9365908243192839,\n",
- "val_loss = 0.58748683818551, val_acc = 0.7267441860465116\n",
- "\n",
- "epoch: 24\n",
- "epoch 24: train loss = 0.5065792173602962, train acc = 0.9470346885490488,\n",
- "val_loss = 0.6247755801954935, val_acc = 0.6395348837209303\n",
- "\n",
- "epoch: 25\n",
- "validation loss decreased (0.578789 ---> 0.532145), val_acc = 0.7790697674418605\n",
- "epoch 25: train loss = 0.47399600875639997, train acc = 0.9593435285341291,\n",
- "val_loss = 0.5321454031522884, val_acc = 0.7790697674418605\n",
- "\n",
- "epoch: 26\n",
- "epoch 26: train loss = 0.4397122574603989, train acc = 0.9668034315553897,\n",
- "val_loss = 0.5539705656295599, val_acc = 0.7674418604651163\n",
- "\n",
- "epoch: 27\n",
- "epoch 27: train loss = 0.4094221261689486, train acc = 0.9701603879149571,\n",
- "val_loss = 0.6161841694698778, val_acc = 0.686046511627907\n",
- "\n",
- "epoch: 28\n",
- "epoch 28: train loss = 0.3771897173559252, train acc = 0.9802312569936591,\n",
- "val_loss = 0.5943776341371758, val_acc = 0.7383720930232558\n",
- "\n",
- "epoch: 29\n",
- "epoch 29: train loss = 0.35380360624186363, train acc = 0.9832152182021634,\n",
- "val_loss = 0.5820831060409546, val_acc = 0.7383720930232558\n",
- "\n",
- "epoch: 30\n",
- "epoch 30: train loss = 0.3256017004140764, train acc = 0.9891831406191719,\n",
- "val_loss = 0.642474444799645, val_acc = 0.7034883720930233\n",
- "\n",
- "epoch: 31\n",
- "epoch 31: train loss = 0.3058795078238281, train acc = 0.990302126072361,\n",
- "val_loss = 0.5841574142145556, val_acc = 0.7732558139534884\n",
- "\n",
- "epoch: 32\n",
- "epoch 32: train loss = 0.28646321554141274, train acc = 0.9962700484893696,\n",
- "val_loss = 0.6434207239816355, val_acc = 0.7325581395348837\n",
- "\n",
- "epoch: 33\n",
- "epoch 33: train loss = 0.26851461289178874, train acc = 0.9947780678851175,\n",
- "val_loss = 0.6213336531506028, val_acc = 0.7558139534883721\n",
- "\n",
- "epoch: 34\n",
- "epoch 34: train loss = 0.2543404344545732, train acc = 0.9977620290936218,\n",
- "val_loss = 0.6432186337404473, val_acc = 0.75\n",
- "\n",
- "epoch: 35\n",
- "epoch 35: train loss = 0.24356636835740866, train acc = 0.9970160387914957,\n",
- "val_loss = 0.6490278299464736, val_acc = 0.7732558139534884\n",
- "\n",
- "epoch: 36\n",
- "epoch 36: train loss = 0.2339746813105363, train acc = 0.999627004848937,\n",
- "val_loss = 0.6225385125293288, val_acc = 0.7558139534883721\n",
- "\n",
- "epoch: 37\n",
- "epoch 37: train loss = 0.22574142918285262, train acc = 1.0,\n",
- "val_loss = 0.6539836512055508, val_acc = 0.7616279069767442\n",
- "\n",
- "!!! overfitted !!!\n",
- "[1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0]\n",
- "[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1]\n",
- "early stoping results:\n",
- "\t 0.7760416666666666\n",
- "\t 0.9671764267064528\n",
- "[1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1]\n",
- "[1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1]\n",
- "full train results:\n",
- "\t 0.7604166666666666\n",
- "\t 0.999627004848937\n",
- "------------fold 2-----------\n",
- "[ 283 1266]\n",
- "0.47221186124580383\n",
- "data shape = (1913, 7, 9, 20, 11)\n",
- "train shape = 2681\n",
- "validation shape = 172\n",
- "test shape = 192\n",
- "calculating total steps...\n"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "Input \u001b[1;32mIn [47]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 12\u001b[0m test_loader \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mDataLoader(test, batch_size\u001b[38;5;241m=\u001b[39mbatch_size, shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcalculating total steps...\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 15\u001b[0m steps \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_steps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_epochs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 16\u001b[0m train_model(train_loader, val_loader, epochs\u001b[38;5;241m=\u001b[39mn_epochs, lr\u001b[38;5;241m=\u001b[39mlr, fold\u001b[38;5;241m=\u001b[39mfold, steps\u001b[38;5;241m=\u001b[39msteps)\n\u001b[0;32m 17\u001b[0m evaluate_model(test_loader, fold\u001b[38;5;241m=\u001b[39mfold)\n",
- "Input \u001b[1;32mIn [11]\u001b[0m, in \u001b[0;36mcalculate_steps\u001b[1;34m(train_loader, epochs)\u001b[0m\n\u001b[0;32m 2\u001b[0m steps \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs):\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data, label \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m 5\u001b[0m steps \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m steps\n",
- "File \u001b[1;32m~\\Desktop\\Master\\bci\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:677\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 676\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__next__\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m--> 677\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprofiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecord_function\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_profile_name\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 679\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n",
- "File \u001b[1;32m~\\Desktop\\Master\\bci\\lib\\site-packages\\torch\\autograd\\profiler.py:443\u001b[0m, in \u001b[0;36mrecord_function.__init__\u001b[1;34m(self, name, args)\u001b[0m\n\u001b[0;32m 440\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_callbacks_on_exit: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m 441\u001b[0m \u001b[38;5;66;03m# Stores underlying RecordFunction as a tensor. TODO: move to custom\u001b[39;00m\n\u001b[0;32m 442\u001b[0m \u001b[38;5;66;03m# class (https://github.com/pytorch/pytorch/issues/35026).\u001b[39;00m\n\u001b[1;32m--> 443\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandle: torch\u001b[38;5;241m.\u001b[39mTensor \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mzeros\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
- "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
- ]
- }
- ],
- "source": [
- "dataset = Dataset(all_data, vowel_label)\n",
- "data = dataset.picturize()\n",
- "\n",
- "for fold, (train_idx, test_idx) in enumerate(skf.split(data, labels)):\n",
- " print('------------fold {}-----------'.format(fold))\n",
- " train, val, test = dataset.split(train_idx, test_idx)\n",
- " train_label = [item[1] for item in train]\n",
- " print(sum(train_label)/len(train_label))\n",
- " dataset.show()\n",
- " train_loader = torch.utils.data.DataLoader(train, batch_size=batch_size, shuffle=True)\n",
- " val_loader = torch.utils.data.DataLoader(val, batch_size=batch_size, shuffle=True)\n",
- " test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=True)\n",
- " \n",
- " print('calculating total steps...')\n",
- " steps = calculate_steps(train_loader, n_epochs)\n",
- " train_model(train_loader, val_loader, epochs=n_epochs, lr=lr, fold=fold, steps=steps)\n",
- " evaluate_model(test_loader, fold=fold)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4d614e0a",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "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": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
- }
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