From ca21607fbd3cefb112907e111524ada609691769 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Rafa=C5=82=20Soba=C5=84ski?= Date: Mon, 24 May 2021 19:26:21 +0200 Subject: [PATCH] solving the task --- .../Logistic_Regression-checkpoint.ipynb | 144 ------------------ 1 file changed, 144 deletions(-) delete mode 100644 .ipynb_checkpoints/Logistic_Regression-checkpoint.ipynb diff --git a/.ipynb_checkpoints/Logistic_Regression-checkpoint.ipynb b/.ipynb_checkpoints/Logistic_Regression-checkpoint.ipynb deleted file mode 100644 index a50ea29..0000000 --- a/.ipynb_checkpoints/Logistic_Regression-checkpoint.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 43, - "id": "f607767c", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import torch\n", - "import gensim.downloader as gensim\n", - "from nltk.tokenize import word_tokenize" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "58212f9c", - "metadata": {}, - "outputs": [], - "source": [ - "x_train = pd.read_table('train/in.tsv', sep='\\t', header = None)\n", - "y_train = pd.read_table('train/expected.tsv', sep='\\t', header = None)\n", - "y_train = y_train[0]\n", - "x_dev = pd.read_table('dev-0/in.tsv', sep='\\t', header = None)\n", - "x_test = pd.read_table('test-A/in.tsv', sep='\\t', header = None)\n", - "\n", - "x_train = x_train[0].str.lower()\n", - "x_dev = x_dev[0].str.lower()\n", - "x_test = x_test[0].str.lower()\n", - "\n", - "x_train = [word_tokenize(x) for x in x_train]\n", - "x_dev = [word_tokenize(x) for x in x_dev]\n", - "x_test = [word_tokenize(x) for x in x_test]" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "d2ef6f47", - "metadata": {}, - "outputs": [], - "source": [ - "word2vec = gensim.load('glove-wiki-gigaword-50')\n", - "\n", - "def document_vector(doc):\n", - " return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(50)], axis=0)\n", - "\n", - "x_train = [document_vector(doc) for doc in x_train]\n", - "x_dev = [document_vector(doc) for doc in x_dev]\n", - "x_test = [document_vector(doc) for doc in x_test]" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "c8f52012", - "metadata": {}, - "outputs": [], - "source": [ - "class NeuralNetworkModel(torch.nn.Module):\n", - " def __init__(self, features):\n", - " super(NeuralNetworkModel, self).__init__()\n", - " self.fc1 = torch.nn.Linear(features, 50)\n", - " self.fc2 = torch.nn.Linear(50, 1)\n", - "\n", - " def forward(self, x):\n", - " x = self.fc1(x)\n", - " x = torch.relu(x)\n", - " x = self.fc2(x)\n", - " x = torch.sigmoid(x)\n", - " return x\n", - " \n", - "nn_model = NeuralNetworkModel(100)\n", - "BATCH_SIZE = 5\n", - "criterion = torch.nn.BCELoss()\n", - "optimizer = torch.optim.SGD(nn_model.parameters(), lr = 0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "47c18496", - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "mat1 and mat2 shapes cannot be multiplied (5x50 and 100x50)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (5x50 and 100x50)" - ] - } - ], - "source": [ - "for epoch in range(5):\n", - " nn_model.train()\n", - " for i in range(0, y_train.shape[0], BATCH_SIZE):\n", - " X = x_train[i:i+BATCH_SIZE]\n", - " X = torch.tensor(X)\n", - " y = y_train[i:i+BATCH_SIZE]\n", - " y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1,1)\n", - "\n", - " outputs = nn_model(X.float())\n", - " loss = criterion(outputs, y)\n", - "\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()" - ] - } - ], - "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": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}