diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..13566b8
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/ium_444354.iml b/.idea/ium_444354.iml
new file mode 100644
index 0000000..d0876a7
--- /dev/null
+++ b/.idea/ium_444354.iml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..d56657a
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..56ed2dd
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.jovianrc b/.jovianrc
new file mode 100644
index 0000000..2f4547b
--- /dev/null
+++ b/.jovianrc
@@ -0,0 +1,3 @@
+{
+ "notebooks": {}
+}
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
index d53aef6..c2f4c89 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -8,20 +8,27 @@ RUN apt install python3-pip -y
RUN apt install unzip -y
RUN pip3 install --upgrade pip
RUN pip3 install --upgrade Pillow
-RUN pip3 install kaggle
RUN pip3 install pandas
RUN pip3 install scikit-learn
RUN pip3 install matplotlib
-RUN mkdir ~/.kaggle/
-COPY ./script.sh ./
-RUN chmod a+x ./script.sh
+RUN pip3 install jovian
+RUN pip3 install torch
+RUN pip3 install seaborn
+RUN pip3 install torchvision
+
+
+# COPY ./script.sh ./
+COPY ./runTrain.sh ./runTrain.sh
+
+# RUN chmod a+x ./script.sh
# Create app directory in image
WORKDIR /app
COPY ./winequality-red.csv ./
# Copy init dataset script to /app directory in image
-COPY ./data_processing.py ./
-# Download kaggle dataset
-#RUN kaggle datasets download -d uciml/red-wine-quality-cortez-et-al-2009
-#RUN unzip -o red-wine-quality-cortez-et-al-2009.zip
+COPY ./pytorch/pytorch.py ./
+
# Script executed after docker run
-CMD python3 ./data_processing.py
\ No newline at end of file
+# CMD python3 ./pytorch.py
+COPY ./runTrain.sh ./
+RUN chmod a+x ./runTrain.sh
+RUN ./runTrain.sh
diff --git a/pytorch/.ipynb_checkpoints/pytorch-checkpoint.ipynb b/pytorch/.ipynb_checkpoints/pytorch-checkpoint.ipynb
new file mode 100644
index 0000000..20f34d4
--- /dev/null
+++ b/pytorch/.ipynb_checkpoints/pytorch-checkpoint.ipynb
@@ -0,0 +1,531 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "e1c5e25d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "import jovian\n",
+ "import torchvision\n",
+ "import matplotlib\n",
+ "import torch.nn as nn\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import torch.nn.functional as F\n",
+ "from torchvision.datasets.utils import download_url\n",
+ "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
+ "import random\n",
+ "import os\n",
+ "import sys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c77ff6aa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " fixed acidity | \n",
+ " volatile acidity | \n",
+ " citric acid | \n",
+ " residual sugar | \n",
+ " chlorides | \n",
+ " free sulfur dioxide | \n",
+ " total sulfur dioxide | \n",
+ " density | \n",
+ " pH | \n",
+ " sulphates | \n",
+ " alcohol | \n",
+ " quality | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 7.4 | \n",
+ " 0.70 | \n",
+ " 0.00 | \n",
+ " 1.9 | \n",
+ " 0.076 | \n",
+ " 11.0 | \n",
+ " 34.0 | \n",
+ " 0.9978 | \n",
+ " 3.51 | \n",
+ " 0.56 | \n",
+ " 9.4 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 7.8 | \n",
+ " 0.88 | \n",
+ " 0.00 | \n",
+ " 2.6 | \n",
+ " 0.098 | \n",
+ " 25.0 | \n",
+ " 67.0 | \n",
+ " 0.9968 | \n",
+ " 3.20 | \n",
+ " 0.68 | \n",
+ " 9.8 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 7.8 | \n",
+ " 0.76 | \n",
+ " 0.04 | \n",
+ " 2.3 | \n",
+ " 0.092 | \n",
+ " 15.0 | \n",
+ " 54.0 | \n",
+ " 0.9970 | \n",
+ " 3.26 | \n",
+ " 0.65 | \n",
+ " 9.8 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 11.2 | \n",
+ " 0.28 | \n",
+ " 0.56 | \n",
+ " 1.9 | \n",
+ " 0.075 | \n",
+ " 17.0 | \n",
+ " 60.0 | \n",
+ " 0.9980 | \n",
+ " 3.16 | \n",
+ " 0.58 | \n",
+ " 9.8 | \n",
+ " 6 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 7.4 | \n",
+ " 0.70 | \n",
+ " 0.00 | \n",
+ " 1.9 | \n",
+ " 0.076 | \n",
+ " 11.0 | \n",
+ " 34.0 | \n",
+ " 0.9978 | \n",
+ " 3.51 | \n",
+ " 0.56 | \n",
+ " 9.4 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
+ "0 7.4 0.70 0.00 1.9 0.076 \n",
+ "1 7.8 0.88 0.00 2.6 0.098 \n",
+ "2 7.8 0.76 0.04 2.3 0.092 \n",
+ "3 11.2 0.28 0.56 1.9 0.075 \n",
+ "4 7.4 0.70 0.00 1.9 0.076 \n",
+ "\n",
+ " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
+ "0 11.0 34.0 0.9978 3.51 0.56 \n",
+ "1 25.0 67.0 0.9968 3.20 0.68 \n",
+ "2 15.0 54.0 0.9970 3.26 0.65 \n",
+ "3 17.0 60.0 0.9980 3.16 0.58 \n",
+ "4 11.0 34.0 0.9978 3.51 0.56 \n",
+ "\n",
+ " alcohol quality \n",
+ "0 9.4 5 \n",
+ "1 9.8 5 \n",
+ "2 9.8 5 \n",
+ "3 9.8 6 \n",
+ "4 9.4 5 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataframe_raw = pd.read_csv(\"winequality-red.csv\")\n",
+ "dataframe_raw.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "99f42861",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(['fixed acidity',\n",
+ " 'volatile acidity',\n",
+ " 'citric acid',\n",
+ " 'residual sugar',\n",
+ " 'chlorides',\n",
+ " 'free sulfur dioxide',\n",
+ " 'total sulfur dioxide',\n",
+ " 'density',\n",
+ " 'pH',\n",
+ " 'sulphates',\n",
+ " 'alcohol'],\n",
+ " ['quality'])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "input_cols=list(dataframe_raw.columns)[:-1]\n",
+ "output_cols = ['quality']\n",
+ "input_cols,output_cols"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "87011c12",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([[ 7.4 , 0.7 , 0. , ..., 3.51 , 0.56 , 9.4 ],\n",
+ " [ 7.8 , 0.88 , 0. , ..., 3.2 , 0.68 , 9.8 ],\n",
+ " [ 7.8 , 0.76 , 0.04 , ..., 3.26 , 0.65 , 9.8 ],\n",
+ " ...,\n",
+ " [ 6.3 , 0.51 , 0.13 , ..., 3.42 , 0.75 , 11. ],\n",
+ " [ 5.9 , 0.645, 0.12 , ..., 3.57 , 0.71 , 10.2 ],\n",
+ " [ 6. , 0.31 , 0.47 , ..., 3.39 , 0.66 , 11. ]]),\n",
+ " array([[5],\n",
+ " [5],\n",
+ " [5],\n",
+ " ...,\n",
+ " [6],\n",
+ " [5],\n",
+ " [6]], dtype=int64))"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def dataframe_to_arrays(dataframe):\n",
+ " dataframe1 = dataframe_raw.copy(deep=True)\n",
+ " inputs_array = dataframe1[input_cols].to_numpy()\n",
+ " targets_array = dataframe1[output_cols].to_numpy()\n",
+ " return inputs_array, targets_array\n",
+ "\n",
+ "inputs_array, targets_array = dataframe_to_arrays(dataframe_raw)\n",
+ "inputs_array, targets_array"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "705fb5b7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(tensor([[ 7.4000, 0.7000, 0.0000, ..., 3.5100, 0.5600, 9.4000],\n",
+ " [ 7.8000, 0.8800, 0.0000, ..., 3.2000, 0.6800, 9.8000],\n",
+ " [ 7.8000, 0.7600, 0.0400, ..., 3.2600, 0.6500, 9.8000],\n",
+ " ...,\n",
+ " [ 6.3000, 0.5100, 0.1300, ..., 3.4200, 0.7500, 11.0000],\n",
+ " [ 5.9000, 0.6450, 0.1200, ..., 3.5700, 0.7100, 10.2000],\n",
+ " [ 6.0000, 0.3100, 0.4700, ..., 3.3900, 0.6600, 11.0000]]),\n",
+ " tensor([[5.],\n",
+ " [5.],\n",
+ " [5.],\n",
+ " ...,\n",
+ " [6.],\n",
+ " [5.],\n",
+ " [6.]]))"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "inputs = torch.from_numpy(inputs_array).type(torch.float)\n",
+ "targets = torch.from_numpy(targets_array).type(torch.float)\n",
+ "inputs,targets"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "71f14b4a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dataset = TensorDataset(inputs, targets)\n",
+ "dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "c4f8cd40",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "train_ds, val_ds = random_split(dataset, [1300, 299])\n",
+ "batch_size=50\n",
+ "train_loader = DataLoader(train_ds, batch_size, shuffle=True)\n",
+ "val_loader = DataLoader(val_ds, batch_size)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "56f75067",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class WineQuality(nn.Module):\n",
+ " def __init__(self):\n",
+ " super().__init__()\n",
+ " self.linear = nn.Linear(input_size,output_size) \n",
+ " \n",
+ " def forward(self, xb): \n",
+ " out = self.linear(xb)\n",
+ " return out\n",
+ " \n",
+ " def training_step(self, batch):\n",
+ " inputs, targets = batch \n",
+ " # Generate predictions\n",
+ " out = self(inputs) \n",
+ " # Calcuate loss\n",
+ " loss = F.l1_loss(out,targets) \n",
+ " return loss\n",
+ " \n",
+ " def validation_step(self, batch):\n",
+ " inputs, targets = batch\n",
+ " # Generate predictions\n",
+ " out = self(inputs)\n",
+ " # Calculate loss\n",
+ " loss = F.l1_loss(out,targets) \n",
+ " return {'val_loss': loss.detach()}\n",
+ " \n",
+ " def validation_epoch_end(self, outputs):\n",
+ " batch_losses = [x['val_loss'] for x in outputs]\n",
+ " epoch_loss = torch.stack(batch_losses).mean() \n",
+ " return {'val_loss': epoch_loss.item()}\n",
+ " \n",
+ " def epoch_end(self, epoch, result, num_epochs):\n",
+ " # Print result every 100th epoch\n",
+ " if (epoch+1) % 100 == 0 or epoch == num_epochs-1:\n",
+ " print(\"Epoch [{}], val_loss: {:.4f}\".format(epoch+1, result['val_loss']))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "57f354ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_size = len(input_cols)\n",
+ "output_size = len(output_cols)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "4a926cfa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model=WineQuality()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "3df1733d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate(model, val_loader):\n",
+ " outputs = [model.validation_step(batch) for batch in val_loader]\n",
+ " return model.validation_epoch_end(outputs)\n",
+ "\n",
+ "def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):\n",
+ " history = []\n",
+ " optimizer = opt_func(model.parameters(), lr)\n",
+ " for epoch in range(epochs):\n",
+ " for batch in train_loader:\n",
+ " loss = model.training_step(batch)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " optimizer.zero_grad()\n",
+ " result = evaluate(model, val_loader)\n",
+ " model.epoch_end(epoch, result, epochs)\n",
+ " history.append(result)\n",
+ " return history"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "3ed5f872",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch [100], val_loss: 4.1732\n",
+ "Epoch [200], val_loss: 1.6444\n",
+ "Epoch [300], val_loss: 1.4860\n",
+ "Epoch [400], val_loss: 1.4119\n",
+ "Epoch [500], val_loss: 1.3407\n",
+ "Epoch [600], val_loss: 1.2709\n",
+ "Epoch [700], val_loss: 1.2045\n",
+ "Epoch [800], val_loss: 1.1401\n",
+ "Epoch [900], val_loss: 1.0783\n",
+ "Epoch [1000], val_loss: 1.0213\n",
+ "Epoch [1100], val_loss: 0.9678\n",
+ "Epoch [1200], val_loss: 0.9186\n",
+ "Epoch [1300], val_loss: 0.8729\n",
+ "Epoch [1400], val_loss: 0.8320\n",
+ "Epoch [1500], val_loss: 0.7959\n"
+ ]
+ }
+ ],
+ "source": [
+ "epochs = 1500\n",
+ "lr = 1e-6\n",
+ "history5 = fit(epochs, lr, model, train_loader, val_loader)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "413ab394",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict_single(input, target, model):\n",
+ " inputs = input.unsqueeze(0)\n",
+ " predictions = model(inputs)\n",
+ " prediction = predictions[0].detach()\n",
+ "\n",
+ " return \"Target: \"+str(target)+\"----- Prediction: \"+str(prediction)+\"\\n\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "b1ab4522",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Target: tensor([5.])----- Prediction: tensor([4.9765])\n",
+ "Target: tensor([5.])----- Prediction: tensor([6.6649])\n",
+ "Target: tensor([5.])----- Prediction: tensor([5.2627])\n",
+ "Target: tensor([7.])----- Prediction: tensor([5.7054])\n",
+ "Target: tensor([5.])----- Prediction: tensor([5.1168])\n",
+ "Target: tensor([7.])----- Prediction: tensor([5.3928])\n",
+ "Target: tensor([5.])----- Prediction: tensor([4.8501])\n",
+ "Target: tensor([4.])----- Prediction: tensor([5.4210])\n",
+ "Target: tensor([5.])----- Prediction: tensor([4.6719])\n",
+ "Target: tensor([5.])----- Prediction: tensor([7.8635])\n"
+ ]
+ }
+ ],
+ "source": [
+ "#wylosuj 10 próbek predykcji\n",
+ "for i in random.sample(range(0, len(val_ds)), 10):\n",
+ " input_, target = val_ds[i]\n",
+ " print(predict_single(input_, target, model),end=\"\")\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "a754aaff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open(\"result.txt\", \"w+\") as file:\n",
+ " for i in range(0, len(val_ds), 1):\n",
+ " input_, target = val_ds[i]\n",
+ " file.write(str(predict_single(input_, target, model)))"
+ ]
+ }
+ ],
+ "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
+}
diff --git a/pytorch/.jovianrc b/pytorch/.jovianrc
new file mode 100644
index 0000000..2f4547b
--- /dev/null
+++ b/pytorch/.jovianrc
@@ -0,0 +1,3 @@
+{
+ "notebooks": {}
+}
\ No newline at end of file
diff --git a/pytorch/Dockerfile b/pytorch/Dockerfile
new file mode 100644
index 0000000..57e6670
--- /dev/null
+++ b/pytorch/Dockerfile
@@ -0,0 +1,33 @@
+FROM ubuntu:latest
+# Install required dependencies
+RUN apt update
+RUN apt-get update
+RUN apt install -y figlet
+RUN export PATH=”$PATH:/usr/local/bin/python”
+RUN apt install python3-pip -y
+RUN apt install unzip -y
+RUN pip3 install --upgrade pip
+RUN pip3 install --upgrade Pillow
+RUN pip3 install pandas
+RUN pip3 install scikit-learn
+RUN pip3 install matplotlib
+RUN pip3 install jovian
+RUN pip3 install torch
+RUN pip3 install seaborn
+RUN pip3 install random
+RUN pip3 install torchvision
+
+
+# COPY ./script.sh ./
+# RUN chmod a+x ./script.sh
+# Create app directory in image
+WORKDIR /app
+COPY ./winequality-red.csv ./
+# Copy init dataset script to /app directory in image
+COPY ./pytorch.py ./
+
+# Script executed after docker run
+# CMD python3 ./pytorch.py
+
+RUN chmod o+wrx ./runTrain.sh
+RUN ./runTrain.sh
\ No newline at end of file
diff --git a/pytorch.ipynb b/pytorch/pytorch.ipynb
similarity index 87%
rename from pytorch.ipynb
rename to pytorch/pytorch.ipynb
index 3cddf1a..20f34d4 100644
--- a/pytorch.ipynb
+++ b/pytorch/pytorch.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 18,
"id": "e1c5e25d",
"metadata": {},
"outputs": [],
@@ -18,7 +18,9 @@
"import torch.nn.functional as F\n",
"from torchvision.datasets.utils import download_url\n",
"from torch.utils.data import DataLoader, TensorDataset, random_split\n",
- "import random"
+ "import random\n",
+ "import os\n",
+ "import sys"
]
},
{
@@ -295,7 +297,7 @@
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 6,
@@ -366,7 +368,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"id": "57f354ce",
"metadata": {},
"outputs": [],
@@ -377,7 +379,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"id": "4a926cfa",
"metadata": {},
"outputs": [],
@@ -387,7 +389,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"id": "3df1733d",
"metadata": {},
"outputs": [],
@@ -413,7 +415,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"id": "3ed5f872",
"metadata": {},
"outputs": [
@@ -421,21 +423,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch [100], val_loss: 5.1117\n",
- "Epoch [200], val_loss: 1.7651\n",
- "Epoch [300], val_loss: 1.4800\n",
- "Epoch [400], val_loss: 1.3942\n",
- "Epoch [500], val_loss: 1.3119\n",
- "Epoch [600], val_loss: 1.2326\n",
- "Epoch [700], val_loss: 1.1571\n",
- "Epoch [800], val_loss: 1.0863\n",
- "Epoch [900], val_loss: 1.0224\n",
- "Epoch [1000], val_loss: 0.9642\n",
- "Epoch [1100], val_loss: 0.9100\n",
- "Epoch [1200], val_loss: 0.8617\n",
- "Epoch [1300], val_loss: 0.8200\n",
- "Epoch [1400], val_loss: 0.7816\n",
- "Epoch [1500], val_loss: 0.7484\n"
+ "Epoch [100], val_loss: 4.1732\n",
+ "Epoch [200], val_loss: 1.6444\n",
+ "Epoch [300], val_loss: 1.4860\n",
+ "Epoch [400], val_loss: 1.4119\n",
+ "Epoch [500], val_loss: 1.3407\n",
+ "Epoch [600], val_loss: 1.2709\n",
+ "Epoch [700], val_loss: 1.2045\n",
+ "Epoch [800], val_loss: 1.1401\n",
+ "Epoch [900], val_loss: 1.0783\n",
+ "Epoch [1000], val_loss: 1.0213\n",
+ "Epoch [1100], val_loss: 0.9678\n",
+ "Epoch [1200], val_loss: 0.9186\n",
+ "Epoch [1300], val_loss: 0.8729\n",
+ "Epoch [1400], val_loss: 0.8320\n",
+ "Epoch [1500], val_loss: 0.7959\n"
]
}
],
@@ -447,7 +449,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 27,
"id": "413ab394",
"metadata": {},
"outputs": [],
@@ -456,15 +458,13 @@
" inputs = input.unsqueeze(0)\n",
" predictions = model(inputs)\n",
" prediction = predictions[0].detach()\n",
- " #print(\"Input:\", input)\n",
- " #print(\"Target:\", target)\n",
- " #print(\"Prediction:\", prediction)\n",
- " print(\"Target: \", target, \"----- Prediction: \", prediction)"
+ "\n",
+ " return \"Target: \"+str(target)+\"----- Prediction: \"+str(prediction)+\"\\n\""
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 32,
"id": "b1ab4522",
"metadata": {},
"outputs": [
@@ -472,16 +472,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Target: tensor([6.]) ----- Prediction: tensor([5.1011])\n",
- "Target: tensor([6.]) ----- Prediction: tensor([7.1398])\n",
- "Target: tensor([5.]) ----- Prediction: tensor([5.1009])\n",
- "Target: tensor([6.]) ----- Prediction: tensor([5.2282])\n",
- "Target: tensor([5.]) ----- Prediction: tensor([4.8219])\n",
- "Target: tensor([6.]) ----- Prediction: tensor([4.8082])\n",
- "Target: tensor([7.]) ----- Prediction: tensor([5.0764])\n",
- "Target: tensor([5.]) ----- Prediction: tensor([6.3668])\n",
- "Target: tensor([6.]) ----- Prediction: tensor([5.0642])\n",
- "Target: tensor([5.]) ----- Prediction: tensor([5.4656])\n"
+ "Target: tensor([5.])----- Prediction: tensor([4.9765])\n",
+ "Target: tensor([5.])----- Prediction: tensor([6.6649])\n",
+ "Target: tensor([5.])----- Prediction: tensor([5.2627])\n",
+ "Target: tensor([7.])----- Prediction: tensor([5.7054])\n",
+ "Target: tensor([5.])----- Prediction: tensor([5.1168])\n",
+ "Target: tensor([7.])----- Prediction: tensor([5.3928])\n",
+ "Target: tensor([5.])----- Prediction: tensor([4.8501])\n",
+ "Target: tensor([4.])----- Prediction: tensor([5.4210])\n",
+ "Target: tensor([5.])----- Prediction: tensor([4.6719])\n",
+ "Target: tensor([5.])----- Prediction: tensor([7.8635])\n"
]
}
],
@@ -489,17 +489,22 @@
"#wylosuj 10 próbek predykcji\n",
"for i in random.sample(range(0, len(val_ds)), 10):\n",
" input_, target = val_ds[i]\n",
- " predict_single(input_, target, model)\n",
+ " print(predict_single(input_, target, model),end=\"\")\n",
" "
]
},
{
"cell_type": "code",
- "execution_count": null,
- "id": "0237aad2",
+ "execution_count": 36,
+ "id": "a754aaff",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "with open(\"result.txt\", \"w+\") as file:\n",
+ " for i in range(0, len(val_ds), 1):\n",
+ " input_, target = val_ds[i]\n",
+ " file.write(str(predict_single(input_, target, model)))"
+ ]
}
],
"metadata": {
diff --git a/pytorch/pytorch.py b/pytorch/pytorch.py
new file mode 100644
index 0000000..3c2e037
--- /dev/null
+++ b/pytorch/pytorch.py
@@ -0,0 +1,187 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[18]:
+
+
+import torch
+import jovian
+import torchvision
+import matplotlib
+import torch.nn as nn
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import torch.nn.functional as F
+from torchvision.datasets.utils import download_url
+from torch.utils.data import DataLoader, TensorDataset, random_split
+import random
+import os
+import sys
+
+
+# In[2]:
+
+
+dataframe_raw = pd.read_csv("winequality-red.csv")
+dataframe_raw.head()
+
+
+# In[3]:
+
+
+input_cols=list(dataframe_raw.columns)[:-1]
+output_cols = ['quality']
+input_cols,output_cols
+
+
+# In[4]:
+
+
+def dataframe_to_arrays(dataframe):
+ dataframe1 = dataframe_raw.copy(deep=True)
+ inputs_array = dataframe1[input_cols].to_numpy()
+ targets_array = dataframe1[output_cols].to_numpy()
+ return inputs_array, targets_array
+
+inputs_array, targets_array = dataframe_to_arrays(dataframe_raw)
+inputs_array, targets_array
+
+
+# In[5]:
+
+
+inputs = torch.from_numpy(inputs_array).type(torch.float)
+targets = torch.from_numpy(targets_array).type(torch.float)
+inputs,targets
+
+
+# In[6]:
+
+
+dataset = TensorDataset(inputs, targets)
+dataset
+
+
+# In[7]:
+
+
+train_ds, val_ds = random_split(dataset, [1300, 299])
+batch_size=50
+train_loader = DataLoader(train_ds, batch_size, shuffle=True)
+val_loader = DataLoader(val_ds, batch_size)
+
+
+# In[8]:
+
+
+class WineQuality(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.linear = nn.Linear(input_size,output_size)
+
+ def forward(self, xb):
+ out = self.linear(xb)
+ return out
+
+ def training_step(self, batch):
+ inputs, targets = batch
+ # Generate predictions
+ out = self(inputs)
+ # Calcuate loss
+ loss = F.l1_loss(out,targets)
+ return loss
+
+ def validation_step(self, batch):
+ inputs, targets = batch
+ # Generate predictions
+ out = self(inputs)
+ # Calculate loss
+ loss = F.l1_loss(out,targets)
+ return {'val_loss': loss.detach()}
+
+ def validation_epoch_end(self, outputs):
+ batch_losses = [x['val_loss'] for x in outputs]
+ epoch_loss = torch.stack(batch_losses).mean()
+ return {'val_loss': epoch_loss.item()}
+
+ def epoch_end(self, epoch, result, num_epochs):
+ # Print result every 100th epoch
+ if (epoch+1) % 100 == 0 or epoch == num_epochs-1:
+ print("Epoch [{}], val_loss: {:.4f}".format(epoch+1, result['val_loss']))
+
+
+# In[9]:
+
+
+input_size = len(input_cols)
+output_size = len(output_cols)
+
+
+# In[10]:
+
+
+model=WineQuality()
+
+
+# In[11]:
+
+
+def evaluate(model, val_loader):
+ outputs = [model.validation_step(batch) for batch in val_loader]
+ return model.validation_epoch_end(outputs)
+
+def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
+ history = []
+ optimizer = opt_func(model.parameters(), lr)
+ for epoch in range(epochs):
+ for batch in train_loader:
+ loss = model.training_step(batch)
+ loss.backward()
+ optimizer.step()
+ optimizer.zero_grad()
+ result = evaluate(model, val_loader)
+ model.epoch_end(epoch, result, epochs)
+ history.append(result)
+ return history
+
+
+# In[12]:
+
+
+epochs = 1500
+lr = 1e-6
+history5 = fit(epochs, lr, model, train_loader, val_loader)
+
+
+# In[27]:
+
+
+def predict_single(input, target, model):
+ inputs = input.unsqueeze(0)
+ predictions = model(inputs)
+ prediction = predictions[0].detach()
+
+ return "Target: "+str(target)+"----- Prediction: "+str(prediction)+"\n"
+
+
+# In[32]:
+
+
+#wylosuj 10 próbek predykcji
+for i in random.sample(range(0, len(val_ds)), 10):
+ input_, target = val_ds[i]
+ print(predict_single(input_, target, model),end="")
+
+
+
+# In[36]:
+
+
+with open("result.txt", "w+") as file:
+ for i in range(0, len(val_ds), 1):
+ input_, target = val_ds[i]
+ file.write(str(predict_single(input_, target, model)))
+
+
+
diff --git a/pytorch/result.txt b/pytorch/result.txt
new file mode 100644
index 0000000..8077cd4
--- /dev/null
+++ b/pytorch/result.txt
@@ -0,0 +1,299 @@
+Target: tensor([4.])----- Prediction: tensor([2.5912])
+Target: tensor([7.])----- Prediction: tensor([5.3811])
+Target: tensor([6.])----- Prediction: tensor([2.4516])
+Target: tensor([5.])----- Prediction: tensor([6.6527])
+Target: tensor([5.])----- Prediction: tensor([3.6947])
+Target: tensor([7.])----- Prediction: tensor([3.0992])
+Target: tensor([5.])----- Prediction: tensor([3.5280])
+Target: tensor([6.])----- Prediction: tensor([2.8694])
+Target: tensor([5.])----- Prediction: tensor([10.5723])
+Target: tensor([6.])----- Prediction: tensor([1.5417])
+Target: tensor([6.])----- Prediction: tensor([1.5702])
+Target: tensor([6.])----- Prediction: tensor([3.8206])
+Target: tensor([6.])----- Prediction: tensor([5.5910])
+Target: tensor([7.])----- Prediction: tensor([2.1009])
+Target: tensor([5.])----- Prediction: tensor([1.9532])
+Target: tensor([6.])----- Prediction: tensor([5.4789])
+Target: tensor([5.])----- Prediction: tensor([3.4121])
+Target: tensor([6.])----- Prediction: tensor([5.6153])
+Target: tensor([6.])----- Prediction: tensor([2.7008])
+Target: tensor([6.])----- Prediction: tensor([2.4302])
+Target: tensor([5.])----- Prediction: tensor([11.2693])
+Target: tensor([7.])----- Prediction: tensor([2.9486])
+Target: tensor([6.])----- Prediction: tensor([2.0228])
+Target: tensor([5.])----- Prediction: tensor([5.8602])
+Target: tensor([5.])----- Prediction: tensor([3.8908])
+Target: tensor([6.])----- Prediction: tensor([10.1888])
+Target: tensor([5.])----- Prediction: tensor([6.6399])
+Target: tensor([6.])----- Prediction: tensor([3.7961])
+Target: tensor([6.])----- Prediction: tensor([3.8239])
+Target: tensor([5.])----- Prediction: tensor([3.6467])
+Target: tensor([6.])----- Prediction: tensor([5.6153])
+Target: tensor([5.])----- Prediction: tensor([4.1886])
+Target: tensor([6.])----- Prediction: tensor([7.4703])
+Target: tensor([5.])----- Prediction: tensor([6.7674])
+Target: tensor([6.])----- Prediction: tensor([3.9402])
+Target: tensor([5.])----- Prediction: tensor([10.8020])
+Target: tensor([5.])----- Prediction: tensor([3.7203])
+Target: tensor([6.])----- Prediction: tensor([4.3365])
+Target: tensor([6.])----- Prediction: tensor([4.0502])
+Target: tensor([5.])----- Prediction: tensor([5.1851])
+Target: tensor([7.])----- Prediction: tensor([3.8488])
+Target: tensor([5.])----- Prediction: tensor([2.4501])
+Target: tensor([6.])----- Prediction: tensor([2.9132])
+Target: tensor([7.])----- Prediction: tensor([3.3335])
+Target: tensor([5.])----- Prediction: tensor([7.8335])
+Target: tensor([6.])----- Prediction: tensor([2.5696])
+Target: tensor([6.])----- Prediction: tensor([2.7571])
+Target: tensor([6.])----- Prediction: tensor([5.0917])
+Target: tensor([5.])----- Prediction: tensor([12.2102])
+Target: tensor([5.])----- Prediction: tensor([7.3395])
+Target: tensor([6.])----- Prediction: tensor([2.2568])
+Target: tensor([7.])----- Prediction: tensor([3.2961])
+Target: tensor([5.])----- Prediction: tensor([1.6766])
+Target: tensor([5.])----- Prediction: tensor([3.5993])
+Target: tensor([6.])----- Prediction: tensor([5.0940])
+Target: tensor([5.])----- Prediction: tensor([9.0951])
+Target: tensor([6.])----- Prediction: tensor([3.0024])
+Target: tensor([5.])----- Prediction: tensor([3.1072])
+Target: tensor([7.])----- Prediction: tensor([7.0583])
+Target: tensor([5.])----- Prediction: tensor([3.1533])
+Target: tensor([5.])----- Prediction: tensor([11.8913])
+Target: tensor([5.])----- Prediction: tensor([1.7950])
+Target: tensor([6.])----- Prediction: tensor([3.5572])
+Target: tensor([6.])----- Prediction: tensor([2.9728])
+Target: tensor([7.])----- Prediction: tensor([2.1861])
+Target: tensor([6.])----- Prediction: tensor([3.7599])
+Target: tensor([6.])----- Prediction: tensor([4.5269])
+Target: tensor([6.])----- Prediction: tensor([3.1787])
+Target: tensor([7.])----- Prediction: tensor([2.6388])
+Target: tensor([5.])----- Prediction: tensor([6.9238])
+Target: tensor([7.])----- Prediction: tensor([4.8372])
+Target: tensor([6.])----- Prediction: tensor([4.9583])
+Target: tensor([5.])----- Prediction: tensor([2.8367])
+Target: tensor([5.])----- Prediction: tensor([3.2148])
+Target: tensor([5.])----- Prediction: tensor([2.2810])
+Target: tensor([5.])----- Prediction: tensor([5.5056])
+Target: tensor([6.])----- Prediction: tensor([1.5980])
+Target: tensor([6.])----- Prediction: tensor([3.2662])
+Target: tensor([6.])----- Prediction: tensor([2.0195])
+Target: tensor([5.])----- Prediction: tensor([10.5990])
+Target: tensor([7.])----- Prediction: tensor([2.8631])
+Target: tensor([5.])----- Prediction: tensor([8.1595])
+Target: tensor([6.])----- Prediction: tensor([3.9324])
+Target: tensor([6.])----- Prediction: tensor([3.2511])
+Target: tensor([6.])----- Prediction: tensor([3.6911])
+Target: tensor([5.])----- Prediction: tensor([3.7448])
+Target: tensor([4.])----- Prediction: tensor([1.5203])
+Target: tensor([6.])----- Prediction: tensor([3.9098])
+Target: tensor([5.])----- Prediction: tensor([3.7416])
+Target: tensor([7.])----- Prediction: tensor([2.1332])
+Target: tensor([6.])----- Prediction: tensor([6.7936])
+Target: tensor([5.])----- Prediction: tensor([5.2619])
+Target: tensor([5.])----- Prediction: tensor([2.4828])
+Target: tensor([6.])----- Prediction: tensor([2.9184])
+Target: tensor([7.])----- Prediction: tensor([3.9367])
+Target: tensor([6.])----- Prediction: tensor([4.4000])
+Target: tensor([5.])----- Prediction: tensor([4.0671])
+Target: tensor([8.])----- Prediction: tensor([8.0165])
+Target: tensor([5.])----- Prediction: tensor([4.5995])
+Target: tensor([6.])----- Prediction: tensor([7.7532])
+Target: tensor([5.])----- Prediction: tensor([9.6909])
+Target: tensor([5.])----- Prediction: tensor([5.7997])
+Target: tensor([6.])----- Prediction: tensor([8.8374])
+Target: tensor([6.])----- Prediction: tensor([4.0067])
+Target: tensor([5.])----- Prediction: tensor([10.5642])
+Target: tensor([5.])----- Prediction: tensor([2.3271])
+Target: tensor([5.])----- Prediction: tensor([2.0700])
+Target: tensor([6.])----- Prediction: tensor([1.7528])
+Target: tensor([6.])----- Prediction: tensor([5.2573])
+Target: tensor([7.])----- Prediction: tensor([3.1217])
+Target: tensor([5.])----- Prediction: tensor([3.4221])
+Target: tensor([4.])----- Prediction: tensor([2.1967])
+Target: tensor([6.])----- Prediction: tensor([1.8576])
+Target: tensor([5.])----- Prediction: tensor([3.9776])
+Target: tensor([4.])----- Prediction: tensor([1.8588])
+Target: tensor([7.])----- Prediction: tensor([8.4651])
+Target: tensor([5.])----- Prediction: tensor([3.0248])
+Target: tensor([5.])----- Prediction: tensor([9.5106])
+Target: tensor([6.])----- Prediction: tensor([2.3674])
+Target: tensor([7.])----- Prediction: tensor([7.9203])
+Target: tensor([6.])----- Prediction: tensor([5.6564])
+Target: tensor([5.])----- Prediction: tensor([4.9836])
+Target: tensor([6.])----- Prediction: tensor([3.6858])
+Target: tensor([6.])----- Prediction: tensor([4.3583])
+Target: tensor([7.])----- Prediction: tensor([2.9855])
+Target: tensor([5.])----- Prediction: tensor([7.3182])
+Target: tensor([5.])----- Prediction: tensor([7.9802])
+Target: tensor([5.])----- Prediction: tensor([2.2287])
+Target: tensor([6.])----- Prediction: tensor([3.9656])
+Target: tensor([5.])----- Prediction: tensor([2.4347])
+Target: tensor([6.])----- Prediction: tensor([5.8240])
+Target: tensor([5.])----- Prediction: tensor([2.6369])
+Target: tensor([7.])----- Prediction: tensor([1.8155])
+Target: tensor([5.])----- Prediction: tensor([10.1038])
+Target: tensor([5.])----- Prediction: tensor([2.3575])
+Target: tensor([5.])----- Prediction: tensor([3.9901])
+Target: tensor([5.])----- Prediction: tensor([2.4601])
+Target: tensor([6.])----- Prediction: tensor([2.8243])
+Target: tensor([5.])----- Prediction: tensor([7.7295])
+Target: tensor([5.])----- Prediction: tensor([3.4800])
+Target: tensor([6.])----- Prediction: tensor([2.6094])
+Target: tensor([6.])----- Prediction: tensor([2.6338])
+Target: tensor([6.])----- Prediction: tensor([3.7549])
+Target: tensor([6.])----- Prediction: tensor([3.0682])
+Target: tensor([7.])----- Prediction: tensor([23.9401])
+Target: tensor([7.])----- Prediction: tensor([2.9943])
+Target: tensor([6.])----- Prediction: tensor([5.7540])
+Target: tensor([7.])----- Prediction: tensor([3.5938])
+Target: tensor([6.])----- Prediction: tensor([3.8617])
+Target: tensor([6.])----- Prediction: tensor([4.4898])
+Target: tensor([6.])----- Prediction: tensor([4.1527])
+Target: tensor([6.])----- Prediction: tensor([13.8834])
+Target: tensor([5.])----- Prediction: tensor([3.4153])
+Target: tensor([5.])----- Prediction: tensor([10.9910])
+Target: tensor([6.])----- Prediction: tensor([3.2680])
+Target: tensor([5.])----- Prediction: tensor([2.7166])
+Target: tensor([6.])----- Prediction: tensor([2.7429])
+Target: tensor([6.])----- Prediction: tensor([1.8801])
+Target: tensor([5.])----- Prediction: tensor([4.7673])
+Target: tensor([6.])----- Prediction: tensor([2.3313])
+Target: tensor([5.])----- Prediction: tensor([8.0485])
+Target: tensor([5.])----- Prediction: tensor([2.2194])
+Target: tensor([7.])----- Prediction: tensor([4.6185])
+Target: tensor([6.])----- Prediction: tensor([3.9581])
+Target: tensor([6.])----- Prediction: tensor([3.2511])
+Target: tensor([5.])----- Prediction: tensor([9.4976])
+Target: tensor([5.])----- Prediction: tensor([2.8048])
+Target: tensor([7.])----- Prediction: tensor([2.3068])
+Target: tensor([5.])----- Prediction: tensor([5.2228])
+Target: tensor([5.])----- Prediction: tensor([3.4355])
+Target: tensor([6.])----- Prediction: tensor([5.8229])
+Target: tensor([6.])----- Prediction: tensor([4.9710])
+Target: tensor([7.])----- Prediction: tensor([4.0948])
+Target: tensor([5.])----- Prediction: tensor([9.8520])
+Target: tensor([6.])----- Prediction: tensor([1.6064])
+Target: tensor([7.])----- Prediction: tensor([4.0091])
+Target: tensor([6.])----- Prediction: tensor([2.1055])
+Target: tensor([5.])----- Prediction: tensor([5.5266])
+Target: tensor([5.])----- Prediction: tensor([1.5365])
+Target: tensor([5.])----- Prediction: tensor([2.0232])
+Target: tensor([6.])----- Prediction: tensor([4.7462])
+Target: tensor([8.])----- Prediction: tensor([3.0388])
+Target: tensor([6.])----- Prediction: tensor([7.3761])
+Target: tensor([8.])----- Prediction: tensor([4.0455])
+Target: tensor([7.])----- Prediction: tensor([8.4651])
+Target: tensor([7.])----- Prediction: tensor([2.2083])
+Target: tensor([5.])----- Prediction: tensor([13.7213])
+Target: tensor([6.])----- Prediction: tensor([2.4370])
+Target: tensor([5.])----- Prediction: tensor([4.2159])
+Target: tensor([6.])----- Prediction: tensor([3.0294])
+Target: tensor([5.])----- Prediction: tensor([11.3286])
+Target: tensor([4.])----- Prediction: tensor([3.1563])
+Target: tensor([5.])----- Prediction: tensor([3.0204])
+Target: tensor([6.])----- Prediction: tensor([3.1787])
+Target: tensor([5.])----- Prediction: tensor([3.4895])
+Target: tensor([5.])----- Prediction: tensor([3.9059])
+Target: tensor([7.])----- Prediction: tensor([2.2940])
+Target: tensor([5.])----- Prediction: tensor([6.0839])
+Target: tensor([6.])----- Prediction: tensor([3.4874])
+Target: tensor([5.])----- Prediction: tensor([12.2298])
+Target: tensor([6.])----- Prediction: tensor([1.9277])
+Target: tensor([5.])----- Prediction: tensor([4.9739])
+Target: tensor([7.])----- Prediction: tensor([4.5098])
+Target: tensor([5.])----- Prediction: tensor([3.4803])
+Target: tensor([6.])----- Prediction: tensor([2.3278])
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diff --git a/pytorch/winequality-red.csv b/pytorch/winequality-red.csv
new file mode 100644
index 0000000..62e674c
--- /dev/null
+++ b/pytorch/winequality-red.csv
@@ -0,0 +1,1600 @@
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+7.5,0.52,0.4,2.2,0.06,12.0,20.0,0.99474,3.26,0.64,11.8,6
+8.0,0.3,0.63,1.6,0.081,16.0,29.0,0.9958799999999999,3.3,0.78,10.8,6
+6.2,0.7,0.15,5.1,0.076,13.0,27.0,0.9962200000000001,3.54,0.6,11.9,6
+6.8,0.67,0.15,1.8,0.11800000000000001,13.0,20.0,0.9954,3.42,0.67,11.3,6
+6.2,0.56,0.09,1.7,0.053,24.0,32.0,0.9940200000000001,3.54,0.6,11.3,5
+7.4,0.35,0.33,2.4,0.068,9.0,26.0,0.9947,3.36,0.6,11.9,6
+6.2,0.56,0.09,1.7,0.053,24.0,32.0,0.9940200000000001,3.54,0.6,11.3,5
+6.1,0.715,0.1,2.6,0.053,13.0,27.0,0.9936200000000001,3.57,0.5,11.9,5
+6.2,0.46,0.29,2.1,0.07400000000000001,32.0,98.0,0.9957799999999999,3.33,0.62,9.8,5
+6.7,0.32,0.44,2.4,0.061,24.0,34.0,0.99484,3.29,0.8,11.6,7
+7.2,0.39,0.44,2.6,0.066,22.0,48.0,0.9949399999999999,3.3,0.84,11.5,6
+7.5,0.31,0.41,2.4,0.065,34.0,60.0,0.99492,3.34,0.85,11.4,6
+5.8,0.61,0.11,1.8,0.066,18.0,28.0,0.9948299999999999,3.55,0.66,10.9,6
+7.2,0.66,0.33,2.5,0.068,34.0,102.0,0.9941399999999999,3.27,0.78,12.8,6
+6.6,0.725,0.2,7.8,0.073,29.0,79.0,0.9977,3.29,0.54,9.2,5
+6.3,0.55,0.15,1.8,0.077,26.0,35.0,0.9931399999999999,3.32,0.82,11.6,6
+5.4,0.74,0.09,1.7,0.08900000000000001,16.0,26.0,0.9940200000000001,3.67,0.56,11.6,6
+6.3,0.51,0.13,2.3,0.076,29.0,40.0,0.99574,3.42,0.75,11.0,6
+6.8,0.62,0.08,1.9,0.068,28.0,38.0,0.99651,3.42,0.82,9.5,6
+6.2,0.6,0.08,2.0,0.09,32.0,44.0,0.9949,3.45,0.58,10.5,5
+5.9,0.55,0.1,2.2,0.062,39.0,51.0,0.9951200000000001,3.52,0.76,11.2,6
+6.3,0.51,0.13,2.3,0.076,29.0,40.0,0.99574,3.42,0.75,11.0,6
+5.9,0.645,0.12,2.0,0.075,32.0,44.0,0.9954700000000001,3.57,0.71,10.2,5
+6.0,0.31,0.47,3.6,0.067,18.0,42.0,0.99549,3.39,0.66,11.0,6
diff --git a/runTrain.sh b/runTrain.sh
new file mode 100644
index 0000000..130ca74
--- /dev/null
+++ b/runTrain.sh
@@ -0,0 +1,2 @@
+#!/bin/bash
+python3 ./pytorch.py
\ No newline at end of file