From 54d71d631e92bdb93cf3388699b7b54649ddc017 Mon Sep 17 00:00:00 2001 From: Adrian Charkiewicz Date: Sat, 7 May 2022 21:17:26 +0200 Subject: [PATCH] no need for copy of pytorch.py --- pytorch/pytorch — kopia.py | 204 ----------------------------------- pytorch/pytorch.py | 4 +- 2 files changed, 2 insertions(+), 206 deletions(-) delete mode 100644 pytorch/pytorch — kopia.py diff --git a/pytorch/pytorch — kopia.py b/pytorch/pytorch — kopia.py deleted file mode 100644 index f88cb99..0000000 --- a/pytorch/pytorch — kopia.py +++ /dev/null @@ -1,204 +0,0 @@ -#!/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 -from sacred import Experiment -from sacred.observers import FileStorageObserver -from sacred.observers import MongoObserver - - -# In[2]: - -ex = Experiment(save_git_info=False) -ex.observers.append(FileStorageObserver('runs')) -@ex.config -def config(): - epochs = 1500 - - - - -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) - -@ex.capture -def fit(lr, model, train_loader, val_loader, opt_func=torch.optim.SGD, epochs, _run): - 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 = int(sys.argv[1]) - - - - -# 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))) - -@ex.main -def main(): - lr = 1e-6 - history5 = fit(lr, model, train_loader, val_loader, epochs) - -ex.run() - diff --git a/pytorch/pytorch.py b/pytorch/pytorch.py index 6a65f14..f88cb99 100644 --- a/pytorch/pytorch.py +++ b/pytorch/pytorch.py @@ -143,7 +143,7 @@ def evaluate(model, val_loader): return model.validation_epoch_end(outputs) @ex.capture -def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD, _run): +def fit(lr, model, train_loader, val_loader, opt_func=torch.optim.SGD, epochs, _run): history = [] optimizer = opt_func(model.parameters(), lr) for epoch in range(epochs): @@ -198,7 +198,7 @@ with open("result.txt", "w+") as file: @ex.main def main(): lr = 1e-6 - history5 = fit(epochs, lr, model, train_loader, val_loader) + history5 = fit(lr, model, train_loader, val_loader, epochs) ex.run()