ium_444354/pytorch/pytorch.py
Adrian Charkiewicz 3033b4db86
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s444354-training/pipeline/head There was a failure building this commit
parametrization of number of epochs
2022-05-07 12:19:39 +02:00

188 lines
3.9 KiB
Python

#!/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 = sys.argv[1]
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)))