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def accuracy(out, y_true):
    y_hat = out.argmax(dim=-1).view(y_true.size(0), -1)
    y_true = y_true.view(y_true.size(0), -1)
    match = y_hat == y_true
    return match.float().mean()
  

class Accuracy(Callback):

    def epoch_started(self, **kwargs):

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class RollingLoss(Callback):

    def __init__(self, smooth=0.98):
        self.smooth = smooth

    def batch_ended(self, phase, **kwargs):
        prev = phase.rolling_loss
        a = self.smooth
        avg_loss = a * prev + (1 - a) * phase.batch_loss
        debias_loss = avg_loss / (1 - a ** phase.batch_index)

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def train(model, opt, phases, callbacks=None, epochs=1, device=default_device, loss_fn=F.nll_loss):
    model.to(device)
    
    cb = callbacks
    
    cb.training_started(phases=phases, optimizer=opt)
    
    for epoch in range(1, epochs + 1):
        cb.epoch_started(epoch=epoch)

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model = create_model(params)
phases = create_train_valid_data()
opt = optim.SGD(model.params, lr=1e-3)

model.to(device)
    
for epoch in range(1, epochs + 1):

    for phase in phases:
        n = len(phase.loader)

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"""
Converting FER2013 dataset from CSV representation into folder with images.

The dataset is taken from:
    https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge

Encoding:
    (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).

"""

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"""
Training ResNet18 model on 50000 samples per category.
"""
import sys

from fastai import defaults
from fastai.vision import create_cnn, get_transforms
from fastai.metrics import accuracy
from fastai.callbacks import EarlyStoppingCallback, SaveModelCallback, CSVLogger
from fastai.vision.data import ImageItemList, imagenet_stats

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