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PyTorch教程4.3之基本分類模型

2512975 2023-06-05 | pdf | 0.13 MB | 次下載 | 免費

資料介紹

您可能已經(jīng)注意到，在回歸的情況下，從頭開始的實現(xiàn)和使用框架功能的簡潔實現(xiàn)非常相似。分類也是如此。由于本書中的許多模型都處理分類，因此值得添加專門支持此設(shè)置的功能。本節(jié)為分類模型提供了一個基類，以簡化以后的代碼。

						import torch
from d2l import torch as d2l

						from mxnet import autograd, gluon, np, npx
from d2l import mxnet as d2l

npx.set_np()

						from functools import partial
import jax
import optax
from jax import numpy as jnp
from d2l import jax as d2l

						 

						No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)

					

						import tensorflow as tf
from d2l import tensorflow as d2l

4.3.1. 類`Classifier`_

我們在下面定義Classifier類。在中，validation_step我們報告了驗證批次的損失值和分類準確度。我們?yōu)槊總€批次繪制一個更新num_val_batches 。這有利于在整個驗證數(shù)據(jù)上生成平均損失和準確性。如果最后一批包含的示例較少，則這些平均數(shù)并不完全正確，但我們忽略了這一微小差異以保持代碼簡單。

							class Classifier(d2l.Module): #@save
  """The base class of classification models."""
  def validation_step(self, batch):
    Y_hat = self(*batch[:-1])
    self.plot('loss', self.loss(Y_hat, batch[-1]), train=False)
    self.plot('acc', self.accuracy(Y_hat, batch[-1]), train=False)

							 

We define the Classifier class below. In the validation_step we report both the loss value and the classification accuracy on a validation batch. We draw an update for every num_val_batches batches. This has the benefit of generating the averaged loss and accuracy on the whole validation data. These average numbers are not exactly correct if the last batch contains fewer examples, but we ignore this minor difference to keep the code simple.

							class Classifier(d2l.Module): #@save
  """The base class of classification models."""
  def validation_step(self, batch):
    Y_hat = self(*batch[:-1])
    self.plot('loss', self.loss(Y_hat, batch[-1]), train=False)
    self.plot('acc', self.accuracy(Y_hat, batch[-1]), train=False)

							 

We also redefine the training_step method for JAX since all models that will subclass Classifier later will have a loss that returns auxiliary data. This auxiliary data can be used for models with batch normalization (to be explained in Section 8.5), while in all other cases we will make the loss also return a placeholder (empty dictionary) to represent the auxiliary data.

							class Classifier(d2l.Module): #@save
  """The base class of classification models."""
  def training_step(self, params, batch, state):
    # Here value is a tuple since models with BatchNorm layers require
    # the loss to return auxiliary data
    value, grads = jax.value_and_grad(
      self.loss, has_aux=True)(params, batch[:-1], batch[-1], state)
    l, _ = value
    self.plot("loss", l, train=True)
    return value, grads

  def validation_step(self, params, batch, state):
    # Discard the second returned value. It is used for training models
    # with BatchNorm layers since loss also returns auxiliary data
    l, _ = self.loss(params, batch[:-1], batch[-1], state)
    self.plot('loss', l, train=False)
    self.plot('acc', self.accuracy(params, batch[:-1], batch[-1], state),
         train=False)

							 

							class Classifier(d2l.Module): #@save
  """The base class of classification models."""
  def validation_step(self, batch):
    Y_hat = self(*batch[:-1])
    self.plot('loss', self.loss(Y_hat, batch[-1]), train=False)
    self.plot('acc', self.accuracy(Y_hat, batch[-1]), train=False)

							 

默認情況下，我們使用隨機梯度下降優(yōu)化器，在小批量上運行，就像我們在線性回歸的上下文中所做的那樣。

							@d2l.add_to_class(d2l.Module) #@save
def configure_optimizers(self):
  return torch.optim.SGD(self.parameters(), lr=self.lr)

							 

							@d2l.add_to_class(d2l.Module) #@save
def configure_optimizers(self):
  params = self.parameters()
  if isinstance(params, list):
    return d2l.SGD(params, self.lr)
  return gluon.Trainer(params, 'sgd', {'learning_rate': self.lr})

							 

							@d2l.add_to_class(d2l.Module) #@save
def configure_optimizers(self):
  return optax.sgd(self.lr)

							 

							@d2l.add_to_class(d2l.Module) #@save
def configure_optimizers(self):
  return tf.keras.optimizers.SGD(self.lr)

							 

4.3.2. 準確性

給定預(yù)測概率分布y_hat，每當我們必須輸出硬預(yù)測時，我們通常會選擇預(yù)測概率最高的類別。事實上，許多應(yīng)用程序需要我們做出選擇。例如，Gmail 必須將電子郵件分類為“主要”、“社交”、“更新”、“論壇”或“垃圾郵件”。它可能會在內(nèi)部估計概率，但最終它必須在類別中選擇一個。

當預(yù)測與標簽 class 一致時y，它們是正確的。分類準確度是所有正確預(yù)測的分數(shù)。盡管直接優(yōu)化精度可能很困難（不可微分），但它通常是我們最關(guān)心的性能指標。它通常是基準測試中的相關(guān)數(shù)量。因此，我們幾乎總是在訓練分類器時報告它。

準確度計算如下。首先，如果y_hat是一個矩陣，我們假設(shè)第二個維度存儲每個類別的預(yù)測分數(shù)。我們使用argmax每行中最大條目的索引來獲取預(yù)測類。然后我們將預(yù)測的類別與真實的元素進行比較y。由于相等運算符== 對數(shù)據(jù)類型敏感，因此我們轉(zhuǎn)換的y_hat數(shù)據(jù)類型以匹配的數(shù)據(jù)類型y。結(jié)果是一個包含條目 0（假）和 1（真）的張量。求和得出正確預(yù)測的數(shù)量。

							@d2l.add_to_class(Classifier) #@save
def accuracy(self, Y_hat, Y, averaged=True):
  """Compute the number of correct predictions."""
  Y_hat = Y_hat.reshape((-1, Y_hat.shape[-1]))
  preds = Y_hat.argmax(axis=1).type(Y.dtype)
  compare = (preds == Y.reshape(-1)).type(torch.float32)
  return compare.mean() if averaged else compare

							 

							@d2l.add_to_class(Classifier) #@save
def accuracy(self, Y_hat, Y, averaged=True):
  """Compute the number of correct predictions."""
  Y_hat = Y_hat.reshape((-1, Y_hat.shape[-1]))
  preds = Y_hat.argmax(axis=1).astype(Y.dtype)
  compare = (preds == Y.reshape(-1)).astype(np.float32)
  return compare.mean() if averaged else compare

@d2l.add_to_class(d2l.Module) #@save
def get_scratch_params(self):
  params = []
  for attr in dir(self):
    a = getattr(self, attr)
    if isinstance(a, np.ndarray):
      params.append(a)
    if isinstance(a, d2l.Module):
      params.extend(a.get_scratch_params())
  return params

@d2l.add_to_class(d2l.Module) #@save
def parameters(self):
  params = self.collect_params()
  return params if isinstance(params, gluon.parameter.ParameterDict) and
						