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PyTorch教程23.7之效用函數(shù)和類

2072272 2023-06-06 | pdf | 0.18 MB | 次下載 | 免費(fèi)

資料介紹

本節(jié)包含本書中使用的實(shí)用函數(shù)和類的實(shí)現(xiàn)。

						import collections
import inspect
from IPython import display
from torch import nn
from d2l import torch as d2l

						 

						import collections
import inspect
import random
from IPython import display
from mxnet import autograd, gluon, np, npx
from mxnet.gluon import nn
from d2l import mxnet as d2l

npx.set_np()

						 

						import collections
import inspect
import jax
from IPython import display
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 collections
import inspect
import tensorflow as tf
from IPython import display
from d2l import tensorflow as d2l

						 

超參數(shù)。

				@d2l.add_to_class(d2l.HyperParameters) #@save
def save_hyperparameters(self, ignore=[]):
  """Save function arguments into class attributes."""
  frame = inspect.currentframe().f_back
  _, _, _, local_vars = inspect.getargvalues(frame)
  self.hparams = {k:v for k, v in local_vars.items()
          if k not in set(ignore+['self']) and not k.startswith('_')}
  for k, v in self.hparams.items():
    setattr(self, k, v)

				 

進(jìn)度條。

				@d2l.add_to_class(d2l.ProgressBoard) #@save
def draw(self, x, y, label, every_n=1):
  Point = collections.namedtuple('Point', ['x', 'y'])
  if not hasattr(self, 'raw_points'):
    self.raw_points = collections.OrderedDict()
    self.data = collections.OrderedDict()
  if label not in self.raw_points:
    self.raw_points[label] = []
    self.data[label] = []
  points = self.raw_points[label]
  line = self.data[label]
  points.append(Point(x, y))
  if len(points) != every_n:
    return
  mean = lambda x: sum(x) / len(x)
  line.append(Point(mean([p.x for p in points]),
           mean([p.y for p in points])))
  points.clear()
  if not self.display:
    return
  d2l.use_svg_display()
  if self.fig is None:
    self.fig = d2l.plt.figure(figsize=self.figsize)
  plt_lines, labels = [], []
  for (k, v), ls, color in zip(self.data.items(), self.ls, self.colors):
    plt_lines.append(d2l.plt.plot([p.x for p in v], [p.y for p in v],
                   linestyle=ls, color=color)[0])
    labels.append(k)
  axes = self.axes if self.axes else d2l.plt.gca()
  if self.xlim: axes.set_xlim(self.xlim)
  if self.ylim: axes.set_ylim(self.ylim)
  if not self.xlabel: self.xlabel = self.x
  axes.set_xlabel(self.xlabel)
  axes.set_ylabel(self.ylabel)
  axes.set_xscale(self.xscale)
  axes.set_yscale(self.yscale)
  axes.legend(plt_lines, labels)
  display.display(self.fig)
  display.clear_output(wait=True)

				 

添加 FrozenLake 環(huán)境

						def frozen_lake(seed): #@save
  # See https://www.gymlibrary.dev/environments/toy_text/frozen_lake/ to learn more about this env
  # How to process env.P.items is adpated from https://sites.google.com/view/deep-rl-bootcamp/labs

  env = gym.make('FrozenLake-v1', is_slippery=False)
  env.seed(seed)
  env.action_space.np_random.seed(seed)
  env.action_space.seed(seed)
  env_info = {}
  env_info['desc'] = env.desc # 2D array specifying what each grid item means
  env_info['num_states'] = env.nS # Number of observations/states or obs/state dim
  env_info['num_actions'] = env.nA # Number of actions or action dim
  # Define indices for (transition probability, nextstate, reward, done) tuple
  env_info['trans_prob_idx'] = 0 # Index of transition probability entry
  env_info['nextstate_idx'] = 1 # Index of next state entry
  env_info['reward_idx'] = 2 # Index of reward entry
  env_info['done_idx'] = 3 # Index of done entry
  env_info['mdp'] = {}
  env_info['env'] = env

  for (s, others) in env.P.items():
    # others(s) = {a0: [ (p(s'|s,a0), s', reward, done),...], a1:[...], ...}

    for (a, pxrds) in others.items():
      # pxrds is [(p1,next1,r1,d1),(p2,next2,r2,d2),..].
      # e.g. [(0.3, 0, 0, False), (0.3, 0, 0, False), (0.3, 4, 1, False)]
      env_info['mdp'][(s,a)] = pxrds

  return env_info

						 

創(chuàng)造環(huán)境

						def make_env(name ='', seed=0): #@save
  # Input parameters:
  # name: specifies a gym environment.
  # For Value iteration, only FrozenLake-v1 is supported.
  if name == 'FrozenLake-v1':
    return frozen_lake(seed)

  else:
    raise ValueError("%s env is not supported in this Notebook")

						 

示值函數(shù)

						def show_value_function_progress(env_desc, V, pi): #@save
  # This function visualizes how value and policy changes over time.
  # V: [num_iters, num_states]
  # pi: [num_iters, num_states]
  # How to visualize value function is adapted (but changed) from: https://sites.google.com/view/deep-rl-bootcamp/labs

  num_iters = V.shape[0]
  fig, ax = plt.subplots(figsize=(15, 15))

  for k in range(V.shape[0]):
    plt.subplot(4, 4, k + 1)
    plt.imshow(V[k].reshape(4,4), cmap="bone")
    ax = plt.gca()
    ax.set_xticks(np.arange(0, 5)-.5, minor=True)
    ax.set_yticks(np.arange(0, 5)-.5, minor=True)
    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
    ax.tick_params(which="minor", bottom=False, left=False)
    ax.set_xticks([])
    ax.set_yticks([])

    # LEFT action: 0, DOWN action: 1
    # RIGHT action: 2, UP action: 3
    action2dxdy = {0:(-.25, 0),1: (0, .25),
            2:(0.25, 0),3: (-.25, 0)}

    for y in range(4):
      for x in range(4):
        action = pi[k].reshape(4,4)[y, x]
        dx, dy = action2dxdy[action]

        if env_desc[y,x].decode() == 'H':
          ax.text(x, y, str(env_desc[y,x].decode()),
            ha="center", va="center", color="y",
             size=20, fontweight='bold')

        elif env_desc[y,x].decode() == 'G':
          ax.text(x, y, str(env_desc[y,x].decode()),
            ha="center", va="center", color="w",
             size=20, fontweight='bold')

        else:
          ax.text(x, y, str(env_desc[y,x].decode()),
            ha="center", va="center", color="g",
             size=15, fontweight='bold')

        # No arrow for cells with G and H labels
        if env_desc[y,x].decode() != 'G' and env_desc[y,x].decode() != 'H':
          ax.arrow(x, y, dx, dy, color='r', head_width=0.2, head_length=0.15)

    ax.set_title("Step = " + str(k + 1),
					