umicv cv-summary1-全连接神经网络模块化实现

• 全连接神经网络模块化实现
  • Linear与Relu单层实现
  • LossLayer实现
  • 多层神经网络
  • 不同梯度下降方法
  • Dropout层

今天这篇博文针对Assignment3的全连接网络作业，对前面学习的内容进行一些总结

在前面的作业中我们建立神经网络的操作比较简单，也不具有模块化的特征，在A3作业中，引导我们对前面的比如linear layer,Relu layer,Loss layer以及dropout layer(这个前面课程内容未涉及 但是在cs231n中有出现),以及梯度下降不同方法(SGD,SGD+Momentum,RMSprop,Adam)等等进行模块化的实现

Linear与Relu单层实现

class Linear(object):

  @staticmethod
  def forward(x, w, b):
    """
    Computes the forward pass for an linear (fully-connected) layer.
    The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N
    examples, where each example x[i] has shape (d_1, ..., d_k). We will
    reshape each input into a vector of dimension D = d_1 * ... * d_k, and
    then transform it to an output vector of dimension M.
    Inputs:
    - x: A tensor containing input data, of shape (N, d_1, ..., d_k)
    - w: A tensor of weights, of shape (D, M)
    - b: A tensor of biases, of shape (M,)
    Returns a tuple of:
    - out: output, of shape (N, M)
    - cache: (x, w, b)
    """
    out = None
    out = x.view(x.shape[0],-1).mm(w)+b
    cache = (x, w, b)
    return out, cache

  @staticmethod
  def backward(dout, cache):
    """
    Computes the backward pass for an linear layer.
    Inputs:
    - dout: Upstream derivative, of shape (N, M)
    - cache: Tuple of:
      - x: Input data, of shape (N, d_1, ... d_k)
      - w: Weights, of shape (D, M)
      - b: Biases, of shape (M,)
    Returns a tuple of:
    - dx: Gradient with respect to x, of shape (N, d1, ..., d_k)
    - dw: Gradient with respect to w, of shape (D, M)
    - db: Gradient with respect to b, of shape (M,)
    """
    x, w, b = cache
    dx, dw, db = None, None, None
    db = dout.sum(dim = 0)
    dx = dout.mm(w.t()).view(x.shape)
    dw = x.view(x.shape[0],-1).t().mm(dout)
    return dx, dw, db


class ReLU(object):

  @staticmethod
  def forward(x):
    """
    Computes the forward pass for a layer of rectified linear units (ReLUs).
    Input:
    - x: Input; a tensor of any shape
    Returns a tuple of:
    - out: Output, a tensor of the same shape as x
    - cache: x
    """
    out = None
    out = x.clone()
    out[out<0] = 0
    cache = x
    return out, cache

  @staticmethod
  def backward(dout, cache):
    """
    Computes the backward pass for a layer of rectified linear units (ReLUs).
    Input:
    - dout: Upstream derivatives, of any shape
    - cache: Input x, of same shape as dout
    Returns:
    - dx: Gradient with respect to x
    """
    dx, x = None, cache
    dx = dout.clone()
    dx[x<0] = 0
    return dx


class Linear_ReLU(object):

  @staticmethod
  def forward(x, w, b):
    """
    Convenience layer that performs an linear transform followed by a ReLU.

    Inputs:
    - x: Input to the linear layer
    - w, b: Weights for the linear layer
    Returns a tuple of:
    - out: Output from the ReLU
    - cache: Object to give to the backward pass
    """
    a, fc_cache = Linear.forward(x, w, b)
    out, relu_cache = ReLU.forward(a)
    cache = (fc_cache, relu_cache)
    return out, cache

  @staticmethod
  def backward(dout, cache):
    """
    Backward pass for the linear-relu convenience layer
    """
    fc_cache, relu_cache = cache
    da = ReLU.backward(dout, relu_cache)
    dx, dw, db = Linear.backward(da, fc_cache)
    return dx, dw, db

从上面的代码我们可以看到，针对linear与relu层，我们可以将前向传播与反向传播分开实现，具体过程在上一篇我的博文中有讨论：https://www.cnblogs.com/dyccyber/p/17764347.html
不同的是我们要对x进行一个reshape，将其转换为N*D的矩阵，才能与矩阵进行点积
在分别实现了linear与relu之后，因为神经网络的架构往往是在linear之后立马加入一个relu层，所以我们可以再建立一个linear-relu class，将这两个层的前向与反向传播合并

LossLayer实现

def svm_loss(x, y):
  """
  Computes the loss and gradient using for multiclass SVM classification.
  Inputs:
  - x: Input data, of shape (N, C) where x[i, j] is the score for the jth
    class for the ith input.
  - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
    0 <= y[i] < C
  Returns a tuple of:
  - loss: Scalar giving the loss
  - dx: Gradient of the loss with respect to x
  """
  N = x.shape[0]
  correct_class_scores = x[torch.arange(N), y]
  margins = (x - correct_class_scores[:, None] + 1.0).clamp(min=0.)
  margins[torch.arange(N), y] = 0.
  loss = margins.sum() / N
  num_pos = (margins > 0).sum(dim=1)
  dx = torch.zeros_like(x)
  dx[margins > 0] = 1.
  dx[torch.arange(N), y] -= num_pos.to(dx.dtype)
  dx /= N
  return loss, dx


def softmax_loss(x, y):
  """
  Computes the loss and gradient for softmax classification.
  Inputs:
  - x: Input data, of shape (N, C) where x[i, j] is the score for the jth
    class for the ith input.
  - y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
    0 <= y[i] < C
  Returns a tuple of:
  - loss: Scalar giving the loss
  - dx: Gradient of the loss with respect to x
  """
  shifted_logits = x - x.max(dim=1, keepdim=True).values
  Z = shifted_logits.exp().sum(dim=1, keepdim=True)
  log_probs = shifted_logits - Z.log()
  probs = log_probs.exp()
  N = x.shape[0]
  loss = (-1.0/ N) * log_probs[torch.arange(N), y].sum()
  dx = probs.clone()
  dx[torch.arange(N), y] -= 1
  dx /= N
  return loss, dx

上面损失函数层我们在之前已经实现过，具体实现需要用到一些矩阵微分的知识，具体可以参考这两篇博文：
http://giantpandacv.com/academic/算法科普/深度学习基础/SVM Loss以及梯度推导/
https://blog.csdn.net/qq_27261889/article/details/82915598

多层神经网络

关于多层神经网络，首先是类的初始化定义，我们可以看神经网络的结构{linear - relu - [dropout]} x (L - 1) - linear - softmax，有L-1个linear层与relu层与dropout层的组合，最后再以linear-softmax的结构结束输出结果，初始化我们要遍历每个隐藏层，初始化权重矩阵与偏置项，最后再去初始化最后一个linear层，要注意矩阵的维度

class FullyConnectedNet(object):
  """
  A fully-connected neural network with an arbitrary number of hidden layers,
  ReLU nonlinearities, and a softmax loss function.
  For a network with L layers, the architecture will be:

  {linear - relu - [dropout]} x (L - 1) - linear - softmax

  where dropout is optional, and the {...} block is repeated L - 1 times.

  Similar to the TwoLayerNet above, learnable parameters are stored in the
  self.params dictionary and will be learned using the Solver class.
  """

  def __init__(self, hidden_dims, input_dim=3*32*32, num_classes=10,
               dropout=0.0, reg=0.0, weight_scale=1e-2, seed=None,
               dtype=torch.float, device='cpu'):
    """
    Initialize a new FullyConnectedNet.

    Inputs:
    - hidden_dims: A list of integers giving the size of each hidden layer.
    - input_dim: An integer giving the size of the input.
    - num_classes: An integer giving the number of classes to classify.
    - dropout: Scalar between 0 and 1 giving the drop probability for networks
      with dropout. If dropout=0 then the network should not use dropout.
    - reg: Scalar giving L2 regularization strength.
    - weight_scale: Scalar giving the standard deviation for random
      initialization of the weights.
    - seed: If not None, then pass this random seed to the dropout layers. This
      will make the dropout layers deteriminstic so we can gradient check the
      model.
    - dtype: A torch data type object; all computations will be performed using
      this datatype. float is faster but less accurate, so you should use
      double for numeric gradient checking.
    - device: device to use for computation. 'cpu' or 'cuda'
    """
    self.use_dropout = dropout != 0
    self.reg = reg
    self.num_layers = 1 + len(hidden_dims)
    self.dtype = dtype
    self.params = {}

    ############################################################################
    # TODO: Initialize the parameters of the network, storing all values in    #
    # the self.params dictionary. Store weights and biases for the first layer #
    # in W1 and b1; for the second layer use W2 and b2, etc. Weights should be #
    # initialized from a normal distribution centered at 0 with standard       #
    # deviation equal to weight_scale. Biases should be initialized to zero.   #
    ############################################################################
    # Replace "pass" statement with your code
    last_dim = input_dim
    for n ,hidden_dim in enumerate(hidden_dims):
      i = n+1
      self.params['W{}'.format(i)] = torch.zeros(last_dim, hidden_dim, dtype=dtype,device = device)
      self.params['W{}'.format(i)] += weight_scale*torch.randn(last_dim, hidden_dim, dtype=dtype,device= device)
      self.params['b{}'.format(i)] = torch.zeros(hidden_dim, dtype=dtype,device= device)
      last_dim = hidden_dim
    i+=1
    self.params['W{}'.format(i)] = torch.zeros(last_dim, num_classes, dtype=dtype,device = device)
    self.params['W{}'.format(i)] += weight_scale*torch.randn(last_dim, num_classes, dtype=dtype,device= device)
    self.params['b{}'.format(i)] = torch.zeros(num_classes, dtype=dtype,device= device)
   

    # When using dropout we need to pass a dropout_param dictionary to each
    # dropout layer so that the layer knows the dropout probability and the mode
    # (train / test). You can pass the same dropout_param to each dropout layer.
    self.dropout_param = {}
    if self.use_dropout:
      self.dropout_param = {'mode': 'train', 'p': dropout}
      if seed is not None:
        self.dropout_param['seed'] = seed

其次，我们可以定义save与load函数，对模型参数等等进行存储与加载：

def save(self, path):
    checkpoint = {
      'reg': self.reg,
      'dtype': self.dtype,
      'params': self.params,
      'num_layers': self.num_layers,
      'use_dropout': self.use_dropout,
      'dropout_param': self.dropout_param,
    }
      
    torch.save(checkpoint, path)
    print("Saved in {}".format(path))


  def load(self, path, dtype, device):
    checkpoint = torch.load(path, map_location='cpu')
    self.params = checkpoint['params']
    self.dtype = dtype
    self.reg = checkpoint['reg']
    self.num_layers = checkpoint['num_layers']
    self.use_dropout = checkpoint['use_dropout']
    self.dropout_param = checkpoint['dropout_param']

    for p in self.params:
      self.params[p] = self.params[p].type(dtype).to(device)

    print("load checkpoint file: {}".format(path))

最后是前向传播与反向传播的实现，这里直接使用前面基础的linear与relu的前向与反向传播即可，注意一下神经网络的结构，不要把顺序搞错即可

def loss(self, X, y=None):
    """
    Compute loss and gradient for the fully-connected net.
    Input / output: Same as TwoLayerNet above.
    """
    X = X.to(self.dtype)
    mode = 'test' if y is None else 'train'

    # Set train/test mode for batchnorm params and dropout param since they
    # behave differently during training and testing.
    if self.use_dropout:
      self.dropout_param['mode'] = mode
    scores = None
    ############################################################################
    # TODO: Implement the forward pass for the fully-connected net, computing  #
    # the class scores for X and storing them in the scores variable.          #
    #                                                                          #
    # When using dropout, you'll need to pass self.dropout_param to each       #
    # dropout forward pass.                                                    #
    ############################################################################
    # Replace "pass" statement with your code
    cache_dict = {}
    last_out = X
    for n  in range(self.num_layers-1):
      i=n+1
      last_out, cache_dict['cache_LR{}'.format(i)] = Linear_ReLU.forward(last_out,self.params['W{}'.format(i)],self.params['b{}'.format(i)])
      if self.use_dropout:
        last_out, cache_dict['cache_Dropout{}'.format(i)] =  Dropout.forward(last_out,self.dropout_param)
    i+=1
    last_out, cache_dict['cache_L{}'.format(i)] = Linear.forward(last_out,self.params['W{}'.format(i)],self.params['b{}'.format(i)])
    scores = last_out

    # If test mode return early
    if mode == 'test':
      return scores

    loss, grads = 0.0, {}
    ############################################################################
    # TODO: Implement the backward pass for the fully-connected net. Store the #
    # loss in the loss variable and gradients in the grads dictionary. Compute #
    # data loss using softmax, and make sure that grads[k] holds the gradients #
    # for self.params[k]. Don't forget to add L2 regularization!               #
    # NOTE: To ensure that your implementation matches ours and you pass the   #
    # automated tests, make sure that your L2 regularization includes a factor #
    # of 0.5 to simplify the expression for the gradient.                      #
    ############################################################################
    # Replace "pass" statement with your code
    loss, dout = softmax_loss(scores, y)
    loss += (self.params['W{}'.format(i)]*self.params['W{}'.format(i)]).sum()*self.reg
    last_dout, dw, db  = Linear.backward(dout, cache_dict['cache_L{}'.format(i)])
    grads['W{}'.format(i)] = dw + 2*self.params['W{}'.format(i)]*self.reg
    grads['b{}'.format(i)] = db
    for n  in range(self.num_layers-1)[::-1]:
      i = n +1
      if self.use_dropout:
        last_dout =  Dropout.backward(last_dout, cache_dict['cache_Dropout{}'.format(i)])
      last_dout, dw, db  = Linear_ReLU.backward(last_dout, cache_dict['cache_LR{}'.format(i)])
      grads['W{}'.format(i)] = dw + 2*self.params['W{}'.format(i)]*self.reg
      grads['b{}'.format(i)] = db
      loss += (self.params['W{}'.format(i)]*self.params['W{}'.format(i)]).sum()*self.reg
    return loss, grads

不同梯度下降方法

SGD,SGD+Momentum,RMSprop,Adam(Momentum+RMSprop+bias)的实现
具体原理介绍可参考之前的一篇博文：https://www.cnblogs.com/dyccyber/p/17759697.html
这里特别提及一下在Adam中我们加入了偏置项，是为了防止在初期进行梯度下降的过程中，下降的过快

def sgd(w, dw, config=None):
    """
    Performs vanilla stochastic gradient descent.
    config format:
    - learning_rate: Scalar learning rate.
    """
    if config is None: config = {}
    config.setdefault('learning_rate', 1e-2)

    w -= config['learning_rate'] * dw
    return w, config

def sgd_momentum(w, dw, config=None):
  """
  Performs stochastic gradient descent with momentum.
  config format:
  - learning_rate: Scalar learning rate.
  - momentum: Scalar between 0 and 1 giving the momentum value.
    Setting momentum = 0 reduces to sgd.
  - velocity: A numpy array of the same shape as w and dw used to store a
    moving average of the gradients.
  """
  if config is None: config = {}
  config.setdefault('learning_rate', 1e-2)
  config.setdefault('momentum', 0.9)
  v = config.get('velocity', torch.zeros_like(w))

  next_w = None
  #############################################################################
  # TODO: Implement the momentum update formula. Store the updated value in   #
  # the next_w variable. You should also use and update the velocity v.       #
  #############################################################################
  # Replace "pass" statement with your code
  v = config['momentum']*v - config['learning_rate'] * dw
  next_w = w + v
  #############################################################################
  #                              END OF YOUR CODE                             #
  #############################################################################
  config['velocity'] = v

  return next_w, config

def rmsprop(w, dw, config=None):
  """
  Uses the RMSProp update rule, which uses a moving average of squared
  gradient values to set adaptive per-parameter learning rates.
  config format:
  - learning_rate: Scalar learning rate.
  - decay_rate: Scalar between 0 and 1 giving the decay rate for the squared
    gradient cache.
  - epsilon: Small scalar used for smoothing to avoid dividing by zero.
  - cache: Moving average of second moments of gradients.
  """
  if config is None: config = {}
  config.setdefault('learning_rate', 1e-2)
  config.setdefault('decay_rate', 0.99)
  config.setdefault('epsilon', 1e-8)
  config.setdefault('cache', torch.zeros_like(w))

  next_w = None
  ###########################################################################
  # TODO: Implement the RMSprop update formula, storing the next value of w #
  # in the next_w variable. Don't forget to update cache value stored in    #
  # config['cache'].                                                        #
  ###########################################################################
  # Replace "pass" statement with your code
  config['cache'] = config['decay_rate'] * config['cache'] + (1 - config['decay_rate']) * dw**2
  w  +=  -config['learning_rate'] * dw / (torch.sqrt(config['cache']) + config['epsilon'])
  next_w = w
  ###########################################################################
  #                             END OF YOUR CODE                            #
  ###########################################################################

  return next_w, config

def adam(w, dw, config=None):
  """
  Uses the Adam update rule, which incorporates moving averages of both the
  gradient and its square and a bias correction term.
  config format:
  - learning_rate: Scalar learning rate.
  - beta1: Decay rate for moving average of first moment of gradient.
  - beta2: Decay rate for moving average of second moment of gradient.
  - epsilon: Small scalar used for smoothing to avoid dividing by zero.
  - m: Moving average of gradient.
  - v: Moving average of squared gradient.
  - t: Iteration number.
  """
  if config is None: config = {}
  config.setdefault('learning_rate', 1e-3)
  config.setdefault('beta1', 0.9)
  config.setdefault('beta2', 0.999)
  config.setdefault('epsilon', 1e-8)
  config.setdefault('m', torch.zeros_like(w))
  config.setdefault('v', torch.zeros_like(w))
  config.setdefault('t', 0)

  next_w = None
  #############################################################################
  # TODO: Implement the Adam update formula, storing the next value of w in   #
  # the next_w variable. Don't forget to update the m, v, and t variables     #
  # stored in config.                                                         #
  #                                                                           #
  # NOTE: In order to match the reference output, please modify t _before_    #
  # using it in any calculations.                                             #
  #############################################################################
  # Replace "pass" statement with your code
  config['t'] += 1
  config['m'] = config['beta1']*config['m'] + (1-config['beta1'])*dw
  mt = config['m'] / (1-config['beta1']**config['t'])
  config['v'] = config['beta2']*config['v'] + (1-config['beta2'])*(dw*dw)
  vc = config['v'] / (1-(config['beta2']**config['t']))
  w = w - (config['learning_rate'] * mt)/ (torch.sqrt(vc) + config['epsilon'])
  next_w = w
  #############################################################################
  #                              END OF YOUR CODE                             #
  #############################################################################

  return next_w, config

Dropout层

注意在前面多层全连接网络的实现中，dropout只有在我们进行train的时候才使用，在test的时候是不使用的
dropout层是一个非常高效与简单的正则化方法，具体来说，在训练时，dropout 是通过仅以一定概率 p 保持神经元活跃来实现的，如果我们设置的随机数小于p就将其设置为零，如下图所示：

用另一种视角去看，dropout实际上是一种对全神经网络进行抽样的方法，可以减少不同神经元之间复杂的关系
具体论文原文见：https://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf
代码实现：文章来源地址https://www.toymoban.com/news/detail-711439.html

class Dropout(object):

  @staticmethod
  def forward(x, dropout_param):
    """
    Performs the forward pass for (inverted) dropout.
    Inputs:
    - x: Input data: tensor of any shape
    - dropout_param: A dictionary with the following keys:
      - p: Dropout parameter. We *drop* each neuron output with probability p.
      - mode: 'test' or 'train'. If the mode is train, then perform dropout;
      if the mode is test, then just return the input.
      - seed: Seed for the random number generator. Passing seed makes this
      function deterministic, which is needed for gradient checking but not
      in real networks.
    Outputs:
    - out: Tensor of the same shape as x.
    - cache: tuple (dropout_param, mask). In training mode, mask is the dropout
      mask that was used to multiply the input; in test mode, mask is None.
    NOTE: Please implement **inverted** dropout, not the vanilla version of dropout.
    See http://cs231n.github.io/neural-networks-2/#reg for more details.
    NOTE 2: Keep in mind that p is the probability of **dropping** a neuron
    output; this might be contrary to some sources, where it is referred to
    as the probability of keeping a neuron output.
    """
    p, mode = dropout_param['p'], dropout_param['mode']
    if 'seed' in dropout_param:
      torch.manual_seed(dropout_param['seed'])

    mask = None
    out = None

    if mode == 'train':
      ###########################################################################
      # TODO: Implement training phase forward pass for inverted dropout.       #
      # Store the dropout mask in the mask variable.                            #
      ###########################################################################
      # Replace "pass" statement with your code
      mask = torch.rand(x.shape) > p
      out = x.clone()
      out[mask] = 0
      ###########################################################################
      #                             END OF YOUR CODE                            #
      ###########################################################################
    elif mode == 'test':
      ###########################################################################
      # TODO: Implement the test phase forward pass for inverted dropout.       #
      ###########################################################################
      # Replace "pass" statement with your code
      out = x
    cache = (dropout_param, mask)

    return out, cache

  @staticmethod
  def backward(dout, cache):
    """
    Perform the backward pass for (inverted) dropout.
    Inputs:
    - dout: Upstream derivatives, of any shape
    - cache: (dropout_param, mask) from Dropout.forward.
    """
    dropout_param, mask = cache
    mode = dropout_param['mode']

    dx = None
    if mode == 'train':
      ###########################################################################
      # TODO: Implement training phase backward pass for inverted dropout       #
      ###########################################################################
      # Replace "pass" statement with your code
      dx = dout
      dx[mask] = 0
    elif mode == 'test':
      dx = dout
    return dx

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