主要目的是儲存一下一個比較完整的全連線神經網路程式碼，不帶說明了，程式碼說明也比較詳細。

dataset.py

# -*- coding: utf-8 -*-

import numpy as np

def unpickle(file):
    import cPickle
    fo = open(file, 'rb')
    dict = cPickle.load(fo)
    fo.close()
    return dict

#load dataset of cifar10
def load_CIFAR10(cifar10_dir):
    #get the training data
    X_train = []
    y_train = []
    for i in range(1,6):
        dic = unpickle(cifar10_dir+"\\data_batch_"+str(i))
        for item in dic["data"]:
            X_train.append(item)
        for item in dic["labels"]:
            y_train.append(item)
            
    #get test data
    X_test = []
    y_test = []
    #do not know why the path is not just right as above,add a extra\
    dic = unpickle(cifar10_dir+"\\test_batch")
    for item in dic["data"]:
       X_test.append(item)
    for item in dic["labels"]:
       y_test.append(item)
    
    X_train = np.asarray(X_train)
    y_train = np.asarray(y_train)
    X_test = np.asarray(X_test)
    y_test = np.array(y_test)
    return X_train, y_train, X_test, y_test

def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):  
    """ 
    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare 
    it for the linear classifier. These are the same steps as we used for the SVM, 
    but condensed to a single function.  
    """  
    # Load the raw CIFAR-10 data 
    cifar10_dir = 'E:\python\cs231n\cifar-10-batches-py'   # make a change
    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)  
    # Subsample the data    
    mask = range(num_training, num_training + num_validation)    
    X_val = X_train[mask]                   # (1000,32,32,3)    
    y_val = y_train[mask]                   # (1000L,)   
    mask = range(num_training)    
    X_train = X_train[mask]                 # (49000,32,32,3)    
    y_train = y_train[mask]                 # (49000L,)    
    mask = range(num_test)   
    X_test = X_test[mask]                   # (1000,32,32,3)    
    y_test = y_test[mask]                   # (1000L,)    

    # preprocessing: subtract the mean image    
    mean_image = np.mean(X_train, axis=0)    
    X_train -= mean_image   
    X_val -= mean_image    
    X_test -= mean_image    

    # Reshape data to rows    
    X_train = X_train.reshape(num_training, -1)      # (49000,3072)    
    X_val = X_val.reshape(num_validation, -1)        # (1000,3072)    
    X_test = X_test.reshape(num_test, -1)            # (1000,3072)   
    
    data = {}
    data['X_train'] = X_train
    data['y_train'] = y_train
    data['X_val'] = X_val
    data['y_val'] = y_val
    data['X_test'] = X_test
    data['y_test'] = y_test

    return data
 

# -*- coding: utf-8 -*-

__coauthor__ = 'Andrew'
# 2.25.2017 #

import  numpy as np
import layers

class TwoLayerNet(object):   
    """    
    A two-layer fully-connected neural network with ReLU nonlinearity and    
    softmax loss that uses a modular layer design. We assume an input dimension    
    of D, a hidden dimension of H, and perform classification over C classes.    

    The architecure should be affine - relu - affine - softmax.    

    Note that this class does not implement gradient descent; instead, it    
    will interact with a separate Solver object that is responsible for running    
    optimization.    

    The learnable parameters of the model are stored in the dictionary    
    self.params that maps parameter names to numpy arrays.   
    """
    def __init__(self, input_dim=3*32*32, hidden_dim=100, num_classes=10,           
                              weight_scale=1e-3, reg=0.0):    
        """    
        Initialize a new network.   
        Inputs:    
        - input_dim: An integer giving the size of the input    
        - hidden_dim: An integer giving the size of the hidden layer    
        - num_classes: An integer giving the number of classes to classify    
        - dropout: Scalar between 0 and 1 giving dropout strength.    
        - weight_scale: Scalar giving the standard deviation for random 
                        initialization of the weights.    
        - reg: Scalar giving L2 regularization strength.    
        """    
        self.params = {}    
        self.reg = reg   
        self.params['W1'] = weight_scale * np.random.randn(input_dim, hidden_dim)     
        self.params['b1'] = np.zeros((1, hidden_dim))    
        self.params['W2'] = weight_scale * np.random.randn(hidden_dim, num_classes)  
        self.params['b2'] = np.zeros((1, num_classes))

    def loss(self, X, y=None):    
        """   
        Compute loss and gradient for a minibatch of data.    
        Inputs:    
        - X: Array of input data of shape (N, d_1, ..., d_k)    
        - y: Array of labels, of shape (N,). y[i] gives the label for X[i].  
        Returns:   
        If y is None, then run a test-time forward pass of the model and return:    
        - scores: Array of shape (N, C) giving classification scores, where              
                  scores[i, c] is the classification score for X[i] and class c. 
        If y is not None, then run a training-time forward and backward pass and    
        return a tuple of:    
        - loss: Scalar value giving the loss   
        - grads: Dictionary with the same keys as self.params, mapping parameter             
                 names to gradients of the loss with respect to those parameters.    
        """
        scores = None
        N = X.shape[0]
        # Unpack variables from the params dictionary
        W1, b1 = self.params['W1'], self.params['b1']
        W2, b2 = self.params['W2'], self.params['b2']
        h1, cache1 = layers.affine_forward(X, W1, b1)
        h1, cacheh1 = layers.relu_forward(h1)
        out, cache2 = layers.affine_forward(h1, W2, b2)
        scores = out              # (N,C)
        # If y is None then we are in test mode so just return scores
        if y is None:   
            return scores

        loss, grads = 0, {}
        data_loss, dscores = layers.softmax_loss(scores, y)
        reg_loss = 0.5 * self.reg * np.sum(W1*W1) + 0.5 * self.reg * np.sum(W2*W2)
        loss = data_loss + reg_loss

        # Backward pass: compute gradients
        dh1, dW2, db2 = layers.affine_backward(dscores, cache2)
        dh1 = layers.relu_backward(dh1,cacheh1)
        dX, dW1, db1 = layers.affine_backward(dh1, cache1)
        
        # Add the regularization gradient contribution
        dW2 += self.reg * W2
        dW1 += self.reg * W1
        grads['W1'] = dW1
        grads['b1'] = db1
        grads['W2'] = dW2
        grads['b2'] = db2

        return loss, grads
 

# -*- coding: utf-8 -*-
__coauthor__ = 'Andrew'
# 2 .26.2017

import numpy as np

def affine_forward(x, w, b):   
    """    
    Computes the forward pass for an affine (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 numpy array containing input data, of shape (N, d_1, ..., d_k)    
    - w: A numpy array of weights, of shape (D, M)    
    - b: A numpy array of biases, of shape (M,)   
    Returns a tuple of:    
    - out: output, of shape (N, M)    
    - cache: (x, w, b)   
    """
    out = None
    # Reshape x into rows
    N = x.shape[0]
    x_row = x.reshape(N, -1)         # (N,D)
    out = np.dot(x_row, w) + b       # (N,M)
    cache = (x, w, b)

    return out, cache

    
def affine_backward(dout, cache):   
    """    
    Computes the backward pass for an affine 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)    
    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   
    dx = np.dot(dout, w.T)                       # (N,D)    
    dx = np.reshape(dx, x.shape)                 # (N,d1,...,d_k)   
    x_row = x.reshape(x.shape[0], -1)            # (N,D)    
    dw = np.dot(x_row.T, dout)                   # (D,M)    
    db = np.sum(dout, axis=0, keepdims=True)     # (1,M)    

    return dx, dw, db

def relu_forward(x):   
    """    
    Computes the forward pass for a layer of rectified linear units (ReLUs).    
    Input:    
    - x: Inputs, of any shape    
    Returns a tuple of:    
    - out: Output, of the same shape as x    
    - cache: x    
    """   
    out = None    
    out = ReLU(x)    
    cache = x    

    return out, cache

def relu_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    
    dx[x <= 0] = 0    

    return dx

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[np.arange(N), y]    
    margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)    
    margins[np.arange(N), y] = 0   
    loss = np.sum(margins) / N   
    num_pos = np.sum(margins > 0, axis=1)    
    dx = np.zeros_like(x)   
    dx[margins > 0] = 1    
    dx[np.arange(N), y] -= num_pos    
    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   
    """    
    probs = np.exp(x - np.max(x, axis=1, keepdims=True))    
    probs /= np.sum(probs, axis=1, keepdims=True)    
    N = x.shape[0]   
    loss = -np.sum(np.log(probs[np.arange(N), y])) / N    
    dx = probs.copy()    
    dx[np.arange(N), y] -= 1    
    dx /= N    

    return loss, dx

def ReLU(x):    
    """ReLU non-linearity."""    
    return np.maximum(0, x)
 

# -*- coding: utf-8 -*-
__coauthor__ = 'Andrew'
# 2 .26.2017

import numpy as np

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', np.zeros_like(w))    
    next_w = None    
    v = config['momentum'] * v - config['learning_rate'] * dw    
    next_w = w + v    
    config['velocity'] = v    

    return next_w, config

def rmsprop(x, dx, 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', np.zeros_like(x))    
    next_x = None    
    cache = config['cache']    
    decay_rate = config['decay_rate']    
    learning_rate = config['learning_rate']    
    epsilon = config['epsilon']    
    cache = decay_rate * cache + (1 - decay_rate) * (dx**2)    
    x += - learning_rate * dx / (np.sqrt(cache) + epsilon)  
    config['cache'] = cache    
    next_x = x    

    return next_x, config

def adam(x, dx, 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', np.zeros_like(x))    
    config.setdefault('v', np.zeros_like(x))    
    config.setdefault('t', 0)   
    next_x = None    
    m = config['m']    
    v = config['v']    
    beta1 = config['beta1']    
    beta2 = config['beta2']    
    learning_rate = config['learning_rate']    
    epsilon = config['epsilon']   
    t = config['t']    
    t += 1    
    m = beta1 * m + (1 - beta1) * dx    
    v = beta2 * v + (1 - beta2) * (dx**2)    
    m_bias = m / (1 - beta1**t)    
    v_bias = v / (1 - beta2**t)    
    x += - learning_rate * m_bias / (np.sqrt(v_bias) + epsilon)    
    next_x = x    
    config['m'] = m    
    config['v'] = v    
    config['t'] = t    

    return next_x, config

# -*- coding: utf-8 -*-
import numpy as np

import optim


class Solver(object):
  """
  A Solver encapsulates all the logic necessary for training classification
  models. The Solver performs stochastic gradient descent using different
  update rules defined in optim.py.

  The solver accepts both training and validataion data and labels so it can
  periodically check classification accuracy on both training and validation
  data to watch out for overfitting.

  To train a model, you will first construct a Solver instance, passing the
  model, dataset, and various optoins (learning rate, batch size, etc) to the
  constructor. You will then call the train() method to run the optimization
  procedure and train the model.
  
  After the train() method returns, model.params will contain the parameters
  that performed best on the validation set over the course of training.
  In addition, the instance variable solver.loss_history will contain a list
  of all losses encountered during training and the instance variables
  solver.train_acc_history and solver.val_acc_history will be lists containing
  the accuracies of the model on the training and validation set at each epoch.
  
  Example usage might look something like this:
  
  data = {
    'X_train': # training data
    'y_train': # training labels
    'X_val': # validation data
    'X_train': # validation labels
  }
  model = MyAwesomeModel(hidden_size=100, reg=10)
  solver = Solver(model, data,
                  update_rule='sgd',
                  optim_config={
                    'learning_rate': 1e-3,
                  },
                  lr_decay=0.95,
                  num_epochs=10, batch_size=100,
                  print_every=100)
  solver.train()


  A Solver works on a model object that must conform to the following API:

  - model.params must be a dictionary mapping string parameter names to numpy
    arrays containing parameter values.

  - model.loss(X, y) must be a function that computes training-time loss and
    gradients, and test-time classification scores, with the following inputs
    and outputs:

    Inputs:
    - X: Array giving a minibatch of input data of shape (N, d_1, ..., d_k)
    - y: Array of labels, of shape (N,) giving labels for X where y[i] is the
      label for X[i].

    Returns:
    If y is None, run a test-time forward pass and return:
    - scores: Array of shape (N, C) giving classification scores for X where
      scores[i, c] gives the score of class c for X[i].

    If y is not None, run a training time forward and backward pass and return
    a tuple of:
    - loss: Scalar giving the loss
    - grads: Dictionary with the same keys as self.params mapping parameter
      names to gradients of the loss with respect to those parameters.
  """

  def __init__(self, model, data, **kwargs):
    """
    Construct a new Solver instance.
    
    Required arguments:
    - model: A model object conforming to the API described above
    - data: A dictionary of training and validation data with the following:
      'X_train': Array of shape (N_train, d_1, ..., d_k) giving training images
      'X_val': Array of shape (N_val, d_1, ..., d_k) giving validation images
      'y_train': Array of shape (N_train,) giving labels for training images
      'y_val': Array of shape (N_val,) giving labels for validation images
      
    Optional arguments:
    - update_rule: A string giving the name of an update rule in optim.py.
      Default is 'sgd'.
    - optim_config: A dictionary containing hyperparameters that will be
      passed to the chosen update rule. Each update rule requires different
      hyperparameters (see optim.py) but all update rules require a
      'learning_rate' parameter so that should always be present.
    - lr_decay: A scalar for learning rate decay; after each epoch the learning
      rate is multiplied by this value.
    - batch_size: Size of minibatches used to compute loss and gradient during
      training.
    - num_epochs: The number of epochs to run for during training.
    - print_every: Integer; training losses will be printed every print_every
      iterations.
    - verbose: Boolean; if set to false then no output will be printed during
      training.
    """
    self.model = model
    self.X_train = data['X_train']
    self.y_train = data['y_train']
    self.X_val = data['X_val']
    self.y_val = data['y_val']
    
    # Unpack keyword arguments
    self.update_rule = kwargs.pop('update_rule', 'sgd')
    self.optim_config = kwargs.pop('optim_config', {})
    self.lr_decay = kwargs.pop('lr_decay', 1.0)
    self.batch_size = kwargs.pop('batch_size', 100)
    self.num_epochs = kwargs.pop('num_epochs', 10)

    self.print_every = kwargs.pop('print_every', 10)
    self.verbose = kwargs.pop('verbose', True)

    # Throw an error if there are extra keyword arguments
    if len(kwargs) > 0:
      extra = ', '.join('"%s"' % k for k in kwargs.keys())
      raise ValueError('Unrecognized arguments %s' % extra)

    # Make sure the update rule exists, then replace the string
    # name with the actual function
    if not hasattr(optim, self.update_rule):
      raise ValueError('Invalid update_rule "%s"' % self.update_rule)
    self.update_rule = getattr(optim, self.update_rule)

    self._reset()


  def _reset(self):
    """
    Set up some book-keeping variables for optimization. Don't call this
    manually.
    """
    # Set up some variables for book-keeping
    self.epoch = 0
    self.best_val_acc = 0
    self.best_params = {}
    self.loss_history = []
    self.train_acc_history = []
    self.val_acc_history = []

    # Make a deep copy of the optim_config for each parameter
    self.optim_configs = {}
    for p in self.model.params:
      d = {k: v for k, v in self.optim_config.iteritems()}
      self.optim_configs[p] = d


  def _step(self):
    """
    Make a single gradient update. This is called by train() and should not
    be called manually.
    """
    # Make a minibatch of training data
    num_train = self.X_train.shape[0]
    batch_mask = np.random.choice(num_train, self.batch_size)
    X_batch = self.X_train[batch_mask]
    y_batch = self.y_train[batch_mask]

    # Compute loss and gradient
    loss, grads = self.model.loss(X_batch, y_batch)
    self.loss_history.append(loss)

    # Perform a parameter update
    for p, w in self.model.params.iteritems():
      dw = grads[p]
      config = self.optim_configs[p]
      next_w, next_config = self.update_rule(w, dw, config) #因為有很多update的方法
      self.model.params[p] = next_w
      self.optim_configs[p] = next_config


  def check_accuracy(self, X, y, num_samples=None, batch_size=100):
    """
    Check accuracy of the model on the provided data.
    
    Inputs:
    - X: Array of data, of shape (N, d_1, ..., d_k)
    - y: Array of labels, of shape (N,)
    - num_samples: If not None, subsample the data and only test the model
      on num_samples datapoints.
    - batch_size: Split X and y into batches of this size to avoid using too
      much memory.
      
    Returns:
    - acc: Scalar giving the fraction of instances that were correctly
      classified by the model.
    """
    
    # Maybe subsample the data
    N = X.shape[0]
    if num_samples is not None and N > num_samples:
      mask = np.random.choice(N, num_samples)
      N = num_samples
      X = X[mask]
      y = y[mask]

    # Compute predictions in batches
    num_batches = N / batch_size
    if N % batch_size != 0:
      num_batches += 1
    y_pred = []
    for i in xrange(num_batches):
      start = i * batch_size
      end = (i + 1) * batch_size
      scores = self.model.loss(X[start:end])
      y_pred.append(np.argmax(scores, axis=1))
    y_pred = np.hstack(y_pred)
    acc = np.mean(y_pred == y)

    return acc


  def train(self):
    """
    Run optimization to train the model.
    """
    num_train = self.X_train.shape[0]
    iterations_per_epoch = max(num_train / self.batch_size, 1)
    num_iterations = self.num_epochs * iterations_per_epoch

    for t in xrange(num_iterations):
      self._step()

      # Maybe print training loss
      if self.verbose and t % self.print_every == 0:
        print '(Iteration %d / %d) loss: %f' % (
               t + 1, num_iterations, self.loss_history[-1])

      # At the end of every epoch, increment the epoch counter and decay the
      # learning rate.
      epoch_end = (t + 1) % iterations_per_epoch == 0
      if epoch_end:
        self.epoch += 1
        for k in self.optim_configs:
          self.optim_configs[k]['learning_rate'] *= self.lr_decay

      # Check train and val accuracy on the first iteration, the last
      # iteration, and at the end of each epoch.
      first_it = (t == 0)
      last_it = (t == num_iterations + 1)
      if first_it or last_it or epoch_end:
        train_acc = self.check_accuracy(self.X_train, self.y_train,
                                        num_samples=1000)
        val_acc = self.check_accuracy(self.X_val, self.y_val)
        self.train_acc_history.append(train_acc)
        self.val_acc_history.append(val_acc)

        if self.verbose:
          print '(Epoch %d / %d) train acc: %f; val_acc: %f' % (
                 self.epoch, self.num_epochs, train_acc, val_acc)

        # Keep track of the best model
        if val_acc > self.best_val_acc:
          self.best_val_acc = val_acc
          self.best_params = {}
          for k, v in self.model.params.iteritems():
            self.best_params[k] = v.copy()

    # At the end of training swap the best params into the model
    self.model.params = self.best_params

# -*- coding: utf-8 -*-

import matplotlib.pyplot as plt
import numpy as np
import fc_net
from solver import Solver
import dataset

data = dataset.get_CIFAR10_data()
model = fc_net.TwoLayerNet(reg=0.9,weight_scale=1e-4)
solver = Solver(model, data,                
                lr_decay=0.95,                
                print_every=100, num_epochs=40, batch_size=400, 
                update_rule='sgd_momentum',                
                optim_config={'learning_rate': 5e-4, 'momentum': 0.5})

solver.train()                 


best_model = model
y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)
y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)
print 'Validation set accuracy: ', (y_val_pred == data['y_val']).mean()
print 'Test set accuracy: ', (y_test_pred == data['y_test']).mean()