[CS231n@Stanford] Assignment1-Q4 (python) Two layer neural network实现

 import numpy as np

    
 import matplotlib.pyplot as plt
    
  
    
  
    
 class TwoLayerNet(object):
    
   """
    
   A two-layer fully-connected neural network. The net has an input dimension of
    
   N, a hidden layer dimension of H, and performs classification over C classes.
    
   We train the network with a softmax loss function and L2 regularization on the
    
   weight matrices. The network uses a ReLU nonlinearity after the first fully
    
   connected layer.
    
   13.   In other words, the network has the following architecture:
    
   15.   input - fully connected layer - ReLU - fully connected layer - softmax
    
   17.   The outputs of the second fully-connected layer are the scores for each class.
    
   """
    
  
    
   def __init__(self, input_size, hidden_size, output_size, std=1e-4):
    
     """
    
     Initialize the model. Weights are initialized to small random values and
    
     biases are initialized to zero. Weights and biases are stored in the
    
     variable self.params, which is a dictionary with the following keys:
    
   26.     W1: First layer weights; has shape (D, H)
    
     b1: First layer biases; has shape (H,)
    
     W2: Second layer weights; has shape (H, C)
    
     b2: Second layer biases; has shape (C,)
    
   31.     Inputs:
    
     - input_size: The dimension D of the input data.
    
     - hidden_size: The number of neurons H in the hidden layer.
    
     - output_size: The number of classes C.
    
     """
    
     self.params = {}
    
     self.params['W1'] = std * np.random.randn(input_size, hidden_size)
    
     self.params['b1'] = np.zeros(hidden_size)
    
     self.params['W2'] = std * np.random.randn(hidden_size, output_size)
    
     self.params['b2'] = np.zeros(output_size)
    
  
    
   def loss(self, X, y=None, reg=0.0):
    
     """
    
     Compute the loss and gradients for a two layer fully connected neural
    
     network.
    
   47.     Inputs:
    
     - X: Input data of shape (N, D). Each X[i] is a training sample.
    
     - y: Vector of training labels. y[i] is the label for X[i], and each y[i] is
    
       an integer in the range 0 <= y[i] < C. This parameter is optional; if it
    
       is not passed then we only return scores, and if it is passed then we
    
       instead return the loss and gradients.
    
     - reg: Regularization strength.
    
   55.     Returns:
    
     If y is None, return a matrix scores of shape (N, C) where scores[i, c] is
    
     the score for class c on input X[i].
    
   59.     If y is not None, instead return a tuple of:
    
     - loss: Loss (data loss and regularization loss) for this batch of training
    
       samples.
    
     - grads: Dictionary mapping parameter names to gradients of those parameters
    
       with respect to the loss function; has the same keys as self.params.
    
     """
    
     # Unpack variables from the params dictionary
    
     W1, b1 = self.params['W1'], self.params['b1']
    
     W2, b2 = self.params['W2'], self.params['b2']
    
     N, D = X.shape
    
  
    
     # Compute the forward pass
    
     scores = None
    
     #############################################################################
    
     # TODO: Perform the forward pass, computing the class scores for the input. #
    
     # Store the result in the scores variable, which should be an array of      #
    
     # shape (N, C).                                                             #
    
     #############################################################################
    
     
    
     f = lambda x: np.maximum(0,x)
    
     h1 = f(np.dot(X, W1)+b1)
    
     h2 = np.dot(h1,W2)+b2
    
  
    
     scores = h2
    
     
    
     
    
     pass
    
     #############################################################################
    
     #                              END OF YOUR CODE                             #
    
     #############################################################################
    
     
    
     # If the targets are not given then jump out, we're done
    
     if y is None:
    
       return scores
    
  
    
     # Compute the loss
    
     loss = None
    
     #############################################################################
    
     # TODO: Finish the forward pass, and compute the loss. This should include  #
    
     # both the data loss and L2 regularization for W1 and W2. Store the result  #
    
     # in the variable loss, which should be a scalar. Use the Softmax           #
    
     # classifiers loss. So that your results match ours, multiply the            #
    
     # regularization loss by 0.5                                                #
    
     #############################################################################
    
     
    
     
    
     shift_scores = scores - np.max(scores, axis = 1).reshape(-1,1)
    
     softmax_output = np.exp(shift_scores)/np.sum(np.exp(shift_scores), axis = 1).reshape(-1,1)
    
     loss = -np.sum(np.log(softmax_output[np.arange(N), y]))
    
     loss /= N 
    
     loss +=  reg * 0.5* (np.sum(W1 * W1)+np.sum(W2 * W2))
    
     
    
     
    
     
    
     pass
    
     #############################################################################
    
     #                              END OF YOUR CODE                             #
    
     #############################################################################
    
  
    
     # Backward pass: compute gradients
    
     grads = {}
    
     #############################################################################
    
     # TODO: Compute the backward pass, computing the derivatives of the weights #
    
     # and biases. Store the results in the grads dictionary. For example,       #
    
     # grads['W1'] should store the gradient on W1, and be a matrix of same size #
    
     #############################################################################
    
     
    
     softmax_output[np.arange(N), y] += -1
    
     grads['W2'] =  1.0/N * h1.T.dot(softmax_output) + reg* W2
    
     grads['b2'] =  1.0/N * np.sum(softmax_output, axis = 0) 
    
     
    
     dh1 = softmax_output.dot(W2.T)
    
     dh1_ReLU = (X.dot(W1)+b1 >0)*dh1
    
     
    
     grads['W1'] = 1.0/N * X.T.dot(dh1_ReLU) + reg* W1
    
     grads['b1'] = 1.0/N * np.sum(dh1_ReLU, axis = 0) 
    
     
    
     
    
     pass
    
     #############################################################################
    
     #                              END OF YOUR CODE                             #
    
     #############################################################################
    
  
    
     return loss, grads
    
  
    
   def train(self, X, y, X_val, y_val,
    
         learning_rate=1e-3, learning_rate_decay=0.95,
    
         reg=1e-5, num_iters=100,
    
         batch_size=200, verbose=False):
    
     """
    
     Train this neural network using stochastic gradient descent.
    
   151.     Inputs:
    
     - X: A numpy array of shape (N, D) giving training data.
    
     - y: A numpy array f shape (N,) giving training labels; y[i] = c means that
    
       X[i] has label c, where 0 <= c < C.
    
     - X_val: A numpy array of shape (N_val, D) giving validation data.
    
     - y_val: A numpy array of shape (N_val,) giving validation labels.
    
     - learning_rate: Scalar giving learning rate for optimization.
    
     - learning_rate_decay: Scalar giving factor used to decay the learning rate
    
       after each epoch.
    
     - reg: Scalar giving regularization strength.
    
     - num_iters: Number of steps to take when optimizing.
    
     - batch_size: Number of training examples to use per step.
    
     - verbose: boolean; if true print progress during optimization.
    
     """
    
     num_train = X.shape[0]
    
     iterations_per_epoch = max(num_train / batch_size, 1)
    
  
    
     # Use SGD to optimize the parameters in self.model
    
     loss_history = []
    
     train_acc_history = []
    
     val_acc_history = []
    
  
    
     for it in xrange(num_iters):
    
       X_batch = None
    
       y_batch = None
    
  
    
       #########################################################################
    
       # TODO: Create a random minibatch of training data and labels, storing  #
    
       # them in X_batch and y_batch respectively.                             #
    
       #########################################################################
    
       
    
       sample_index = np.random.choice(num_train, batch_size ,replace = True)
    
       X_batch = X[sample_index,:]
    
       y_batch = y[sample_index]
    
       
    
       pass
    
       #########################################################################
    
       #                             END OF YOUR CODE                          #
    
       #########################################################################
    
  
    
       # Compute loss and gradients using the current minibatch
    
       loss, grads = self.loss(X_batch, y=y_batch, reg=reg)
    
       loss_history.append(loss)
    
  
    
       #########################################################################
    
       # TODO: Use the gradients in the grads dictionary to update the         #
    
       # parameters of the network (stored in the dictionary self.params)      #
    
       # using stochastic gradient descent. You'll need to use the gradients   #
    
       # stored in the grads dictionary defined above.                         #
    
       #########################################################################
    
       
    
       
    
       self.params['W1'] = self.params['W1'] -learning_rate*grads['W1'] 
    
       self.params['W2'] = self.params['W2'] -learning_rate*grads['W2'] 
    
       self.params['b1'] = self.params['b1'] -learning_rate*grads['b1']
    
       self.params['b2'] = self.params['b2'] -learning_rate*grads['b2']       
    
       
    
       pass
    
       #########################################################################
    
       #                             END OF YOUR CODE                          #
    
       #########################################################################
    
  
    
       if verbose and it % 100 == 0:
    
     print 'iteration %d / %d: loss %f' % (it, num_iters, loss)
    
  
    
       # Every epoch, check train and val accuracy and decay learning rate.
    
       if it % iterations_per_epoch == 0:
    
     # Check accuracy
    
     train_acc = (self.predict(X_batch) == y_batch).mean()
    
     val_acc = (self.predict(X_val) == y_val).mean()
    
     train_acc_history.append(train_acc)
    
     val_acc_history.append(val_acc)
    
  
    
     # Decay learning rate
    
     learning_rate *= learning_rate_decay
    
  
    
     return {
    
       'loss_history': loss_history,
    
       'train_acc_history': train_acc_history,
    
       'val_acc_history': val_acc_history,
    
     }
    
  
    
   def predict(self, X):
    
     """
    
     Use the trained weights of this two-layer network to predict labels for
    
     data points. For each data point we predict scores for each of the C
    
     classes, and assign each data point to the class with the highest score.
    
   239.     Inputs:
    
     - X: A numpy array of shape (N, D) giving N D-dimensional data points to
    
       classify.
    
   243.     Returns:
    
     - y_pred: A numpy array of shape (N,) giving predicted labels for each of
    
       the elements of X. For all i, y_pred[i] = c means that X[i] is predicted
    
       to have class c, where 0 <= c < C.
    
     """
    
     y_pred = None
    
  
    
     ###########################################################################
    
     # TODO: Implement this function; it should be VERY simple!                #
    
     ###########################################################################
    
     
    
     f = lambda x: np.maximum(0,x)
    
     h1 = f(np.dot(X, self.params['W1'])+self.params['b1'])
    
     h2 = np.dot(h1,self.params['W2'])+self.params['b2']
    
     scores = h2
    
  
    
     y_pred = np.argmax(scores, axis = 1)
    
     
    
     
    
     pass
    
     ###########################################################################
    
     #                              END OF YOUR CODE                           #
    
     ###########################################################################
    
  
    
     return y_pred
    
  
    
    
    
    
    AI写代码

 from vis_utils import visualize_grid

    
  
    
 # Visualize the weights of the network
    
  
    
 def show_net_weights(net):
    
   W1 = net.params['W1']
    
   W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)
    
   plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))
    
   plt.gca().axis('off')
    
   plt.show()
    
  
    
  
    
 best_net = None # store the best model into this 
    
  
    
 results = {} 
    
 best_val = -1  
    
 learning_rate = [8e-4, 9e-4]  
    
 regularization_strengths = [0.01, 0.005]  
    
 learning_rate_decay= [0.97, 0.95]
    
  
    
 #################################################################################
    
 # TODO: Tune hyperparameters using the validation set. Store your best trained  #
    
 # model in best_net.                                                            #
    
 #                                                                               #
    
 # To help debug your network, it may help to use visualizations similar to the  #
    
 # ones we used above; these visualizations will have significant qualitative    #
    
 # differences from the ones we saw above for the poorly tuned network.          #
    
 #                                                                               #
    
 # Tweaking hyperparameters by hand can be fun, but you might find it useful to  #
    
 # write code to sweep through possible combinations of hyperparameters          #
    
 # automatically like we did on the previous exercises.                          #
    
 #################################################################################
    
  
    
 iters = 2000    
    
 for lr in learning_rate:    
    
     for reg in regularization_strengths:    
    
     for lrd in learning_rate_decay:
    
         print lr,reg,lrd
    
         net = TwoLayerNet(input_size, hidden_size, num_classes) 
    
         net.train(X_train, y_train, X_val, y_val, learning_rate=lr, reg=reg, learning_rate_decay=lrd, num_iters=iters, verbose=False)    
    
         
    
         y_train_pred = net.predict(X_train)    
    
         acc_train = np.mean(y_train == y_train_pred)    
    
         
    
         y_val_pred = net.predict(X_val)    
    
         acc_val = np.mean(y_val == y_val_pred)    
    
     
    
         results[(lr, reg, lrd)] = (acc_train, acc_val)    
    
         
    
         if best_val < acc_val:    
    
             best_val = acc_val    
    
             best_net = net   
    
  
    
  
    
 pass
    
 #################################################################################
    
 #                               END OF YOUR CODE                                #
    
 #################################################################################
    
 # Print out results.
    
 for lr, reg, lrd in sorted(results):
    
     train_accuracy, val_accuracy = results[(lr, reg, lrd)]
    
     print 'lr %e reg %e lrd %e train accuracy: %f val accuracy: %f' % (
    
             lr, reg, lrd, train_accuracy, val_accuracy)
    
     
    
 print 'best validation accuracy achieved during cross-validation: %f' % best_val 
    
 # visualize the weights of the best network
    
 show_net_weights(best_net)
    
  
    
 test_acc = (best_net.predict(X_test) == y_test).mean()
    
 print 'Test accuracy: ', test_acc
    
    
    
    
    AI写代码