text: from builtins import object
  import numpy as np
  
  from cs231n.layers import *
  from cs231n.fast_layers import *
  from cs231n.layer_utils import *
  
  
  class ThreeLayerConvNet(object):
      """
      A three-layer convolutional network with the following architecture:
  
      conv - relu - 2x2 max pool - affine - relu - affine - softmax
  
      The network operates on minibatches of data that have shape (N, C, H, W)
      consisting of N images, each with height H and width W and with C input
      channels.
      """
  
      def __init__(self, input_dim=(3, 32, 32), num_filters=32, filter_size=7,
                   hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0,
                   dtype=np.float32):
          """
          Initialize a new network.
  
          Inputs:
          - input_dim: Tuple (C, H, W) giving size of input data
          - num_filters: Number of filters to use in the convolutional layer
          - filter_size: Size of filters to use in the convolutional layer
          - hidden_dim: Number of units to use in the fully-connected hidden layer
          - num_classes: Number of scores to produce from the final affine layer.
          - weight_scale: Scalar giving standard deviation for random initialization
            of weights.
          - reg: Scalar giving L2 regularization strength
          - dtype: numpy datatype to use for computation.
          """
          self.params = {}
          self.reg = reg
          self.dtype = dtype
  
          ############################################################################
          # TODO: Initialize weights and biases for the three-layer convolutional    #
          # network. Weights should be initialized from a Gaussian with standard     #
          # deviation equal to weight_scale; biases should be initialized to zero.   #
          # All weights and biases should be stored in the dictionary self.params.   #
          # Store weights and biases for the convolutional layer using the keys 'W1' #
          # and 'b1'; use keys 'W2' and 'b2' for the weights and biases of the       #
          # hidden affine layer, and keys 'W3' and 'b3' for the weights and biases   #
          # of the output affine layer.                                              #
          ############################################################################
          C, H, W = input_dim
          self.params['W1'] = np.random.normal(0, weight_scale,
                                               (num_filters, C, filter_size, filter_size))
          self.params['b1'] = np.zeros(num_filters)
  
          pad = (filter_size - 1) // 2
          Hp = 1 + (H + 2 * pad - filter_size)
          Wp = 1 + (W + 2 * pad - filter_size)
          self.params['W2'] = np.random.normal(0, weight_scale,
                                               ((num_filters*Hp*Wp/4), hidden_dim))
  
          self.params['b2'] = np.zeros(hidden_dim)
  
          self.params['W3'] = np.random.normal(0, weight_scale, (hidden_dim, num_classes))
          self.params['b3'] = np.zeros(num_classes)
          ############################################################################
          #                             END OF YOUR CODE                             #
          ############################################################################
  
          for k, v in self.params.items():
              self.params[k] = v.astype(dtype)
  
  
      def loss(self, X, y=None):
          """
          Evaluate loss and gradient for the three-layer convolutional network.
  
          Input / output: Same API as TwoLayerNet in fc_net.py.
          """
          W1, b1 = self.params['W1'], self.params['b1']
          W2, b2 = self.params['W2'], self.params['b2']
          W3, b3 = self.params['W3'], self.params['b3']
  
          # pass conv_param to the forward pass for the convolutional layer
          filter_size = W1.shape[2]
          conv_param = {'stride': 1, 'pad': (filter_size - 1) // 2}
  
          # pass pool_param to the forward pass for the max-pooling layer
          pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}
  
          scores = None
          ############################################################################
          # TODO: Implement the forward pass for the three-layer convolutional net,  #
          # computing the class scores for X and storing them in the scores          #
          # variable.                                                                #
          ############################################################################
          out, cache1 = conv_relu_pool_forward(X, W1, b1, conv_param, pool_param)
          out, cache2 = affine_relu_forward(out, W2, b2)
          scores, cache3 = affine_forward(out, W3, b3)
          ############################################################################
          #                             END OF YOUR CODE                             #
          ############################################################################
  
          if y is None:
              return scores
  
          loss, grads = 0, {}
          ############################################################################
          # TODO: Implement the backward pass for the three-layer convolutional net, #
          # storing the loss and gradients in the loss and grads variables. 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!               #
          ############################################################################
          loss, dL = softmax_loss(scores, y)
          loss += 0.5 * self.reg * (np.sum(W1*W1) + np.sum(W2*W2) + np.sum(W3*W3))
  
          dx, dW3, db3 = affine_backward(dL, cache3)
          dx, dW2, db2 = affine_relu_backward(dx, cache2)
          dx, dW1, db1 = conv_relu_pool_backward(dx, cache1)
  
          grads['W3'] = dW3 + self.reg * W3
          grads['b3'] = db3
          grads['W2'] = dW2 + self.reg * W2
          grads['b2'] = db2
          grads['W1'] = dW1 + self.reg * W1
          grads['b1'] = db1
  
          ############################################################################
          #                             END OF YOUR CODE                             #
          ############################################################################
  
          return loss, grads
  