cnn.py cs231n

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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.
    """
    C,H,W=input_dim

    self.params = {}
    self.reg = reg
    self.dtype = dtype
    self.params['W1']=np.random.randn(num_filters,C,filter_size,filter_size)*weight_scale
    self.params['b1']=np.zeros(num_filters,)
    self.params['W2']=np.random.randn(num_filters*H*W/4,hidden_dim)*weight_scale
    self.params['b2']=np.zeros(hidden_dim,)
    self.params['W3']=np.random.randn(hidden_dim,num_classes)*weight_scale
    self.params['b3']=np.zeros(num_classes,)
    # why randn needs int while seros needs tuple!!!!
    for k, v in self.params.iteritems():
      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
    out1,cache1=conv_relu_pool_forward(X,W1,b1,conv_param,pool_param)

    out=out1.reshape(out1.shape[0],-1)

    out,cache2=affine_relu_forward(out,W2,b2)

    scores,cache3=affine_forward(out,W3,b3)

    if y is None:
      return scores

    loss, grads = 0, {}
    loss,dout=softmax_loss(scores,y)

    loss+=self.reg*0.5*np.sum(W3**2)
    loss+=self.reg*0.5*np.sum(W2**2)
    loss+=self.reg*0.5*np.sum(W1**2)

    dout,grads['W3'],grads['b3']=affine_backward(dout,cache3)
    grads['W3']+=W3*self.reg

    dout,grads['W2'],grads['b2']=affine_relu_backward(dout,cache2)
    grads['W2']+=W2*self.reg

    dout=dout.reshape(*out1.shape)
    dout,grads['W1'],grads['b1']=conv_relu_pool_backward(dout,cache1)
    grads['W1']+=W1*self.reg

    ############################################################################
    #                             END OF YOUR CODE                             #
    ############################################################################

    return loss, grads

pass

　　

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