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'] = weight_scale * np.random.randn(num_filters, C, filter_size, filter_size)
self.params['b1'] = np.zeros(num_filters)
self.params['W2'] = weight_scale * np.random.randn((H / 2)*(W / 2)*num_filters, hidden_dim)
self.params['b2'] = np.zeros(hidden_dim)
self.params['W3'] = weight_scale * np.random.randn(hidden_dim, num_classes)
self.params['b3'] = np.zeros(num_classes)
#pass
############################################################################
#? ? ? ? ? ? ? ? ? ? ? ? ? ? END OF YOUR CODE? ? ? ? ? ? ? ? ? ? ? ? ? ? #
############################################################################
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
############################################################################
# TODO: Implement the forward pass for the three-layer convolutional net,? #
# computing the class scores for X and storing them in the scores? ? ? ? ? #
# variable.? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? #
############################################################################
conv_forward_out_1, cache_forward_1 = conv_relu_pool_forward(X, self.params['W1'], self.params['b1'], conv_param, pool_param)
affine_forward_out_2, cache_forward_2 = affine_forward(conv_forward_out_1, self.params['W2'], self.params['b2'])
affine_relu_2, cache_relu_2 = relu_forward(affine_forward_out_2)
scores, cache_forward_3 = affine_forward(affine_relu_2, self.params['W3'], self.params['b3'])
#pass
############################################################################
#? ? ? ? ? ? ? ? ? ? ? ? ? ? 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, dout = softmax_loss(scores, y)
# Add regularization
loss += self.reg * 0.5 * (np.sum(self.params['W1'] ** 2) + np.sum(self.params['W2'] ** 2) + np.sum(self.params['W3'] ** 2))
dX3, grads['W3'], grads['b3'] = affine_backward(dout, cache_forward_3)
dX2 = relu_backward(dX3, cache_relu_2)
dX2, grads['W2'], grads['b2'] = affine_backward(dX2, cache_forward_2)
dX1, grads['W1'], grads['b1'] = conv_relu_pool_backward(dX2, cache_forward_1)
grads['W3'] = grads['W3'] + self.reg * self.params['W3']
grads['W2'] = grads['W2'] + self.reg * self.params['W2']
grads['W1'] = grads['W1'] + self.reg * self.params['W1']
#pass
############################################################################
#? ? ? ? ? ? ? ? ? ? ? ? ? ? END OF YOUR CODE? ? ? ? ? ? ? ? ? ? ? ? ? ? #
############################################################################
return loss, grads
pass
