from?layer_utils?import?*

class?ThreelayerConvNet（object）:????
????“““????
????A?three-layer?convolutional?network?with?the?following?architecture:???????
???????conv?-?relu?-?2x2?max?pool?-?affine?-?relu?-?affine?-?softmax
????“““

????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）:
????????self.params?=?{}
????????self.reg?=?reg
????????self.dtype?=?dtype

????????#?Initialize?weights?and?biases
????????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（num_filters*H*W/4?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）

????????for?k?v?in?self.params.iteritems（）:????
????????????self.params[k]?=?v.astype（dtype）


????def?loss（self?X?y=None）:
????????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}

????????#?compute?the?forward?pass
????????a1?cache1?=?conv_relu_pool_forward（X?W1?b1?conv_param?pool_param）
????????a2?cache2?=?affine_relu_forward（a1?W2?b2）
????????scores?cache3?=?affine_forward（a2?W3?b3）

????????if?y?is?None:????
????????????return?scores

????????#?compute?the?backward?pass
????????data_loss?dscores?=?softmax_loss（scores?y）
????????da2?dW3?db3?=?affine_backward（dscores?cache3）
????????da1?dW2?db2?=?affine_relu_backward（da2?cache2）
????????dX?dW1?db1?=?conv_relu_pool_backward（da1?cache1）

????????#?Add?regularization
????????dW1?+=?self.reg?*?W1
????????dW2?+=?self.reg?*?W2
????????dW3?+=?self.reg?*?W3
????????reg_loss?=?0.5?*?self.reg?*?sum（np.sum（W?*?W）?for?W?in?[W1?W2?W3]）

????????loss?=?data_loss?+?reg_loss
????????grads?=?{‘W1‘:?dW1?‘b1‘:?db1?‘W2‘:?dW2?‘b2‘:?db2?‘W3‘:?dW3?‘b3‘:?db3}

????????return?loss?grads

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????2491??2017-01-04?13:06??cnn.py
?????文件????????6884??2017-01-04?14:37??data_utils.py
?????文件????????2410??2017-01-04?12:45??layer_utils.py
?????文件????????7691??2017-01-04?14:41??layers.py
?????文件????????3966??2017-01-03?12:05??optim.py
?????文件????????9587??2017-01-04?14:35??solver.py
?????文件????????1668??2017-01-04?16:45??start.py