中的fc6和fc7，将它们做了对比，发现结果不一样，说明vgg16的fc6和fc7只是初始化了faster rcnn中heat_to_tail中的fc6和fc7，之后后者被训练。..._scope + "/fc6/weights": fc6_conv, self...._scope + '/fc6/weights:0'], tf.reshape(fc6_conv, self._variables_to_fix[self...._variables_to_fix['my/vgg_16/fc6/weights:0'], tf.reshape(fc6_conv,self...._variables_to_fix['my/vgg_16/fc6/weights:0'].get_shape()))) sess.run(tf.assign(self.
top: "pool5" pooling_param { pool: MAX kernel_size: 3 stride: 2 } } layer { name: "fc6..." type: "InnerProduct" bottom: "pool5" top: "fc6" param { lr_mult: 1 decay_mult: 1...inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6..." top: "fc6" } layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param...{ dropout_ratio: 0.5 } } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "
VOCdevkit/VOC2010 ~/caffe/examles/fcn.berkeleyvision.org/data/pascal/VOC2010 这是建立软连接，至于为什么到第9步就知道了，至于这个linux...fcn.berkeleyvision.org/pascalcontext-fcn32s目录下创建snapshot/train 8、更改层名 由于下载的vgg16layer.caffemodel中也有fc6...，fc7和train.txt、val.txt中的fc6、fc7不一致，会导致错误， 所以我们把train.txt、val.txt中的所有fc6、fc7改成fc6x和fc7x，包括里面的blob名，目的是不让这个权重值传过去...o(￣ヘ￣o＃) 这个问题是这样的，出在第8步预训练的VGG16的weights的传递过程中，咱们之前把VGG16卷积层的weights拷过去了，然后由于FCN是把全连接层都改成了卷积层，所以我们对于FC6...可以看到fc6和fc7的权重也被合理reshape之后coercing过去了 ⑥看一下现在的loss下降的速度 开始时： ? 1个小时后： ?
roi_pooling_param { pooled_w: 6 pooled_h: 6 spatial_scale: 0.0625 # 1/16 } } layer { name: "fc6..." type: "InnerProduct" bottom: "roi_pool_conv5" top: "fc6" param { lr_mult: 1.0 } param { lr_mult...inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6..." top: "fc6" } layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param...dropout_ratio: 0.5 scale_train: false } } layer { name: "fc7" type: "InnerProduct" bottom: "fc6
{ pooled_w: 6 pooled_h: 6 spatial_scale: 0.0625 # 1/16 } } layer { name: "fc6..." type: "InnerProduct" bottom: "roi_pool_conv5" top: "fc6" param { lr_mult: 1.0 } param...inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6..." top: "fc6" } layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param...dropout_ratio: 0.5 scale_train: false } } layer { name: "fc7" type: "InnerProduct" bottom: "fc6
cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO, spatial_scale=spatial_scale ) model.FC('pool5', 'fc6...', dim_in * 7 * 7, 4096) model.Relu('fc6', 'fc6') model.FC('fc6', 'fc7', 4096, 4096) blob_out...cfg.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO, spatial_scale=spatial_scale ) model.FC('pool5', 'fc6...', dim_in * 6 * 6, 4096) model.Relu('fc6', 'fc6') model.FC('fc6', 'fc7', 4096, 1024) blob_out
memory_data_param { batch_size: 4 channels: 1 height: 1 width: 2 } }layer { name: "fc6...type: "xavier" } bias_filler { type: "constant" } } }layer { name: "fc6sig" bottom: "fc6..." top: "fc6" type: "Sigmoid" }layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7"...layer { name: "fc6" type: "InnerProduct" bottom: "fulldata" top: "fc6" inner_product_param {...type: "xavier" } bias_filler { type: "constant" } } }layer { name: "fc6sig" bottom: "fc6
1], padding='VALID', name='pool5') with tf.name_scope("fc6...weights and inputs and add bias act = tf.nn.xw_plus_b(flattened, weights, biases, name="fc6...") fc6 = tf.nn.relu(act) dp6 = tf.nn.dropout(fc6,keep_prob=self.KEEP_PROB)
roi_pooling_param { pooled_w: 7 pooled_h: 7 spatial_scale: 0.0625 # 1/16 } } layer { name: "fc6..." type: "InnerProduct" bottom: "pool5" top: "fc6" param { name: "fc6_w" lr_mult: 1...inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6..." top: "fc6" } layer { name: "drop6" type: "Dropout" bottom: "fc6" top: "fc6" dropout_param...{ dropout_ratio: 0.5 } } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "
conv3阶段DFD（data flow diagram）： 4. conv4阶段DFD（data flow diagram）： 5. conv5阶段DFD（data flow diagram）： 6. fc6...I0721 10:38:16.749074 4692 net.cpp:74] Creating Layer fc6 I0721 10:38:16.749083 4692 net.cpp:84]...fc6 <- pool5 I0721 10:38:16.749091 4692 net.cpp:110] fc6 -> fc6 I0721 10:38:17.160079 4692 net.cpp...:125] Top shape: 256 4096 1 1 (1048576) I0721 10:38:17.160148 4692 net.cpp:151] fc6 needs backward...17.160166 4692 net.cpp:74] Creating Layer relu6 I0721 10:38:17.160177 4692 net.cpp:84] relu6 <- fc6
" type: "InnerProduct" bottom: "pool5" top: "fc6" inner_product_param { num_output: 4096... } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "drop6" ...type: "Dropout" bottom: "fc6" top: "fc6" dropout_param { dropout_ratio: 0.5 } } layer { ...fc6 <- pool5 I0122 23:02:21.947065 2968 net.cpp:382] fc6 -> fc6 I0122 23:02:21.989847 2968 net.cpp:...I0122 23:02:22.017619 2968 net.cpp:202] fc6 does not need backward computation.
0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { bottom: "fc6..." top: "fc6" name: "relu6" type: "ReLU" } layer { bottom: "fc6" top: "fc6" name: "drop6"...type: "Dropout" dropout_param { dropout_ratio: 0.5 } } layer { bottom: "fc6" top: "fc7"..." top: "fc6" name: "relu6" type: "ReLU" } layer { bottom: "fc6" top: "fc6" name: "drop6"...type: "Dropout" dropout_param { dropout_ratio: 0.5 } } layer { bottom: "fc6" top: "fc7"
conv3阶段DFD（data flow diagram）： 4、conv4阶段DFD（data flow diagram）： 5、 conv5阶段DFD（data flow diagram）： 6、fc6...I0721 10:38:16.749074 4692 net.cpp:74] Creating Layer fc6 I0721 10:38:16.749083 4692 net.cpp:84]...fc6 fc6 I0721 10:38:17.160079 4692 net.cpp...:125] Top shape: 256 4096 1 1 (1048576) I0721 10:38:17.160148 4692 net.cpp:151] fc6 needs backward...17.160166 4692 net.cpp:74] Creating Layer relu6 I0721 10:38:17.160177 4692 net.cpp:84] relu6 <- fc6
shape { dim: 1 dim: 9216 } data_filler { type: "gaussian" std: 0.01 } } } layer { name: "fc6..." type: "InnerProduct" bottom: "dummy_roi_pool_conv5" top: "fc6" param { lr_mult: 0 decay_mult...inner_product_param { num_output: 4096 } } layer { name: "relu6" type: "ReLU" bottom: "fc6..." top: "fc6" } layer { name: "fc7" type: "InnerProduct" bottom: "fc6" top: "fc7" param {
网络结构 AlexNet整体的网络结构包括：1个输入层（input layer）、5个卷积层（C1、C2、C3、C4、C5）、2个全连接层（FC6、FC7）和1个输出层（output layer）。...13x13x192x2=64896 C4 C4层的FeatureMap的神经元个数 13x13x192x2=64896 C5 C5层的FeatureMap的神经元个数 13x13x128x2=43264 FC6...FC6全连接层神经元个数 4096 FC7 FC7全连接层神经元个数 4096 Output layer 输出层神经元个数 1000 整个AlexNet网络包含的神经元个数为： 290400 +...3x3x192，192个卷积核，2组，偏置参数 (3x3x192+1)x192x2=663936 C5 卷积核3x3x192，128个卷积核，2组，偏置参数 (3x3x192+1)x128x2=442624 FC6...在AlexNet网络中，全连接层FC6、FC7就使用了Dropout方法。 Dropout应该算是AlexNet中一个很大的创新，现在神经网络中的必备结构之一。
.Dimension,'Normalization','none','Name','action') fullyConnectedLayer(hiddenLayerSize2,'Name','fc6...criticNetwork = connectLayers(criticNetwork,'fc5','cat1/in2'); criticNetwork = connectLayers(criticNetwork,'fc6
在第一个S7-400站点中创建FC5,FC6发送与接收块和DB1,DB2数据块，在OB1主循环程序中编写程序调用FC5,如图所示 在另一个S7-400站点中，采用同样的方法添加相应的模块，并在OB1中调用...FC6 程序编制完成后，将各自程序下载到相应的CPU中，即可实现两个CPU之间的数据传输。
I0728 09:33:23.682938 9633 net.cpp:91] Creating Layer fc6 I0728 09:33:23.682941 9633 net.cpp:411]...fc6 <- pool5 I0728 09:33:23.682945 9633 net.cpp:369] fc6 -> fc6 I0728 09:33:23.682950 9633 net.cpp:...121] Setting up fc6 I0728 09:33:23.687371 9633 net.cpp:128] Top shape: 1 1024 63 47 (3032064) I0728...I0728 09:33:23.687402 9633 net.cpp:358] relu6 -> fc6 (in-place) I0728 09:33:23.687407 9633 net.cpp...I0728 09:33:23.687422 9633 net.cpp:358] drop6 -> fc6 (in-place) I0728 09:33:23.687425 9633 net.cpp
pool5 在上一小节已经讨论过了，那么 fc6 和 f7 就成了研究的对象。...fc6 与 pool5 构成全连接，为了计算 feature 它会乘以一个 4096x9216 的权重矩阵，然后在与一组 bias 相加，所以它有 3700 多万的参数。...但经过作者在 PASCAL 上不做 fine-tune 处理，直接测试，可以发现 fc7 的意义没有 fc6 大，甚至移除它之后，对于 mAP 结果指标没有影响。...更惊喜的事情是，同时移除 fc6 和 fc7 并没有多大的损失，甚至结果还要好一点点。 所以，神经网络最神奇的力量来自卷积层，而不是全连接层。...结果证明，fine-tune 后 fc6 与 fc7 提升的效果明显。
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