keras - 计算 Keras 中的神经元（具有不同的层），我的方法正确吗？
问题描述
我正在尝试确定我的 Keras 网络中的“神经元/节点”的数量，而不是参数。我正在使用一个已经实现的变体，所以我没有自己开发任何东西。
我知道，我可以通过摘要获得网络的概述和参数数量。这里的问题是，我不想知道我有多少参数，而是知道有多少“神经元”。背景是，对于 8 到 8 个全连接层，我得到 64 个参数。但我想达到 16。我也知道，使用 Conv2D 层的整个故事并不是那么容易制作的。
我的第一种方法是将 output_shape 变量的所有值相乘，然后将它们相加。我可以这样做还是错了？
这就是列表形式模型摘要：
Layer (type) Output Shape
================================================================
input_image (InputLayer) (None, None, None, 1)
zero_padding2d_1 (ZeroPadding2D) (None, None, None, 1)
conv1 (Conv2D) (None, None, None, 64)
bn_conv1 (BatchNorm) (None, None, None, 64)
activation_1 (Activation) (None, None, None, 64)
max_pooling2d_1 (MaxPooling2D) (None, None, None, 64)
res2a_branch2a (Conv2D) (None, None, None, 64)
bn2a_branch2a (BatchNorm) (None, None, None, 64)
activation_2 (Activation) (None, None, None, 64)
res2a_branch2b (Conv2D) (None, None, None, 64)
bn2a_branch2b (BatchNorm) (None, None, None, 64)
activation_3 (Activation) (None, None, None, 64)
res2a_branch2c (Conv2D) (None, None, None, 256)
res2a_branch1 (Conv2D) (None, None, None, 256)
bn2a_branch2c (BatchNorm) (None, None, None, 256)
bn2a_branch1 (BatchNorm) (None, None, None, 256)
add_1 (Add) (None, None, None, 256)
res2a_out (Activation) (None, None, None, 256)
res2b_branch2a (Conv2D) (None, None, None, 64)
bn2b_branch2a (BatchNorm) (None, None, None, 64)
activation_4 (Activation) (None, None, None, 64)
res2b_branch2b (Conv2D) (None, None, None, 64)
bn2b_branch2b (BatchNorm) (None, None, None, 64)
activation_5 (Activation) (None, None, None, 64)
res2b_branch2c (Conv2D) (None, None, None, 256)
bn2b_branch2c (BatchNorm) (None, None, None, 256)
add_2 (Add) (None, None, None, 256)
res2b_out (Activation) (None, None, None, 256)
res2c_branch2a (Conv2D) (None, None, None, 64)
bn2c_branch2a (BatchNorm) (None, None, None, 64)
activation_6 (Activation) (None, None, None, 64)
res2c_branch2b (Conv2D) (None, None, None, 64)
bn2c_branch2b (BatchNorm) (None, None, None, 64)
activation_7 (Activation) (None, None, None, 64)
res2c_branch2c (Conv2D) (None, None, None, 256)
bn2c_branch2c (BatchNorm) (None, None, None, 256)
add_3 (Add) (None, None, None, 256)
res2c_out (Activation) (None, None, None, 256)
res3a_branch2a (Conv2D) (None, None, None, 128)
bn3a_branch2a (BatchNorm) (None, None, None, 128)
activation_8 (Activation) (None, None, None, 128)
res3a_branch2b (Conv2D) (None, None, None, 128)
bn3a_branch2b (BatchNorm) (None, None, None, 128)
activation_9 (Activation) (None, None, None, 128)
res3a_branch2c (Conv2D) (None, None, None, 512)
res3a_branch1 (Conv2D) (None, None, None, 512)
bn3a_branch2c (BatchNorm) (None, None, None, 512)
bn3a_branch1 (BatchNorm) (None, None, None, 512)
add_4 (Add) (None, None, None, 512)
res3a_out (Activation) (None, None, None, 512)
res3b_branch2a (Conv2D) (None, None, None, 128)
bn3b_branch2a (BatchNorm) (None, None, None, 128)
activation_10 (Activation) (None, None, None, 128)
res3b_branch2b (Conv2D) (None, None, None, 128)
bn3b_branch2b (BatchNorm) (None, None, None, 128)
activation_11 (Activation) (None, None, None, 128)
res3b_branch2c (Conv2D) (None, None, None, 512)
bn3b_branch2c (BatchNorm) (None, None, None, 512)
add_5 (Add) (None, None, None, 512)
res3b_out (Activation) (None, None, None, 512)
res3c_branch2a (Conv2D) (None, None, None, 128)
bn3c_branch2a (BatchNorm) (None, None, None, 128)
activation_12 (Activation) (None, None, None, 128)
res3c_branch2b (Conv2D) (None, None, None, 128)
bn3c_branch2b (BatchNorm) (None, None, None, 128)
activation_13 (Activation) (None, None, None, 128)
res3c_branch2c (Conv2D) (None, None, None, 512)
bn3c_branch2c (BatchNorm) (None, None, None, 512)
add_6 (Add) (None, None, None, 512)
res3c_out (Activation) (None, None, None, 512)
res3d_branch2a (Conv2D) (None, None, None, 128)
bn3d_branch2a (BatchNorm) (None, None, None, 128)
activation_14 (Activation) (None, None, None, 128)
res3d_branch2b (Conv2D) (None, None, None, 128)
bn3d_branch2b (BatchNorm) (None, None, None, 128)
activation_15 (Activation) (None, None, None, 128)
res3d_branch2c (Conv2D) (None, None, None, 512)
bn3d_branch2c (BatchNorm) (None, None, None, 512)
add_7 (Add) (None, None, None, 512)
res3d_out (Activation) (None, None, None, 512)
res4a_branch2a (Conv2D) (None, None, None, 256)
bn4a_branch2a (BatchNorm) (None, None, None, 256)
activation_16 (Activation) (None, None, None, 256)
res4a_branch2b (Conv2D) (None, None, None, 256)
bn4a_branch2b (BatchNorm) (None, None, None, 256)
activation_17 (Activation) (None, None, None, 256)
res4a_branch2c (Conv2D) (None, None, None, 1024)
res4a_branch1 (Conv2D) (None, None, None, 1024)
bn4a_branch2c (BatchNorm) (None, None, None, 1024)
bn4a_branch1 (BatchNorm) (None, None, None, 1024)
add_8 (Add) (None, None, None, 1024)
res4a_out (Activation) (None, None, None, 1024)
res4b_branch2a (Conv2D) (None, None, None, 256)
bn4b_branch2a (BatchNorm) (None, None, None, 256)
activation_18 (Activation) (None, None, None, 256)
res4b_branch2b (Conv2D) (None, None, None, 256)
bn4b_branch2b (BatchNorm) (None, None, None, 256)
activation_19 (Activation) (None, None, None, 256)
res4b_branch2c (Conv2D) (None, None, None, 1024)
bn4b_branch2c (BatchNorm) (None, None, None, 1024)
add_9 (Add) (None, None, None, 1024)
res4b_out (Activation) (None, None, None, 1024)
res4c_branch2a (Conv2D) (None, None, None, 256)
bn4c_branch2a (BatchNorm) (None, None, None, 256)
activation_20 (Activation) (None, None, None, 256)
res4c_branch2b (Conv2D) (None, None, None, 256)
bn4c_branch2b (BatchNorm) (None, None, None, 256)
activation_21 (Activation) (None, None, None, 256)
res4c_branch2c (Conv2D) (None, None, None, 1024)
bn4c_branch2c (BatchNorm) (None, None, None, 1024)
add_10 (Add) (None, None, None, 1024)
res4c_out (Activation) (None, None, None, 1024)
res4d_branch2a (Conv2D) (None, None, None, 256)
bn4d_branch2a (BatchNorm) (None, None, None, 256)
activation_22 (Activation) (None, None, None, 256)
res4d_branch2b (Conv2D) (None, None, None, 256)
bn4d_branch2b (BatchNorm) (None, None, None, 256)
activation_23 (Activation) (None, None, None, 256)
res4d_branch2c (Conv2D) (None, None, None, 1024)
bn4d_branch2c (BatchNorm) (None, None, None, 1024)
add_11 (Add) (None, None, None, 1024)
res4d_out (Activation) (None, None, None, 1024)
res4e_branch2a (Conv2D) (None, None, None, 256)
bn4e_branch2a (BatchNorm) (None, None, None, 256)
activation_24 (Activation) (None, None, None, 256)
res4e_branch2b (Conv2D) (None, None, None, 256)
bn4e_branch2b (BatchNorm) (None, None, None, 256)
activation_25 (Activation) (None, None, None, 256)
res4e_branch2c (Conv2D) (None, None, None, 1024)
bn4e_branch2c (BatchNorm) (None, None, None, 1024)
add_12 (Add) (None, None, None, 1024)
res4e_out (Activation) (None, None, None, 1024)
res4f_branch2a (Conv2D) (None, None, None, 256)
bn4f_branch2a (BatchNorm) (None, None, None, 256)
activation_26 (Activation) (None, None, None, 256)
res4f_branch2b (Conv2D) (None, None, None, 256)
bn4f_branch2b (BatchNorm) (None, None, None, 256)
activation_27 (Activation) (None, None, None, 256)
res4f_branch2c (Conv2D) (None, None, None, 1024)
bn4f_branch2c (BatchNorm) (None, None, None, 1024)
add_13 (Add) (None, None, None, 1024)
res4f_out (Activation) (None, None, None, 1024)
res5a_branch2a (Conv2D) (None, None, None, 512)
bn5a_branch2a (BatchNorm) (None, None, None, 512)
activation_28 (Activation) (None, None, None, 512)
res5a_branch2b (Conv2D) (None, None, None, 512)
bn5a_branch2b (BatchNorm) (None, None, None, 512)
activation_29 (Activation) (None, None, None, 512)
res5a_branch2c (Conv2D) (None, None, None, 2048)
res5a_branch1 (Conv2D) (None, None, None, 2048)
bn5a_branch2c (BatchNorm) (None, None, None, 2048)
bn5a_branch1 (BatchNorm) (None, None, None, 2048)
add_14 (Add) (None, None, None, 2048)
res5a_out (Activation) (None, None, None, 2048)
res5b_branch2a (Conv2D) (None, None, None, 512)
bn5b_branch2a (BatchNorm) (None, None, None, 512)
activation_30 (Activation) (None, None, None, 512)
res5b_branch2b (Conv2D) (None, None, None, 512)
bn5b_branch2b (BatchNorm) (None, None, None, 512)
activation_31 (Activation) (None, None, None, 512)
res5b_branch2c (Conv2D) (None, None, None, 2048)
bn5b_branch2c (BatchNorm) (None, None, None, 2048)
add_15 (Add) (None, None, None, 2048)
res5b_out (Activation) (None, None, None, 2048)
res5c_branch2a (Conv2D) (None, None, None, 512)
bn5c_branch2a (BatchNorm) (None, None, None, 512)
activation_32 (Activation) (None, None, None, 512)
res5c_branch2b (Conv2D) (None, None, None, 512)
bn5c_branch2b (BatchNorm) (None, None, None, 512)
activation_33 (Activation) (None, None, None, 512)
res5c_branch2c (Conv2D) (None, None, None, 2048)
bn5c_branch2c (BatchNorm) (None, None, None, 2048)
add_16 (Add) (None, None, None, 2048)
res5c_out (Activation) (None, None, None, 2048)
fpn_c5p5 (Conv2D) (None, None, None, 256)
fpn_p5upsampled (UpSampling2D) (None, None, None, 256)
fpn_c4p4 (Conv2D) (None, None, None, 256)
fpn_p4add (Add) (None, None, None, 256)
fpn_p4upsampled (UpSampling2D) (None, None, None, 256)
fpn_c3p3 (Conv2D) (None, None, None, 256)
fpn_p3add (Add) (None, None, None, 256)
fpn_p3upsampled (UpSampling2D) (None, None, None, 256)
fpn_c2p2 (Conv2D) (None, None, None, 256)
fpn_p2add (Add) (None, None, None, 256)
fpn_p5 (Conv2D) (None, None, None, 256)
fpn_p2 (Conv2D) (None, None, None, 256)
fpn_p3 (Conv2D) (None, None, None, 256)
fpn_p4 (Conv2D) (None, None, None, 256)
fpn_p6 (MaxPooling2D) (None, None, None, 256)
rpn_model (Model) [(None, None, 2),
(None, None, 2),
(None, None, 4)]
rpn_class (Concatenate) (None, None, 2)
rpn_bbox (Concatenate) (None, None, 4)
input_anchors (InputLayer) (None, None, 4)
ROI (ProposalLayer) (None, 1000, 4)
input_image_meta (InputLayer) (None, 18)
roi_align_classifier (PyramidROIAlign) (None, 1000, 7, 7, 256)
mrcnn_class_conv1 (TimeDistributed) (None, 1000, 1, 1, 1024)
mrcnn_class_bn1 (TimeDistributed) (None, 1000, 1, 1, 1024)
activation_34 (Activation) (None, 1000, 1, 1, 1024)
mrcnn_class_conv2 (TimeDistributed) (None, 1000, 1, 1, 1024)
mrcnn_class_bn2 (TimeDistributed) (None, 1000, 1, 1, 1024)
activation_35 (Activation) (None, 1000, 1, 1, 1024)
pool_squeeze (Lambda) (None, 1000, 1024)
mrcnn_class_logits (TimeDistributed) (None, 1000, 6)
mrcnn_bbox_fc (TimeDistributed) (None, 1000, 24)
mrcnn_class (TimeDistributed) (None, 1000, 6)
mrcnn_bbox (Reshape) (None, 1000, 6, 4)
mrcnn_detection (DetectionLayer) (None, 100, 6)
lambda_3 (Lambda) (None, 100, 4)
roi_align_mask (PyramidROIAlign) (None, 100, 14, 14, 256)
mrcnn_mask_conv1 (TimeDistributed) (None, 100, 14, 14, 256)
mrcnn_mask_bn1 (TimeDistributed) (None, 100, 14, 14, 256)
activation_37 (Activation) (None, 100, 14, 14, 256)
mrcnn_mask_conv2 (TimeDistributed) (None, 100, 14, 14, 256)
mrcnn_mask_bn2 (TimeDistributed) (None, 100, 14, 14, 256)
activation_38 (Activation) (None, 100, 14, 14, 256)
mrcnn_mask_conv3 (TimeDistributed) (None, 100, 14, 14, 256)
mrcnn_mask_bn3 (TimeDistributed) (None, 100, 14, 14, 256)
activation_39 (Activation) (None, 100, 14, 14, 256)
mrcnn_mask_conv4 (TimeDistributed) (None, 100, 14, 14, 256)
mrcnn_mask_bn4 (TimeDistributed) (None, 100, 14, 14, 256)
activation_40 (Activation) (None, 100, 14, 14, 256)
mrcnn_mask_deconv (TimeDistributed) (None, 100, 28, 28, 256)
mrcnn_mask (TimeDistributed) (None, 100, 28, 28, 6)
================================================================
Total params: 44,678,198
Trainable params: 44,618,934
Non-trainable params: 59,264
我数了一下神经元 105,641,486。这看起来是错误的，因为除了权重（参数）之外还有很多。我不确定我是否真的可以添加所有图层？
如果有人想知道我为什么要这样做。我想将它与生物神经网络进行比较，我只有大脑的神经元数量，而不是那里的所有连接。我知道它们没有可比性，但对于我想做的事情来说已经足够了。
感谢您的提示和帮助
解决方案
一些东西：
- 在卷积层中，
neurons == filters - 如果您计算其他层，例如激活、填充和池化/采样，您将计算其他不存在的神经元（这些层没有神经元）
BatchNormalization图层确实有参数，但我不确定您是否要将它们视为具有神经元。然而，除了不可训练的均值和方差参数外，它们还具有可学习的缩放和偏差参数。use_bias=False（总是在批规范之前的任何层中 使用的一个很好的理由）
因此，只需计算每个 Conv 层中的过滤器数量。如果需要，请添加 BatchNorm 通道。
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