• <div><p>hi , I try to test inceptionv3, I change the forward and model. It appears the following problems <code>Traceback (most recent call last): File "inceptionv3_train.py", line 346, in &...
• InceptionV3的PyTorch实现：https://github.com/pytorch/vision/blob/master/torchvision/models/inception.py 2a表示第2组的第1个Block，同一组的空间维度相同 但为何没有3a, 5a？ (299, 299, 3) →【1a, ...
InceptionV3的PyTorch实现：https://github.com/pytorch/vision/blob/master/torchvision/models/inception.py
2a表示第2组的第1个Block，同一组的空间维度相同
但为何没有3a, 5a？
(299, 299, 3)

→【1a, Cout=32, f=3, s=2】→(149, 149, 32)

→【2a, Cout=32, f=3】→(147, 147, 32)→【2b, Cout=64, f=3, p=1】→(147, 147, 64)
→【max pool, f=3, s=2】→(73, 73, 64)

→【3b, Cout=80, f=1】→(73, 73, 80)

→【4a, Cout=192, f=3】→(71, 71, 192)→【max pool, f=3, s=2】→(35, 35, 192)

→【Mixed_5b, InceptionA】→(35, 35, 256)→【Mixed_5c, InceptionA】→(35, 35, 288)
→【Mixed_5d, InceptionA】→(35, 35, 288)

→【Mixed_6a, InceptionB】→(17, 17, 768)
→【Mixed_6b, InceptionC】→(17, 17, 768)→【Mixed_6c, InceptionC】→(17, 17, 768)
→【Mixed_6d, InceptionC】→(17, 17, 768)→【Mixed_6e, InceptionC】→(17, 17, 768)

→【Mixed_7a, InceptionD】→(8, 8, 1280)
→【Mixed_7b, InceptionE】→(8, 8, 2048)→【Mixed_7c, InceptionE】→(8, 8, 2048)

→【global avg pool】→(2048,)→【dropout】→(2048,)→【fc】→(1000,)

分支：→【Mixed_6e, InceptionC】→(17, 17, 768)→【InceptionAux】→(1000,)


InceptionA使用了3次，分别用在Mixed_5b, Mixed_5c, Mixed_5d中，包含参数pool_features，输入为(35, 35, in_channels)，输出固定为(35, 35, 224+pool features)
(35, 35, 192)→【Mixed_5b，InceptionA, pool_features=32】→(35, 35, 224+32=256)
(35, 35, 256)→【Mixed_5c，InceptionA, pool_features=64】→(35, 35, 224+64=288)
(35, 35, 288)→【Mixed_5d，InceptionA, pool_features=64】→(35, 35, 224+64=288)

以(35, 35, 192)→【Mixed_5b, InceptionA, pool_features=32】→(35, 35, 256)为例
输入：(35, 35, 192)

分支1：→【BasicConv2d, Cout=64, f=1】→(35, 35, 64)

分支2：→【BasicConv2d, Cout=48, f=1】→(35, 35, 48)
→【BasicConv2d, Cout=64, f=5, p=2】→(35, 35, 64)

分支3：→【BasicConv2d, Cout=64, f=1】→(35, 35, 64)
→【BasicConv2d, Cout=96, f=3, p=1】→(35, 35, 96)
→【BasicConv2d, Cout=96, f=3, p=1】→(35, 35, 96)

分支4：→【avg pool, f=3, s=1, p=1】→(35, 35, 192)
→【BasicConv2d, Cout=pool_features, f=1】→(35, 35, pool_features)

合并：(35, 35, 224+pool_features)


InceptionB只使用了1次，用在Mixed_6a中
(35, 35, 288)→【Mixed_6a, InceptionB】→(17, 17, 768)，空间维度减半，通道数增加到大约2.7倍
输入：(35, 35, 288)

分支1：→【BasicConv2d, Cout=384, f=3, s=2】→(17, 17, 384)

分支2：→【BasicConv2d, Cout=64, f=1】→(35, 35, 64)
→【BasicConv2d, Cout=96, f=3, p=1】→(35, 35, 96)
→【BasicConv2d, Cout=96, f=3, s=2】→(17, 17, 96)

分支3：→【max pool, f=3, s=2】→(17, 17, 288)

合并：(17, 17, 384+96+288=768)


InceptionC使用了4次，分别用在Mixed_6b, Mixed_6c, Mixed_6d, Mixed_6e中，输入和输出均为(17, 17, 768)，只是参数channels_7x7不同，参数channels_7x7简记为c7
Mixed_6b，c7=128
Mixed_6c，c7=160
Mixed_6d，c7=160
Mixed_6e，c7=192

输入：(17, 17, 768)

分支1：→【BasicConv2d, Cout=192, f=1】→(17, 17, 192)

分支2：→【BasicConv2d, Cout=c7, f=1】→(17, 17, c7)
→【BasicConv2d, Cout=c7, f=(1, 7), p=(0, 3)】→(17, 17, c7)
→【BasicConv2d, Cout=192, f=(7, 1), p=(3, 0)】→(17, 17, 192)

分支3：→【BasicConv2d, Cout=c7, f=1】→(17, 17, c7)
→【BasicConv2d, Cout=c7, f=(7, 1), p=(3, 0)】→(17, 17, c7)
→【BasicConv2d, Cout=c7, f=(1, 7), p=(0, 3)】→(17, 17, c7)
→【BasicConv2d, Cout=c7, f=(7, 1), p=(3, 0)】→(17, 17, c7)
→【BasicConv2d, Cout=192, f=(1, 7), p=(0, 3)】→(17, 17, 192)

分支4：→【avg pool, f=3, s=1, p=1】→(17, 17, 768)
→【BasicConv2d, Cout=192, f=1】→(17, 17, 192)

合并：(17, 17, 192×4=768)


InceptionD只使用了1次，用在Mixed_7a中
(17, 17, 768)→【Mixed_7a, InceptionD】→(8, 8, 1280)，空间维度减半，通道数增加到大约1.7倍
输入：(17, 17, 768)

分支1：→【BasicConv2d, Cout=192, f=1】→(17, 17, 192)
→【BasicConv2d, Cout=320, f=3, s=2】→(8, 8, 320)

分支2：→【BasicConv2d, Cout=192, f=1】→(17, 17, 192)
→【BasicConv2d, Cout=192, f=(1, 7), p=(0, 3)】→(17, 17, 192)
→【BasicConv2d, Cout=192, f=(7, 1), p=(3, 0)】→(17, 17, 192)
→【BasicConv2d, Cout=192, f=3, s=2】→(8, 8, 192)

分支3：→【max pool, f=3, s=2】→(8, 8, 768)

合并：(8, 8, 320+192+768=1280)


InceptionE使用了2次， 分别用在Mixed_7b, Mixed_7c中，输入为(8, 8, in_channels)，输出固定为(8, 8, 2048)
以(8, 8, 1280)→【Mixed_7b, InceptionE】→(8, 8, 2048)为例
输入：(8, 8, 1280)

分支1：→【BasicConv2d, Cout=320, f=1】→(8, 8, 320)

分支2：→【BasicConv2d, Cout=384, f=1】→(8, 8, 384)
分支2-1：→【BasicConv2d, Cout=384, f=(1, 3), p=(0, 1)】→(8, 8, 384)
分支2-2：→【BasicConv2d, Cout=384, f=(3, 1), p=(1, 0)】→(8, 8, 384)
合并：(8, 8, 384×2=768)

分支3：→【BasicConv2d, Cout=448, f=1】→(8, 8, 448)
→【BasicConv2d, Cout=384, f=3, p=1】→(8, 8, 384)
分支3-1：→【BasicConv2d, Cout=384, f=(1, 3), p=(0, 1)】→(8, 8, 384)
分支3-2：→【BasicConv2d, Cout=384, f=(3, 1), p=(1, 0)】→(8, 8, 384)
合并：(8, 8, 384×2=768)

分支4：→【avg pool, f=3, s=1, p=1】→(8, 8, 1280)
→【BasicConv2d, Cout=192, f=1】→(8, 8, 192)

合并：(8, 8, 320+768+768+192=2048)


InceptionAux只用了1次，连接在【Mixed_6e, InceptionC】的输出(17, 17, 768)上
(17, 17, 768)
→【avg pool, f=5, s=3】→(5, 5, 768)
→【BasicConv2d, Cout=128, f=1】→(5, 5, 128)
→【BasicConv2d, Cout=768, f=5】→(1, 1, 768)
→【reshape】→(768,)→【fc】→(1000,)


pool层几个值得注意的地方
(147, 147, 64)→【max pool, f=3, s=2】→(73, 73, 64)
(71, 71, 192)→【max pool, f=3, s=2】→(35, 35, 192)，使用f=3（一般使用f=2）

Keras中的InceptionV3，共313层
参考：https://github.com/keras-team/keras-applications/blob/master/keras_applications/inception_v3.py
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to
==================================================================================================

第0层
__________________________________________________________________________________________________
input_1 (InputLayer)            (None, 299, 299, 3)  0
__________________________________________________________________________________________________

第1-3层，(299, 299, 3)→【1a, Cout=32, f=3, s=2】→(149, 149, 32)
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 149, 149, 32) 864         input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 149, 149, 32) 96          conv2d_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 149, 149, 32) 0           batch_normalization_1[0][0]
__________________________________________________________________________________________________

第4-6层，(149, 149, 32)→【2a, Cout=32, f=3】→(147, 147, 32)
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 147, 147, 32) 9216        activation_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 147, 147, 32) 96          conv2d_2[0][0]
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 147, 147, 32) 0           batch_normalization_2[0][0]
__________________________________________________________________________________________________

第7-9层，(147, 147, 32)→【2b, Cout=64, f=3, p=1】→(147, 147, 64)
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 147, 147, 64) 18432       activation_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 147, 147, 64) 192         conv2d_3[0][0]
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 147, 147, 64) 0           batch_normalization_3[0][0]
__________________________________________________________________________________________________

第10层，(147, 147, 64)→【max pool, f=3, s=2】→(73, 73, 64)
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)  (None, 73, 73, 64)   0           activation_3[0][0]
__________________________________________________________________________________________________

第11-13层，(73, 73, 64)→【3b, Cout=80, f=1】→(73, 73, 80)
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 73, 73, 80)   5120        max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 73, 73, 80)   240         conv2d_4[0][0]
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 73, 73, 80)   0           batch_normalization_4[0][0]
__________________________________________________________________________________________________

第14-16层，(73, 73, 80)→【4a, Cout=192, f=3】→(71, 71, 192)
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 71, 71, 192)  138240      activation_4[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 71, 71, 192)  576         conv2d_5[0][0]
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 71, 71, 192)  0           batch_normalization_5[0][0]
__________________________________________________________________________________________________

第17层，(71, 71, 192)→【max pool, f=3, s=2】→(35, 35, 192)
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)  (None, 35, 35, 192)  0           activation_5[0][0]
__________________________________________________________________________________________________

第18-40层，(35, 35, 192)→【Mixed_5b, InceptionA】→(35, 35, 256)
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 35, 35, 64)   12288       max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 35, 35, 64)   192         conv2d_9[0][0]
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 35, 35, 64)   0           batch_normalization_9[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 35, 35, 48)   9216        max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 35, 35, 96)   55296       activation_9[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 35, 35, 48)   144         conv2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 35, 35, 96)   288         conv2d_10[0][0]
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 35, 35, 48)   0           batch_normalization_7[0][0]
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 35, 35, 96)   0           batch_normalization_10[0][0]
__________________________________________________________________________________________________
average_pooling2d_1 (AveragePoo (None, 35, 35, 192)  0           max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 35, 35, 64)   12288       max_pooling2d_2[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 35, 35, 64)   76800       activation_7[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 35, 35, 96)   82944       activation_10[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 35, 35, 32)   6144        average_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 35, 35, 64)   192         conv2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 35, 35, 64)   192         conv2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 35, 35, 96)   288         conv2d_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 35, 35, 32)   96          conv2d_12[0][0]
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 35, 35, 64)   0           batch_normalization_6[0][0]
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 35, 35, 64)   0           batch_normalization_8[0][0]
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 35, 35, 96)   0           batch_normalization_11[0][0]
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 35, 35, 32)   0           batch_normalization_12[0][0]
__________________________________________________________________________________________________
mixed0 (Concatenate)            (None, 35, 35, 256)  0           activation_6[0][0]
activation_8[0][0]
activation_11[0][0]
activation_12[0][0]
__________________________________________________________________________________________________

第41-63层，(35, 35, 256)→【Mixed_5c, InceptionA】→(35, 35, 288)
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 35, 35, 64)   16384       mixed0[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 35, 35, 64)   192         conv2d_16[0][0]
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 35, 35, 64)   0           batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 35, 35, 48)   12288       mixed0[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 35, 35, 96)   55296       activation_16[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 35, 35, 48)   144         conv2d_14[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 35, 35, 96)   288         conv2d_17[0][0]
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 35, 35, 48)   0           batch_normalization_14[0][0]
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 35, 35, 96)   0           batch_normalization_17[0][0]
__________________________________________________________________________________________________
average_pooling2d_2 (AveragePoo (None, 35, 35, 256)  0           mixed0[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 35, 35, 64)   16384       mixed0[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 35, 35, 64)   76800       activation_14[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 35, 35, 96)   82944       activation_17[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 35, 35, 64)   16384       average_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 35, 35, 64)   192         conv2d_13[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 35, 35, 64)   192         conv2d_15[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 35, 35, 96)   288         conv2d_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 35, 35, 64)   192         conv2d_19[0][0]
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 35, 35, 64)   0           batch_normalization_13[0][0]
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 35, 35, 64)   0           batch_normalization_15[0][0]
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 35, 35, 96)   0           batch_normalization_18[0][0]
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 35, 35, 64)   0           batch_normalization_19[0][0]
__________________________________________________________________________________________________
mixed1 (Concatenate)            (None, 35, 35, 288)  0           activation_13[0][0]
activation_15[0][0]
activation_18[0][0]
activation_19[0][0]
__________________________________________________________________________________________________

第64-86层，(35, 35, 288)→【Mixed_5d, InceptionA】→(35, 35, 288)
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 35, 35, 64)   18432       mixed1[0][0]
__________________________________________________________________________________________________
batch_normalization_23 (BatchNo (None, 35, 35, 64)   192         conv2d_23[0][0]
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 35, 35, 64)   0           batch_normalization_23[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 35, 35, 48)   13824       mixed1[0][0]
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 35, 35, 96)   55296       activation_23[0][0]
__________________________________________________________________________________________________
batch_normalization_21 (BatchNo (None, 35, 35, 48)   144         conv2d_21[0][0]
__________________________________________________________________________________________________
batch_normalization_24 (BatchNo (None, 35, 35, 96)   288         conv2d_24[0][0]
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 35, 35, 48)   0           batch_normalization_21[0][0]
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 35, 35, 96)   0           batch_normalization_24[0][0]
__________________________________________________________________________________________________
average_pooling2d_3 (AveragePoo (None, 35, 35, 288)  0           mixed1[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 35, 35, 64)   18432       mixed1[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 35, 35, 64)   76800       activation_21[0][0]
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 35, 35, 96)   82944       activation_24[0][0]
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, 35, 35, 64)   18432       average_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 35, 35, 64)   192         conv2d_20[0][0]
__________________________________________________________________________________________________
batch_normalization_22 (BatchNo (None, 35, 35, 64)   192         conv2d_22[0][0]
__________________________________________________________________________________________________
batch_normalization_25 (BatchNo (None, 35, 35, 96)   288         conv2d_25[0][0]
__________________________________________________________________________________________________
batch_normalization_26 (BatchNo (None, 35, 35, 64)   192         conv2d_26[0][0]
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 35, 35, 64)   0           batch_normalization_20[0][0]
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 35, 35, 64)   0           batch_normalization_22[0][0]
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 35, 35, 96)   0           batch_normalization_25[0][0]
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 35, 35, 64)   0           batch_normalization_26[0][0]
__________________________________________________________________________________________________
mixed2 (Concatenate)            (None, 35, 35, 288)  0           activation_20[0][0]
activation_22[0][0]
activation_25[0][0]
activation_26[0][0]
__________________________________________________________________________________________________

第87-100层，(35, 35, 288)→【Mixed_6a, InceptionB】→(17, 17, 768)
__________________________________________________________________________________________________
conv2d_28 (Conv2D)              (None, 35, 35, 64)   18432       mixed2[0][0]
__________________________________________________________________________________________________
batch_normalization_28 (BatchNo (None, 35, 35, 64)   192         conv2d_28[0][0]
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 35, 35, 64)   0           batch_normalization_28[0][0]
__________________________________________________________________________________________________
conv2d_29 (Conv2D)              (None, 35, 35, 96)   55296       activation_28[0][0]
__________________________________________________________________________________________________
batch_normalization_29 (BatchNo (None, 35, 35, 96)   288         conv2d_29[0][0]
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 35, 35, 96)   0           batch_normalization_29[0][0]
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, 17, 17, 384)  995328      mixed2[0][0]
__________________________________________________________________________________________________
conv2d_30 (Conv2D)              (None, 17, 17, 96)   82944       activation_29[0][0]
__________________________________________________________________________________________________
batch_normalization_27 (BatchNo (None, 17, 17, 384)  1152        conv2d_27[0][0]
__________________________________________________________________________________________________
batch_normalization_30 (BatchNo (None, 17, 17, 96)   288         conv2d_30[0][0]
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 17, 17, 384)  0           batch_normalization_27[0][0]
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 17, 17, 96)   0           batch_normalization_30[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)  (None, 17, 17, 288)  0           mixed2[0][0]
__________________________________________________________________________________________________
mixed3 (Concatenate)            (None, 17, 17, 768)  0           activation_27[0][0]
activation_30[0][0]
max_pooling2d_3[0][0]
__________________________________________________________________________________________________

第101-132层，(17, 17, 768)→【Mixed_6b, InceptionC】→(17, 17, 768)
__________________________________________________________________________________________________
conv2d_35 (Conv2D)              (None, 17, 17, 128)  98304       mixed3[0][0]
__________________________________________________________________________________________________
batch_normalization_35 (BatchNo (None, 17, 17, 128)  384         conv2d_35[0][0]
__________________________________________________________________________________________________
activation_35 (Activation)      (None, 17, 17, 128)  0           batch_normalization_35[0][0]
__________________________________________________________________________________________________
conv2d_36 (Conv2D)              (None, 17, 17, 128)  114688      activation_35[0][0]
__________________________________________________________________________________________________
batch_normalization_36 (BatchNo (None, 17, 17, 128)  384         conv2d_36[0][0]
__________________________________________________________________________________________________
activation_36 (Activation)      (None, 17, 17, 128)  0           batch_normalization_36[0][0]
__________________________________________________________________________________________________
conv2d_32 (Conv2D)              (None, 17, 17, 128)  98304       mixed3[0][0]
__________________________________________________________________________________________________
conv2d_37 (Conv2D)              (None, 17, 17, 128)  114688      activation_36[0][0]
__________________________________________________________________________________________________
batch_normalization_32 (BatchNo (None, 17, 17, 128)  384         conv2d_32[0][0]
__________________________________________________________________________________________________
batch_normalization_37 (BatchNo (None, 17, 17, 128)  384         conv2d_37[0][0]
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 17, 17, 128)  0           batch_normalization_32[0][0]
__________________________________________________________________________________________________
activation_37 (Activation)      (None, 17, 17, 128)  0           batch_normalization_37[0][0]
__________________________________________________________________________________________________
conv2d_33 (Conv2D)              (None, 17, 17, 128)  114688      activation_32[0][0]
__________________________________________________________________________________________________
conv2d_38 (Conv2D)              (None, 17, 17, 128)  114688      activation_37[0][0]
__________________________________________________________________________________________________
batch_normalization_33 (BatchNo (None, 17, 17, 128)  384         conv2d_33[0][0]
__________________________________________________________________________________________________
batch_normalization_38 (BatchNo (None, 17, 17, 128)  384         conv2d_38[0][0]
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 17, 17, 128)  0           batch_normalization_33[0][0]
__________________________________________________________________________________________________
activation_38 (Activation)      (None, 17, 17, 128)  0           batch_normalization_38[0][0]
__________________________________________________________________________________________________
average_pooling2d_4 (AveragePoo (None, 17, 17, 768)  0           mixed3[0][0]
__________________________________________________________________________________________________
conv2d_31 (Conv2D)              (None, 17, 17, 192)  147456      mixed3[0][0]
__________________________________________________________________________________________________
conv2d_34 (Conv2D)              (None, 17, 17, 192)  172032      activation_33[0][0]
__________________________________________________________________________________________________
conv2d_39 (Conv2D)              (None, 17, 17, 192)  172032      activation_38[0][0]
__________________________________________________________________________________________________
conv2d_40 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_31 (BatchNo (None, 17, 17, 192)  576         conv2d_31[0][0]
__________________________________________________________________________________________________
batch_normalization_34 (BatchNo (None, 17, 17, 192)  576         conv2d_34[0][0]
__________________________________________________________________________________________________
batch_normalization_39 (BatchNo (None, 17, 17, 192)  576         conv2d_39[0][0]
__________________________________________________________________________________________________
batch_normalization_40 (BatchNo (None, 17, 17, 192)  576         conv2d_40[0][0]
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 17, 17, 192)  0           batch_normalization_31[0][0]
__________________________________________________________________________________________________
activation_34 (Activation)      (None, 17, 17, 192)  0           batch_normalization_34[0][0]
__________________________________________________________________________________________________
activation_39 (Activation)      (None, 17, 17, 192)  0           batch_normalization_39[0][0]
__________________________________________________________________________________________________
activation_40 (Activation)      (None, 17, 17, 192)  0           batch_normalization_40[0][0]
__________________________________________________________________________________________________
mixed4 (Concatenate)            (None, 17, 17, 768)  0           activation_31[0][0]
activation_34[0][0]
activation_39[0][0]
activation_40[0][0]
__________________________________________________________________________________________________

第133-164层，(17, 17, 768)→【Mixed_6c, InceptionC】→(17, 17, 768)
__________________________________________________________________________________________________
conv2d_45 (Conv2D)              (None, 17, 17, 160)  122880      mixed4[0][0]
__________________________________________________________________________________________________
batch_normalization_45 (BatchNo (None, 17, 17, 160)  480         conv2d_45[0][0]
__________________________________________________________________________________________________
activation_45 (Activation)      (None, 17, 17, 160)  0           batch_normalization_45[0][0]
__________________________________________________________________________________________________
conv2d_46 (Conv2D)              (None, 17, 17, 160)  179200      activation_45[0][0]
__________________________________________________________________________________________________
batch_normalization_46 (BatchNo (None, 17, 17, 160)  480         conv2d_46[0][0]
__________________________________________________________________________________________________
activation_46 (Activation)      (None, 17, 17, 160)  0           batch_normalization_46[0][0]
__________________________________________________________________________________________________
conv2d_42 (Conv2D)              (None, 17, 17, 160)  122880      mixed4[0][0]
__________________________________________________________________________________________________
conv2d_47 (Conv2D)              (None, 17, 17, 160)  179200      activation_46[0][0]
__________________________________________________________________________________________________
batch_normalization_42 (BatchNo (None, 17, 17, 160)  480         conv2d_42[0][0]
__________________________________________________________________________________________________
batch_normalization_47 (BatchNo (None, 17, 17, 160)  480         conv2d_47[0][0]
__________________________________________________________________________________________________
activation_42 (Activation)      (None, 17, 17, 160)  0           batch_normalization_42[0][0]
__________________________________________________________________________________________________
activation_47 (Activation)      (None, 17, 17, 160)  0           batch_normalization_47[0][0]
__________________________________________________________________________________________________
conv2d_43 (Conv2D)              (None, 17, 17, 160)  179200      activation_42[0][0]
__________________________________________________________________________________________________
conv2d_48 (Conv2D)              (None, 17, 17, 160)  179200      activation_47[0][0]
__________________________________________________________________________________________________
batch_normalization_43 (BatchNo (None, 17, 17, 160)  480         conv2d_43[0][0]
__________________________________________________________________________________________________
batch_normalization_48 (BatchNo (None, 17, 17, 160)  480         conv2d_48[0][0]
__________________________________________________________________________________________________
activation_43 (Activation)      (None, 17, 17, 160)  0           batch_normalization_43[0][0]
__________________________________________________________________________________________________
activation_48 (Activation)      (None, 17, 17, 160)  0           batch_normalization_48[0][0]
__________________________________________________________________________________________________
average_pooling2d_5 (AveragePoo (None, 17, 17, 768)  0           mixed4[0][0]
__________________________________________________________________________________________________
conv2d_41 (Conv2D)              (None, 17, 17, 192)  147456      mixed4[0][0]
__________________________________________________________________________________________________
conv2d_44 (Conv2D)              (None, 17, 17, 192)  215040      activation_43[0][0]
__________________________________________________________________________________________________
conv2d_49 (Conv2D)              (None, 17, 17, 192)  215040      activation_48[0][0]
__________________________________________________________________________________________________
conv2d_50 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_5[0][0]
__________________________________________________________________________________________________
batch_normalization_41 (BatchNo (None, 17, 17, 192)  576         conv2d_41[0][0]
__________________________________________________________________________________________________
batch_normalization_44 (BatchNo (None, 17, 17, 192)  576         conv2d_44[0][0]
__________________________________________________________________________________________________
batch_normalization_49 (BatchNo (None, 17, 17, 192)  576         conv2d_49[0][0]
__________________________________________________________________________________________________
batch_normalization_50 (BatchNo (None, 17, 17, 192)  576         conv2d_50[0][0]
__________________________________________________________________________________________________
activation_41 (Activation)      (None, 17, 17, 192)  0           batch_normalization_41[0][0]
__________________________________________________________________________________________________
activation_44 (Activation)      (None, 17, 17, 192)  0           batch_normalization_44[0][0]
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 17, 17, 192)  0           batch_normalization_49[0][0]
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 17, 17, 192)  0           batch_normalization_50[0][0]
__________________________________________________________________________________________________
mixed5 (Concatenate)            (None, 17, 17, 768)  0           activation_41[0][0]
activation_44[0][0]
activation_49[0][0]
activation_50[0][0]
__________________________________________________________________________________________________

第165-196层，(17, 17, 768)→【Mixed_6d, InceptionC】→(17, 17, 768)
__________________________________________________________________________________________________
conv2d_55 (Conv2D)              (None, 17, 17, 160)  122880      mixed5[0][0]
__________________________________________________________________________________________________
batch_normalization_55 (BatchNo (None, 17, 17, 160)  480         conv2d_55[0][0]
__________________________________________________________________________________________________
activation_55 (Activation)      (None, 17, 17, 160)  0           batch_normalization_55[0][0]
__________________________________________________________________________________________________
conv2d_56 (Conv2D)              (None, 17, 17, 160)  179200      activation_55[0][0]
__________________________________________________________________________________________________
batch_normalization_56 (BatchNo (None, 17, 17, 160)  480         conv2d_56[0][0]
__________________________________________________________________________________________________
activation_56 (Activation)      (None, 17, 17, 160)  0           batch_normalization_56[0][0]
__________________________________________________________________________________________________
conv2d_52 (Conv2D)              (None, 17, 17, 160)  122880      mixed5[0][0]
__________________________________________________________________________________________________
conv2d_57 (Conv2D)              (None, 17, 17, 160)  179200      activation_56[0][0]
__________________________________________________________________________________________________
batch_normalization_52 (BatchNo (None, 17, 17, 160)  480         conv2d_52[0][0]
__________________________________________________________________________________________________
batch_normalization_57 (BatchNo (None, 17, 17, 160)  480         conv2d_57[0][0]
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 17, 17, 160)  0           batch_normalization_52[0][0]
__________________________________________________________________________________________________
activation_57 (Activation)      (None, 17, 17, 160)  0           batch_normalization_57[0][0]
__________________________________________________________________________________________________
conv2d_53 (Conv2D)              (None, 17, 17, 160)  179200      activation_52[0][0]
__________________________________________________________________________________________________
conv2d_58 (Conv2D)              (None, 17, 17, 160)  179200      activation_57[0][0]
__________________________________________________________________________________________________
batch_normalization_53 (BatchNo (None, 17, 17, 160)  480         conv2d_53[0][0]
__________________________________________________________________________________________________
batch_normalization_58 (BatchNo (None, 17, 17, 160)  480         conv2d_58[0][0]
__________________________________________________________________________________________________
activation_53 (Activation)      (None, 17, 17, 160)  0           batch_normalization_53[0][0]
__________________________________________________________________________________________________
activation_58 (Activation)      (None, 17, 17, 160)  0           batch_normalization_58[0][0]
__________________________________________________________________________________________________
average_pooling2d_6 (AveragePoo (None, 17, 17, 768)  0           mixed5[0][0]
__________________________________________________________________________________________________
conv2d_51 (Conv2D)              (None, 17, 17, 192)  147456      mixed5[0][0]
__________________________________________________________________________________________________
conv2d_54 (Conv2D)              (None, 17, 17, 192)  215040      activation_53[0][0]
__________________________________________________________________________________________________
conv2d_59 (Conv2D)              (None, 17, 17, 192)  215040      activation_58[0][0]
__________________________________________________________________________________________________
conv2d_60 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_6[0][0]
__________________________________________________________________________________________________
batch_normalization_51 (BatchNo (None, 17, 17, 192)  576         conv2d_51[0][0]
__________________________________________________________________________________________________
batch_normalization_54 (BatchNo (None, 17, 17, 192)  576         conv2d_54[0][0]
__________________________________________________________________________________________________
batch_normalization_59 (BatchNo (None, 17, 17, 192)  576         conv2d_59[0][0]
__________________________________________________________________________________________________
batch_normalization_60 (BatchNo (None, 17, 17, 192)  576         conv2d_60[0][0]
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 17, 17, 192)  0           batch_normalization_51[0][0]
__________________________________________________________________________________________________
activation_54 (Activation)      (None, 17, 17, 192)  0           batch_normalization_54[0][0]
__________________________________________________________________________________________________
activation_59 (Activation)      (None, 17, 17, 192)  0           batch_normalization_59[0][0]
__________________________________________________________________________________________________
activation_60 (Activation)      (None, 17, 17, 192)  0           batch_normalization_60[0][0]
__________________________________________________________________________________________________
mixed6 (Concatenate)            (None, 17, 17, 768)  0           activation_51[0][0]
activation_54[0][0]
activation_59[0][0]
activation_60[0][0]
__________________________________________________________________________________________________

第197-228层，(17, 17, 768)→【Mixed_6e, InceptionC】→(17, 17, 768)
__________________________________________________________________________________________________
conv2d_65 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]
__________________________________________________________________________________________________
batch_normalization_65 (BatchNo (None, 17, 17, 192)  576         conv2d_65[0][0]
__________________________________________________________________________________________________
activation_65 (Activation)      (None, 17, 17, 192)  0           batch_normalization_65[0][0]
__________________________________________________________________________________________________
conv2d_66 (Conv2D)              (None, 17, 17, 192)  258048      activation_65[0][0]
__________________________________________________________________________________________________
batch_normalization_66 (BatchNo (None, 17, 17, 192)  576         conv2d_66[0][0]
__________________________________________________________________________________________________
activation_66 (Activation)      (None, 17, 17, 192)  0           batch_normalization_66[0][0]
__________________________________________________________________________________________________
conv2d_62 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]
__________________________________________________________________________________________________
conv2d_67 (Conv2D)              (None, 17, 17, 192)  258048      activation_66[0][0]
__________________________________________________________________________________________________
batch_normalization_62 (BatchNo (None, 17, 17, 192)  576         conv2d_62[0][0]
__________________________________________________________________________________________________
batch_normalization_67 (BatchNo (None, 17, 17, 192)  576         conv2d_67[0][0]
__________________________________________________________________________________________________
activation_62 (Activation)      (None, 17, 17, 192)  0           batch_normalization_62[0][0]
__________________________________________________________________________________________________
activation_67 (Activation)      (None, 17, 17, 192)  0           batch_normalization_67[0][0]
__________________________________________________________________________________________________
conv2d_63 (Conv2D)              (None, 17, 17, 192)  258048      activation_62[0][0]
__________________________________________________________________________________________________
conv2d_68 (Conv2D)              (None, 17, 17, 192)  258048      activation_67[0][0]
__________________________________________________________________________________________________
batch_normalization_63 (BatchNo (None, 17, 17, 192)  576         conv2d_63[0][0]
__________________________________________________________________________________________________
batch_normalization_68 (BatchNo (None, 17, 17, 192)  576         conv2d_68[0][0]
__________________________________________________________________________________________________
activation_63 (Activation)      (None, 17, 17, 192)  0           batch_normalization_63[0][0]
__________________________________________________________________________________________________
activation_68 (Activation)      (None, 17, 17, 192)  0           batch_normalization_68[0][0]
__________________________________________________________________________________________________
average_pooling2d_7 (AveragePoo (None, 17, 17, 768)  0           mixed6[0][0]
__________________________________________________________________________________________________
conv2d_61 (Conv2D)              (None, 17, 17, 192)  147456      mixed6[0][0]
__________________________________________________________________________________________________
conv2d_64 (Conv2D)              (None, 17, 17, 192)  258048      activation_63[0][0]
__________________________________________________________________________________________________
conv2d_69 (Conv2D)              (None, 17, 17, 192)  258048      activation_68[0][0]
__________________________________________________________________________________________________
conv2d_70 (Conv2D)              (None, 17, 17, 192)  147456      average_pooling2d_7[0][0]
__________________________________________________________________________________________________
batch_normalization_61 (BatchNo (None, 17, 17, 192)  576         conv2d_61[0][0]
__________________________________________________________________________________________________
batch_normalization_64 (BatchNo (None, 17, 17, 192)  576         conv2d_64[0][0]
__________________________________________________________________________________________________
batch_normalization_69 (BatchNo (None, 17, 17, 192)  576         conv2d_69[0][0]
__________________________________________________________________________________________________
batch_normalization_70 (BatchNo (None, 17, 17, 192)  576         conv2d_70[0][0]
__________________________________________________________________________________________________
activation_61 (Activation)      (None, 17, 17, 192)  0           batch_normalization_61[0][0]
__________________________________________________________________________________________________
activation_64 (Activation)      (None, 17, 17, 192)  0           batch_normalization_64[0][0]
__________________________________________________________________________________________________
activation_69 (Activation)      (None, 17, 17, 192)  0           batch_normalization_69[0][0]
__________________________________________________________________________________________________
activation_70 (Activation)      (None, 17, 17, 192)  0           batch_normalization_70[0][0]
__________________________________________________________________________________________________
mixed7 (Concatenate)            (None, 17, 17, 768)  0           activation_61[0][0]
activation_64[0][0]
activation_69[0][0]
activation_70[0][0]
__________________________________________________________________________________________________

第229-248层，(17, 17, 768)→【Mixed_7a, InceptionD】→(8, 8, 1280)
__________________________________________________________________________________________________
conv2d_73 (Conv2D)              (None, 17, 17, 192)  147456      mixed7[0][0]
__________________________________________________________________________________________________
batch_normalization_73 (BatchNo (None, 17, 17, 192)  576         conv2d_73[0][0]
__________________________________________________________________________________________________
activation_73 (Activation)      (None, 17, 17, 192)  0           batch_normalization_73[0][0]
__________________________________________________________________________________________________
conv2d_74 (Conv2D)              (None, 17, 17, 192)  258048      activation_73[0][0]
__________________________________________________________________________________________________
batch_normalization_74 (BatchNo (None, 17, 17, 192)  576         conv2d_74[0][0]
__________________________________________________________________________________________________
activation_74 (Activation)      (None, 17, 17, 192)  0           batch_normalization_74[0][0]
__________________________________________________________________________________________________
conv2d_71 (Conv2D)              (None, 17, 17, 192)  147456      mixed7[0][0]
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, 17, 17, 192)  258048      activation_74[0][0]
__________________________________________________________________________________________________
batch_normalization_71 (BatchNo (None, 17, 17, 192)  576         conv2d_71[0][0]
__________________________________________________________________________________________________
batch_normalization_75 (BatchNo (None, 17, 17, 192)  576         conv2d_75[0][0]
__________________________________________________________________________________________________
activation_71 (Activation)      (None, 17, 17, 192)  0           batch_normalization_71[0][0]
__________________________________________________________________________________________________
activation_75 (Activation)      (None, 17, 17, 192)  0           batch_normalization_75[0][0]
__________________________________________________________________________________________________
conv2d_72 (Conv2D)              (None, 8, 8, 320)    552960      activation_71[0][0]
__________________________________________________________________________________________________
conv2d_76 (Conv2D)              (None, 8, 8, 192)    331776      activation_75[0][0]
__________________________________________________________________________________________________
batch_normalization_72 (BatchNo (None, 8, 8, 320)    960         conv2d_72[0][0]
__________________________________________________________________________________________________
batch_normalization_76 (BatchNo (None, 8, 8, 192)    576         conv2d_76[0][0]
__________________________________________________________________________________________________
activation_72 (Activation)      (None, 8, 8, 320)    0           batch_normalization_72[0][0]
__________________________________________________________________________________________________
activation_76 (Activation)      (None, 8, 8, 192)    0           batch_normalization_76[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)  (None, 8, 8, 768)    0           mixed7[0][0]
__________________________________________________________________________________________________
mixed8 (Concatenate)            (None, 8, 8, 1280)   0           activation_72[0][0]
activation_76[0][0]
max_pooling2d_4[0][0]
__________________________________________________________________________________________________

第249-279层，(8, 8, 1280)→【Mixed_7b, InceptionE】→(8, 8, 2048)
__________________________________________________________________________________________________
conv2d_81 (Conv2D)              (None, 8, 8, 448)    573440      mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_81 (BatchNo (None, 8, 8, 448)    1344        conv2d_81[0][0]
__________________________________________________________________________________________________
activation_81 (Activation)      (None, 8, 8, 448)    0           batch_normalization_81[0][0]
__________________________________________________________________________________________________
conv2d_78 (Conv2D)              (None, 8, 8, 384)    491520      mixed8[0][0]
__________________________________________________________________________________________________
conv2d_82 (Conv2D)              (None, 8, 8, 384)    1548288     activation_81[0][0]
__________________________________________________________________________________________________
batch_normalization_78 (BatchNo (None, 8, 8, 384)    1152        conv2d_78[0][0]
__________________________________________________________________________________________________
batch_normalization_82 (BatchNo (None, 8, 8, 384)    1152        conv2d_82[0][0]
__________________________________________________________________________________________________
activation_78 (Activation)      (None, 8, 8, 384)    0           batch_normalization_78[0][0]
__________________________________________________________________________________________________
activation_82 (Activation)      (None, 8, 8, 384)    0           batch_normalization_82[0][0]
__________________________________________________________________________________________________
conv2d_79 (Conv2D)              (None, 8, 8, 384)    442368      activation_78[0][0]
__________________________________________________________________________________________________
conv2d_80 (Conv2D)              (None, 8, 8, 384)    442368      activation_78[0][0]
__________________________________________________________________________________________________
conv2d_83 (Conv2D)              (None, 8, 8, 384)    442368      activation_82[0][0]
__________________________________________________________________________________________________
conv2d_84 (Conv2D)              (None, 8, 8, 384)    442368      activation_82[0][0]
__________________________________________________________________________________________________
average_pooling2d_8 (AveragePoo (None, 8, 8, 1280)   0           mixed8[0][0]
__________________________________________________________________________________________________
conv2d_77 (Conv2D)              (None, 8, 8, 320)    409600      mixed8[0][0]
__________________________________________________________________________________________________
batch_normalization_79 (BatchNo (None, 8, 8, 384)    1152        conv2d_79[0][0]
__________________________________________________________________________________________________
batch_normalization_80 (BatchNo (None, 8, 8, 384)    1152        conv2d_80[0][0]
__________________________________________________________________________________________________
batch_normalization_83 (BatchNo (None, 8, 8, 384)    1152        conv2d_83[0][0]
__________________________________________________________________________________________________
batch_normalization_84 (BatchNo (None, 8, 8, 384)    1152        conv2d_84[0][0]
__________________________________________________________________________________________________
conv2d_85 (Conv2D)              (None, 8, 8, 192)    245760      average_pooling2d_8[0][0]
__________________________________________________________________________________________________
batch_normalization_77 (BatchNo (None, 8, 8, 320)    960         conv2d_77[0][0]
__________________________________________________________________________________________________
activation_79 (Activation)      (None, 8, 8, 384)    0           batch_normalization_79[0][0]
__________________________________________________________________________________________________
activation_80 (Activation)      (None, 8, 8, 384)    0           batch_normalization_80[0][0]
__________________________________________________________________________________________________
activation_83 (Activation)      (None, 8, 8, 384)    0           batch_normalization_83[0][0]
__________________________________________________________________________________________________
activation_84 (Activation)      (None, 8, 8, 384)    0           batch_normalization_84[0][0]
__________________________________________________________________________________________________
batch_normalization_85 (BatchNo (None, 8, 8, 192)    576         conv2d_85[0][0]
__________________________________________________________________________________________________
activation_77 (Activation)      (None, 8, 8, 320)    0           batch_normalization_77[0][0]
__________________________________________________________________________________________________
mixed9_0 (Concatenate)          (None, 8, 8, 768)    0           activation_79[0][0]
activation_80[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 8, 8, 768)    0           activation_83[0][0]
activation_84[0][0]
__________________________________________________________________________________________________
activation_85 (Activation)      (None, 8, 8, 192)    0           batch_normalization_85[0][0]
__________________________________________________________________________________________________
mixed9 (Concatenate)            (None, 8, 8, 2048)   0           activation_77[0][0]
mixed9_0[0][0]
concatenate_1[0][0]
activation_85[0][0]
__________________________________________________________________________________________________

第280-310层，(8, 8, 2048)→【Mixed_7c, InceptionE】→(8, 8, 2048)
__________________________________________________________________________________________________
conv2d_90 (Conv2D)              (None, 8, 8, 448)    917504      mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_90 (BatchNo (None, 8, 8, 448)    1344        conv2d_90[0][0]
__________________________________________________________________________________________________
activation_90 (Activation)      (None, 8, 8, 448)    0           batch_normalization_90[0][0]
__________________________________________________________________________________________________
conv2d_87 (Conv2D)              (None, 8, 8, 384)    786432      mixed9[0][0]
__________________________________________________________________________________________________
conv2d_91 (Conv2D)              (None, 8, 8, 384)    1548288     activation_90[0][0]
__________________________________________________________________________________________________
batch_normalization_87 (BatchNo (None, 8, 8, 384)    1152        conv2d_87[0][0]
__________________________________________________________________________________________________
batch_normalization_91 (BatchNo (None, 8, 8, 384)    1152        conv2d_91[0][0]
__________________________________________________________________________________________________
activation_87 (Activation)      (None, 8, 8, 384)    0           batch_normalization_87[0][0]
__________________________________________________________________________________________________
activation_91 (Activation)      (None, 8, 8, 384)    0           batch_normalization_91[0][0]
__________________________________________________________________________________________________
conv2d_88 (Conv2D)              (None, 8, 8, 384)    442368      activation_87[0][0]
__________________________________________________________________________________________________
conv2d_89 (Conv2D)              (None, 8, 8, 384)    442368      activation_87[0][0]
__________________________________________________________________________________________________
conv2d_92 (Conv2D)              (None, 8, 8, 384)    442368      activation_91[0][0]
__________________________________________________________________________________________________
conv2d_93 (Conv2D)              (None, 8, 8, 384)    442368      activation_91[0][0]
__________________________________________________________________________________________________
average_pooling2d_9 (AveragePoo (None, 8, 8, 2048)   0           mixed9[0][0]
__________________________________________________________________________________________________
conv2d_86 (Conv2D)              (None, 8, 8, 320)    655360      mixed9[0][0]
__________________________________________________________________________________________________
batch_normalization_88 (BatchNo (None, 8, 8, 384)    1152        conv2d_88[0][0]
__________________________________________________________________________________________________
batch_normalization_89 (BatchNo (None, 8, 8, 384)    1152        conv2d_89[0][0]
__________________________________________________________________________________________________
batch_normalization_92 (BatchNo (None, 8, 8, 384)    1152        conv2d_92[0][0]
__________________________________________________________________________________________________
batch_normalization_93 (BatchNo (None, 8, 8, 384)    1152        conv2d_93[0][0]
__________________________________________________________________________________________________
conv2d_94 (Conv2D)              (None, 8, 8, 192)    393216      average_pooling2d_9[0][0]
__________________________________________________________________________________________________
batch_normalization_86 (BatchNo (None, 8, 8, 320)    960         conv2d_86[0][0]
__________________________________________________________________________________________________
activation_88 (Activation)      (None, 8, 8, 384)    0           batch_normalization_88[0][0]
__________________________________________________________________________________________________
activation_89 (Activation)      (None, 8, 8, 384)    0           batch_normalization_89[0][0]
__________________________________________________________________________________________________
activation_92 (Activation)      (None, 8, 8, 384)    0           batch_normalization_92[0][0]
__________________________________________________________________________________________________
activation_93 (Activation)      (None, 8, 8, 384)    0           batch_normalization_93[0][0]
__________________________________________________________________________________________________
batch_normalization_94 (BatchNo (None, 8, 8, 192)    576         conv2d_94[0][0]
__________________________________________________________________________________________________
activation_86 (Activation)      (None, 8, 8, 320)    0           batch_normalization_86[0][0]
__________________________________________________________________________________________________
mixed9_1 (Concatenate)          (None, 8, 8, 768)    0           activation_88[0][0]
activation_89[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, 8, 8, 768)    0           activation_92[0][0]
activation_93[0][0]
__________________________________________________________________________________________________
activation_94 (Activation)      (None, 8, 8, 192)    0           batch_normalization_94[0][0]
__________________________________________________________________________________________________
mixed10 (Concatenate)           (None, 8, 8, 2048)   0           activation_86[0][0]
mixed9_1[0][0]
concatenate_2[0][0]
activation_94[0][0]
__________________________________________________________________________________________________

第311-312层，(8, 8, 2048)→【global avg pool】→(2048,)→【dropout】→(2048,)→【fc】→(1000,)
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 2048)         0           mixed10[0][0]
__________________________________________________________________________________________________
predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]
==================================================================================================
Total params: 23,851,784
Trainable params: 23,817,352
Non-trainable params: 34,432
__________________________________________________________________________________________________

Non-trainable params参数均来自bn层
源代码中，x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)，设置scale=False表示省略参数$\gamma$（源代码中对应gamma），理由是：When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer.
例如第1个bn层，(149, 149, 32)→【bn】→(149, 149, 32)，分别对32个通道进行batch norm，每个通道包含3个参数，即$\mu, \sigma, \beta$（源代码中分别对应moving_mean, moving_variance, beta），其中$\mu, \sigma$是Non-trainable的参数，$\beta$是trainable的参数
故该bn层参数总数为：32×3=96，Non-trainable的参数总数为：32×2=64


展开全文
• Inceptionv3的mlmodel模型是用于Xcode的，模型可以识别一张照片的主体事物。目前苹果官网已经没有了Inceptionv3的mlmodel模型，此照片模型依旧可以正常使用。
• inceptionv3结构图visio制作
• <div><p>Hi, I'm currently trying to generate heatmap using grad-cam from my InceptionV3 model but I'm getting weird results: ...
• <ol><li>Supports <code>I3D-inceptionv1</code> and <code>I3D-inceptionv3</code> models for video action recognition from <a href="https://arxiv.org/abs/1705.07750">I3D paper</a>. Performance reproduced...
• 参照原论文使用tensorflow写的一个inceptionv3网络，后续会更新数据集的使用及训练。
• <div><p>Hi, i want to train the inceptionv3 network. I use the following command: <p>python train.py --network inceptionv3 --prefix final\inception\new\ssd --finetune 1 --end-epoch 400 --num-class 1 -...
• 迁移学习inceptionv3模型准备好新的数据集和训练好的模型之后，通过以下代码完成迁移学习。个子文件夹，每个子文件夹代表一种花，表示不同类别，每张图片是RGB彩色模式，大小不相同。先将原始图像数据整理成模型需要...
• <div><p>From master, I modified the <code>activation_maximization.ipynb</code> notebook to replace <code>VGG16</code> with <code>InceptionV3</code>. Basically, I only changed the model instantiation ...
• InceptionV3网络结构图手画笔记图，本结构图是依据inceptionV3代码解释而来：
InceptionV3网络结构图手画笔记图，本结构图是依据inceptionV3代码解释而来：


展开全文
• InceptionV3_Optimization_with_TensorRT
• 本课程讲解内容是基于深度学习框架Keras，对InceptionV3模型进行迁移学习。涉及到迁移学习的必要性，迁移学习方法，迁移学习实战，最后用迁移学习结果去识别图片。
• <p>When trying to import InceptionV3 on Python3.6, using <pre><code>python from keras.applications.inception_v3 import InceptionV3 basemodel = InceptionV3(weights='imagenet', include_...
• InceptionV3.py: import tensorflow as tf import os import flower_photo_dispose as fd from tensorflow.python.platform import gfile print("hello wrold1") model_path = "inception_dec_2015/" model_file =...
InceptionV3.py:

import tensorflow as tf
import os
import flower_photo_dispose as fd
from tensorflow.python.platform import gfile
print("hello wrold1")
model_path = "inception_dec_2015/"
model_file = "tensorflow_inception_graph.pb"

num_steps = 4000
BATCH_SIZE = 100

bottleneck_size = 2048  # InceptionV3模型瓶颈层的节点个数

# 调用create_image_lists()函数获得该函数返回的字典
image_lists = fd.create_image_dict()
num_classes = len(image_lists.keys())  # num_classes=5,因为有5类

# 读取已经训练好的Inception-v3模型。
with gfile.FastGFile(os.path.join(model_path, model_file), 'rb') as f:
graph_def = tf.GraphDef()

# 使用import_graph_def()函数加载读取的InceptionV3模型后会返回
# 图像数据输入节点的张量名称以及计算瓶颈结果所对应的张量，函数原型为
# import_graph_def(graph_def,input_map,return_elements,name,op_dict,producer_op_list)
bottleneck_tensor, jpeg_data_tensor = tf.import_graph_def(graph_def,
return_elements=["pool_3/_reshape:0",
"DecodeJpeg/contents:0"])

x = tf.placeholder(tf.float32, [None, bottleneck_size], name='BottleneckInputPlaceholder')
y_ = tf.placeholder(tf.float32, [None, num_classes], name='GroundTruthInput')

# 定义一层全连接层
with tf.name_scope("final_training_ops"):
weights = tf.Variable(tf.truncated_normal([bottleneck_size, num_classes], stddev=0.001))
biases = tf.Variable(tf.zeros([num_classes]))
logits = tf.matmul(x, weights) + biases
final_tensor = tf.nn.softmax(logits)

# 定义交叉熵损失函数以及train_step使用的随机梯度下降优化器
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_)
cross_entropy_mean = tf.reduce_mean(cross_entropy)

# 定义计算正确率的操作
correct_prediction = tf.equal(tf.argmax(final_tensor, 1), tf.argmax(y_, 1))
evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
for i in range(num_steps):
# 使用get_random_bottlenecks()函数产生训练用的随机的特征向量数据及其对应的label
# 在run()函数内开始训练的过程
train_bottlenecks, train_labels = fd.get_random_bottlenecks(sess, num_classes,
image_lists, BATCH_SIZE,
"training",
jpeg_data_tensor, bottleneck_tensor)
sess.run(train_step, feed_dict={x: train_bottlenecks, y_: train_labels})

# 进行相关的验证，同样是使用get_random_bottlenecks()函数产生随机的特征向量及其
# 对应的label
if i % 100 == 0:
validation_bottlenecks, validation_labels = fd.get_random_bottlenecks(sess,
num_classes, image_lists,
BATCH_SIZE, "validation",
jpeg_data_tensor, bottleneck_tensor)
validation_accuracy = sess.run(evaluation_step, feed_dict={
x: validation_bottlenecks,
y_: validation_labels})
print("Step %d: Validation accuracy = %.1f%%" % (i, validation_accuracy * 100))

# 在最后的测试数据上测试正确率，这里调用的是get_test_bottlenecks()函数，返回
# 所有图片的特征向量作为特征数据
test_bottlenecks, test_labels = fd.get_test_bottlenecks(sess, image_lists, num_classes,
jpeg_data_tensor, bottleneck_tensor)
test_accuracy = sess.run(evaluation_step, feed_dict={x: test_bottlenecks,
y_: test_labels})
print("Finally test accuracy = %.1f%%" % (test_accuracy * 100))


flower_photos_dispose.py:

import glob
import os.path
import random
import numpy as np
from tensorflow.python.platform import gfile
import tensorflow as tf
input_data = "flower_photos"
CACHE_DIR  = "bottleneck"

def create_image_dict():
result = {}
# path是flower_photos文件夹的路径，同时也包含了其子文件夹的路径
# directory的数据形式为一个列表，打印其内容为：
# /home/jiangziyang/flower_photos, /home/jiangziyang/flower_photos/daisy,
# /home/jiangziyang/flower_photos/tulips, /home/jiangziyang/flower_photos/roses,
# /home/jiangziyang/flower_photos/dandelion, /home/jiangziyang/flower_photos/sunflowers
path_list = [x[0] for x in os.walk(input_data)]
is_root_dir = True
for sub_dirs in path_list:
if is_root_dir:
is_root_dir = False
continue  # continue会跳出当前循环执行下一轮的循环

# extension_name列表列出了图片文件可能的扩展名
extension_name = ['jpg', 'jpeg', 'JPG', 'JPEG']
# 创建保存图片文件名的列表
images_list = []
for extension in extension_name:
# join()函数用于拼接路径，用extension_name列表中的元素作为后缀名，比如：
# /home/jiangziyang/flower_photos/daisy/*.jpg
# /home/jiangziyang/flower_photos/daisy/*.jpeg
# /home/jiangziyang/flower_photos/daisy/*.JPG
# /home/jiangziyang/flower_photos/daisy/*.JPEG
file_glob = os.path.join(sub_dirs, '*.' + extension)

# 使用glob()函数获取满足正则表达式的文件名，例如对于
# /home/jiangziyang/flower_photos/daisy/*.jpg，glob()函数会得到该路径下
# 所有后缀名为.jpg的文件，例如下面这个例子：
# /home/jiangziyang/flower_photos/daisy/7924174040_444d5bbb8a.jpg
images_list.extend(glob.glob(file_glob))

# basename()函数会舍弃一个文件名中保存的路径，比如对于
# /home/jiangziyang/flower_photos/daisy，其结果是仅仅保留daisy
# flower_category就是图片的类别，这个类别通过子文件夹名获得
dir_name = os.path.basename(sub_dirs)
flower_category = dir_name

# 初始化每个类别的flower photos对应的训练集图片名列表、测试集图片名列表
# 和验证集图片名列表
training_images = []
testing_images = []
validation_images = []

for image_name in images_list:
# 对于images_name列表中的图片文件名，它也包含了路径名，但我们不需要
# 路径名所以这里使用basename()函数获取文件名
image_name = os.path.basename(image_name)
# random.randint()函数产生均匀分布的整数
score = np.random.randint(100)
if score < 10:
validation_images.append(image_name)
elif score < 20:
testing_images.append(image_name)
else:
training_images.append(image_name)

# 每执行一次最外层的循环，都会刷新一次result，result是一个字典，
# 它的key为flower_category，它的value也是一个字典，以数据集分类的形式存储了
# 所有图片的名称，最后函数将result返回
result[flower_category] = {
"dir": dir_name,
"training": training_images,
"testing": testing_images,
"validation": validation_images,
}
return result

def get_image_path(image_lists, image_dir, flower_category, image_index, data_category):
# category_list用列表的形式保存了某一类花的某一个数据集的内容，
# 其中参数flower_category从函数get_random_bottlenecks()传递过来
category_list = image_lists[flower_category][data_category]

# actual_index是一个图片在category_list列表中的位置序号
# 其中参数image_index也是从函数get_random_bottlenecks()传递过来
actual_index = image_index % len(category_list)

# image_name就是图片的文件名
image_name = category_list[actual_index]

# sub_dir得到flower_photos中某一类花所在的子文件夹名
sub_dir = image_lists[flower_category]["dir"]

# 拼接路径，这个路径包含了文件名，最终返回给create_bottleneck()函数
# 作为每一个图片对应的特征向量的文件
full_path = os.path.join(image_dir, sub_dir, image_name)
return full_path

def create_bottleneck(sess, image_lists, flower_category, image_index,
data_category, jpeg_data_tensor, bottleneck_tensor):
# sub_dir得到的是flower_photos下某一类花的文件夹名，这类花由
# flower_photos参数确定，花的文件夹名由dir参数确定
sub_dir = image_lists[flower_category]["dir"]

# 拼接路径，路径名就是在CACHE_DIR路径的基础上加上sub_dir
sub_dir_path = os.path.join(CACHE_DIR, sub_dir)

# 判断拼接出的路径是否存在，如果不存在，则在CACHE_DIR下创建相应的子文件夹
if not os.path.exists(sub_dir_path):
os.makedirs(sub_dir_path)

# 获取一张图片对应的特征向量的全名，这个全名包括了路径名，而且会在图片的.jpg后面
# 用.txt作为后缀，获取没有.txt缀的文件名使用了get_image_path()函数，
# 该函数会返回带路径的图片名
bottleneck_path = get_image_path(image_lists, CACHE_DIR, flower_category,
image_index, data_category) + ".txt"

# 如果指定名称的特征向量文件不存在，则通过InceptionV3模型计算得到该特征向量
# 计算的结果也会存入文件
if not os.path.exists(bottleneck_path):
# 获取原始的图片名，这个图片名包含了原始图片的完整路径
image_path = get_image_path(image_lists, input_data, flower_category,
image_index, data_category)
# 读取图片的内容

# 将当前图片输入到InceptionV3模型，并计算瓶颈张量的值，所得瓶颈张量的值
# 就是这张图片的特征向量，但是得到的特征向量是四维的，所以还需要通过squeeze()
# 函数压缩成一维的，以方便作为全连层的输入
bottleneck_values = sess.run(bottleneck_tensor, feed_dict={jpeg_data_tensor: image_data})

# 压缩成一维的
bottleneck_values = np.squeeze(bottleneck_values)

# 将计算得到的特征向量存入文件，存入文件前需要为两个值之间加入逗号作为分隔
# 这样可以方便从文件读取数据时的解析过程
bottleneck_string = ','.join(str(x) for x in bottleneck_values)
with open(bottleneck_path, "w") as bottleneck_file:
bottleneck_file.write(bottleneck_string)
else:
# else是特征向量文件已经存在的情况，此时会直接从bottleneck_path获取
# 特征向量数据
with open(bottleneck_path, "r") as bottleneck_file:

# 从文件读取的特征向量数据是字符串的形式，要以逗号为分隔将其转为列表的形式
bottleneck_values = [float(x) for x in bottleneck_string.split(',')]
return bottleneck_values

def get_random_bottlenecks(sess, num_classes, image_lists, batch_size, data_category, jpeg_data_tensor,
bottleneck_tensor):
# 定义bottlenecks用于存储得到的一个batch的特征向量
# 定义labels用于存储这个batch的label标签
bottlenecks = []
labels = []

for i in range(batch_size):
# random_index是从五个花类中随机抽取的类别编号
# image_lists.keys()的值就是五种花的类别名称
random_index = random.randrange(num_classes)
flower_category = list(image_lists.keys())[random_index]

# image_index就是随机抽取的图片的编号，在get_image_path()函数中
# 我们会看到如何通过这个图片编号和random_index确定类别找到图片的文件名
image_index = random.randrange(65536)

# 调用get_or_create_bottleneck()函数获取或者创建图片的特征向量
# 这个函数调用了get_image_path()函数
bottleneck = create_bottleneck(sess, image_lists, flower_category, image_index,
data_category, jpeg_data_tensor, bottleneck_tensor)

# 首先生成每一个标签的答案值，再通过append()函数组织成一个batch列表
# 函数将完整的列表返回
label = np.zeros(num_classes, dtype=np.float32)
label[random_index] = 1.0
labels.append(label)
bottlenecks.append(bottleneck)
# 这个函数的返回值是关于一幅图片的特征向量,以及它对应的标签
return bottlenecks, labels

def get_test_bottlenecks(sess, image_lists, num_classes, jpeg_data_tensor, bottleneck_tensor):
bottlenecks = []
labels = []

# flower_category_list是image_lists中键的列表，打印出来就是这样：
# ['roses', 'sunflowers', 'daisy', 'dandelion', 'tulips']
flower_category_list = list(image_lists.keys())

data_category = "testing"

# 枚举所有的类别和每个类别中的测试图片
# 在外层的for循环中，label_index是flower_category_list列表中的元素下标
# flower_category就是该列表中的值
for label_index, flower_category in enumerate(flower_category_list):

# 在内层的for循环中，通过flower_category和"testing"枚举image_lists中每一类花中
# 用于测试的花名，得到的名字就是unused_base_name，但我们只需要image_index
for image_index, unused_base_name in enumerate(image_lists[flower_category]
["testing"]):
# 调用create_bottleneck()函数创建特征向量，因为在进行训练或验证的过程中
# 用于测试的图片并没有生成相应的特征向量，所以这里要一次性全部生成
bottleneck = create_bottleneck(sess, image_lists, flower_category,
image_index, data_category,
jpeg_data_tensor, bottleneck_tensor)

# 接下来就和get_random_bottlenecks()函数相同了
label = np.zeros(num_classes, dtype=np.float32)
label[label_index] = 1.0
labels.append(label)
bottlenecks.append(bottleneck)
return bottlenecks, labels


展开全文
• 训练好InceptionV3模型以后,将一张图片输入模型,可以得到模型中每一次卷积的输出结果,并可视化出来.为标准的sci模式特征图
• <p>Yet, when I tried to load InceptionV3 model, I get an error. There was not any errors when I converted the model from 'h5' to 'json' but the code below does not work. <p><img alt=...
• 参考博文：https://blog.csdn.net/u014365862/article/details/54380246之前的博客已经介绍过InceptionV3论文，包括实现了InceptionV3的前向传播，很详细的一个版本，接下来还会有更多关于InceptionV3的介绍。...
参考博文：https://blog.csdn.net/u014365862/article/details/54380246之前的博客已经介绍过InceptionV3论文，包括实现了InceptionV3的前向传播，很详细的一个版本，接下来还会有更多关于InceptionV3的介绍。想从InceptionV3入手逐步来理解卷积的构造，模型的构建，训练，测试以及迁移。进入正题1.导入各种包import tensorflow as tf
import os
import tarfile
import requests2.下载模型2.1保存下载路径在inception_pretrain_model_urlinception_pretrain_model_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'2.2创建存放模型的文件夹#创建文件夹的名字及路径（当前路径下）
inception_pretrain_model_dir = "inception_pretrain"
#如果inception_pretrain_model_dir的文件夹不存在，则创建
if not os.path.exists(inception_pretrain_model_dir):
#os.path.exists()函数用来检验给出的路径是否真地存在 返回bool
os.makedirs(inception_pretrain_model_dir)
#makedir(path):创建文件夹，注：创建已存在的文件夹将异常

filename = inception_pretrain_model_url.split('/')[-1]
#filename取以/分开的最后一个字符串即inception-2015-12-05.tgz
filepath = os.path.join(inception_pretrain_model_dir, filename)
#将两个路径连接起来：inception_pretrain\inception-2015-12-05.tgz2.3查看路径下是否有文件，若无就下载# 如果路径名不存在（这里指的是路径下的内容）的话，就开始下载文件
if not os.path.exists(filepath):
print("开始下载: ", filename)
r = requests.get(inception_pretrain_model_url, stream=True)
# requests.get从指定http网站上下载内容
with open(filepath, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
#用with语句来打开文件，就包含关闭的功能。wb是写二进制文件，由于文件过大，批量写（这里是压缩包）2.4解压文件，解压前先打印开始解压提示语print("下载完成, 开始解压: ", filename)
#解压出来的文件其中的classify_image_graph_def.pb 文件就是训练好的Inception-v3模型。
#imagenet_synset_to_human_label_map.txt是类别文件。
tarfile.open(filepath, 'r:gz').extractall(inception_pretrain_model_dir)
# tarfile解压文件3.创建TensorBoard log目录log_dir = 'inception_log'  #目录地址
if not os.path.exists(log_dir):
os.makedirs(log_dir)

# 加载inception graph
inception_graph_def_file = os.path.join(inception_pretrain_model_dir, 'classify_image_graph_def.pb')
with tf.Session() as sess:
with tf.gfile.FastGFile(inception_graph_def_file, 'rb') as f: #以二进制读取文件
graph_def = tf.GraphDef()
# 绘图
tf.import_graph_def(graph_def, name='')
writer = tf.summary.FileWriter(log_dir, sess.graph)
#AttributeError: module 'tensorflow.python.training.training' has no attribute 'SummaryWriter'所以用tf.summary.FileWriter
writer.close()解压完后的文件如下：所有代码运行完，会得到一个events事件，在目录inception_log下，如图：接下来打开这个文件，需要用到命令行。注意，我这个inception_log文件的地址是在D：/python/neural network/Inception/inception_log4.命令行切换到inception_log跟目录切换到目录后就可以使用tensorboard --logdir=log_dir（创建log的地址）打开文件了最后出来的这个网址http://DESKTOP-JIUMT28:6006复制到谷歌浏览器里面去就可以得到可视化图展开看细节好了，不会的宝宝可以私信我
展开全文
• Can you provide pretrained inceptionv3 model? You use the inceptionv3 is not http://mxnet.io/model_zoo/ here inceptionv3?</p><p>该提问来源于开源项目：zhreshold/mxnet-ssd</p></div>
• InceptionV3算法的简介(论文介绍)、架构详解、案例应用等配图集合之详细攻略 目录 InceptionV2 & InceptionV3算法的简介(论文介绍) InceptionV2 & InceptionV3算法的架构详解 1、卷积分解 2、...
• ## InceptionV3代码解析

千次阅读 热门讨论 2018-03-30 16:13:10
• CNN常用模型8 InceptionV3 8 InceptionV3 from keras.models import Model from keras import layers from keras.layers import Activation,Dense,Input,BatchNormalization,Conv2D,MaxPooling2D,AveragePooling2D ...
• 1.inceptionv3的网络结构 2. figure5 figure6 figure7 这里作者对比了减肥之前先降维，或者减肥后再降维两种方法，前者速度快但违反了通用设计准则一，即增加了瓶颈，而后者需要耗费三倍的计算量，似乎看起来都...
• 使用MATLAB自带的inceptionv3模型进行迁移学习，若没有安装inceptionv3模型支持工具，在命令窗口输入inceptionv3，点击下载链接进行安装。 训练环境：Windows10系统，MATLAB20018b，CPU i3 3.7GHz，4GB内存。 使用...
• inceptionv3的网络结构图，直接上图： 1.全局结构图： 2. 在上图的基础上展开一级： 3.在上图的基础上进一步再展开一级： ​​​​...
• InceptionV3论文： Rethinking the Inception Architecture for Computer Vision以及一张网络结构图 MobileNetV1模型_8分类16.3MB InceptionV3模型_8分类83.4MB label_image.py脚本，即加载模型进行,数据仅选了几张...
• 前段时间做了一个简单的图像分类功能，采用Tensorflow-slim下的InceptionV3、InceptionV4网络模型，现在记录下两者在训练过程中的准确率、训练时间等进行一些比较。 项目地址：...
• 图像字幕：使用InceptionV3和光束搜索的图像字幕
• 该文件包含有一个inceptionv3的网络，以及制作和读取TFRecord格式的数据集的方法。

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