参考论文：ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
作者：Xiangyu Zhang ，Xinyu Zhou，Mengxiao Lin，Jian Sun
1、论文摘要
我们引入了一个高效计算的CNN结构名字叫做shuffleNet，这个结构被设计用来解决部署算力非常有限的移动设备问题，这个新的结构使用了两个新的操作,pointwise group convolution 和 channel shuffle能够在极大减少计算量的同时保持一定的精度。我们在ImageNet classification和MS COCO目标检测数据集上做实验论证了ShuffleNet和其他的结构相比有着很好的性能。比如,相比于mobilenet，shufflenet在ImageNet 分类任务上有着更低的top-1错误率(错误率是7.8%)需要的计算量为40MFLOPs。在一个ARM-based移动设备,ShuffleNet相比于AlexNet实现了保持一定的精度的同时，实现了13A的速度。
2 Group Convolution（分组卷积）
第一个创新点就是分组1*1卷积
简单来说分组卷积就是将特征图分为不同的组，再对每组特征图分别进行卷积。这里的分组一般都是分为 n 个等份，理论上其实不是等份也可以，不 过一般为了实现方便都是分为等份。分组卷积的好处主要是可以减少模型的计算量和训练参 数，同时对模型准确率影响不大，甚至有可能会提高模型准确率。
在分组卷积中，每个卷积核只处理部分通道，比如上图中，红色卷积核只处理红色的通道，绿色卷积核只处理绿色通道，黄色卷积核只处理黄色通道。此时每个卷积核有2个通道，每个卷积核生成一张特征图。
下面我们通过几个图来详细了解一下，下图为普通卷积
图中的 Conv 表示卷积。
这里特征图的大小和卷积和的大小都不是重点内容，所以图中没有标出，我们只要能看 出 6 个特征图卷积后得到 12 个特征图就可以了。不过为了让大家理解分组卷积的计算量和 权值数量这里我们举例计算一下，假设特征图大小是 28×28，卷积核大小为 5×5，Same Padding。卷积层权值数量为 5×5×6×12+12=1812，乘法计算量为 5×5×28×28×6× 12=1411200。
下面我们看一下分组卷积，分组卷积一般都是把特征图分为 n 个等份，然后再对 n 个等 份的特征图分别卷积，这里的 n 可以人为设置，如图所示。
图中的 Conv 表示卷积。
为了跟普通卷积对比，所以这里分组卷积的例子输入也是 6 个特征图，输出也是 12 个特 征图。这里我们可以看到把 6 个特征图分为了 3 组，每组 2 个特征图，每组分别进行卷积， 卷积后得到 4 个特征图。最后再把 3 个组共 12 个特征图组合起来。假设特征图大小是 28× 28，卷积核大小为 5×5，Same Padding。这里卷积层权值数量为 5×5×2×4× 3+12=612，乘法计算量为 5×5×28×28×2×4×3=470400。权值数量和计算量都约为普通 卷积的 1/3。
分组卷积过程也可以描述如下：
1、假设输入的形状为 H × W × C ，用 k 个 h × w 的卷积核对其进行卷积操作；
2、把输入分为g组，每组形状为H × W × ( C / g )(假设可以整除)
3、把卷积核也分为g组，每组为k/g(假设可整除)个h × w 卷积核
4、按顺序，每组的输入和该组内的卷积核分别做标准卷积操作，输出 g 组形状为H ′ × W ′ × ( k / g ) ；
5、将这 g 组特征合并起来,得到最终形状为H ′ × W ′ × k 的特征;
下图为分两组时的举例：
或者看下图
左边标准卷积，每个卷积核处理12个通道
右边分组卷积，假设输入的12个通道分为3组，每个卷积核只处理4个通道
图1
图1（a）说明了两个堆叠组卷积层的情况。很明显，某个组的输出只与组内的输入有关。此属性会阻塞通道组之间的信息流并削弱表示。
如果我们允许组卷积从不同组中获取输入数据（如图 1（b）所示），输入和输出通道将完全相关。
对于上一层生成的特征图，我们可以先将每组中的通道划分为几个子组，然后将不同的子组馈入下一层中的每个组。这可以通过通道混洗操作有效而优雅地实现（图 1（c））：假设一个卷积层具有 g 个组，其输出有 g × n 个通道；我们首先将输出通道维度重塑为 (g, n)，转置然后将其展平作为下一层的输入。请注意，即使两个卷积的组数不同，该操作仍然有效。此外，channel shuffle 也是可微分的，这意味着它可以嵌入到网络结构中进行端到端训练。
举个例子来说，如下图。分组卷积生成的三组特征图，第一组1~4；第二组5~8；第三组9~12。先将特征图重塑，为三行N列的矩形。然后进行转置，变成N行三列。最后压平，从二维tensor变成一维tensor，每一组的特征图交叉组合在一起。实现各组之间的信息交融。
图2
图 2. ShuffleNet 单元
a) 具有深度卷积 (DWConv) [3, 12] 的残差单元 [9]；
b) 具有逐点组卷积 (GConv) 和通道混洗的 ShuffleNet 单元；
c) ShuffleNet 单元，stride = 2.
这里需要注意(b)中第一个分组卷积降维，然后通道重排，在进行3*3的DepthWise卷积，最后使用1*1的分组卷积升维，目的还是让残差前后的shape一致，保证可以进行Add操作。
©图是ShuffleNet的下采样模块。注意，这里左右分支不是使用Add操作，因为最后左右分支的shape是不一致的。右分支的通道数和左分支的通道数叠加 == 输出特征图的通道数out_channel(重点，和上面的残差是不一样的)，残差边上使用了池化窗口为3*3，stride=2的平均池化Add是逐元素求个，Concat是某个方向的叠加，这里是再通道方向叠加。
上图为不同分组数的ShuffleNet网络结构，通常我们将g=3的那一个网络作为baseline Network
我们在表 1 中展示了整体 ShuffleNet 架构。所提出的网络主要由一组 ShuffleNet 单元组成，分为三个阶段。每个阶段的第一个构建块应用 stride = 2。一个阶段内的其他超参数保持不变，下一个阶段的输出通道加倍。与 [9] 类似，我们将每个 ShuffleNet 的瓶颈通道数设置为输出通道的 1/4
上表中stage2的第一个block上不用GConv,用普通的1*1卷积，因为此时输入通道数只有24，太少了。且每个stage中的第一个block的stride=2（对应ShuffleNet Unit中的下采样模块,c图），其他block的stride=1(对应ShuffleNet基本模块，图b)
这里只是简单复现下，细节问题并没有管，比如那个stage2的第一个block上不用GConv,用普通的1*1卷积就没管。
1 import tensorflow as tf
2 from tensorflow.keras.layers import concatenate, Conv2D, Activation, BatchNormalization, DepthwiseConv2D
3 from tensorflow.keras.layers import add, AvgPool2D,MaxPool2D,GlobalAveragePooling2D,Dense
4 from tensorflow.keras.models import Model
5 from plot_model import plot_model 1 # 通道重排,跨组信息交互
2 def channel_shuffle(inputs, num_groups):
3 # 先得到输入特征图的shape，b:batch size，h,w:一张图的size，c:通道数
4 b, h, w, c = inputs.shape
5
6 # 确定shape = [b, h, w, num_groups, c//num_groups]。通道维度原来是一个长为c的一维tensor，变成num_groups行n列的矩阵
7 # 在通道维度上将特征图reshape为num_groups行n列的矩阵
8 x_reshaped = tf.reshape(inputs, [-1, h, w, num_groups, c // num_groups])
9
10 # 确定转置的矩形的shape = [b, h, w, c//num_groups, num_groups]
11 # 矩阵转置，最后两个维度从num_groups行n列变成n行num_groups列
12 x_transposed = tf.transpose(x_reshaped, [0, 1, 2, 4, 3])
13
14 # 重新排列，shotcut和x的通道像素交叉排列，通道维度重新变成一维tensor
15 output = tf.reshape(x_transposed, [-1, h, w, c])
16 # 返回通道维度交叉排序后的tensor
17 return output我这里tensorflow版本为2.0，我看官网API中高版本的Conv2D参数中已经有了groups属性了。
1 def group_conv(inputs, filters, kernel, strides, num_groups):
2 conv_side_layers_tmp = tf.split(inputs, num_groups, axis=3)
3 conv_side_layers = []
4 for layer in conv_side_layers_tmp:
5 conv_side_layers.append(tf.keras.layers.Conv2D(filters // num_groups, kernel, strides, padding='same')(layer))
6 x = concatenate(conv_side_layers, axis=-1)
7
8 return x1 # 普通卷积：卷积+批标准化+ReLU激活
2 def conv(inputs, filters, kernel_size, stride=1):
3 x = Conv2D(filters, kernel_size, stride, padding='same', use_bias=False)(inputs)
4 x = BatchNormalization()(x)
5 x = Activation('relu')(x)
6 return x1 # DWConv:深度可分离卷积块(论文中DWConv卷积核全是3*3,步长有1和2两种)
2 def depthwise_conv_bn(inputs, kernel_size, stride=1):
3 x = DepthwiseConv2D(kernel_size=kernel_size,
4 strides=stride,
5 padding='same',
6 use_bias=False)(inputs)
7 x = BatchNormalization()(x)
8 return x注意，这里没有用ReLU
1 # ShuffleNetV1基本模块(Add)
2 def shuffleNetUnitA(inputs, num_groups):
3 in_channels = inputs.shape[-1]
4 out_channels = in_channels
5 bottleneck_channels = out_channels // 4
6
7 # 1*1分组卷积降维
8 x = group_conv(inputs, bottleneck_channels, kernel=1, strides=1, num_groups=num_groups)
9 x = BatchNormalization()(x)
10 x = Activation('relu')(x)
11 # Channel Shuffle
12 x = channel_shuffle(x, num_groups)
13 # 3*3 DWConv
14 x = depthwise_conv_bn(x, kernel_size=3, stride=1)
15 # 1*1分组卷积升维(要保证残差连接前后的shape一致)
16 x = group_conv(x, out_channels, kernel=1, strides=1, num_groups=num_groups)
17 x = BatchNormalization()(x)
18 x = add([inputs, x])
19 x = Activation('relu')(x)
20 return x 1 # ShuffleNetV1下采样模块(下采样模块，concat)
2 def shuffleNetUnitB(inputs, out_channels, num_groups):
3 in_channels = inputs.shape[-1]
4 # 右分支的通道数和左分支的通道数叠加 == 输出特征图的通道数out_channel(重点，和上面的残差是不一样的)
5 out_channels -= in_channels
6 bottleneck_channels = out_channels // 4
7 # （1）右分支
8 # 1*1 GConv
9 x = group_conv(inputs, bottleneck_channels, kernel=1, strides=1, num_groups=num_groups)
10 x = BatchNormalization()(x)
11 x = Activation('relu')(x)
12 # Channel Shuffle
13 x = channel_shuffle(x, num_groups)
14 # 3*3 DWConv,stide=2
15 x = depthwise_conv_bn(x, kernel_size=3, stride=2)
16 # 1*1 GConv
17 x = group_conv(x, out_channels, kernel=1, strides=1, num_groups=num_groups)
18 x = BatchNormalization()(x)
19
20 # (2)左分支:3*3 AVG Pool,stride=2
21 y = AvgPool2D(pool_size=3, strides=2, padding='same')(inputs)
22 # 在通道维度上堆叠
23 x = concatenate([y, x], axis=-1)
24 x = Activation('relu')(x)
25 return x6.5和6.6的通道数需要结合论文好好看，要不你看不懂为什么会这样设计，原论文中都有解释。
每个stage中的第一个block的stride=2(即下采样模块)，其他block的stride=1(即基本模块)
1 def stage(inputs, out_channels, num_groups, n):
2 # 每个stage中的第一个block的stride=2(即下采样模块)，其他block的stride=1(即基本模块)
3 # 都是按照论文搭建的，要去看论文原文，要不你绝对不理解为什么这样搭建，嘿嘿。
4 x = shuffleNetUnitB(inputs, out_channels, num_groups)
5
6 for _ in range(n):
7 x = shuffleNetUnitA(x, num_groups)
8 return x
1 # first_stage_channels为第一个stage的输出通道数
2 # num_groups为分组数量
3 def ShuffleNet(inputs, first_stage_channels, num_groups,num_classes):
4 # 构建网络输入tensor
5 inputs = tf.keras.Input(shape=inputs)
6 # 论文中先用了一个普通卷积和池化
7 x = Conv2D(filters=24,
8 kernel_size=3,
9 strides=2,
10 padding='same')(inputs)
11 x = MaxPool2D(pool_size=3, strides=2, padding='same')(x)
12 # 三个stage,每个stage的第一个block的stride=2
13 # 同一个stage内的其他超参数不变，下一个stage的输出通道数加倍(这个可以通过论文中的表格看出，原文也给了)
14 # n为分组卷积的分组数量，论文中用g表示
15 x = stage(x, first_stage_channels, num_groups, n=3)
16 x = stage(x, first_stage_channels * 2, num_groups, n=7)
17 x = stage(x, first_stage_channels * 4, num_groups, n=3)
18
19 x = GlobalAveragePooling2D()(x)
20 # 我看过其他大佬的文章说compile的时候再用softmax，那样更稳定，有时间再试试吧
21 x = Dense(num_classes, activation='softmax')(x)
22
23 # 完整网络架构
24 model = Model(inputs=inputs, outputs=x)
25 return model1 # 类别数
2 num_classes = 17
3 # 批次大小
4 batch_size = 32
5 # 周期数
6 epochs = 100
7 # 图片大小
8 image_size = 224查看模型摘要
1 model=ShuffleNet(inputs[224,224,3],first_stage_channels=240,num_groups=3,num_classes=17)
2 model.summary() 1 Model: "functional_1"
2 __________________________________________________________________________________________________
3 Layer (type) Output Shape Param # Connected to
4 ==================================================================================================
5 input_1 (InputLayer) [(None, 224, 224, 3) 0
6 __________________________________________________________________________________________________
7 conv2d (Conv2D) (None, 112, 112, 24) 672 input_1[0][0]
8 __________________________________________________________________________________________________
9 max_pooling2d (MaxPooling2D) (None, 56, 56, 24) 0 conv2d[0][0]
10 __________________________________________________________________________________________________
11 tf_op_layer_split (TensorFlowOp [(None, 56, 56, 8), 0 max_pooling2d[0][0]
12 __________________________________________________________________________________________________
13 conv2d_1 (Conv2D) (None, 56, 56, 18) 162 tf_op_layer_split[0][0]
14 __________________________________________________________________________________________________
15 conv2d_2 (Conv2D) (None, 56, 56, 18) 162 tf_op_layer_split[0][1]
16 __________________________________________________________________________________________________
17 conv2d_3 (Conv2D) (None, 56, 56, 18) 162 tf_op_layer_split[0][2]
18 __________________________________________________________________________________________________
19 concatenate (Concatenate) (None, 56, 56, 54) 0 conv2d_1[0][0]
20 conv2d_2[0][0]
21 conv2d_3[0][0]
22 __________________________________________________________________________________________________
23 batch_normalization (BatchNorma (None, 56, 56, 54) 216 concatenate[0][0]
24 __________________________________________________________________________________________________
25 activation (Activation) (None, 56, 56, 54) 0 batch_normalization[0][0]
26 __________________________________________________________________________________________________
27 tf_op_layer_Reshape (TensorFlow [(None, 56, 56, 3, 1 0 activation[0][0]
28 __________________________________________________________________________________________________
29 tf_op_layer_Transpose (TensorFl [(None, 56, 56, 18, 0 tf_op_layer_Reshape[0][0]
30 __________________________________________________________________________________________________
31 tf_op_layer_Reshape_1 (TensorFl [(None, 56, 56, 54)] 0 tf_op_layer_Transpose[0][0]
32 __________________________________________________________________________________________________
33 depthwise_conv2d (DepthwiseConv (None, 28, 28, 54) 486 tf_op_layer_Reshape_1[0][0]
34 __________________________________________________________________________________________________
35 batch_normalization_1 (BatchNor (None, 28, 28, 54) 216 depthwise_conv2d[0][0]
36 __________________________________________________________________________________________________
37 tf_op_layer_split_1 (TensorFlow [(None, 28, 28, 18), 0 batch_normalization_1[0][0]
38 __________________________________________________________________________________________________
39 conv2d_4 (Conv2D) (None, 28, 28, 72) 1368 tf_op_layer_split_1[0][0]
40 __________________________________________________________________________________________________
41 conv2d_5 (Conv2D) (None, 28, 28, 72) 1368 tf_op_layer_split_1[0][1]
42 __________________________________________________________________________________________________
43 conv2d_6 (Conv2D) (None, 28, 28, 72) 1368 tf_op_layer_split_1[0][2]
44 __________________________________________________________________________________________________
45 concatenate_1 (Concatenate) (None, 28, 28, 216) 0 conv2d_4[0][0]
46 conv2d_5[0][0]
47 conv2d_6[0][0]
48 __________________________________________________________________________________________________
49 average_pooling2d (AveragePooli (None, 28, 28, 24) 0 max_pooling2d[0][0]
50 __________________________________________________________________________________________________
51 batch_normalization_2 (BatchNor (None, 28, 28, 216) 864 concatenate_1[0][0]
52 __________________________________________________________________________________________________
53 concatenate_2 (Concatenate) (None, 28, 28, 240) 0 average_pooling2d[0][0]
54 batch_normalization_2[0][0]
55 __________________________________________________________________________________________________
56 activation_1 (Activation) (None, 28, 28, 240) 0 concatenate_2[0][0]
57 __________________________________________________________________________________________________
58 tf_op_layer_split_2 (TensorFlow [(None, 28, 28, 80), 0 activation_1[0][0]
59 __________________________________________________________________________________________________
60 conv2d_7 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_2[0][0]
61 __________________________________________________________________________________________________
62 conv2d_8 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_2[0][1]
63 __________________________________________________________________________________________________
64 conv2d_9 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_2[0][2]
65 __________________________________________________________________________________________________
66 concatenate_3 (Concatenate) (None, 28, 28, 60) 0 conv2d_7[0][0]
67 conv2d_8[0][0]
68 conv2d_9[0][0]
69 __________________________________________________________________________________________________
70 batch_normalization_3 (BatchNor (None, 28, 28, 60) 240 concatenate_3[0][0]
71 __________________________________________________________________________________________________
72 activation_2 (Activation) (None, 28, 28, 60) 0 batch_normalization_3[0][0]
73 __________________________________________________________________________________________________
74 tf_op_layer_Reshape_2 (TensorFl [(None, 28, 28, 3, 2 0 activation_2[0][0]
75 __________________________________________________________________________________________________
76 tf_op_layer_Transpose_1 (Tensor [(None, 28, 28, 20, 0 tf_op_layer_Reshape_2[0][0]
77 __________________________________________________________________________________________________
78 tf_op_layer_Reshape_3 (TensorFl [(None, 28, 28, 60)] 0 tf_op_layer_Transpose_1[0][0]
79 __________________________________________________________________________________________________
80 depthwise_conv2d_1 (DepthwiseCo (None, 28, 28, 60) 540 tf_op_layer_Reshape_3[0][0]
81 __________________________________________________________________________________________________
82 batch_normalization_4 (BatchNor (None, 28, 28, 60) 240 depthwise_conv2d_1[0][0]
83 __________________________________________________________________________________________________
84 tf_op_layer_split_3 (TensorFlow [(None, 28, 28, 20), 0 batch_normalization_4[0][0]
85 __________________________________________________________________________________________________
86 conv2d_10 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_3[0][0]
87 __________________________________________________________________________________________________
88 conv2d_11 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_3[0][1]
89 __________________________________________________________________________________________________
90 conv2d_12 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_3[0][2]
91 __________________________________________________________________________________________________
92 concatenate_4 (Concatenate) (None, 28, 28, 240) 0 conv2d_10[0][0]
93 conv2d_11[0][0]
94 conv2d_12[0][0]
95 __________________________________________________________________________________________________
96 batch_normalization_5 (BatchNor (None, 28, 28, 240) 960 concatenate_4[0][0]
97 __________________________________________________________________________________________________
98 add (Add) (None, 28, 28, 240) 0 activation_1[0][0]
99 batch_normalization_5[0][0]
100 __________________________________________________________________________________________________
101 activation_3 (Activation) (None, 28, 28, 240) 0 add[0][0]
102 __________________________________________________________________________________________________
103 tf_op_layer_split_4 (TensorFlow [(None, 28, 28, 80), 0 activation_3[0][0]
104 __________________________________________________________________________________________________
105 conv2d_13 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_4[0][0]
106 __________________________________________________________________________________________________
107 conv2d_14 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_4[0][1]
108 __________________________________________________________________________________________________
109 conv2d_15 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_4[0][2]
110 __________________________________________________________________________________________________
111 concatenate_5 (Concatenate) (None, 28, 28, 60) 0 conv2d_13[0][0]
112 conv2d_14[0][0]
113 conv2d_15[0][0]
114 __________________________________________________________________________________________________
115 batch_normalization_6 (BatchNor (None, 28, 28, 60) 240 concatenate_5[0][0]
116 __________________________________________________________________________________________________
117 activation_4 (Activation) (None, 28, 28, 60) 0 batch_normalization_6[0][0]
118 __________________________________________________________________________________________________
119 tf_op_layer_Reshape_4 (TensorFl [(None, 28, 28, 3, 2 0 activation_4[0][0]
120 __________________________________________________________________________________________________
121 tf_op_layer_Transpose_2 (Tensor [(None, 28, 28, 20, 0 tf_op_layer_Reshape_4[0][0]
122 __________________________________________________________________________________________________
123 tf_op_layer_Reshape_5 (TensorFl [(None, 28, 28, 60)] 0 tf_op_layer_Transpose_2[0][0]
124 __________________________________________________________________________________________________
125 depthwise_conv2d_2 (DepthwiseCo (None, 28, 28, 60) 540 tf_op_layer_Reshape_5[0][0]
126 __________________________________________________________________________________________________
127 batch_normalization_7 (BatchNor (None, 28, 28, 60) 240 depthwise_conv2d_2[0][0]
128 __________________________________________________________________________________________________
129 tf_op_layer_split_5 (TensorFlow [(None, 28, 28, 20), 0 batch_normalization_7[0][0]
130 __________________________________________________________________________________________________
131 conv2d_16 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_5[0][0]
132 __________________________________________________________________________________________________
133 conv2d_17 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_5[0][1]
134 __________________________________________________________________________________________________
135 conv2d_18 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_5[0][2]
136 __________________________________________________________________________________________________
137 concatenate_6 (Concatenate) (None, 28, 28, 240) 0 conv2d_16[0][0]
138 conv2d_17[0][0]
139 conv2d_18[0][0]
140 __________________________________________________________________________________________________
141 batch_normalization_8 (BatchNor (None, 28, 28, 240) 960 concatenate_6[0][0]
142 __________________________________________________________________________________________________
143 add_1 (Add) (None, 28, 28, 240) 0 activation_3[0][0]
144 batch_normalization_8[0][0]
145 __________________________________________________________________________________________________
146 activation_5 (Activation) (None, 28, 28, 240) 0 add_1[0][0]
147 __________________________________________________________________________________________________
148 tf_op_layer_split_6 (TensorFlow [(None, 28, 28, 80), 0 activation_5[0][0]
149 __________________________________________________________________________________________________
150 conv2d_19 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_6[0][0]
151 __________________________________________________________________________________________________
152 conv2d_20 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_6[0][1]
153 __________________________________________________________________________________________________
154 conv2d_21 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_6[0][2]
155 __________________________________________________________________________________________________
156 concatenate_7 (Concatenate) (None, 28, 28, 60) 0 conv2d_19[0][0]
157 conv2d_20[0][0]
158 conv2d_21[0][0]
159 __________________________________________________________________________________________________
160 batch_normalization_9 (BatchNor (None, 28, 28, 60) 240 concatenate_7[0][0]
161 __________________________________________________________________________________________________
162 activation_6 (Activation) (None, 28, 28, 60) 0 batch_normalization_9[0][0]
163 __________________________________________________________________________________________________
164 tf_op_layer_Reshape_6 (TensorFl [(None, 28, 28, 3, 2 0 activation_6[0][0]
165 __________________________________________________________________________________________________
166 tf_op_layer_Transpose_3 (Tensor [(None, 28, 28, 20, 0 tf_op_layer_Reshape_6[0][0]
167 __________________________________________________________________________________________________
168 tf_op_layer_Reshape_7 (TensorFl [(None, 28, 28, 60)] 0 tf_op_layer_Transpose_3[0][0]
169 __________________________________________________________________________________________________
170 depthwise_conv2d_3 (DepthwiseCo (None, 28, 28, 60) 540 tf_op_layer_Reshape_7[0][0]
171 __________________________________________________________________________________________________
172 batch_normalization_10 (BatchNo (None, 28, 28, 60) 240 depthwise_conv2d_3[0][0]
173 __________________________________________________________________________________________________
174 tf_op_layer_split_7 (TensorFlow [(None, 28, 28, 20), 0 batch_normalization_10[0][0]
175 __________________________________________________________________________________________________
176 conv2d_22 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_7[0][0]
177 __________________________________________________________________________________________________
178 conv2d_23 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_7[0][1]
179 __________________________________________________________________________________________________
180 conv2d_24 (Conv2D) (None, 28, 28, 80) 1680 tf_op_layer_split_7[0][2]
181 __________________________________________________________________________________________________
182 concatenate_8 (Concatenate) (None, 28, 28, 240) 0 conv2d_22[0][0]
183 conv2d_23[0][0]
184 conv2d_24[0][0]
185 __________________________________________________________________________________________________
186 batch_normalization_11 (BatchNo (None, 28, 28, 240) 960 concatenate_8[0][0]
187 __________________________________________________________________________________________________
188 add_2 (Add) (None, 28, 28, 240) 0 activation_5[0][0]
189 batch_normalization_11[0][0]
190 __________________________________________________________________________________________________
191 activation_7 (Activation) (None, 28, 28, 240) 0 add_2[0][0]
192 __________________________________________________________________________________________________
193 tf_op_layer_split_8 (TensorFlow [(None, 28, 28, 80), 0 activation_7[0][0]
194 __________________________________________________________________________________________________
195 conv2d_25 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_8[0][0]
196 __________________________________________________________________________________________________
197 conv2d_26 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_8[0][1]
198 __________________________________________________________________________________________________
199 conv2d_27 (Conv2D) (None, 28, 28, 20) 1620 tf_op_layer_split_8[0][2]
200 __________________________________________________________________________________________________
201 concatenate_9 (Concatenate) (None, 28, 28, 60) 0 conv2d_25[0][0]
202 conv2d_26[0][0]
203 conv2d_27[0][0]
204 __________________________________________________________________________________________________
205 batch_normalization_12 (BatchNo (None, 28, 28, 60) 240 concatenate_9[0][0]
206 __________________________________________________________________________________________________
207 activation_8 (Activation) (None, 28, 28, 60) 0 batch_normalization_12[0][0]
208 __________________________________________________________________________________________________
209 tf_op_layer_Reshape_8 (TensorFl [(None, 28, 28, 3, 2 0 activation_8[0][0]
210 __________________________________________________________________________________________________
211 tf_op_layer_Transpose_4 (Tensor [(None, 28, 28, 20, 0 tf_op_layer_Reshape_8[0][0]
212 __________________________________________________________________________________________________
213 tf_op_layer_Reshape_9 (TensorFl [(None, 28, 28, 60)] 0 tf_op_layer_Transpose_4[0][0]
214 __________________________________________________________________________________________________
215 depthwise_conv2d_4 (DepthwiseCo (None, 14, 14, 60) 540 tf_op_layer_Reshape_9[0][0]
216 __________________________________________________________________________________________________
217 batch_normalization_13 (BatchNo (None, 14, 14, 60) 240 depthwise_conv2d_4[0][0]
218 __________________________________________________________________________________________________
219 tf_op_layer_split_9 (TensorFlow [(None, 14, 14, 20), 0 batch_normalization_13[0][0]
220 __________________________________________________________________________________________________
221 conv2d_28 (Conv2D) (None, 14, 14, 80) 1680 tf_op_layer_split_9[0][0]
222 __________________________________________________________________________________________________
223 conv2d_29 (Conv2D) (None, 14, 14, 80) 1680 tf_op_layer_split_9[0][1]
224 __________________________________________________________________________________________________
225 conv2d_30 (Conv2D) (None, 14, 14, 80) 1680 tf_op_layer_split_9[0][2]
226 __________________________________________________________________________________________________
227 concatenate_10 (Concatenate) (None, 14, 14, 240) 0 conv2d_28[0][0]
228 conv2d_29[0][0]
229 conv2d_30[0][0]
230 __________________________________________________________________________________________________
231 average_pooling2d_1 (AveragePoo (None, 14, 14, 240) 0 activation_7[0][0]
232 __________________________________________________________________________________________________
233 batch_normalization_14 (BatchNo (None, 14, 14, 240) 960 concatenate_10[0][0]
234 __________________________________________________________________________________________________
235 concatenate_11 (Concatenate) (None, 14, 14, 480) 0 average_pooling2d_1[0][0]
236 batch_normalization_14[0][0]
237 __________________________________________________________________________________________________
238 activation_9 (Activation) (None, 14, 14, 480) 0 concatenate_11[0][0]
239 __________________________________________________________________________________________________
240 tf_op_layer_split_10 (TensorFlo [(None, 14, 14, 160) 0 activation_9[0][0]
241 __________________________________________________________________________________________________
242 conv2d_31 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_10[0][0]
243 __________________________________________________________________________________________________
244 conv2d_32 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_10[0][1]
245 __________________________________________________________________________________________________
246 conv2d_33 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_10[0][2]
247 __________________________________________________________________________________________________
248 concatenate_12 (Concatenate) (None, 14, 14, 120) 0 conv2d_31[0][0]
249 conv2d_32[0][0]
250 conv2d_33[0][0]
251 __________________________________________________________________________________________________
252 batch_normalization_15 (BatchNo (None, 14, 14, 120) 480 concatenate_12[0][0]
253 __________________________________________________________________________________________________
254 activation_10 (Activation) (None, 14, 14, 120) 0 batch_normalization_15[0][0]
255 __________________________________________________________________________________________________
256 tf_op_layer_Reshape_10 (TensorF [(None, 14, 14, 3, 4 0 activation_10[0][0]
257 __________________________________________________________________________________________________
258 tf_op_layer_Transpose_5 (Tensor [(None, 14, 14, 40, 0 tf_op_layer_Reshape_10[0][0]
259 __________________________________________________________________________________________________
260 tf_op_layer_Reshape_11 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_5[0][0]
261 __________________________________________________________________________________________________
262 depthwise_conv2d_5 (DepthwiseCo (None, 14, 14, 120) 1080 tf_op_layer_Reshape_11[0][0]
263 __________________________________________________________________________________________________
264 batch_normalization_16 (BatchNo (None, 14, 14, 120) 480 depthwise_conv2d_5[0][0]
265 __________________________________________________________________________________________________
266 tf_op_layer_split_11 (TensorFlo [(None, 14, 14, 40), 0 batch_normalization_16[0][0]
267 __________________________________________________________________________________________________
268 conv2d_34 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_11[0][0]
269 __________________________________________________________________________________________________
270 conv2d_35 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_11[0][1]
271 __________________________________________________________________________________________________
272 conv2d_36 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_11[0][2]
273 __________________________________________________________________________________________________
274 concatenate_13 (Concatenate) (None, 14, 14, 480) 0 conv2d_34[0][0]
275 conv2d_35[0][0]
276 conv2d_36[0][0]
277 __________________________________________________________________________________________________
278 batch_normalization_17 (BatchNo (None, 14, 14, 480) 1920 concatenate_13[0][0]
279 __________________________________________________________________________________________________
280 add_3 (Add) (None, 14, 14, 480) 0 activation_9[0][0]
281 batch_normalization_17[0][0]
282 __________________________________________________________________________________________________
283 activation_11 (Activation) (None, 14, 14, 480) 0 add_3[0][0]
284 __________________________________________________________________________________________________
285 tf_op_layer_split_12 (TensorFlo [(None, 14, 14, 160) 0 activation_11[0][0]
286 __________________________________________________________________________________________________
287 conv2d_37 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_12[0][0]
288 __________________________________________________________________________________________________
289 conv2d_38 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_12[0][1]
290 __________________________________________________________________________________________________
291 conv2d_39 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_12[0][2]
292 __________________________________________________________________________________________________
293 concatenate_14 (Concatenate) (None, 14, 14, 120) 0 conv2d_37[0][0]
294 conv2d_38[0][0]
295 conv2d_39[0][0]
296 __________________________________________________________________________________________________
297 batch_normalization_18 (BatchNo (None, 14, 14, 120) 480 concatenate_14[0][0]
298 __________________________________________________________________________________________________
299 activation_12 (Activation) (None, 14, 14, 120) 0 batch_normalization_18[0][0]
300 __________________________________________________________________________________________________
301 tf_op_layer_Reshape_12 (TensorF [(None, 14, 14, 3, 4 0 activation_12[0][0]
302 __________________________________________________________________________________________________
303 tf_op_layer_Transpose_6 (Tensor [(None, 14, 14, 40, 0 tf_op_layer_Reshape_12[0][0]
304 __________________________________________________________________________________________________
305 tf_op_layer_Reshape_13 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_6[0][0]
306 __________________________________________________________________________________________________
307 depthwise_conv2d_6 (DepthwiseCo (None, 14, 14, 120) 1080 tf_op_layer_Reshape_13[0][0]
308 __________________________________________________________________________________________________
309 batch_normalization_19 (BatchNo (None, 14, 14, 120) 480 depthwise_conv2d_6[0][0]
310 __________________________________________________________________________________________________
311 tf_op_layer_split_13 (TensorFlo [(None, 14, 14, 40), 0 batch_normalization_19[0][0]
312 __________________________________________________________________________________________________
313 conv2d_40 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_13[0][0]
314 __________________________________________________________________________________________________
315 conv2d_41 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_13[0][1]
316 __________________________________________________________________________________________________
317 conv2d_42 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_13[0][2]
318 __________________________________________________________________________________________________
319 concatenate_15 (Concatenate) (None, 14, 14, 480) 0 conv2d_40[0][0]
320 conv2d_41[0][0]
321 conv2d_42[0][0]
322 __________________________________________________________________________________________________
323 batch_normalization_20 (BatchNo (None, 14, 14, 480) 1920 concatenate_15[0][0]
324 __________________________________________________________________________________________________
325 add_4 (Add) (None, 14, 14, 480) 0 activation_11[0][0]
326 batch_normalization_20[0][0]
327 __________________________________________________________________________________________________
328 activation_13 (Activation) (None, 14, 14, 480) 0 add_4[0][0]
329 __________________________________________________________________________________________________
330 tf_op_layer_split_14 (TensorFlo [(None, 14, 14, 160) 0 activation_13[0][0]
331 __________________________________________________________________________________________________
332 conv2d_43 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_14[0][0]
333 __________________________________________________________________________________________________
334 conv2d_44 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_14[0][1]
335 __________________________________________________________________________________________________
336 conv2d_45 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_14[0][2]
337 __________________________________________________________________________________________________
338 concatenate_16 (Concatenate) (None, 14, 14, 120) 0 conv2d_43[0][0]
339 conv2d_44[0][0]
340 conv2d_45[0][0]
341 __________________________________________________________________________________________________
342 batch_normalization_21 (BatchNo (None, 14, 14, 120) 480 concatenate_16[0][0]
343 __________________________________________________________________________________________________
344 activation_14 (Activation) (None, 14, 14, 120) 0 batch_normalization_21[0][0]
345 __________________________________________________________________________________________________
346 tf_op_layer_Reshape_14 (TensorF [(None, 14, 14, 3, 4 0 activation_14[0][0]
347 __________________________________________________________________________________________________
348 tf_op_layer_Transpose_7 (Tensor [(None, 14, 14, 40, 0 tf_op_layer_Reshape_14[0][0]
349 __________________________________________________________________________________________________
350 tf_op_layer_Reshape_15 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_7[0][0]
351 __________________________________________________________________________________________________
352 depthwise_conv2d_7 (DepthwiseCo (None, 14, 14, 120) 1080 tf_op_layer_Reshape_15[0][0]
353 __________________________________________________________________________________________________
354 batch_normalization_22 (BatchNo (None, 14, 14, 120) 480 depthwise_conv2d_7[0][0]
355 __________________________________________________________________________________________________
356 tf_op_layer_split_15 (TensorFlo [(None, 14, 14, 40), 0 batch_normalization_22[0][0]
357 __________________________________________________________________________________________________
358 conv2d_46 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_15[0][0]
359 __________________________________________________________________________________________________
360 conv2d_47 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_15[0][1]
361 __________________________________________________________________________________________________
362 conv2d_48 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_15[0][2]
363 __________________________________________________________________________________________________
364 concatenate_17 (Concatenate) (None, 14, 14, 480) 0 conv2d_46[0][0]
365 conv2d_47[0][0]
366 conv2d_48[0][0]
367 __________________________________________________________________________________________________
368 batch_normalization_23 (BatchNo (None, 14, 14, 480) 1920 concatenate_17[0][0]
369 __________________________________________________________________________________________________
370 add_5 (Add) (None, 14, 14, 480) 0 activation_13[0][0]
371 batch_normalization_23[0][0]
372 __________________________________________________________________________________________________
373 activation_15 (Activation) (None, 14, 14, 480) 0 add_5[0][0]
374 __________________________________________________________________________________________________
375 tf_op_layer_split_16 (TensorFlo [(None, 14, 14, 160) 0 activation_15[0][0]
376 __________________________________________________________________________________________________
377 conv2d_49 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_16[0][0]
378 __________________________________________________________________________________________________
379 conv2d_50 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_16[0][1]
380 __________________________________________________________________________________________________
381 conv2d_51 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_16[0][2]
382 __________________________________________________________________________________________________
383 concatenate_18 (Concatenate) (None, 14, 14, 120) 0 conv2d_49[0][0]
384 conv2d_50[0][0]
385 conv2d_51[0][0]
386 __________________________________________________________________________________________________
387 batch_normalization_24 (BatchNo (None, 14, 14, 120) 480 concatenate_18[0][0]
388 __________________________________________________________________________________________________
389 activation_16 (Activation) (None, 14, 14, 120) 0 batch_normalization_24[0][0]
390 __________________________________________________________________________________________________
391 tf_op_layer_Reshape_16 (TensorF [(None, 14, 14, 3, 4 0 activation_16[0][0]
392 __________________________________________________________________________________________________
393 tf_op_layer_Transpose_8 (Tensor [(None, 14, 14, 40, 0 tf_op_layer_Reshape_16[0][0]
394 __________________________________________________________________________________________________
395 tf_op_layer_Reshape_17 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_8[0][0]
396 __________________________________________________________________________________________________
397 depthwise_conv2d_8 (DepthwiseCo (None, 14, 14, 120) 1080 tf_op_layer_Reshape_17[0][0]
398 __________________________________________________________________________________________________
399 batch_normalization_25 (BatchNo (None, 14, 14, 120) 480 depthwise_conv2d_8[0][0]
400 __________________________________________________________________________________________________
401 tf_op_layer_split_17 (TensorFlo [(None, 14, 14, 40), 0 batch_normalization_25[0][0]
402 __________________________________________________________________________________________________
403 conv2d_52 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_17[0][0]
404 __________________________________________________________________________________________________
405 conv2d_53 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_17[0][1]
406 __________________________________________________________________________________________________
407 conv2d_54 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_17[0][2]
408 __________________________________________________________________________________________________
409 concatenate_19 (Concatenate) (None, 14, 14, 480) 0 conv2d_52[0][0]
410 conv2d_53[0][0]
411 conv2d_54[0][0]
412 __________________________________________________________________________________________________
413 batch_normalization_26 (BatchNo (None, 14, 14, 480) 1920 concatenate_19[0][0]
414 __________________________________________________________________________________________________
415 add_6 (Add) (None, 14, 14, 480) 0 activation_15[0][0]
416 batch_normalization_26[0][0]
417 __________________________________________________________________________________________________
418 activation_17 (Activation) (None, 14, 14, 480) 0 add_6[0][0]
419 __________________________________________________________________________________________________
420 tf_op_layer_split_18 (TensorFlo [(None, 14, 14, 160) 0 activation_17[0][0]
421 __________________________________________________________________________________________________
422 conv2d_55 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_18[0][0]
423 __________________________________________________________________________________________________
424 conv2d_56 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_18[0][1]
425 __________________________________________________________________________________________________
426 conv2d_57 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_18[0][2]
427 __________________________________________________________________________________________________
428 concatenate_20 (Concatenate) (None, 14, 14, 120) 0 conv2d_55[0][0]
429 conv2d_56[0][0]
430 conv2d_57[0][0]
431 __________________________________________________________________________________________________
432 batch_normalization_27 (BatchNo (None, 14, 14, 120) 480 concatenate_20[0][0]
433 __________________________________________________________________________________________________
434 activation_18 (Activation) (None, 14, 14, 120) 0 batch_normalization_27[0][0]
435 __________________________________________________________________________________________________
436 tf_op_layer_Reshape_18 (TensorF [(None, 14, 14, 3, 4 0 activation_18[0][0]
437 __________________________________________________________________________________________________
438 tf_op_layer_Transpose_9 (Tensor [(None, 14, 14, 40, 0 tf_op_layer_Reshape_18[0][0]
439 __________________________________________________________________________________________________
440 tf_op_layer_Reshape_19 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_9[0][0]
441 __________________________________________________________________________________________________
442 depthwise_conv2d_9 (DepthwiseCo (None, 14, 14, 120) 1080 tf_op_layer_Reshape_19[0][0]
443 __________________________________________________________________________________________________
444 batch_normalization_28 (BatchNo (None, 14, 14, 120) 480 depthwise_conv2d_9[0][0]
445 __________________________________________________________________________________________________
446 tf_op_layer_split_19 (TensorFlo [(None, 14, 14, 40), 0 batch_normalization_28[0][0]
447 __________________________________________________________________________________________________
448 conv2d_58 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_19[0][0]
449 __________________________________________________________________________________________________
450 conv2d_59 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_19[0][1]
451 __________________________________________________________________________________________________
452 conv2d_60 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_19[0][2]
453 __________________________________________________________________________________________________
454 concatenate_21 (Concatenate) (None, 14, 14, 480) 0 conv2d_58[0][0]
455 conv2d_59[0][0]
456 conv2d_60[0][0]
457 __________________________________________________________________________________________________
458 batch_normalization_29 (BatchNo (None, 14, 14, 480) 1920 concatenate_21[0][0]
459 __________________________________________________________________________________________________
460 add_7 (Add) (None, 14, 14, 480) 0 activation_17[0][0]
461 batch_normalization_29[0][0]
462 __________________________________________________________________________________________________
463 activation_19 (Activation) (None, 14, 14, 480) 0 add_7[0][0]
464 __________________________________________________________________________________________________
465 tf_op_layer_split_20 (TensorFlo [(None, 14, 14, 160) 0 activation_19[0][0]
466 __________________________________________________________________________________________________
467 conv2d_61 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_20[0][0]
468 __________________________________________________________________________________________________
469 conv2d_62 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_20[0][1]
470 __________________________________________________________________________________________________
471 conv2d_63 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_20[0][2]
472 __________________________________________________________________________________________________
473 concatenate_22 (Concatenate) (None, 14, 14, 120) 0 conv2d_61[0][0]
474 conv2d_62[0][0]
475 conv2d_63[0][0]
476 __________________________________________________________________________________________________
477 batch_normalization_30 (BatchNo (None, 14, 14, 120) 480 concatenate_22[0][0]
478 __________________________________________________________________________________________________
479 activation_20 (Activation) (None, 14, 14, 120) 0 batch_normalization_30[0][0]
480 __________________________________________________________________________________________________
481 tf_op_layer_Reshape_20 (TensorF [(None, 14, 14, 3, 4 0 activation_20[0][0]
482 __________________________________________________________________________________________________
483 tf_op_layer_Transpose_10 (Tenso [(None, 14, 14, 40, 0 tf_op_layer_Reshape_20[0][0]
484 __________________________________________________________________________________________________
485 tf_op_layer_Reshape_21 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_10[0][0]
486 __________________________________________________________________________________________________
487 depthwise_conv2d_10 (DepthwiseC (None, 14, 14, 120) 1080 tf_op_layer_Reshape_21[0][0]
488 __________________________________________________________________________________________________
489 batch_normalization_31 (BatchNo (None, 14, 14, 120) 480 depthwise_conv2d_10[0][0]
490 __________________________________________________________________________________________________
491 tf_op_layer_split_21 (TensorFlo [(None, 14, 14, 40), 0 batch_normalization_31[0][0]
492 __________________________________________________________________________________________________
493 conv2d_64 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_21[0][0]
494 __________________________________________________________________________________________________
495 conv2d_65 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_21[0][1]
496 __________________________________________________________________________________________________
497 conv2d_66 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_21[0][2]
498 __________________________________________________________________________________________________
499 concatenate_23 (Concatenate) (None, 14, 14, 480) 0 conv2d_64[0][0]
500 conv2d_65[0][0]
501 conv2d_66[0][0]
502 __________________________________________________________________________________________________
503 batch_normalization_32 (BatchNo (None, 14, 14, 480) 1920 concatenate_23[0][0]
504 __________________________________________________________________________________________________
505 add_8 (Add) (None, 14, 14, 480) 0 activation_19[0][0]
506 batch_normalization_32[0][0]
507 __________________________________________________________________________________________________
508 activation_21 (Activation) (None, 14, 14, 480) 0 add_8[0][0]
509 __________________________________________________________________________________________________
510 tf_op_layer_split_22 (TensorFlo [(None, 14, 14, 160) 0 activation_21[0][0]
511 __________________________________________________________________________________________________
512 conv2d_67 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_22[0][0]
513 __________________________________________________________________________________________________
514 conv2d_68 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_22[0][1]
515 __________________________________________________________________________________________________
516 conv2d_69 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_22[0][2]
517 __________________________________________________________________________________________________
518 concatenate_24 (Concatenate) (None, 14, 14, 120) 0 conv2d_67[0][0]
519 conv2d_68[0][0]
520 conv2d_69[0][0]
521 __________________________________________________________________________________________________
522 batch_normalization_33 (BatchNo (None, 14, 14, 120) 480 concatenate_24[0][0]
523 __________________________________________________________________________________________________
524 activation_22 (Activation) (None, 14, 14, 120) 0 batch_normalization_33[0][0]
525 __________________________________________________________________________________________________
526 tf_op_layer_Reshape_22 (TensorF [(None, 14, 14, 3, 4 0 activation_22[0][0]
527 __________________________________________________________________________________________________
528 tf_op_layer_Transpose_11 (Tenso [(None, 14, 14, 40, 0 tf_op_layer_Reshape_22[0][0]
529 __________________________________________________________________________________________________
530 tf_op_layer_Reshape_23 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_11[0][0]
531 __________________________________________________________________________________________________
532 depthwise_conv2d_11 (DepthwiseC (None, 14, 14, 120) 1080 tf_op_layer_Reshape_23[0][0]
533 __________________________________________________________________________________________________
534 batch_normalization_34 (BatchNo (None, 14, 14, 120) 480 depthwise_conv2d_11[0][0]
535 __________________________________________________________________________________________________
536 tf_op_layer_split_23 (TensorFlo [(None, 14, 14, 40), 0 batch_normalization_34[0][0]
537 __________________________________________________________________________________________________
538 conv2d_70 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_23[0][0]
539 __________________________________________________________________________________________________
540 conv2d_71 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_23[0][1]
541 __________________________________________________________________________________________________
542 conv2d_72 (Conv2D) (None, 14, 14, 160) 6560 tf_op_layer_split_23[0][2]
543 __________________________________________________________________________________________________
544 concatenate_25 (Concatenate) (None, 14, 14, 480) 0 conv2d_70[0][0]
545 conv2d_71[0][0]
546 conv2d_72[0][0]
547 __________________________________________________________________________________________________
548 batch_normalization_35 (BatchNo (None, 14, 14, 480) 1920 concatenate_25[0][0]
549 __________________________________________________________________________________________________
550 add_9 (Add) (None, 14, 14, 480) 0 activation_21[0][0]
551 batch_normalization_35[0][0]
552 __________________________________________________________________________________________________
553 activation_23 (Activation) (None, 14, 14, 480) 0 add_9[0][0]
554 __________________________________________________________________________________________________
555 tf_op_layer_split_24 (TensorFlo [(None, 14, 14, 160) 0 activation_23[0][0]
556 __________________________________________________________________________________________________
557 conv2d_73 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_24[0][0]
558 __________________________________________________________________________________________________
559 conv2d_74 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_24[0][1]
560 __________________________________________________________________________________________________
561 conv2d_75 (Conv2D) (None, 14, 14, 40) 6440 tf_op_layer_split_24[0][2]
562 __________________________________________________________________________________________________
563 concatenate_26 (Concatenate) (None, 14, 14, 120) 0 conv2d_73[0][0]
564 conv2d_74[0][0]
565 conv2d_75[0][0]
566 __________________________________________________________________________________________________
567 batch_normalization_36 (BatchNo (None, 14, 14, 120) 480 concatenate_26[0][0]
568 __________________________________________________________________________________________________
569 activation_24 (Activation) (None, 14, 14, 120) 0 batch_normalization_36[0][0]
570 __________________________________________________________________________________________________
571 tf_op_layer_Reshape_24 (TensorF [(None, 14, 14, 3, 4 0 activation_24[0][0]
572 __________________________________________________________________________________________________
573 tf_op_layer_Transpose_12 (Tenso [(None, 14, 14, 40, 0 tf_op_layer_Reshape_24[0][0]
574 __________________________________________________________________________________________________
575 tf_op_layer_Reshape_25 (TensorF [(None, 14, 14, 120) 0 tf_op_layer_Transpose_12[0][0]
576 __________________________________________________________________________________________________
577 depthwise_conv2d_12 (DepthwiseC (None, 7, 7, 120) 1080 tf_op_layer_Reshape_25[0][0]
578 __________________________________________________________________________________________________
579 batch_normalization_37 (BatchNo (None, 7, 7, 120) 480 depthwise_conv2d_12[0][0]
580 __________________________________________________________________________________________________
581 tf_op_layer_split_25 (TensorFlo [(None, 7, 7, 40), ( 0 batch_normalization_37[0][0]
582 __________________________________________________________________________________________________
583 conv2d_76 (Conv2D) (None, 7, 7, 160) 6560 tf_op_layer_split_25[0][0]
584 __________________________________________________________________________________________________
585 conv2d_77 (Conv2D) (None, 7, 7, 160) 6560 tf_op_layer_split_25[0][1]
586 __________________________________________________________________________________________________
587 conv2d_78 (Conv2D) (None, 7, 7, 160) 6560 tf_op_layer_split_25[0][2]
588 __________________________________________________________________________________________________
589 concatenate_27 (Concatenate) (None, 7, 7, 480) 0 conv2d_76[0][0]
590 conv2d_77[0][0]
591 conv2d_78[0][0]
592 __________________________________________________________________________________________________
593 average_pooling2d_2 (AveragePoo (None, 7, 7, 480) 0 activation_23[0][0]
594 __________________________________________________________________________________________________
595 batch_normalization_38 (BatchNo (None, 7, 7, 480) 1920 concatenate_27[0][0]
596 __________________________________________________________________________________________________
597 concatenate_28 (Concatenate) (None, 7, 7, 960) 0 average_pooling2d_2[0][0]
598 batch_normalization_38[0][0]
599 __________________________________________________________________________________________________
600 activation_25 (Activation) (None, 7, 7, 960) 0 concatenate_28[0][0]
601 __________________________________________________________________________________________________
602 tf_op_layer_split_26 (TensorFlo [(None, 7, 7, 320), 0 activation_25[0][0]
603 __________________________________________________________________________________________________
604 conv2d_79 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_26[0][0]
605 __________________________________________________________________________________________________
606 conv2d_80 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_26[0][1]
607 __________________________________________________________________________________________________
608 conv2d_81 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_26[0][2]
609 __________________________________________________________________________________________________
610 concatenate_29 (Concatenate) (None, 7, 7, 240) 0 conv2d_79[0][0]
611 conv2d_80[0][0]
612 conv2d_81[0][0]
613 __________________________________________________________________________________________________
614 batch_normalization_39 (BatchNo (None, 7, 7, 240) 960 concatenate_29[0][0]
615 __________________________________________________________________________________________________
616 activation_26 (Activation) (None, 7, 7, 240) 0 batch_normalization_39[0][0]
617 __________________________________________________________________________________________________
618 tf_op_layer_Reshape_26 (TensorF [(None, 7, 7, 3, 80) 0 activation_26[0][0]
619 __________________________________________________________________________________________________
620 tf_op_layer_Transpose_13 (Tenso [(None, 7, 7, 80, 3) 0 tf_op_layer_Reshape_26[0][0]
621 __________________________________________________________________________________________________
622 tf_op_layer_Reshape_27 (TensorF [(None, 7, 7, 240)] 0 tf_op_layer_Transpose_13[0][0]
623 __________________________________________________________________________________________________
624 depthwise_conv2d_13 (DepthwiseC (None, 7, 7, 240) 2160 tf_op_layer_Reshape_27[0][0]
625 __________________________________________________________________________________________________
626 batch_normalization_40 (BatchNo (None, 7, 7, 240) 960 depthwise_conv2d_13[0][0]
627 __________________________________________________________________________________________________
628 tf_op_layer_split_27 (TensorFlo [(None, 7, 7, 80), ( 0 batch_normalization_40[0][0]
629 __________________________________________________________________________________________________
630 conv2d_82 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_27[0][0]
631 __________________________________________________________________________________________________
632 conv2d_83 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_27[0][1]
633 __________________________________________________________________________________________________
634 conv2d_84 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_27[0][2]
635 __________________________________________________________________________________________________
636 concatenate_30 (Concatenate) (None, 7, 7, 960) 0 conv2d_82[0][0]
637 conv2d_83[0][0]
638 conv2d_84[0][0]
639 __________________________________________________________________________________________________
640 batch_normalization_41 (BatchNo (None, 7, 7, 960) 3840 concatenate_30[0][0]
641 __________________________________________________________________________________________________
642 add_10 (Add) (None, 7, 7, 960) 0 activation_25[0][0]
643 batch_normalization_41[0][0]
644 __________________________________________________________________________________________________
645 activation_27 (Activation) (None, 7, 7, 960) 0 add_10[0][0]
646 __________________________________________________________________________________________________
647 tf_op_layer_split_28 (TensorFlo [(None, 7, 7, 320), 0 activation_27[0][0]
648 __________________________________________________________________________________________________
649 conv2d_85 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_28[0][0]
650 __________________________________________________________________________________________________
651 conv2d_86 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_28[0][1]
652 __________________________________________________________________________________________________
653 conv2d_87 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_28[0][2]
654 __________________________________________________________________________________________________
655 concatenate_31 (Concatenate) (None, 7, 7, 240) 0 conv2d_85[0][0]
656 conv2d_86[0][0]
657 conv2d_87[0][0]
658 __________________________________________________________________________________________________
659 batch_normalization_42 (BatchNo (None, 7, 7, 240) 960 concatenate_31[0][0]
660 __________________________________________________________________________________________________
661 activation_28 (Activation) (None, 7, 7, 240) 0 batch_normalization_42[0][0]
662 __________________________________________________________________________________________________
663 tf_op_layer_Reshape_28 (TensorF [(None, 7, 7, 3, 80) 0 activation_28[0][0]
664 __________________________________________________________________________________________________
665 tf_op_layer_Transpose_14 (Tenso [(None, 7, 7, 80, 3) 0 tf_op_layer_Reshape_28[0][0]
666 __________________________________________________________________________________________________
667 tf_op_layer_Reshape_29 (TensorF [(None, 7, 7, 240)] 0 tf_op_layer_Transpose_14[0][0]
668 __________________________________________________________________________________________________
669 depthwise_conv2d_14 (DepthwiseC (None, 7, 7, 240) 2160 tf_op_layer_Reshape_29[0][0]
670 __________________________________________________________________________________________________
671 batch_normalization_43 (BatchNo (None, 7, 7, 240) 960 depthwise_conv2d_14[0][0]
672 __________________________________________________________________________________________________
673 tf_op_layer_split_29 (TensorFlo [(None, 7, 7, 80), ( 0 batch_normalization_43[0][0]
674 __________________________________________________________________________________________________
675 conv2d_88 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_29[0][0]
676 __________________________________________________________________________________________________
677 conv2d_89 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_29[0][1]
678 __________________________________________________________________________________________________
679 conv2d_90 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_29[0][2]
680 __________________________________________________________________________________________________
681 concatenate_32 (Concatenate) (None, 7, 7, 960) 0 conv2d_88[0][0]
682 conv2d_89[0][0]
683 conv2d_90[0][0]
684 __________________________________________________________________________________________________
685 batch_normalization_44 (BatchNo (None, 7, 7, 960) 3840 concatenate_32[0][0]
686 __________________________________________________________________________________________________
687 add_11 (Add) (None, 7, 7, 960) 0 activation_27[0][0]
688 batch_normalization_44[0][0]
689 __________________________________________________________________________________________________
690 activation_29 (Activation) (None, 7, 7, 960) 0 add_11[0][0]
691 __________________________________________________________________________________________________
692 tf_op_layer_split_30 (TensorFlo [(None, 7, 7, 320), 0 activation_29[0][0]
693 __________________________________________________________________________________________________
694 conv2d_91 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_30[0][0]
695 __________________________________________________________________________________________________
696 conv2d_92 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_30[0][1]
697 __________________________________________________________________________________________________
698 conv2d_93 (Conv2D) (None, 7, 7, 80) 25680 tf_op_layer_split_30[0][2]
699 __________________________________________________________________________________________________
700 concatenate_33 (Concatenate) (None, 7, 7, 240) 0 conv2d_91[0][0]
701 conv2d_92[0][0]
702 conv2d_93[0][0]
703 __________________________________________________________________________________________________
704 batch_normalization_45 (BatchNo (None, 7, 7, 240) 960 concatenate_33[0][0]
705 __________________________________________________________________________________________________
706 activation_30 (Activation) (None, 7, 7, 240) 0 batch_normalization_45[0][0]
707 __________________________________________________________________________________________________
708 tf_op_layer_Reshape_30 (TensorF [(None, 7, 7, 3, 80) 0 activation_30[0][0]
709 __________________________________________________________________________________________________
710 tf_op_layer_Transpose_15 (Tenso [(None, 7, 7, 80, 3) 0 tf_op_layer_Reshape_30[0][0]
711 __________________________________________________________________________________________________
712 tf_op_layer_Reshape_31 (TensorF [(None, 7, 7, 240)] 0 tf_op_layer_Transpose_15[0][0]
713 __________________________________________________________________________________________________
714 depthwise_conv2d_15 (DepthwiseC (None, 7, 7, 240) 2160 tf_op_layer_Reshape_31[0][0]
715 __________________________________________________________________________________________________
716 batch_normalization_46 (BatchNo (None, 7, 7, 240) 960 depthwise_conv2d_15[0][0]
717 __________________________________________________________________________________________________
718 tf_op_layer_split_31 (TensorFlo [(None, 7, 7, 80), ( 0 batch_normalization_46[0][0]
719 __________________________________________________________________________________________________
720 conv2d_94 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_31[0][0]
721 __________________________________________________________________________________________________
722 conv2d_95 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_31[0][1]
723 __________________________________________________________________________________________________
724 conv2d_96 (Conv2D) (None, 7, 7, 320) 25920 tf_op_layer_split_31[0][2]
725 __________________________________________________________________________________________________
726 concatenate_34 (Concatenate) (None, 7, 7, 960) 0 conv2d_94[0][0]
727 conv2d_95[0][0]
728 conv2d_96[0][0]
729 __________________________________________________________________________________________________
730 batch_normalization_47 (BatchNo (None, 7, 7, 960) 3840 concatenate_34[0][0]
731 __________________________________________________________________________________________________
732 add_12 (Add) (None, 7, 7, 960) 0 activation_29[0][0]
733 batch_normalization_47[0][0]
734 __________________________________________________________________________________________________
735 activation_31 (Activation) (None, 7, 7, 960) 0 add_12[0][0]
736 __________________________________________________________________________________________________
737 global_average_pooling2d (Globa (None, 960) 0 activation_31[0][0]
738 __________________________________________________________________________________________________
739 dense (Dense) (None, 17) 16337 global_average_pooling2d[0][0]
740 ==================================================================================================
741 Total params: 902,741
742 Trainable params: 879,053
743 Non-trainable params: 23,688数据增强
1 # 训练集数据进行数据增强
2 train_datagen = ImageDataGenerator(
3 rotation_range=20, # 随机旋转度数
4 width_shift_range=0.1, # 随机水平平移
5 height_shift_range=0.1, # 随机竖直平移
6 rescale=1 / 255, # 数据归一化
7 shear_range=10, # 随机错切变换
8 zoom_range=0.1, # 随机放大
9 horizontal_flip=True, # 水平翻转
10 brightness_range=(0.7, 1.3), # 亮度变化
11 fill_mode='nearest', # 填充方式
12 )
13 # 测试集数据只需要归一化就可以
14 test_datagen = ImageDataGenerator(
15 rescale=1 / 255, # 数据归一化
16 )数据生成器
1 # 训练集数据生成器，可以在训练时自动产生数据进行训练
2 # 从'data/train'获得训练集数据
3 # 获得数据后会把图片resize为image_size×image_size的大小
4 # generator每次会产生batch_size个数据
5 train_generator = train_datagen.flow_from_directory(
6 '../data/train',
7 target_size=(image_size, image_size),
8 batch_size=batch_size,
9 )
10
11 # 测试集数据生成器
12 test_generator = test_datagen.flow_from_directory(
13 '../data/test',
14 target_size=(image_size, image_size),
15 batch_size=batch_size,
16 )
17 # 字典的键为17个文件夹的名字，值为对应的分类编号
18 print(train_generator.class_indices)
回调设置
1 # 学习率调节函数，逐渐减小学习率
2 def adjust_learning_rate(epoch):
3 # 前40周期
4 if epoch<=40:
5 lr = 1e-4
6 # 前40到80周期
7 elif epoch>40 and epoch<=80:
8 lr = 1e-5
9 # 80到100周期
10 else:
11 lr = 1e-6
12 return lr
13
14 # 定义优化器
15 adam = Adam(lr=1e-4)
16
17 # 读取模型
18 checkpoint_save_path = "./checkpoint/ShuffleNetV1.ckpt"
19 if os.path.exists(checkpoint_save_path + '.index'):
20 print('-------------load the model-----------------')
21 model.load_weights(checkpoint_save_path)
22 # 保存模型
23 cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
24 save_weights_only=True,
25 save_best_only=True)
26
27 # 定义学习率衰减策略
28 callbacks = []
29 callbacks.append(LearningRateScheduler(adjust_learning_rate))
30 callbacks.append(cp_callback)1 # 定义优化器，loss function，训练过程中计算准确率
2 model.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])
3
4 # Tensorflow2.1版本(包括2.1)之后可以直接使用fit训练模型
5 history = model.fit(x=train_generator,epochs=epochs,validation_data=test_generator,callbacks=callbacks)
1 Epoch 1/100
2 34/34 [==============================] - 18s 526ms/step - loss: 2.9157 - accuracy: 0.1544 - val_loss: 2.8353 - val_accuracy: 0.0588
3 Epoch 2/100
4 34/34 [==============================] - 16s 478ms/step - loss: 2.2678 - accuracy: 0.2822 - val_loss: 2.8387 - val_accuracy: 0.0588
5 Epoch 3/100
6 34/34 [==============================] - 16s 483ms/step - loss: 1.9987 - accuracy: 0.3778 - val_loss: 2.8482 - val_accuracy: 0.0588
7 Epoch 4/100
8 34/34 [==============================] - 16s 481ms/step - loss: 1.8269 - accuracy: 0.4200 - val_loss: 2.8607 - val_accuracy: 0.0625
9 Epoch 5/100
10 34/34 [==============================] - 17s 487ms/step - loss: 1.7022 - accuracy: 0.4651 - val_loss: 2.8800 - val_accuracy: 0.0588
11 Epoch 6/100
12 34/34 [==============================] - 16s 479ms/step - loss: 1.5436 - accuracy: 0.5138 - val_loss: 2.9101 - val_accuracy: 0.0588
13 Epoch 7/100
14 34/34 [==============================] - 16s 484ms/step - loss: 1.4806 - accuracy: 0.5221 - val_loss: 2.9455 - val_accuracy: 0.0588
15 Epoch 8/100
16 34/34 [==============================] - 17s 486ms/step - loss: 1.3658 - accuracy: 0.5699 - val_loss: 3.0113 - val_accuracy: 0.0588
17 Epoch 9/100
18 34/34 [==============================] - 16s 478ms/step - loss: 1.3244 - accuracy: 0.5772 - val_loss: 3.0792 - val_accuracy: 0.0588
19 Epoch 10/100
20 34/34 [==============================] - 16s 480ms/step - loss: 1.2401 - accuracy: 0.6029 - val_loss: 3.2077 - val_accuracy: 0.0588
21 Epoch 11/100
22 34/34 [==============================] - 16s 480ms/step - loss: 1.1940 - accuracy: 0.6048 - val_loss: 3.3061 - val_accuracy: 0.0588
23 Epoch 12/100
24 34/34 [==============================] - 16s 480ms/step - loss: 1.1689 - accuracy: 0.6213 - val_loss: 3.4508 - val_accuracy: 0.0772
25 Epoch 13/100
26 34/34 [==============================] - 16s 481ms/step - loss: 1.1282 - accuracy: 0.6241 - val_loss: 3.5842 - val_accuracy: 0.0919
27 Epoch 14/100
28 34/34 [==============================] - 16s 479ms/step - loss: 1.0759 - accuracy: 0.6572 - val_loss: 3.5697 - val_accuracy: 0.1140
29 Epoch 15/100
30 34/34 [==============================] - 17s 488ms/step - loss: 1.0264 - accuracy: 0.6618 - val_loss: 3.6231 - val_accuracy: 0.1176
31 Epoch 16/100
32 34/34 [==============================] - 16s 475ms/step - loss: 1.0210 - accuracy: 0.6719 - val_loss: 3.3065 - val_accuracy: 0.1471
33 Epoch 17/100
34 34/34 [==============================] - 16s 477ms/step - loss: 0.9543 - accuracy: 0.6939 - val_loss: 2.9929 - val_accuracy: 0.2169
35 Epoch 18/100
36 34/34 [==============================] - 17s 493ms/step - loss: 0.9405 - accuracy: 0.6939 - val_loss: 2.3133 - val_accuracy: 0.2941
37 Epoch 19/100
38 34/34 [==============================] - 17s 488ms/step - loss: 0.9069 - accuracy: 0.7031 - val_loss: 1.8180 - val_accuracy: 0.4081
39 Epoch 20/100
40 34/34 [==============================] - 17s 491ms/step - loss: 0.8797 - accuracy: 0.7050 - val_loss: 1.7820 - val_accuracy: 0.4926
41 Epoch 21/100
42 34/34 [==============================] - 17s 493ms/step - loss: 0.8911 - accuracy: 0.7215 - val_loss: 1.4993 - val_accuracy: 0.5625
43 Epoch 22/100
44 34/34 [==============================] - 17s 496ms/step - loss: 0.8480 - accuracy: 0.7215 - val_loss: 1.4438 - val_accuracy: 0.5699
45 Epoch 23/100
46 34/34 [==============================] - 17s 496ms/step - loss: 0.7861 - accuracy: 0.7472 - val_loss: 1.2822 - val_accuracy: 0.6103
47 Epoch 24/100
48 34/34 [==============================] - 16s 477ms/step - loss: 0.8111 - accuracy: 0.7307 - val_loss: 1.7404 - val_accuracy: 0.5404
49 Epoch 25/100
50 34/34 [==============================] - 16s 477ms/step - loss: 0.7855 - accuracy: 0.7316 - val_loss: 1.4184 - val_accuracy: 0.6287
51 Epoch 26/100
52 34/34 [==============================] - 17s 491ms/step - loss: 0.7679 - accuracy: 0.7555 - val_loss: 1.2750 - val_accuracy: 0.6213
53 Epoch 27/100
54 34/34 [==============================] - 16s 481ms/step - loss: 0.7451 - accuracy: 0.7454 - val_loss: 1.4109 - val_accuracy: 0.5882
55 Epoch 28/100
56 34/34 [==============================] - 16s 482ms/step - loss: 0.7201 - accuracy: 0.7408 - val_loss: 1.3238 - val_accuracy: 0.6507
57 Epoch 29/100
58 34/34 [==============================] - 16s 475ms/step - loss: 0.7238 - accuracy: 0.7629 - val_loss: 1.2997 - val_accuracy: 0.6360
59 Epoch 30/100
60 34/34 [==============================] - 16s 482ms/step - loss: 0.6587 - accuracy: 0.7849 - val_loss: 1.2941 - val_accuracy: 0.6471
61 Epoch 31/100
62 34/34 [==============================] - 17s 488ms/step - loss: 0.6508 - accuracy: 0.7858 - val_loss: 1.4063 - val_accuracy: 0.6397
63 Epoch 32/100
64 34/34 [==============================] - 17s 495ms/step - loss: 0.6294 - accuracy: 0.8006 - val_loss: 1.1997 - val_accuracy: 0.6691
65 Epoch 33/100
66 34/34 [==============================] - 16s 482ms/step - loss: 0.6579 - accuracy: 0.7665 - val_loss: 1.2146 - val_accuracy: 0.6434
67 Epoch 34/100
68 34/34 [==============================] - 16s 481ms/step - loss: 0.6207 - accuracy: 0.7941 - val_loss: 1.3010 - val_accuracy: 0.6360
69 Epoch 35/100
70 34/34 [==============================] - 16s 480ms/step - loss: 0.5873 - accuracy: 0.8033 - val_loss: 1.2448 - val_accuracy: 0.6618
71 Epoch 36/100
72 34/34 [==============================] - 16s 477ms/step - loss: 0.5755 - accuracy: 0.7941 - val_loss: 1.2584 - val_accuracy: 0.6434
73 Epoch 37/100
74 34/34 [==============================] - 17s 491ms/step - loss: 0.5668 - accuracy: 0.8189 - val_loss: 1.1914 - val_accuracy: 0.6728
75 Epoch 38/100
76 34/34 [==============================] - 16s 485ms/step - loss: 0.5527 - accuracy: 0.8107 - val_loss: 1.2917 - val_accuracy: 0.6434
77 Epoch 39/100
78 34/34 [==============================] - 16s 475ms/step - loss: 0.5715 - accuracy: 0.8088 - val_loss: 1.2613 - val_accuracy: 0.6581
79 Epoch 40/100
80 34/34 [==============================] - 16s 477ms/step - loss: 0.5286 - accuracy: 0.8272 - val_loss: 1.2632 - val_accuracy: 0.6581
81 Epoch 41/100
82 34/34 [==============================] - 17s 495ms/step - loss: 0.5098 - accuracy: 0.8327 - val_loss: 1.1116 - val_accuracy: 0.6985
83 Epoch 42/100
84 34/34 [==============================] - 17s 493ms/step - loss: 0.4543 - accuracy: 0.8428 - val_loss: 1.0116 - val_accuracy: 0.7206
85 Epoch 43/100
86 34/34 [==============================] - 17s 492ms/step - loss: 0.4145 - accuracy: 0.8695 - val_loss: 0.9978 - val_accuracy: 0.7206
87 Epoch 44/100
88 34/34 [==============================] - 17s 489ms/step - loss: 0.4421 - accuracy: 0.8612 - val_loss: 0.9956 - val_accuracy: 0.7169
89 Epoch 45/100
90 34/34 [==============================] - 17s 486ms/step - loss: 0.4015 - accuracy: 0.8750 - val_loss: 0.9964 - val_accuracy: 0.7206
91 Epoch 46/100
92 34/34 [==============================] - 16s 476ms/step - loss: 0.3937 - accuracy: 0.8805 - val_loss: 1.0131 - val_accuracy: 0.7279
93 Epoch 47/100
94 34/34 [==============================] - 16s 477ms/step - loss: 0.4492 - accuracy: 0.8539 - val_loss: 1.0172 - val_accuracy: 0.7243
95 Epoch 48/100
96 34/34 [==============================] - 16s 483ms/step - loss: 0.4238 - accuracy: 0.8649 - val_loss: 1.0143 - val_accuracy: 0.7279
97 Epoch 49/100
98 34/34 [==============================] - 16s 480ms/step - loss: 0.4192 - accuracy: 0.8658 - val_loss: 1.0148 - val_accuracy: 0.7206
99 Epoch 50/100
100 34/34 [==============================] - 16s 479ms/step - loss: 0.4071 - accuracy: 0.8695 - val_loss: 1.0148 - val_accuracy: 0.7316
101 Epoch 51/100
102 34/34 [==============================] - 16s 483ms/step - loss: 0.4115 - accuracy: 0.8557 - val_loss: 1.0180 - val_accuracy: 0.7463
103 Epoch 52/100
104 34/34 [==============================] - 16s 480ms/step - loss: 0.4106 - accuracy: 0.8704 - val_loss: 1.0214 - val_accuracy: 0.7353
105 Epoch 53/100
106 34/34 [==============================] - 16s 477ms/step - loss: 0.4026 - accuracy: 0.8713 - val_loss: 1.0171 - val_accuracy: 0.7390
107 Epoch 54/100
108 34/34 [==============================] - 16s 476ms/step - loss: 0.4073 - accuracy: 0.8750 - val_loss: 1.0067 - val_accuracy: 0.7426
109 Epoch 55/100
110 34/34 [==============================] - 16s 485ms/step - loss: 0.3608 - accuracy: 0.8842 - val_loss: 1.0039 - val_accuracy: 0.7500
111 Epoch 56/100
112 34/34 [==============================] - 16s 478ms/step - loss: 0.4101 - accuracy: 0.8658 - val_loss: 1.0056 - val_accuracy: 0.7426
113 Epoch 57/100
114 34/34 [==============================] - 16s 478ms/step - loss: 0.3797 - accuracy: 0.8787 - val_loss: 0.9983 - val_accuracy: 0.7426
115 Epoch 58/100
116 34/34 [==============================] - 16s 484ms/step - loss: 0.4077 - accuracy: 0.8695 - val_loss: 1.0055 - val_accuracy: 0.7390
117 Epoch 59/100
118 34/34 [==============================] - 16s 474ms/step - loss: 0.4265 - accuracy: 0.8649 - val_loss: 1.0111 - val_accuracy: 0.7500
119 Epoch 60/100
120 34/34 [==============================] - 17s 492ms/step - loss: 0.3475 - accuracy: 0.8961 - val_loss: 0.9925 - val_accuracy: 0.7500
121 Epoch 61/100
122 34/34 [==============================] - 17s 497ms/step - loss: 0.3883 - accuracy: 0.8796 - val_loss: 0.9693 - val_accuracy: 0.7390
123 Epoch 62/100
124 34/34 [==============================] - 17s 494ms/step - loss: 0.3615 - accuracy: 0.8934 - val_loss: 0.9608 - val_accuracy: 0.7463
125 Epoch 63/100
126 34/34 [==============================] - 16s 480ms/step - loss: 0.4017 - accuracy: 0.8695 - val_loss: 0.9706 - val_accuracy: 0.7610
127 Epoch 64/100
128 34/34 [==============================] - 17s 495ms/step - loss: 0.3727 - accuracy: 0.8851 - val_loss: 0.9587 - val_accuracy: 0.7574
129 Epoch 65/100
130 34/34 [==============================] - 16s 480ms/step - loss: 0.4053 - accuracy: 0.8750 - val_loss: 0.9707 - val_accuracy: 0.7390
131 Epoch 66/100
132 34/34 [==============================] - 16s 481ms/step - loss: 0.3722 - accuracy: 0.8860 - val_loss: 0.9836 - val_accuracy: 0.7426
133 Epoch 67/100
134 34/34 [==============================] - 16s 477ms/step - loss: 0.3841 - accuracy: 0.8824 - val_loss: 0.9597 - val_accuracy: 0.7426
135 Epoch 68/100
136 34/34 [==============================] - 16s 480ms/step - loss: 0.4085 - accuracy: 0.8621 - val_loss: 0.9813 - val_accuracy: 0.7390
137 Epoch 69/100
138 34/34 [==============================] - 16s 479ms/step - loss: 0.3578 - accuracy: 0.8943 - val_loss: 0.9673 - val_accuracy: 0.7463
139 Epoch 70/100
140 34/34 [==============================] - 16s 478ms/step - loss: 0.3628 - accuracy: 0.8915 - val_loss: 0.9789 - val_accuracy: 0.7353
141 Epoch 71/100
142 34/34 [==============================] - 17s 486ms/step - loss: 0.3904 - accuracy: 0.8686 - val_loss: 0.9738 - val_accuracy: 0.7537
143 Epoch 72/100
144 34/34 [==============================] - 16s 484ms/step - loss: 0.3585 - accuracy: 0.8943 - val_loss: 0.9827 - val_accuracy: 0.7574
145 Epoch 73/100
146 34/34 [==============================] - 16s 479ms/step - loss: 0.3593 - accuracy: 0.8879 - val_loss: 0.9722 - val_accuracy: 0.7574
147 Epoch 74/100
148 34/34 [==============================] - 16s 481ms/step - loss: 0.3636 - accuracy: 0.8869 - val_loss: 0.9719 - val_accuracy: 0.7500
149 Epoch 75/100
150 34/34 [==============================] - 16s 479ms/step - loss: 0.3776 - accuracy: 0.8787 - val_loss: 0.9690 - val_accuracy: 0.7500
151 Epoch 76/100
152 34/34 [==============================] - 17s 490ms/step - loss: 0.3815 - accuracy: 0.8768 - val_loss: 0.9569 - val_accuracy: 0.7574
153 Epoch 77/100
154 34/34 [==============================] - 16s 480ms/step - loss: 0.3608 - accuracy: 0.8833 - val_loss: 0.9737 - val_accuracy: 0.7537
155 Epoch 78/100
156 34/34 [==============================] - 17s 496ms/step - loss: 0.3801 - accuracy: 0.8722 - val_loss: 0.9393 - val_accuracy: 0.7610
157 Epoch 79/100
158 34/34 [==============================] - 16s 478ms/step - loss: 0.3385 - accuracy: 0.8943 - val_loss: 0.9577 - val_accuracy: 0.7500
159 Epoch 80/100
160 34/34 [==============================] - 16s 477ms/step - loss: 0.3565 - accuracy: 0.8869 - val_loss: 0.9693 - val_accuracy: 0.7537
161 Epoch 81/100
162 34/34 [==============================] - 16s 482ms/step - loss: 0.3614 - accuracy: 0.8961 - val_loss: 0.9911 - val_accuracy: 0.7574
163 Epoch 82/100
164 34/34 [==============================] - 16s 478ms/step - loss: 0.3581 - accuracy: 0.8814 - val_loss: 0.9848 - val_accuracy: 0.7574
165 Epoch 83/100
166 34/34 [==============================] - 16s 479ms/step - loss: 0.3880 - accuracy: 0.8833 - val_loss: 0.9779 - val_accuracy: 0.7574
167 Epoch 84/100
168 34/34 [==============================] - 16s 478ms/step - loss: 0.3549 - accuracy: 0.8851 - val_loss: 0.9744 - val_accuracy: 0.7537
169 Epoch 85/100
170 34/34 [==============================] - 17s 487ms/step - loss: 0.3552 - accuracy: 0.8934 - val_loss: 0.9736 - val_accuracy: 0.7537
171 Epoch 86/100
172 34/34 [==============================] - 16s 483ms/step - loss: 0.3681 - accuracy: 0.8842 - val_loss: 0.9715 - val_accuracy: 0.7537
173 Epoch 87/100
174 34/34 [==============================] - 16s 479ms/step - loss: 0.3416 - accuracy: 0.8934 - val_loss: 0.9662 - val_accuracy: 0.7537
175 Epoch 88/100
176 34/34 [==============================] - 17s 486ms/step - loss: 0.3562 - accuracy: 0.8934 - val_loss: 0.9662 - val_accuracy: 0.7537
177 Epoch 89/100
178 34/34 [==============================] - 17s 485ms/step - loss: 0.3682 - accuracy: 0.8851 - val_loss: 0.9636 - val_accuracy: 0.7500
179 Epoch 90/100
180 34/34 [==============================] - 16s 480ms/step - loss: 0.3739 - accuracy: 0.8787 - val_loss: 0.9614 - val_accuracy: 0.7500
181 Epoch 91/100
182 34/34 [==============================] - 16s 483ms/step - loss: 0.3442 - accuracy: 0.8943 - val_loss: 0.9631 - val_accuracy: 0.7500
183 Epoch 92/100
184 34/34 [==============================] - 16s 478ms/step - loss: 0.3598 - accuracy: 0.8915 - val_loss: 0.9624 - val_accuracy: 0.7500
185 Epoch 93/100
186 34/34 [==============================] - 16s 474ms/step - loss: 0.3845 - accuracy: 0.8778 - val_loss: 0.9641 - val_accuracy: 0.7500
187 Epoch 94/100
188 34/34 [==============================] - 16s 478ms/step - loss: 0.3526 - accuracy: 0.8915 - val_loss: 0.9655 - val_accuracy: 0.7574
189 Epoch 95/100
190 34/34 [==============================] - 16s 477ms/step - loss: 0.3581 - accuracy: 0.8869 - val_loss: 0.9652 - val_accuracy: 0.7574
191 Epoch 96/100
192 34/34 [==============================] - 16s 477ms/step - loss: 0.3392 - accuracy: 0.8897 - val_loss: 0.9608 - val_accuracy: 0.7537
193 Epoch 97/100
194 34/34 [==============================] - 16s 478ms/step - loss: 0.3525 - accuracy: 0.8943 - val_loss: 0.9628 - val_accuracy: 0.7537
195 Epoch 98/100
196 34/34 [==============================] - 16s 480ms/step - loss: 0.3532 - accuracy: 0.8943 - val_loss: 0.9632 - val_accuracy: 0.7537
197 Epoch 99/100
198 34/34 [==============================] - 16s 479ms/step - loss: 0.3759 - accuracy: 0.8851 - val_loss: 0.9571 - val_accuracy: 0.7574
199 Epoch 100/100
200 34/34 [==============================] - 16s 476ms/step - loss: 0.3292 - accuracy: 0.8971 - val_loss: 0.9545 - val_accuracy: 0.7537
没有预训练的权重，训练出来的结果肯定不是特别好，但是还算能接受吧。
可视化
1 # 画出训练集准确率曲线图
2 plt.plot(np.arange(epochs),history.history['accuracy'],c='b',label='train_accuracy')
3 # 画出验证集准确率曲线图
4 plt.plot(np.arange(epochs),history.history['val_accuracy'],c='y',label='val_accuracy')
5 # 图例
6 plt.legend()
7 # x坐标描述
8 plt.xlabel('epochs')
9 # y坐标描述
10 plt.ylabel('accuracy')
11 # 显示图像
12 plt.show()
1 # 画出训练集loss曲线图
2 plt.plot(np.arange(epochs),history.history['loss'],c='b',label='train_loss')
3 # 画出验证集loss曲线图
4 plt.plot(np.arange(epochs),history.history['val_loss'],c='y',label='val_loss')
5 # 图例
6 plt.legend()
7 # x坐标描述
8 plt.xlabel('epochs')
9 # y坐标描述
10 plt.ylabel('loss')
11 # 显示图像
12 plt.show()
这里给出1000分类的模型结构图，图太大，翻起来不好翻，需要pdf的留下邮箱我发给你。
声明：本文参照CSDN博主「别团等shy哥发育」的文章
原文链接：https://blog.csdn.net/qq_43753724/article/details/126415101
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