政安晨：【Keras机器学习实践要点】（二十一）—— MobileViT：基于变换器的移动友好图像分类模型

目录

简介

导入

超参数

MobileViT 实用程序

政安晨的个人主页：政安晨

欢迎 👍点赞✍评论⭐收藏

收录专栏: TensorFlow与Keras机器学习实战

希望政安晨的博客能够对您有所裨益，如有不足之处，欢迎在评论区提出指正！

本文目标：MobileViT 利用卷积和变换器的综合优势进行图像分类。

简介


在本示例中，我们实现了 MobileViT 架构（Mehta 等人），该架构结合了 Transformers（Vaswani 等人）和卷积的优点。通过变换器，我们可以捕捉长距离依赖关系，从而实现全局表示。通过卷积，我们可以捕捉空间关系，从而建立局部模型。

除了结合变换器和卷积的特性，作者还介绍了 MobileViT，将其作为通用的移动友好骨干，用于不同的图像识别任务。他们的研究结果表明，从性能上看，MobileViT 优于其他具有相同或更高复杂度的模型（例如 MobileNetV3），同时在移动设备上也很高效。

注：本示例应在 Tensorflow 2.13 及更高版本上运行。

导入

import os
import tensorflow as tfos.environ["KERAS_BACKEND"] = "tensorflow"import keras
from keras import layers
from keras import backendimport tensorflow_datasets as tfdstfds.disable_progress_bar()

超参数

# Values are from table 4.
patch_size = 4  # 2x2, for the Transformer blocks.
image_size = 256
expansion_factor = 2  # expansion factor for the MobileNetV2 blocks.

MobileViT 实用程序

MobileViT 架构由以下模块组成：

处理输入图像的阶梯式 3x3 卷积。
MobileNetV2 风格的反转残差块，用于降低中间特征图的分辨率。
MobileViT 块，结合了变换器和卷积的优势。

如下图所示（摘自论文原文）：

def conv_block(x, filters=16, kernel_size=3, strides=2):conv_layer = layers.Conv2D(filters,kernel_size,strides=strides,activation=keras.activations.swish,padding="same",)return conv_layer(x)# Reference: https://github.com/keras-team/keras/blob/e3858739d178fe16a0c77ce7fab88b0be6dbbdc7/keras/applications/imagenet_utils.py#L413C17-L435def correct_pad(inputs, kernel_size):img_dim = 2 if backend.image_data_format() == "channels_first" else 1input_size = inputs.shape[img_dim : (img_dim + 2)]if isinstance(kernel_size, int):kernel_size = (kernel_size, kernel_size)if input_size[0] is None:adjust = (1, 1)else:adjust = (1 - input_size[0] % 2, 1 - input_size[1] % 2)correct = (kernel_size[0] // 2, kernel_size[1] // 2)return ((correct[0] - adjust[0], correct[0]),(correct[1] - adjust[1], correct[1]),)# Reference: https://git.io/JKgtCdef inverted_residual_block(x, expanded_channels, output_channels, strides=1):m = layers.Conv2D(expanded_channels, 1, padding="same", use_bias=False)(x)m = layers.BatchNormalization()(m)m = keras.activations.swish(m)if strides == 2:m = layers.ZeroPadding2D(padding=correct_pad(m, 3))(m)m = layers.DepthwiseConv2D(3, strides=strides, padding="same" if strides == 1 else "valid", use_bias=False)(m)m = layers.BatchNormalization()(m)m = keras.activations.swish(m)m = layers.Conv2D(output_channels, 1, padding="same", use_bias=False)(m)m = layers.BatchNormalization()(m)if keras.ops.equal(x.shape[-1], output_channels) and strides == 1:return layers.Add()([m, x])return m# Reference:
# https://keras.io/examples/vision/image_classification_with_vision_transformer/def mlp(x, hidden_units, dropout_rate):for units in hidden_units:x = layers.Dense(units, activation=keras.activations.swish)(x)x = layers.Dropout(dropout_rate)(x)return xdef transformer_block(x, transformer_layers, projection_dim, num_heads=2):for _ in range(transformer_layers):# Layer normalization 1.x1 = layers.LayerNormalization(epsilon=1e-6)(x)# Create a multi-head attention layer.attention_output = layers.MultiHeadAttention(num_heads=num_heads, key_dim=projection_dim, dropout=0.1)(x1, x1)# Skip connection 1.x2 = layers.Add()([attention_output, x])# Layer normalization 2.x3 = layers.LayerNormalization(epsilon=1e-6)(x2)# MLP.x3 = mlp(x3,hidden_units=[x.shape[-1] * 2, x.shape[-1]],dropout_rate=0.1,)# Skip connection 2.x = layers.Add()([x3, x2])return xdef mobilevit_block(x, num_blocks, projection_dim, strides=1):# Local projection with convolutions.local_features = conv_block(x, filters=projection_dim, strides=strides)local_features = conv_block(local_features, filters=projection_dim, kernel_size=1, strides=strides)# Unfold into patches and then pass through Transformers.num_patches = int((local_features.shape[1] * local_features.shape[2]) / patch_size)non_overlapping_patches = layers.Reshape((patch_size, num_patches, projection_dim))(local_features)global_features = transformer_block(non_overlapping_patches, num_blocks, projection_dim)# Fold into conv-like feature-maps.folded_feature_map = layers.Reshape((*local_features.shape[1:-1], projection_dim))(global_features)# Apply point-wise conv -> concatenate with the input features.folded_feature_map = conv_block(folded_feature_map, filters=x.shape[-1], kernel_size=1, strides=strides)local_global_features = layers.Concatenate(axis=-1)([x, folded_feature_map])# Fuse the local and global features using a convoluion layer.local_global_features = conv_block(local_global_features, filters=projection_dim, strides=strides)return local_global_features

更多关于 MobileViT 区块的信息：

首先，特征表示（A）要经过卷积块，以捕捉局部关系。这里单个条目的预期形状是（h, w, num_channels）。
然后，它们会被展开成另一个形状为（p, n, num_channels）的向量，其中 p 是一个小块的面积，n 是（h * w）/p。展开后的矢量会经过一个变换器模块，以捕捉补丁之间的全局关系。

输出向量（B）再次被折叠成一个形状（h、w、num_channels）类似于卷积产生的特征图的向量。
然后，向量 A 和 B 再经过两个卷积层，将局部和全局表征融合在一起。请注意，此时最终向量的空间分辨率保持不变。作者还解释了 MobileViT 块如何与 CNN 的卷积块相似。

更多详情，请参阅原始论文。

接下来，我们将这些模块组合在一起，实现 MobileViT 架构（XXS 变体）。

def create_mobilevit(num_classes=5):inputs = keras.Input((image_size, image_size, 3))x = layers.Rescaling(scale=1.0 / 255)(inputs)# Initial conv-stem -> MV2 block.x = conv_block(x, filters=16)x = inverted_residual_block(x, expanded_channels=16 * expansion_factor, output_channels=16)# Downsampling with MV2 block.x = inverted_residual_block(x, expanded_channels=16 * expansion_factor, output_channels=24, strides=2)x = inverted_residual_block(x, expanded_channels=24 * expansion_factor, output_channels=24)x = inverted_residual_block(x, expanded_channels=24 * expansion_factor, output_channels=24)# First MV2 -> MobileViT block.x = inverted_residual_block(x, expanded_channels=24 * expansion_factor, output_channels=48, strides=2)x = mobilevit_block(x, num_blocks=2, projection_dim=64)# Second MV2 -> MobileViT block.x = inverted_residual_block(x, expanded_channels=64 * expansion_factor, output_channels=64, strides=2)x = mobilevit_block(x, num_blocks=4, projection_dim=80)# Third MV2 -> MobileViT block.x = inverted_residual_block(x, expanded_channels=80 * expansion_factor, output_channels=80, strides=2)x = mobilevit_block(x, num_blocks=3, projection_dim=96)x = conv_block(x, filters=320, kernel_size=1, strides=1)# Classification head.x = layers.GlobalAvgPool2D()(x)outputs = layers.Dense(num_classes, activation="softmax")(x)return keras.Model(inputs, outputs)mobilevit_xxs = create_mobilevit()
mobilevit_xxs.summary()

演绎如下：
 

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            
__________________________________________________________________________________________________
rescaling (Rescaling)           (None, 256, 256, 3)  0           input_1[0][0]                    
__________________________________________________________________________________________________
conv2d (Conv2D)                 (None, 128, 128, 16) 448         rescaling[0][0]                  
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 128, 128, 32) 512         conv2d[0][0]                     
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 128, 128, 32) 128         conv2d_1[0][0]                   
__________________________________________________________________________________________________
tf.nn.silu (TFOpLambda)         (None, 128, 128, 32) 0           batch_normalization[0][0]        
__________________________________________________________________________________________________
depthwise_conv2d (DepthwiseConv (None, 128, 128, 32) 288         tf.nn.silu[0][0]                 
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 128, 128, 32) 128         depthwise_conv2d[0][0]           
__________________________________________________________________________________________________
tf.nn.silu_1 (TFOpLambda)       (None, 128, 128, 32) 0           batch_normalization_1[0][0]      
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 128, 128, 16) 512         tf.nn.silu_1[0][0]               
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 128, 128, 16) 64          conv2d_2[0][0]                   
__________________________________________________________________________________________________
add (Add)                       (None, 128, 128, 16) 0           batch_normalization_2[0][0]      conv2d[0][0]                     
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 128, 128, 32) 512         add[0][0]                        
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 128, 128, 32) 128         conv2d_3[0][0]                   
__________________________________________________________________________________________________
tf.nn.silu_2 (TFOpLambda)       (None, 128, 128, 32) 0           batch_normalization_3[0][0]      
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D)  (None, 129, 129, 32) 0           tf.nn.silu_2[0][0]               
__________________________________________________________________________________________________
depthwise_conv2d_1 (DepthwiseCo (None, 64, 64, 32)   288         zero_padding2d[0][0]             
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 64, 64, 32)   128         depthwise_conv2d_1[0][0]         
__________________________________________________________________________________________________
tf.nn.silu_3 (TFOpLambda)       (None, 64, 64, 32)   0           batch_normalization_4[0][0]      
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 64, 64, 24)   768         tf.nn.silu_3[0][0]               
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 64, 64, 24)   96          conv2d_4[0][0]                   
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 64, 64, 48)   1152        batch_normalization_5[0][0]      
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 64, 64, 48)   192         conv2d_5[0][0]                   
__________________________________________________________________________________________________
tf.nn.silu_4 (TFOpLambda)       (None, 64, 64, 48)   0           batch_normalization_6[0][0]      
__________________________________________________________________________________________________
depthwise_conv2d_2 (DepthwiseCo (None, 64, 64, 48)   432         tf.nn.silu_4[0][0]               
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 64, 64, 48)   192         depthwise_conv2d_2[0][0]         
__________________________________________________________________________________________________
tf.nn.silu_5 (TFOpLambda)       (None, 64, 64, 48)   0           batch_normalization_7[0][0]      
__________________________________________________________________________________________________
conv2d_6 (Conv2D)               (None, 64, 64, 24)   1152        tf.nn.silu_5[0][0]               
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 64, 64, 24)   96          conv2d_6[0][0]                   
__________________________________________________________________________________________________
add_1 (Add)                     (None, 64, 64, 24)   0           batch_normalization_8[0][0]      batch_normalization_5[0][0]      
__________________________________________________________________________________________________
conv2d_7 (Conv2D)               (None, 64, 64, 48)   1152        add_1[0][0]                      
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 64, 64, 48)   192         conv2d_7[0][0]                   
__________________________________________________________________________________________________
tf.nn.silu_6 (TFOpLambda)       (None, 64, 64, 48)   0           batch_normalization_9[0][0]      
__________________________________________________________________________________________________
depthwise_conv2d_3 (DepthwiseCo (None, 64, 64, 48)   432         tf.nn.silu_6[0][0]               
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 64, 64, 48)   192         depthwise_conv2d_3[0][0]         
__________________________________________________________________________________________________
tf.nn.silu_7 (TFOpLambda)       (None, 64, 64, 48)   0           batch_normalization_10[0][0]     
__________________________________________________________________________________________________
conv2d_8 (Conv2D)               (None, 64, 64, 24)   1152        tf.nn.silu_7[0][0]               
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 64, 64, 24)   96          conv2d_8[0][0]                   
__________________________________________________________________________________________________
add_2 (Add)                     (None, 64, 64, 24)   0           batch_normalization_11[0][0]     add_1[0][0]                      
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 64, 64, 48)   1152        add_2[0][0]                      
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 64, 64, 48)   192         conv2d_9[0][0]                   
__________________________________________________________________________________________________
tf.nn.silu_8 (TFOpLambda)       (None, 64, 64, 48)   0           batch_normalization_12[0][0]     
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 65, 65, 48)   0           tf.nn.silu_8[0][0]               
__________________________________________________________________________________________________
depthwise_conv2d_4 (DepthwiseCo (None, 32, 32, 48)   432         zero_padding2d_1[0][0]           
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 32, 32, 48)   192         depthwise_conv2d_4[0][0]         
__________________________________________________________________________________________________
tf.nn.silu_9 (TFOpLambda)       (None, 32, 32, 48)   0           batch_normalization_13[0][0]     
__________________________________________________________________________________________________
conv2d_10 (Conv2D)              (None, 32, 32, 48)   2304        tf.nn.silu_9[0][0]               
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 32, 32, 48)   192         conv2d_10[0][0]                  
__________________________________________________________________________________________________
conv2d_11 (Conv2D)              (None, 32, 32, 64)   27712       batch_normalization_14[0][0]     
__________________________________________________________________________________________________
conv2d_12 (Conv2D)              (None, 32, 32, 64)   4160        conv2d_11[0][0]                  
__________________________________________________________________________________________________
reshape (Reshape)               (None, 4, 256, 64)   0           conv2d_12[0][0]                  
__________________________________________________________________________________________________
layer_normalization (LayerNorma (None, 4, 256, 64)   128         reshape[0][0]                    
__________________________________________________________________________________________________
multi_head_attention (MultiHead (None, 4, 256, 64)   33216       layer_normalization[0][0]        layer_normalization[0][0]        
__________________________________________________________________________________________________
add_3 (Add)                     (None, 4, 256, 64)   0           multi_head_attention[0][0]       reshape[0][0]                    
__________________________________________________________________________________________________
layer_normalization_1 (LayerNor (None, 4, 256, 64)   128         add_3[0][0]                      
__________________________________________________________________________________________________
dense (Dense)                   (None, 4, 256, 128)  8320        layer_normalization_1[0][0]      
__________________________________________________________________________________________________
dropout (Dropout)               (None, 4, 256, 128)  0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 4, 256, 64)   8256        dropout[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 4, 256, 64)   0           dense_1[0][0]                    
__________________________________________________________________________________________________
add_4 (Add)                     (None, 4, 256, 64)   0           dropout_1[0][0]                  add_3[0][0]                      
__________________________________________________________________________________________________
layer_normalization_2 (LayerNor (None, 4, 256, 64)   128         add_4[0][0]                      
__________________________________________________________________________________________________
multi_head_attention_1 (MultiHe (None, 4, 256, 64)   33216       layer_normalization_2[0][0]      layer_normalization_2[0][0]      
__________________________________________________________________________________________________
add_5 (Add)                     (None, 4, 256, 64)   0           multi_head_attention_1[0][0]     add_4[0][0]                      
__________________________________________________________________________________________________
layer_normalization_3 (LayerNor (None, 4, 256, 64)   128         add_5[0][0]                      
__________________________________________________________________________________________________
dense_2 (Dense)                 (None, 4, 256, 128)  8320        layer_normalization_3[0][0]      
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 4, 256, 128)  0           dense_2[0][0]                    
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 4, 256, 64)   8256        dropout_2[0][0]                  
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 4, 256, 64)   0           dense_3[0][0]                    
__________________________________________________________________________________________________
add_6 (Add)                     (None, 4, 256, 64)   0           dropout_3[0][0]                  add_5[0][0]                      
__________________________________________________________________________________________________
reshape_1 (Reshape)             (None, 32, 32, 64)   0           add_6[0][0]                      
__________________________________________________________________________________________________
conv2d_13 (Conv2D)              (None, 32, 32, 48)   3120        reshape_1[0][0]                  
__________________________________________________________________________________________________
concatenate (Concatenate)       (None, 32, 32, 96)   0           batch_normalization_14[0][0]     conv2d_13[0][0]                  
__________________________________________________________________________________________________
conv2d_14 (Conv2D)              (None, 32, 32, 64)   55360       concatenate[0][0]                
__________________________________________________________________________________________________
conv2d_15 (Conv2D)              (None, 32, 32, 128)  8192        conv2d_14[0][0]                  
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 32, 32, 128)  512         conv2d_15[0][0]                  
__________________________________________________________________________________________________
tf.nn.silu_10 (TFOpLambda)      (None, 32, 32, 128)  0           batch_normalization_15[0][0]     
__________________________________________________________________________________________________
zero_padding2d_2 (ZeroPadding2D (None, 33, 33, 128)  0           tf.nn.silu_10[0][0]              
__________________________________________________________________________________________________
depthwise_conv2d_5 (DepthwiseCo (None, 16, 16, 128)  1152        zero_padding2d_2[0][0]           
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 16, 16, 128)  512         depthwise_conv2d_5[0][0]         
__________________________________________________________________________________________________
tf.nn.silu_11 (TFOpLambda)      (None, 16, 16, 128)  0           batch_normalization_16[0][0]     
__________________________________________________________________________________________________
conv2d_16 (Conv2D)              (None, 16, 16, 64)   8192        tf.nn.silu_11[0][0]              
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 16, 16, 64)   256         conv2d_16[0][0]                  
__________________________________________________________________________________________________
conv2d_17 (Conv2D)              (None, 16, 16, 80)   46160       batch_normalization_17[0][0]     
__________________________________________________________________________________________________
conv2d_18 (Conv2D)              (None, 16, 16, 80)   6480        conv2d_17[0][0]                  
__________________________________________________________________________________________________
reshape_2 (Reshape)             (None, 4, 64, 80)    0           conv2d_18[0][0]                  
__________________________________________________________________________________________________
layer_normalization_4 (LayerNor (None, 4, 64, 80)    160         reshape_2[0][0]                  
__________________________________________________________________________________________________
multi_head_attention_2 (MultiHe (None, 4, 64, 80)    51760       layer_normalization_4[0][0]      layer_normalization_4[0][0]      
__________________________________________________________________________________________________
add_7 (Add)                     (None, 4, 64, 80)    0           multi_head_attention_2[0][0]     reshape_2[0][0]                  
__________________________________________________________________________________________________
layer_normalization_5 (LayerNor (None, 4, 64, 80)    160         add_7[0][0]                      
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 4, 64, 160)   12960       layer_normalization_5[0][0]      
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 4, 64, 160)   0           dense_4[0][0]                    
__________________________________________________________________________________________________
dense_5 (Dense)                 (None, 4, 64, 80)    12880       dropout_4[0][0]                  
__________________________________________________________________________________________________
dropout_5 (Dropout)             (None, 4, 64, 80)    0           dense_5[0][0]                    
__________________________________________________________________________________________________
add_8 (Add)                     (None, 4, 64, 80)    0           dropout_5[0][0]                  add_7[0][0]                      
__________________________________________________________________________________________________
layer_normalization_6 (LayerNor (None, 4, 64, 80)    160         add_8[0][0]                      
__________________________________________________________________________________________________
multi_head_attention_3 (MultiHe (None, 4, 64, 80)    51760       layer_normalization_6[0][0]      layer_normalization_6[0][0]      
__________________________________________________________________________________________________
add_9 (Add)                     (None, 4, 64, 80)    0           multi_head_attention_3[0][0]     add_8[0][0]                      
__________________________________________________________________________________________________
layer_normalization_7 (LayerNor (None, 4, 64, 80)    160         add_9[0][0]                      
__________________________________________________________________________________________________
dense_6 (Dense)                 (None, 4, 64, 160)   12960       layer_normalization_7[0][0]      
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 4, 64, 160)   0           dense_6[0][0]                    
__________________________________________________________________________________________________
dense_7 (Dense)                 (None, 4, 64, 80)    12880       dropout_6[0][0]                  
__________________________________________________________________________________________________
dropout_7 (Dropout)             (None, 4, 64, 80)    0           dense_7[0][0]                    
__________________________________________________________________________________________________
add_10 (Add)                    (None, 4, 64, 80)    0           dropout_7[0][0]                  add_9[0][0]                      
__________________________________________________________________________________________________
layer_normalization_8 (LayerNor (None, 4, 64, 80)    160         add_10[0][0]                     
__________________________________________________________________________________________________
multi_head_attention_4 (MultiHe (None, 4, 64, 80)    51760       layer_normalization_8[0][0]      layer_normalization_8[0][0]      
__________________________________________________________________________________________________
add_11 (Add)                    (None, 4, 64, 80)    0           multi_head_attention_4[0][0]     add_10[0][0]                     
__________________________________________________________________________________________________
layer_normalization_9 (LayerNor (None, 4, 64, 80)    160         add_11[0][0]                     
__________________________________________________________________________________________________
dense_8 (Dense)                 (None, 4, 64, 160)   12960       layer_normalization_9[0][0]      
__________________________________________________________________________________________________
dropout_8 (Dropout)             (None, 4, 64, 160)   0           dense_8[0][0]                    
__________________________________________________________________________________________________
dense_9 (Dense)                 (None, 4, 64, 80)    12880       dropout_8[0][0]                  
__________________________________________________________________________________________________
dropout_9 (Dropout)             (None, 4, 64, 80)    0           dense_9[0][0]                    
__________________________________________________________________________________________________
add_12 (Add)                    (None, 4, 64, 80)    0           dropout_9[0][0]                  add_11[0][0]                     
__________________________________________________________________________________________________
layer_normalization_10 (LayerNo (None, 4, 64, 80)    160         add_12[0][0]                     
__________________________________________________________________________________________________
multi_head_attention_5 (MultiHe (None, 4, 64, 80)    51760       layer_normalization_10[0][0]     layer_normalization_10[0][0]     
__________________________________________________________________________________________________
add_13 (Add)                    (None, 4, 64, 80)    0           multi_head_attention_5[0][0]     add_12[0][0]                     
__________________________________________________________________________________________________
layer_normalization_11 (LayerNo (None, 4, 64, 80)    160         add_13[0][0]                     
__________________________________________________________________________________________________
dense_10 (Dense)                (None, 4, 64, 160)   12960       layer_normalization_11[0][0]     
__________________________________________________________________________________________________
dropout_10 (Dropout)            (None, 4, 64, 160)   0           dense_10[0][0]                   
__________________________________________________________________________________________________
dense_11 (Dense)                (None, 4, 64, 80)    12880       dropout_10[0][0]                 
__________________________________________________________________________________________________
dropout_11 (Dropout)            (None, 4, 64, 80)    0           dense_11[0][0]                   
__________________________________________________________________________________________________
add_14 (Add)                    (None, 4, 64, 80)    0           dropout_11[0][0]                 add_13[0][0]                     
__________________________________________________________________________________________________
reshape_3 (Reshape)             (None, 16, 16, 80)   0           add_14[0][0]                     
__________________________________________________________________________________________________
conv2d_19 (Conv2D)              (None, 16, 16, 64)   5184        reshape_3[0][0]                  
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 16, 16, 128)  0           batch_normalization_17[0][0]     conv2d_19[0][0]                  
__________________________________________________________________________________________________
conv2d_20 (Conv2D)              (None, 16, 16, 80)   92240       concatenate_1[0][0]              
__________________________________________________________________________________________________
conv2d_21 (Conv2D)              (None, 16, 16, 160)  12800       conv2d_20[0][0]                  
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 16, 16, 160)  640         conv2d_21[0][0]                  
__________________________________________________________________________________________________
tf.nn.silu_12 (TFOpLambda)      (None, 16, 16, 160)  0           batch_normalization_18[0][0]     
__________________________________________________________________________________________________
zero_padding2d_3 (ZeroPadding2D (None, 17, 17, 160)  0           tf.nn.silu_12[0][0]              
__________________________________________________________________________________________________
depthwise_conv2d_6 (DepthwiseCo (None, 8, 8, 160)    1440        zero_padding2d_3[0][0]           
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 8, 8, 160)    640         depthwise_conv2d_6[0][0]         
__________________________________________________________________________________________________
tf.nn.silu_13 (TFOpLambda)      (None, 8, 8, 160)    0           batch_normalization_19[0][0]     
__________________________________________________________________________________________________
conv2d_22 (Conv2D)              (None, 8, 8, 80)     12800       tf.nn.silu_13[0][0]              
__________________________________________________________________________________________________
batch_normalization_20 (BatchNo (None, 8, 8, 80)     320         conv2d_22[0][0]                  
__________________________________________________________________________________________________
conv2d_23 (Conv2D)              (None, 8, 8, 96)     69216       batch_normalization_20[0][0]     
__________________________________________________________________________________________________
conv2d_24 (Conv2D)              (None, 8, 8, 96)     9312        conv2d_23[0][0]                  
__________________________________________________________________________________________________
reshape_4 (Reshape)             (None, 4, 16, 96)    0           conv2d_24[0][0]                  
__________________________________________________________________________________________________
layer_normalization_12 (LayerNo (None, 4, 16, 96)    192         reshape_4[0][0]                  
__________________________________________________________________________________________________
multi_head_attention_6 (MultiHe (None, 4, 16, 96)    74400       layer_normalization_12[0][0]     layer_normalization_12[0][0]     
__________________________________________________________________________________________________
add_15 (Add)                    (None, 4, 16, 96)    0           multi_head_attention_6[0][0]     reshape_4[0][0]                  
__________________________________________________________________________________________________
layer_normalization_13 (LayerNo (None, 4, 16, 96)    192         add_15[0][0]                     
__________________________________________________________________________________________________
dense_12 (Dense)                (None, 4, 16, 192)   18624       layer_normalization_13[0][0]     
__________________________________________________________________________________________________
dropout_12 (Dropout)            (None, 4, 16, 192)   0           dense_12[0][0]                   
__________________________________________________________________________________________________
dense_13 (Dense)                (None, 4, 16, 96)    18528       dropout_12[0][0]                 
__________________________________________________________________________________________________
dropout_13 (Dropout)            (None, 4, 16, 96)    0           dense_13[0][0]                   
__________________________________________________________________________________________________
add_16 (Add)                    (None, 4, 16, 96)    0           dropout_13[0][0]                 add_15[0][0]                     
__________________________________________________________________________________________________
layer_normalization_14 (LayerNo (None, 4, 16, 96)    192         add_16[0][0]                     
__________________________________________________________________________________________________
multi_head_attention_7 (MultiHe (None, 4, 16, 96)    74400       layer_normalization_14[0][0]     layer_normalization_14[0][0]     
__________________________________________________________________________________________________
add_17 (Add)                    (None, 4, 16, 96)    0           multi_head_attention_7[0][0]     add_16[0][0]                     
__________________________________________________________________________________________________
layer_normalization_15 (LayerNo (None, 4, 16, 96)    192         add_17[0][0]                     
__________________________________________________________________________________________________
dense_14 (Dense)                (None, 4, 16, 192)   18624       layer_normalization_15[0][0]     
__________________________________________________________________________________________________
dropout_14 (Dropout)            (None, 4, 16, 192)   0           dense_14[0][0]                   
__________________________________________________________________________________________________
dense_15 (Dense)                (None, 4, 16, 96)    18528       dropout_14[0][0]                 
__________________________________________________________________________________________________
dropout_15 (Dropout)            (None, 4, 16, 96)    0           dense_15[0][0]                   
__________________________________________________________________________________________________
add_18 (Add)                    (None, 4, 16, 96)    0           dropout_15[0][0]                 add_17[0][0]                     
__________________________________________________________________________________________________
layer_normalization_16 (LayerNo (None, 4, 16, 96)    192         add_18[0][0]                     
__________________________________________________________________________________________________
multi_head_attention_8 (MultiHe (None, 4, 16, 96)    74400       layer_normalization_16[0][0]     layer_normalization_16[0][0]     
__________________________________________________________________________________________________
add_19 (Add)                    (None, 4, 16, 96)    0           multi_head_attention_8[0][0]     add_18[0][0]                     
__________________________________________________________________________________________________
layer_normalization_17 (LayerNo (None, 4, 16, 96)    192         add_19[0][0]                     
__________________________________________________________________________________________________
dense_16 (Dense)                (None, 4, 16, 192)   18624       layer_normalization_17[0][0]     
__________________________________________________________________________________________________
dropout_16 (Dropout)            (None, 4, 16, 192)   0           dense_16[0][0]                   
__________________________________________________________________________________________________
dense_17 (Dense)                (None, 4, 16, 96)    18528       dropout_16[0][0]                 
__________________________________________________________________________________________________
dropout_17 (Dropout)            (None, 4, 16, 96)    0           dense_17[0][0]                   
__________________________________________________________________________________________________
add_20 (Add)                    (None, 4, 16, 96)    0           dropout_17[0][0]                 add_19[0][0]                     
__________________________________________________________________________________________________
reshape_5 (Reshape)             (None, 8, 8, 96)     0           add_20[0][0]                     
__________________________________________________________________________________________________
conv2d_25 (Conv2D)              (None, 8, 8, 80)     7760        reshape_5[0][0]                  
__________________________________________________________________________________________________
concatenate_2 (Concatenate)     (None, 8, 8, 160)    0           batch_normalization_20[0][0]     conv2d_25[0][0]                  
__________________________________________________________________________________________________
conv2d_26 (Conv2D)              (None, 8, 8, 96)     138336      concatenate_2[0][0]              
__________________________________________________________________________________________________
conv2d_27 (Conv2D)              (None, 8, 8, 320)    31040       conv2d_26[0][0]                  
__________________________________________________________________________________________________
global_average_pooling2d (Globa (None, 320)          0           conv2d_27[0][0]                  
__________________________________________________________________________________________________
dense_18 (Dense)                (None, 5)            1605        global_average_pooling2d[0][0]   
==================================================================================================
Total params: 1,307,621
Trainable params: 1,305,077
Non-trainable params: 2,544
__________________________________________________________________________________________________---
## Dataset preparationWe will be using the
[`tf_flowers`](https://www.tensorflow.org/datasets/catalog/tf_flowers)
dataset to demonstrate the model. Unlike other Transformer-based architectures,
MobileViT uses a simple augmentation pipeline primarily because it has the properties
of a CNN.```python
batch_size = 64
auto = tf.data.AUTOTUNE
resize_bigger = 280
num_classes = 5def preprocess_dataset(is_training=True):def _pp(image, label):if is_training:# Resize to a bigger spatial resolution and take the random# crops.image = tf.image.resize(image, (resize_bigger, resize_bigger))image = tf.image.random_crop(image, (image_size, image_size, 3))image = tf.image.random_flip_left_right(image)else:image = tf.image.resize(image, (image_size, image_size))label = tf.one_hot(label, depth=num_classes)return image, labelreturn _ppdef prepare_dataset(dataset, is_training=True):if is_training:dataset = dataset.shuffle(batch_size * 10)dataset = dataset.map(preprocess_dataset(is_training), num_parallel_calls=auto)return dataset.batch(batch_size).prefetch(auto)

咱们使用多尺度数据采样器来帮助模型学习不同尺度的表征。

train_dataset, val_dataset = tfds.load("tf_flowers", split=["train[:90%]", "train[90%:]"], as_supervised=True
)num_train = train_dataset.cardinality()
num_val = val_dataset.cardinality()
print(f"Number of training examples: {num_train}")
print(f"Number of validation examples: {num_val}")train_dataset = prepare_dataset(train_dataset, is_training=True)
val_dataset = prepare_dataset(val_dataset, is_training=False)

演绎如下：
 

Number of training examples: 3303
Number of validation examples: 367

--- ## Train a MobileViT (XXS) model

learning_rate = 0.002
label_smoothing_factor = 0.1
epochs = 30optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
loss_fn = keras.losses.CategoricalCrossentropy(label_smoothing=label_smoothing_factor)def run_experiment(epochs=epochs):mobilevit_xxs = create_mobilevit(num_classes=num_classes)mobilevit_xxs.compile(optimizer=optimizer, loss=loss_fn, metrics=["accuracy"])# When using `save_weights_only=True` in `ModelCheckpoint`, the filepath provided must end in `.weights.h5`checkpoint_filepath = "/tmp/checkpoint.weights.h5"checkpoint_callback = keras.callbacks.ModelCheckpoint(checkpoint_filepath,monitor="val_accuracy",save_best_only=True,save_weights_only=True,)mobilevit_xxs.fit(train_dataset,validation_data=val_dataset,epochs=epochs,callbacks=[checkpoint_callback],)mobilevit_xxs.load_weights(checkpoint_filepath)_, accuracy = mobilevit_xxs.evaluate(val_dataset)print(f"Validation accuracy: {round(accuracy * 100, 2)}%")return mobilevit_xxsmobilevit_xxs = run_experiment()

演绎：

Epoch 1/30
52/52 [==============================] - 47s 459ms/step - loss: 1.3397 - accuracy: 0.4832 - val_loss: 1.7250 - val_accuracy: 0.1662
Epoch 2/30
52/52 [==============================] - 21s 404ms/step - loss: 1.1167 - accuracy: 0.6210 - val_loss: 1.9844 - val_accuracy: 0.1907
Epoch 3/30
52/52 [==============================] - 21s 403ms/step - loss: 1.0217 - accuracy: 0.6709 - val_loss: 1.8187 - val_accuracy: 0.1907
Epoch 4/30
52/52 [==============================] - 21s 409ms/step - loss: 0.9682 - accuracy: 0.7048 - val_loss: 2.0329 - val_accuracy: 0.1907
Epoch 5/30
52/52 [==============================] - 21s 408ms/step - loss: 0.9552 - accuracy: 0.7196 - val_loss: 2.1150 - val_accuracy: 0.1907
Epoch 6/30
52/52 [==============================] - 21s 407ms/step - loss: 0.9186 - accuracy: 0.7318 - val_loss: 2.9713 - val_accuracy: 0.1907
Epoch 7/30
52/52 [==============================] - 21s 407ms/step - loss: 0.8986 - accuracy: 0.7457 - val_loss: 3.2062 - val_accuracy: 0.1907
Epoch 8/30
52/52 [==============================] - 21s 408ms/step - loss: 0.8831 - accuracy: 0.7542 - val_loss: 3.8631 - val_accuracy: 0.1907
Epoch 9/30
52/52 [==============================] - 21s 408ms/step - loss: 0.8433 - accuracy: 0.7714 - val_loss: 1.8029 - val_accuracy: 0.3542
Epoch 10/30
52/52 [==============================] - 21s 408ms/step - loss: 0.8489 - accuracy: 0.7763 - val_loss: 1.7920 - val_accuracy: 0.4796
Epoch 11/30
52/52 [==============================] - 21s 409ms/step - loss: 0.8256 - accuracy: 0.7884 - val_loss: 1.4992 - val_accuracy: 0.5477
Epoch 12/30
52/52 [==============================] - 21s 407ms/step - loss: 0.7859 - accuracy: 0.8123 - val_loss: 0.9236 - val_accuracy: 0.7330
Epoch 13/30
52/52 [==============================] - 21s 409ms/step - loss: 0.7702 - accuracy: 0.8159 - val_loss: 0.8059 - val_accuracy: 0.8011
Epoch 14/30
52/52 [==============================] - 21s 403ms/step - loss: 0.7670 - accuracy: 0.8153 - val_loss: 1.1535 - val_accuracy: 0.7084
Epoch 15/30
52/52 [==============================] - 21s 408ms/step - loss: 0.7332 - accuracy: 0.8344 - val_loss: 0.7746 - val_accuracy: 0.8147
Epoch 16/30
52/52 [==============================] - 21s 404ms/step - loss: 0.7284 - accuracy: 0.8335 - val_loss: 1.0342 - val_accuracy: 0.7330
Epoch 17/30
52/52 [==============================] - 21s 409ms/step - loss: 0.7484 - accuracy: 0.8262 - val_loss: 1.0523 - val_accuracy: 0.7112
Epoch 18/30
52/52 [==============================] - 21s 408ms/step - loss: 0.7209 - accuracy: 0.8450 - val_loss: 0.8146 - val_accuracy: 0.8174
Epoch 19/30
52/52 [==============================] - 21s 409ms/step - loss: 0.7141 - accuracy: 0.8435 - val_loss: 0.8016 - val_accuracy: 0.7875
Epoch 20/30
52/52 [==============================] - 21s 410ms/step - loss: 0.7075 - accuracy: 0.8435 - val_loss: 0.9352 - val_accuracy: 0.7439
Epoch 21/30
52/52 [==============================] - 21s 406ms/step - loss: 0.7066 - accuracy: 0.8504 - val_loss: 1.0171 - val_accuracy: 0.7139
Epoch 22/30
52/52 [==============================] - 21s 405ms/step - loss: 0.6913 - accuracy: 0.8532 - val_loss: 0.7059 - val_accuracy: 0.8610
Epoch 23/30
52/52 [==============================] - 21s 408ms/step - loss: 0.6681 - accuracy: 0.8671 - val_loss: 0.8007 - val_accuracy: 0.8147
Epoch 24/30
52/52 [==============================] - 21s 409ms/step - loss: 0.6636 - accuracy: 0.8747 - val_loss: 0.9490 - val_accuracy: 0.7302
Epoch 25/30
52/52 [==============================] - 21s 408ms/step - loss: 0.6637 - accuracy: 0.8722 - val_loss: 0.6913 - val_accuracy: 0.8556
Epoch 26/30
52/52 [==============================] - 21s 406ms/step - loss: 0.6443 - accuracy: 0.8837 - val_loss: 1.0483 - val_accuracy: 0.7139
Epoch 27/30
52/52 [==============================] - 21s 407ms/step - loss: 0.6555 - accuracy: 0.8695 - val_loss: 0.9448 - val_accuracy: 0.7602
Epoch 28/30
52/52 [==============================] - 21s 409ms/step - loss: 0.6409 - accuracy: 0.8807 - val_loss: 0.9337 - val_accuracy: 0.7302
Epoch 29/30
52/52 [==============================] - 21s 408ms/step - loss: 0.6300 - accuracy: 0.8910 - val_loss: 0.7461 - val_accuracy: 0.8256
Epoch 30/30
52/52 [==============================] - 21s 408ms/step - loss: 0.6093 - accuracy: 0.8968 - val_loss: 0.8651 - val_accuracy: 0.7766
6/6 [==============================] - 0s 65ms/step - loss: 0.7059 - accuracy: 0.8610
Validation accuracy: 86.1%

--- ## 结果和 TFLite 转换 大约有一百万个参数，在 256x256 分辨率下达到 ~85% top-1 的准确率是一个很好的结果。这款 MobileViT 移动设备与 TensorFlow Lite (TFLite) 完全兼容，可以用以下代码进行转换：

# Serialize the model as a SavedModel.
tf.saved_model.save(mobilevit_xxs, "mobilevit_xxs")# Convert to TFLite. This form of quantization is called
# post-training dynamic-range quantization in TFLite.
converter = tf.lite.TFLiteConverter.from_saved_model("mobilevit_xxs")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,  # Enable TensorFlow Lite ops.tf.lite.OpsSet.SELECT_TF_OPS,  # Enable TensorFlow ops.
]
tflite_model = converter.convert()
open("mobilevit_xxs.tflite", "wb").write(tflite_model)

要了解有关 TFLite 中可用的不同量化配方以及使用 TFLite 模型运行推理的更多信息，请查阅 [本官方资源](https://www.tensorflow.org/lite/performance/post_training_quantization)。

您可以使用[Hugging Face Hub](https://huggingface.co/keras-io/mobile-vit-xxs)上托管的训练有素的模型，并尝试[Hugging Face Spaces](https://huggingface.co/spaces/keras-io/Flowers-Classification-MobileViT)上的演示。

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.mzph.cn/news/802020.shtml

如若内容造成侵权/违法违规/事实不符，请联系多彩编程网进行投诉反馈email:809451989@qq.com，一经查实，立即删除！

相关文章

AI 创业指难（一） ：Stable Diffusion AI 绘画怎么用

AI 创业指难（一） ：Stable Diffusion AI 绘画怎么用

一. 前言 一年不到，AI对生活和工作的影响已经逐步体现。所以千万别掉队了，也许 AI 不能成为我们的主要工作，但是如何借助 AI 实现副业的扩展同样值得思考。 这一篇就来讲一个 AI 绘画工具，这个工具我也是才上手不久，…
阅读更多...
冻干可以长期给猫咪吃吗？五款顶尖生骨肉冻干盘点推荐

冻干可以长期给猫咪吃吗？五款顶尖生骨肉冻干盘点推荐

近年来，冻干猫粮因其高品质而备受喜爱，吸引了无数猫主人的目光，像我这样的养猫老手早已开始冻干喂养。但对于新手养猫的人来说，他们可能会对冻干猫粮感到陌生，并产生疑问：这到底是什么？冻干可以…
阅读更多...
.NET 设计模式—装饰器模式（Decorator Pattern）

.NET 设计模式—装饰器模式（Decorator Pattern）

简介 装饰者模式（Decorator Pattern）是一种结构型设计模式，它允许你在不改变对象接口的前提下，动态地将新行为附加到对象上。这种模式是通过创建一个包装（或装饰）对象，将要被装饰的对象包裹起来…
阅读更多...
看看你的身体出现了哪些症状，要当心了！

看看你的身体出现了哪些症状，要当心了！

身体出现以下五个症状，你要小心了。 夜间尿频，不要以为晚上喝水喝多了，很有可能是你的血糖升高了，血糖过高的人，口腔很容易受到刺激，而感到非常的干燥，所以会通过补充水分的方式来缓解&#xff…
阅读更多...
基于java+springboot+vue实现的农产品销售系统（文末源码+Lw）23-231

基于java+springboot+vue实现的农产品销售系统（文末源码+Lw）23-231

摘 要 如今社会上各行各业，都喜欢用自己行业的专属软件工作，互联网发展到这个时候，人们已经发现离不开了互联网。新技术的产生，往往能解决一些老技术的弊端问题。因为传统乐乐农产品销售系统信息管理难度大，容错率低…
阅读更多...
Springboot-redis整合

Springboot-redis整合

Springboot-redis命令行封装 前言 Redis（Remote Dictionary Server），即远程字典服务，是一个开源的使用ANSI C语言编写的、支持网络、可基于内存亦可持久化的日志型、Key-Value数据库，并提供多种语言的API。Redis也是现…
阅读更多...
CSS导读 (Emmet语法)

CSS导读 (Emmet语法)

（大家好，今天我们将继续来学习CSS的相关知识，大家可以在评论区进行互动答疑哦~加油！💕） 目录 续：七、Chrome调试工具 一、Emmet语法 1.1 快速生成HTML结构语法 1.2 快速生成CSS样式语法 &…
阅读更多...
LangChain-10 Agents langchainhub 共享的提示词Prompt

LangChain-10 Agents langchainhub 共享的提示词Prompt

LangChainHub 的思路真的很好，通过Hub的方式将Prompt 共享起来，大家可以通过很方便的手段，短短的几行代码就可以使用共享的Prompt。 我个人非常看好这个项目。 官方推荐使用LangChainHub，但是它在GitHub已经一年没有更新了&#x…
阅读更多...
flutter多入口点entrypoint

flutter多入口点entrypoint

native中引擎对象本身消耗内存(每个引擎对象约莫消耗42MB内存) 多引擎：native多引擎>启动>flutter多入口点entrypoint>多main函数>多子包元素集>多(子)程序 单引擎(复用)：native单引擎>复用启动>flutter多入口点entrypoint>多m…
阅读更多...
高等数学基础篇之关于圆，椭圆，圆环的应用

高等数学基础篇之关于圆，椭圆，圆环的应用

文章目录 前言 1.圆 1.1标准方程 1.2偏心圆 1.3参数方程 2.椭圆 2.1标准方程 2.2参数方程 2.3极坐标 3.圆环 4.扇形 前言 这篇文章主要是应对二重积分出现的一些关于圆的积分域，让大家大概了解一下，不是很详细，因为二重积分对几何…
阅读更多...
uniapp请求后端接口

uniapp请求后端接口

新建文件夹utils const request (config) > {// 拼接完整的接口路径config.url http://mm.test.cn config.url;//这里拼接的是访问后端接口的地址，http://mm.test.cn/prod-api/testconsole.log(config.url)//判断是都携带参数if(!config.data){config.data …
阅读更多...
7-26 单词长度

7-26 单词长度

题解&#xff1a; #include <bits/stdc.h> using namespace std; int main() {string s;getline(cin,s); //读取一行字符串char c; //记录字符int cnt 0; //用来记录长度int flag 0; //用来判断是否已经输出了第一个单词的长度for (int i 0;i<s.size(); i)…
阅读更多...
阿里云新手用户建站必看攻略，从注册域名到网站上线需完成步骤

阿里云新手用户建站必看攻略，从注册域名到网站上线需完成步骤

无论是个人还是企业新手用户&#xff0c;搭建个人或者企业网站都必须进过注册域名、购买云服务器、搭建网站、ICP备案、解析域名等步骤&#xff0c;本文为大家展示阿里云新手用户建站过程中从注册域名到网站上线需要完成的具体步骤。 1、选购域名 域名是互联网世界的门牌号码&…
阅读更多...
什么是HW，企业如何进行HW保障？

什么是HW，企业如何进行HW保障？

文章目录 一、什么是HW二、HW行动具体采取了哪些攻防演练措施三、攻击方一般的攻击流程和方法四、企业HW保障方案1.建意识2.摸家底3.固城池4.配神器5.增值守 一、什么是HW 网络安全形势近年出现新变化&#xff0c;网络安全态势变得越来越复杂&#xff0c;黑客攻击入侵、勒索病…
阅读更多...
【JavaWeb】Day37.MySQL概述——数据库设计-DML

【JavaWeb】Day37.MySQL概述——数据库设计-DML

数据库操作-DML DML英文全称是Data Manipulation Language(数据操作语言)&#xff0c;用来对数据库中表的数据记录进行增、删、改操作。 1.增加(insert) insert语法&#xff1a; 向指定字段添加数据 insert into 表名 (字段名1, 字段名2) values (值1, 值2); 全部字段添加数据…
阅读更多...
回归预测 | MATLAB实现BO-GRNN贝叶斯优化广义回归神经网络多输入单输出预测

回归预测 | MATLAB实现BO-GRNN贝叶斯优化广义回归神经网络多输入单输出预测

回归预测 | MATLAB实现BO-GRNN贝叶斯优化广义回归神经网络多输入单输出预测 目录 回归预测 | MATLAB实现BO-GRNN贝叶斯优化广义回归神经网络多输入单输出预测预测效果基本介绍程序设计参考资料预测效果 基本介绍
阅读更多...
如何实现小程序滑动删除组件+全选批量删除组件

如何实现小程序滑动删除组件+全选批量删除组件

如何实现小程序滑动删除组件全选批量删除组件 一、简介 如何实现小程序滑动删除组件全选批量删除组件 采用 uni-app 实现&#xff0c;可以适用微信小程序、其他各种小程序以及 APP、Web等多个平台 具体实现步骤如下&#xff1a; 下载开发者工具 HbuilderX进入 【Dcloud 插…
阅读更多...
使用Datax自定义采集组件Reader/Writer实现国产数据库支持以及_Datax数据清洗/过滤规则功能自定义---大数据之DataX工作笔记007

使用Datax自定义采集组件Reader/Writer实现国产数据库支持以及_Datax数据清洗/过滤规则功能自定义---大数据之DataX工作笔记007

我们基于datax来做的自己的数据采集系统,现在基本的数据采集已经实现了,也就是调用datax的数据采集能力,实现在已支持的数据库之间同步数据.我们是基于datax-web实现的,里面都有开源的代码了,可以分析以后拿过来用,这个过程并不复杂,而且,结合xxljob的web那个开源项目,也可以让…
阅读更多...
Redis 和 Mysql 数据库数据如何保持一致性

Redis 和 Mysql 数据库数据如何保持一致性

Redis 和 Mysql 数据库数据如何保持一致性 保持Redis和MySQL数据库数据一致性是一个常见且重要的问题&#xff0c;特别是在使用Redis作为MySQL数据库的缓存层时。以下是几种常用的保证二者数据一致性的策略和方法&#xff1a; 双写一致性&#xff08;同步更新&#xff09;&…
阅读更多...
Windows完全卸载MySQL后再下载安装（附安装包）

Windows完全卸载MySQL后再下载安装（附安装包）

目录 友情提醒第一章&#xff1a;如何完全卸载干净mysql教程&#xff08;三个步骤完全卸载&#xff09;1&#xff09;步骤一&#xff1a;卸载程序2&#xff09;步骤二&#xff1a;删除文件3&#xff09;步骤三&#xff1a;删除注册表信息 第二章&#xff1a;下载软件两种方式1&…
阅读更多...
推荐文章
最新文章

Copyright @ 2022~2023