TensorFlow tf.keras.layers.Permute

本文介绍如何使用Keras中的Permute层调整张量的维度顺序。通过一个具体示例,展示如何交换输入张量的第一维度和第二维度,从而改变模型的输出形状。

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更改维度的顺序,维度索引从1开始

model = Sequential()
model.add(Permute((2, 1), input_shape=(10, 64))) # 交换第一维度和第二维度
# now: model.output_shape == (None, 64, 10)
# note: `None` is the batch dimension
```

参数|描述
--|--
dims|(int,int)


参考:
[官网](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Permute?hl=en#class_permute)
请作为资深开发工程师,解释我给出的代码。请逐行分析我的代码并给出你对这段代码的理解。 我给出的代码是: 【# 导入必要的库 Import the necessary libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import torch import math import torch.nn as nn from scipy.stats import pearsonr from sklearn.metrics import accuracy_score from sklearn.linear_model import LinearRegression from collections import deque from tensorflow.keras import layers import tensorflow.keras.backend as K from tensorflow.keras.layers import LSTM,Dense,Dropout,SimpleRNN,Input,Conv1D,Activation,BatchNormalization,Flatten,Permute from tensorflow.python import keras from tensorflow.python.keras.layers import Layer from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.metrics import r2_score from sklearn.preprocessing import MinMaxScaler import tensorflow as tf from tensorflow.keras import Sequential, layers, utils, losses from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard from tensorflow.keras.layers import Conv2D,Input,Conv1D from tensorflow.keras.models import Model from PIL import * from tensorflow.keras import regularizers from tensorflow.keras.layers import Dropout from tensorflow.keras.callbacks import EarlyStopping import seaborn as sns from sklearn.decomposition import PCA import numpy as np import matplotlib.pyplot as plt from scipy.signal import filtfilt from scipy.fftpack import fft from sklearn.model_selection import train_test_split import warnings warnings.filterwarnings('ignore')】
03-13
from __future__ import print_function import keras from tensorflow.keras.layers import Input, Flatten, Concatenate, add, dot, Multiply, Add, Lambda,Permute,Reshape,Dot from tensorflow.keras.layers import Dense, Conv1D, BatchNormalization, ReLU, MaxPool1D, Input, Flatten, Concatenate, Dropout, LayerNormalization from tensorflow.keras.layers import GlobalAveragePooling1D from tensorflow.keras.optimizers import Adam from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau from tensorflow.keras.models import Model from tensorflow.keras import backend as K import tensorflow as tf import numpy as np import scipy.io as scio from sklearn.model_selection import KFold from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from tensorflow.keras.utils import plot_model from tensorflow.keras import regularizers from tensorflow.compat.v1.profiler import ProfileOptionBuilder from tensorflow.keras.regularizers import l2 import os # 设置随机种子以确保结果可复现 seed = 42 tf.random.set_seed(seed) np.random.seed(seed) ### 参数设置 ### epoch = 100 # 训练轮数 batch_size = 32 # 批量大小 lr = 0.0002 # 学习率 drop_out = 0.1 # Dropout率 L2 = 0.000001 # L2正则化系数 fc1 = 256 # 全连接层1的神经元数量 fc2 = 128 # 全连接层2的神经元数量 input_shape = (80, 1) # 输入形状 embed_dim = 128 # Transformer嵌入维度 num_heads = 8 # 多头注意力头数 ff_dim = 512 # Feed-Forward网络维度 num_transformer_blocks = 4 # Transformer块数 dropout_rate = 0.1 # Dropout率 # 用于存储训练、验证和测试结果的列表 #train_result_MAE_stress = [] #train_result_RMSE_stress = [] #val_result_MAE_stress = [] #val_result_RMSE_stress = [] #test_result_MAE_stress = [] #test_result_RMSE_stress = [] # 创建保存模型和结果的目录 os.makedirs('models', exist_ok=True) os.makedirs('plots', exist_ok=True) # 归一化函数1将数据归一化到[0, 1]区间。 def normalization(array): new_list = [] min = np.min(array) # 计算数组的最小值 max = np.max(array) # 计算数组的最大值 for wave in array: new_wave = (wave - min) / ( max - min) # 均值归
最新发布
03-19
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