TensorFlow tf.keras.layers.GlobalAveragePooling1D

本文深入解析了TensorFlow中Embedding层的工作原理,详细解释了steps和features参数的意义,通过实例展示了如何使用Embedding层处理序列数据。文章涵盖了数据格式、输入输出形状以及具体的应用代码。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

参数

steps 是时间序列的意思,就是一句话包含了多少个词,features是每个词的特征,可以联想图片处理中一个像素点对应3个通道(channel,就是feathures,其对应的就是filters)

参数描述
data_formatchannels_last (default):(batch, steps, features);channels_first:(batch, features, steps)

输入形状

三维的张量
channels_last:(batch_size, steps, features)
channels_first:(batch_size, features, steps)

输出形状

二维张量:(batch_size, features)

应用:

from tensorflow import keras
import numpy as np

data = np.array([[0,0,0],[1,1,1]])
emb = keras.layers.Embedding(input_dim=2, output_dim=3, input_length=3)
emb(data)
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')】
最新发布
03-13
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值