classification

问题 What is the threshold value that is used by TF by default to classify an input image as being a certain class? For example, say I have 3 classes 0 , 1 , 2 , and the labels for images are one-hot encoded like so: [1, 0, 0] , meaning this image has label of class 0. Now when a model outputs a prediction after softmax like this one: [0.39, 0.56, 0.05] does TF use 0.5 as the threshold so the class it predicts is class 1? What if all the predictions were below 0.5 like [0.33, 0.33, 0.33] what

问题 In a CNN for binary classification of images, should the shape of output be (number of images, 1) or (number of images, 2)? Specifically, here are 2 kinds of last layer in a CNN: keras.layers.Dense(2, activation = 'softmax')(previousLayer) or keras.layers.Dense(1, activation = 'softmax')(previousLayer) In the first case, for every image there are 2 output values (probability of belonging to group 1 and probability of belonging to group 2). In the second case, each image has only 1 output

问题 I am trying to build a Fake news classifier and I am quite new in this field. I have a column "title_1_en" which has the title for fake news and another column called "title_2_en". There are 3 target labels; "agreed", "disagreed", and "unrelated" if the title of the news in column "title_2_en" agrees, disagrees or is unrelated to that in the first column. I have tried calculating basic cosine similarity between the two titles after converting the words of the sentences into vectors. This has

问题 I am trying to build a Fake news classifier and I am quite new in this field. I have a column "title_1_en" which has the title for fake news and another column called "title_2_en". There are 3 target labels; "agreed", "disagreed", and "unrelated" if the title of the news in column "title_2_en" agrees, disagrees or is unrelated to that in the first column. I have tried calculating basic cosine similarity between the two titles after converting the words of the sentences into vectors. This has

问题 I am trying to build a Fake news classifier and I am quite new in this field. I have a column "title_1_en" which has the title for fake news and another column called "title_2_en". There are 3 target labels; "agreed", "disagreed", and "unrelated" if the title of the news in column "title_2_en" agrees, disagrees or is unrelated to that in the first column. I have tried calculating basic cosine similarity between the two titles after converting the words of the sentences into vectors. This has

问题 I trained Resnet-50 classification network to classify my objects and I use the following code to evaluate the network. from tensorflow.keras.models import load_model import cv2 import numpy as np import os class_names = ["x", "y", "b","g", "xx", "yy", "bb","gg", "xyz","xzy","yy"] model = load_model('transfer_resnet.h5') model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) imgg = cv2.imread('/path to image/a1.jpg') img = cv2.resize(imgg,(224,224)) img = np

问题 I need to build a software that does audio recognition from a small audio sample (A) inside other audio samples (B), and output how many times A appears inside the audio from B (if there is a match). What I have: A database with hundreds of audios Input: New audio Expected Output: A boolean if the input matches a sample from the database, and how many times appeared the input inside the matched audio (from the db). Any code, open source project, guides, books, videos, tutorial, etc... is

问题 I need to build a software that does audio recognition from a small audio sample (A) inside other audio samples (B), and output how many times A appears inside the audio from B (if there is a match). What I have: A database with hundreds of audios Input: New audio Expected Output: A boolean if the input matches a sample from the database, and how many times appeared the input inside the matched audio (from the db). Any code, open source project, guides, books, videos, tutorial, etc... is

问题 df1 = data.frame(id=c('A1','2','B3','4','5','6','7','8','9','10'),s1c1=c(0,0.2,0,0.5,0.8,0,0,0,0,0),s1c2=c(0,0,0.3,0,0,0.9,0.3,0,0,0),s1c3=c(0.1,0,0,0,0,0,0,0.2,0.8,0.1)) df2 = data.frame(id=c('A1','2','B3','4','5','6','7','8','9','10'),s2c1=c(0,0.22,0,0.35,0.8,0,0,0,0,0),s2c2=c(0,0,0.23,0,0,0.7,0.3,0,0,0),s2c3=c(0.2,0,0,0,0,0,0,0.4,0.9,0.4)) df <- merge(df1,df2, by="id",all=TRUE) df$class <- c(0,0,0,0,0,1,1,0,0,0) > df id s1c1 s1c2 s1c3 s2c1 s2c2 s2c3 class 10 0.0 0.0 0.1 0.00 0.00 0.4 0 2 0

问题 I know the general rule that we should test a trained classifier only on the testing set. But now comes the question: When I have an already trained and tested classifier ready, can I apply it to the same dataset that was the base of the training and testing set? Or do I have to apply it to a new predicting set that is different from the training+testing set? And what if I predict a label column of a time series (edited later: I do not mean to create a classical time series analysis here, but