【发布时间】：2023-02-16 19:51:01
【问题描述】：
我有一个挑战，我正在努力解决这个问题以便继续前进，这是我模型操作的最后一块拼图。
我想做什么？：* 根据此处的示例，通过此处的拆分操作验证 Xval_test 变量中使用的视频 In Python sklearn, how do I retrieve the names of samples/variables in test/training data? ：
X_train, Xval_test, Y_train, Yval_test = train_test_split(
X, Y, train_size=0.8, test_size=0.2, random_state=1, shuffle=True)
1.我试过什么？： 正在通过 file_path 名称从实际标签调用名称，但这不起作用。 （每次代码运行时，都会从文件路径中获取名称，而不是从实际的拆分操作 Xval_test 变量中获取名称。这会在 model.fit() 过程中导致问题，因为它将一维扁平张量更改为（多行， 1 列）
file_paths = []
for file_name in os.listdir(root):
file_path = os.path.join(root, file_name)
if os.path.isfile(file_path):
file_paths.append(file_path)
print('**********************************************************')
print('ALL Directory File Paths Completed', file_paths)
我不确定文件是否按照 split() 论坛的指导方针通过我的弱尝试正确地洗牌了。 （据我所知，每次我运行代码时，这些文件都会被混洗到一个新的 Xval_test 集，相对于它指定的分割参数 80:20。
2.我尝试调用 model.predict()，它没有显示我希望它显示的标签（也许我用错误的方式调用索引，我不知道）。
my_pred = model.predict(Xval_test).argmax(axis=1)
我尝试调用 np.argmax():( 我知道 X_test 中的文件总数是 16，基于 split()）
Y_valpred = np.argmax(model.predict(Xval_test), axis=1) # model
这只返回类标签而不是它的内容，例如数据存储区中的类是包含（步行和击剑）的文件夹，而不是实际的视频标签，例如（walking0.avi ....100/n 和 fencing0.avi.....100n/）！！！？？？ ！
我不确定获取文件夹内容标签的操作，即实际文件本身。这是我试图从 X_test 变量中获取的。
（或者它可能是我使用的错误变量或功能，我又缺乏理解这一点的知识，请协助以便我可以进入下一阶段）。
3.我尝试打印之前操作中的所有变量，以查看该名称标签的存储位置以及它存储在姓名根据我的操作，下面的变量：（但是我如何将这些文件夹内容的文件标签转发给 X_test 变量或根据我的选择将 model.predict() 与其他指标一起输出到列中。到目前为止，这会导致 model.fit() 出现问题功能？？？）
for files3 in files2:
name = os.path.join(namelist, files3)
name1 = name.strip("./dataset/")
name2 = name1.strip("Fencing/")
name3 = name2.strip("Stabing/")
name3 = name3.replace('.av', '')
name4 = name3.split()
# print("This is name1 ", name1)
# name5 = pd.DataFrame({"vid_names": name4}).to_csv("results.csv")
# name1 = name1.replace('[]', '')
with open('vid_names.csv', 'a',newline='') as f:
writer = csv.writer(f)
writer = writer.writerow(name4)
# print("My Video Names => ", name3)
3A.在此先感谢您，感谢您提供的任何指导，请协助！
问题： ＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃ 问题：1。是否可以查看 X_Test 变量中分割了哪些视频标签？
问题：1A。如果是，我可以在这里请求您的指导，请问如何做到这一点？： 我已经研究了几个星期，但似乎无法解决这个问题，我们将不胜感激您的努力。
问题：2. 我的预期结果：我正在尝试访问预测。所以，最后我会得到一个相对于实际视频标签的输出，它暗示了预测操作中使用的实际视频及其类标签（见下文）：
最初，model.predict() 操作输出与类标签相关的数值数据。
我也在尝试访问实际的文件标签： 例如，我希望预测结果如下所示：
X_test_labs Pred_labs Actual_File Pred_Score
0 Fencing Fencing fencing0.avi 0.99650866
1 Walking Fencing walking6.avi 0.9948837
2 Walking Walking walking21.avi 0.9967557
3 Fencing Fencing fencing32.avi 0.9930409
4 Walking Fencing walking43.avi 0.9961387
5 Walking Walking walking48.avi 0.6467387
6 Walking Walking walking50.avi 0.5465369
7 Walking Walking walking9.avi 0.3478027
8 Fencing Fencing fencing22.avi 0.1247543
9 Fencing Fencing fencing46.avi 0.7477777
10 Walking Walking walking37.avi 0.8499399
11 Fencing Fencing fencing19.avi 0.8887722
12 Walking Walking walking12.avi 0.7775351
13 Fencing Fencing fencing33.avi 0.4323323
14 Fencing Fencing fencing51.avi 0.7812434
15 Fencing Fencing fencing8.avi 0.8723476
我不确定如何完成这项任务，这对我来说比预期的要棘手一些
这是我的代码*
'''*******Load Dependencies********'''
from keras.regularizers import l2
from keras.layers import Dense
from keras_tqdm import TQDMNotebookCallback
from tqdm.keras import TqdmCallback
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import math
import tensorflow as tf
from tqdm import tqdm
import videoto3d
import seaborn as sns
import scikitplot as skplt
from sklearn import preprocessing
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
from keras.utils.vis_utils import plot_model
from keras.utils import np_utils
from tensorflow.keras.optimizers import Adam
from keras.models import Sequential
from keras.losses import categorical_crossentropy
from keras.layers import (Activation, Conv3D, Dense, Dropout, Flatten,MaxPooling3D)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import argparse
import time
import sys
import openpyxl
import os
import re
import csv
from keras import models
import cv2
import pickle
import glob
from numpy import load
np.seterr(divide='ignore', invalid='ignore')
print('**********************************************************')
print('Graphical Representation Of Accuracy & Validation Results Completed')
def plot_history(history, result_dir):
plt.plot(history.history['val_accuracy'], marker='.')
plt.plot(history.history['accuracy'], marker='.')
plt.title('model accuracy')
plt.xlabel('epoch')
plt.ylabel('accuracy')
plt.grid()
plt.legend(['Val_acc', 'Test_acc'], loc='lower right')
plt.savefig(os.path.join(result_dir, 'model_accuracy.png'))
plt.close()
plt.plot(history.history['val_loss'], marker='.')
plt.plot(history.history['loss'], marker='.')
plt.title('model Loss')
plt.xlabel('epoch')
plt.ylabel('loss')
plt.grid()
plt.legend(['Val_loss', 'Test_loss'], loc='upper right')
plt.savefig(os.path.join(result_dir, 'model_loss.png'))
plt.close()
# Saving History Accuracy & Validation Acuuracy Results To Directory
print('**********************************************************')
print('Generating History Acuuracy Results Completed')
def save_history(history, result_dir):
loss = history.history['loss']
acc = history.history['accuracy']
val_loss = history.history['val_loss']
val_acc = history.history['val_accuracy']
nb_epoch = len(acc)
# Creating The Results File To Directory = Store Results
print('**********************************************************')
print('Saving History Acuuracy Results To Directory Completed')
with open(os.path.join(result_dir, 'result.txt'), 'w') as fp:
fp.write('epoch\tloss\tacc\tval_loss\tval_acc\n')
# print(fp)
for i in range(nb_epoch):
fp.write('{}\t{}\t{}\t{}\t{}\n'.format(
i, loss[i], acc[i], val_loss[i], val_acc[i]))
print('**********************************************************')
print('Loading All Specified Video Data Samples From Directory Completed')
def loaddata(video_dir, vid3d, nclass, result_dir, color=False, skip=True):
files = os.listdir(video_dir)
with open('files.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(files)
root = '/Users/symbadian/3DCNN_latest_Version/3DCNNtesting/dataset/'
dirlist = [item for item in os.listdir(
root) if os.path.isdir(os.path.join(root, item))]
print('Get the filesname and path')
print('DIRLIST Directory Completed', dirlist)
file_paths = []
for file_name in os.listdir(root):
file_path = os.path.join(root, file_name)
if os.path.isfile(file_path):
file_paths.append(file_path)
print('**********************************************************')
print('ALL Directory File Paths Completed', file_paths)
roots, dirsy, fitte = next(os.walk(root), ([], [], []))
print('**********************************************************')
print('ALL Directory ROOTED', roots, fitte, dirsy)
X = []
print('X labels==>', X) # This stores all variable data in an object format
labellist = []
pbar = tqdm(total=len(files)) # generate progress bar for file processing
print('**********************************************************')
print('Generating/Join Class Labels For Video Dataset For Input Completed')
# Accessing files and labels from dataset directory
for filename in files:
pbar.update(1)
if filename == '.DS_Store':#.DS_Store
continue
namelist = os.path.join(video_dir, filename)
files2 = os.listdir(namelist)
###############################################################################
######### NEEDS TO FIX THIS Data Adding to CSV Rather Than REWRITTING #########
for files3 in files2:
name = os.path.join(namelist, files3)
#Call a function that extract the frames details of all file names
label = vid3d.get_UCF_classname(filename)
if label not in labellist:
if len(labellist) >= nclass:
continue
labellist.append(label)
# This X variable is the point where the lables are store (I think??!?!)
X.append(vid3d.video3d(name, color=color, skip=skip))
pbar.close()
# generating labellist/ writing to directory
print('******************************************************')
print('Saving All Class Labels For Referencing To Directory Completed')
with open(os.path.join(result_dir, 'classes.txt'), 'w') as fp:
for i in range(len(labellist)):
# print('These are labellist i classes',i) #Not This
fp.write('{}\n'.format(labellist[i]))
# print('These are my labels: ==>',mylabel)
for num, label in enumerate(labellist):
for i in range(len(labels)):
if label == labels[i]:
labels[i] = num
# print('This is labels i',labels[i]) #Not this
if color: # conforming image channels of image for input sequence
return np.array(X).transpose((0, 2, 3, 4, 1)), labels
else:
return np.array(X).transpose((0, 2, 3, 1)), labels
print('**********************************************************')
print('Generating Args Informative Messages/ Tuning Parameters Options Completed')
def main():
parser = argparse.ArgumentParser(description='A 3D Convolution Model For Action Recognition')
parser.add_argument('--batch', type=int, default=130)
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--videos', type=str, default='dataset',help='Directory Where Videos Are Stored')# UCF101
parser.add_argument('--nclass', type=int, default= 2)
parser.add_argument('--output', type=str, required=True)
parser.add_argument('--color', type=bool, default=False)
parser.add_argument('--skip', type=bool, default=True)
parser.add_argument('--depth', type=int, default=10)
args = parser.parse_args()
# print('This is the Option Arguments ==>',args)
print('**********************************************************')
print('Specifying Input Size and Channels Completed')
img_rows, img_cols, frames = 32, 32, args.depth
channel = 3 if args.color else 1
print('**********************************************************')
print('Saving Dataset As NPZ To Directory Completed')
fname_npz = 'dataset_{}_{}_{}.npz'.format(args.nclass, args.depth, args.skip)
vid3d = videoto3d.Videoto3D(img_rows, img_cols, frames)
nb_classes = args.nclass
# loading the data
if os.path.exists(fname_npz):
loadeddata = np.load(fname_npz)
X, Y = loadeddata["X"], loadeddata["Y"]
else:
x, y = loaddata(args.videos, vid3d, args.nclass,args.output, args.color, args.skip)
X = x.reshape((x.shape[0], img_rows, img_cols, frames, channel))
Y = np_utils.to_categorical(y, nb_classes)
X = X.astype('float32')
#save npzdata to file
np.savez(fname_npz, X=X, Y=Y)
print('Saved Dataset To dataset.npz. Completed')
print('X_shape:{}\nY_shape:{}'.format(X.shape, Y.shape))
print('**********************************************************')
print('Initialise Model Layers & Layer Parameters Completed')
# Sequential groups a linear stack of layers into a tf.keras.Model.
# Sequential provides training and inference features on this model
model = Sequential()
model.add(Conv3D(32, kernel_size=(3, 3, 3),input_shape=(X.shape[1:]), padding='same'))
model.add(Activation('relu'))
model.add(Conv3D(32, kernel_size=(3, 3, 3), padding='same'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), padding='same'))
model.add(Conv3D(64, kernel_size=(3, 3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv3D(64, kernel_size=(3, 3, 3), padding='same'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), padding='same'))
model.add(Conv3D(128, kernel_size=(3, 3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv3D(128, kernel_size=(3, 3, 3), padding='same'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), padding='same'))
model.add(Dropout(0.5))
model.add(Conv3D(256, kernel_size=(3, 3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv3D(256, kernel_size=(3, 3, 3), padding='same'))
model.add(MaxPooling3D(pool_size=(3, 3, 3), padding='same'))
model.add(Dropout(0.5))
model.add(Flatten())
# Dense function to convert FCL to 512 values
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes, activation='softmax'))
model.compile(loss=categorical_crossentropy,optimizer=Adam(), metrics=['accuracy'])
model.summary()
print('this is the model shape')
model.output_shape
plot_model(model, show_shapes=True,to_file=os.path.join(args.output, 'model.png'))
print('**********************************************************')
print("Train Test Method HoldOut Performance")
X_train, Xval_test, Y_train, Yval_test = train_test_split(
X, Y, train_size=0.8, test_size=0.2, random_state=1, stratify=Y, shuffle=True)
print('**********************************************************')
print('Deploying Data Fitting/ Performance Accuracy Guidance Completed')
#Stop operations when experiencing no learning
rlronp = tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss", factor=0.5, patience=1, mode='auto', min_delta=0.0001, cooldown=1, min_lr=0.0001)
# Fit the training data
history = model.fit(X_train, Y_train, validation_split=0.20, batch_size=args.batch,epochs=args.epoch, verbose=1, callbacks=[rlronp], shuffle=True)
# Predict X_Test (Xval_test) data and Labels
predict_labels = model.predict(Xval_test, batch_size=args.batch,verbose=1,use_multiprocessing=True)
classes = np.argmax(predict_labels, axis = 1)
label = np.argmax(Yval_test,axis = 1)
print('This the BATCH size', args.batch)
print('This the DEPTH size', args.depth)
print('This the EPOCH size', args.epoch)
print('This the TRAIN SPLIT size', len(X_train))
print('This the TEST SPLIT size', len(Xval_test))
# https://stackoverflow.com/questions/52261597/keras-model-fit-verbose-formatting
# A json file enhances the model performance by a simple to save/load model
model_json = model.to_json()
if not os.path.isdir(args.output):
os.makedirs(args.output)
with open(os.path.join(args.output, 'ucf101_3dcnnmodel.json'), 'w') as json_file:
json_file.write(model_json)
# hd5 contains multidimensional arrays of scientific data
model.save_weights(os.path.join(args.output, 'ucf101_3dcnnmodel.hd5'))
''' Evaluation is a process
'''
print('**********************************************************')
print('Displying Test Loss & Test Accuracy Completed')
loss, acc = model.evaluate(Xval_test, Yval_test, verbose=2, batch_size=args.batch, use_multiprocessing=True) # verbose 0
print('this is args output', args.output)
plot_history(history, args.output)
save_history(history, args.output)
print('**********************************************************')
# Generating Picture Of Confusion matrix
print('**********************************************************')
print('Generating CM InputData/Classification Report Completed')
#Ground truth (correct) target values.
y_valtest_arg = np.argmax(Yval_test, axis=1)
#Estimated targets as returned by a classifier
Y_valpred = np.argmax(model.predict(Xval_test), axis=1) # model
print('y_valtest_arg Shape is ==>', y_valtest_arg.shape)
print('Y_valpred Shape is ==>', Y_valpred.shape)
print('**********************************************************')
print('Classification_Report On Model Performance Completed==')
print(classification_report(y_valtest_arg.round(), Y_valpred.round(), target_names=filehandle, zero_division=1))
'''Intitate Confusion Matrix'''
# print('Model Confusion Matrix Per Test Data Completed===>')
cm = confusion_matrix(y_valtest_arg, Y_valpred, normalize=None)
print('Display Confusion Matrix ===>', cm)
print('**********************************************************')
print('Model Overall Accuracy')
print('Model Test loss:', loss)
print('**********************************************************')
print('Model Test accuracy:', acc)
print('**********************************************************')
if __name__ == '__main__':
main()
【问题讨论】：
标签： arguments artificial-intelligence prediction evaluation train-test-split