我为视频分类编写了一个简单的convLSTM脚本，但出现了一个错误

Question

我创建了一个对视频进行分类的ConvlTM网络。该数据集由6个类组成，其中包含视频

我得到错误“NameError:name'y_pred'未定义”，它与最后第三行代码相关。您不需要运行代码，因为它可能是语法错误或类似的错误

为什么我会犯这个错误

import keras
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
 
import os
import cv2
import numpy as np
from sklearn.model_selection import train_test_split
import keras_metrics as km
 
 
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import multilabel_confusion_matrix
 
data_dir = "video_data/"
img_height , img_width = 64, 64
seq_len = 70
 
classes = ["Apply Eye Makeup", "Archery", "Apply Lipstick", "Baby Crawling", "Balance Beam", "Band Marching"]
 
#  Creating frames from videos
 
def frames_extraction(video_path):
    frames_list = []
     
    vidObj = cv2.VideoCapture(video_path)
    # Used as counter variable 
    count = 1
 
    while count <= seq_len: 
         
        success, image = vidObj.read() 
        if success:
            image = cv2.resize(image, (img_height, img_width))
            frames_list.append(image)
            count += 1
        else:
            print("Defected frame")
            break
 
            
    return frames_list
 
def create_data(input_dir):
    X = []
    Y = []
     
    classes_list = os.listdir(input_dir)
     
    for c in classes_list:
        print(c)
        files_list = os.listdir(os.path.join(input_dir, c))
        for f in files_list:
           frames = frames_extraction(os.path.join(os.path.join(input_dir, c), f))
            if len(frames) == seq_len:
                X.append(frames)
             
                y = [0]*len(classes)
                y[classes.index(c)] = 1
                Y.append(y)
     
    X = np.asarray(X)
    Y = np.asarray(Y)
    return X, Y
 
X, Y = create_data(data_dir)
 
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, shuffle=True, random_state=0)
 
model = Sequential()
model.add(ConvLSTM2D(filters = 64, kernel_size = (3, 3), return_sequences = False, data_format = "channels_last", input_shape = (seq_len, img_height, img_width, 3)))
model.add(Dropout(0.2))
model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.3))
model.add(Dense(6, activation = "softmax"))
 
model.summary()
 
opt = keras.optimizers.SGD(lr=0.001)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=["accuracy"])
 
earlystop = EarlyStopping(patience=7)
callbacks = [earlystop]
 
history = model.fit(x = X_train, y = y_train, epochs=40, batch_size = 8 , shuffle=True, validation_split=0.2, callbacks=callbacks)
 
y_pred = np.argmax(y_pred, axis = 1)
y_test = np.argmax(y_test, axis = 1)
 
print(classification_report(y_test, y_pred))

Answer 1

消息中的错误不言自明：未定义y_pred

你必须首先预测你的数据，做一些类似于y_pred = model.predict(X_test)的事情，然后，你可以argmax等等

[编辑]：总之，将最后三行替换为

y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis = 1)
 
print(classification_report(y_test, y_pred))