CNN + RNN错误“ __init __（）至少接受4个参数（给定4个）”

Question

我最近学习了有关深度学习的教程，现在我正尝试自己创建一个。 想法是拍摄视频，将其分割为单个帧，然后通过神经网络进行输入。 因为它是jpg，但我是CNN。 但是我没有对这张图片进行分类，而且我想获得浮点数。 那就是为什么我虽然使用RNN。 我找到了支持Keras的库来支持这一点：但是在这一点上，我坚持了下来。 （在Python 2.7上运行）

错误信息：

runfile('/Users/tobias/anaconda3/envs/opencv/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py', wdir='/Users/tobias/anaconda3/envs/opencv/lib/python2.7/site-packages/spyder/utils/site')

runfile('/Users/tobias/Desktop/Projekt/Speed_ANN.py', wdir='/Users/tobias/Desktop/Projekt')
Traceback (most recent call last):

  File "<ipython-input-14-b3a54cae7fa1>", line 1, in <module>
    runfile('/Users/tobias/Desktop/Projekt/Speed_ANN.py', wdir='/Users/tobias/Desktop/Projekt')

  File "/Users/tobias/anaconda3/envs/opencv/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 880, in runfile
    execfile(filename, namespace)

  File "/Users/tobias/anaconda3/envs/opencv/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
    builtins.execfile(filename, *where)

  File "/Users/tobias/Desktop/Projekt/Speed_ANN.py", line 38, in <module>
    classifier.add(TimeDistributedConvolution2D(32,(3,3),input_shape = (64, 64, 3),activation = 'relu'))

TypeError: __init__() takes at least 4 arguments (4 given)

我有4个论点？ 我放错了吗？

这是我的代码：您还需要kera-extra.py吗？ 这是我添加的库

"""
Creator: Tobias
Date: 15.05.17
"""
#Initialising video preprocessing
import cv2
import numpy as np
import pandas as pd

#Initialising all Libarys for Deep Learning
from keras.models import Sequential
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers.extra import TimeDistributedConvolution2D
from keras.layers.extra import TimeDistributedFlatten
from keras.layers.extra import TimeDistributedMaxPooling2D
"""
#Loading .txt with speed values
speed_values = pd.read_csv('data/train.txt')

#Loading Video in Python
video = cv2.VideoCapture('data/train.mp4')
success,image = video.read()
count = 0
success = True
#Splitting video in single images in jpg
while success:
    success,image = video.read()
    #cv2.imwrite('data/video_jpg/',speed_values[success],'.jpg')
    cv2.imwrite("data/video_jpg/%f.jpg" %speed_values.iloc[count,:].values,image) 
    count += 1 
print('Video Succefully Converted to jpg')
"""


classifier = Sequential()
# Initialising the CNN and building CNN
classifier.add(TimeDistributedConvolution2D(32,(3,3),input_shape = (64, 64, 3),activation = 'relu'))
classifier.add(TimeDistributedConvolution2D(16, 3, 3, border_mode='valid',activation = 'relu'))
classifier.add(Flatten())
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.compile(optimizer = 'adam', loss = 'mean_squared_error',metrics = ['accuracy'])

#Preprocessing the video data for CNN part 2

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('data/training/train_data',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('data/training/test_data',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

classifier.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 5,
                         validation_data = test_set,
                         validation_steps = 2000)

// EDIT @JohanL我按照您的建议进行了更改，但是即使它具有所需的所有参数，它仍然有一个错误。

classifier = Sequential()
# Initialising the CNN and building CNN
classifier.add(TimeDistributedConvolution2D(32,3,3,input_shape = (64, 64, 3),activation = 'relu'))

现在，它给了我：使用标准CNN可以正常工作，所以在keras.extra库中可能有问题吗？

classifier = Sequential()
# Initialising the CNN and building CNN
classifier.add(TimeDistributedConvolution2D(32,3,3,input_shape = (64, 64, 3),activation = 'relu'))
Traceback (most recent call last):



  File "<ipython-input-20-085e686ea1fc>", line 3, in <module>
    classifier.add(TimeDistributedConvolution2D(32,3,3,input_shape = (64, 64, 3),activation = 'relu'))

  File "/Users/tobias/anaconda3/envs/opencv/lib/python2.7/site-packages/keras/models.py", line 433, in add
    layer(x)

  File "/Users/tobias/anaconda3/envs/opencv/lib/python2.7/site-packages/keras/engine/topology.py", line 558, in __call__
    self.build(input_shapes[0])

TypeError: build() takes exactly 1 argument (2 given)

Answer 1

这是TimeDistributedConvolution2D的init函数的定义中的方法标头：

def __init__(self, nb_filter, nb_row, nb_col,
             init='glorot_uniform', activation='linear', weights=None,
             border_mode='valid', subsample=(1, 1), dim_ordering='th',
             W_regularizer=None, b_regularizer=None, activity_regularizer=None,
             W_constraint=None, b_constraint=None, **kwargs):

可以看出，有四个没有默认值的参数（三个加自身）。 这四个参数必须给出。 我假设您不打算以元组为参数，而是：

classifier.add(TimeDistributedConvolution2D(32,3,3,input_shape = (64, 64, 3),activation = 'relu'))

其中将具有足够数量的args。

但是，当您提供具有默认值的参数时，错误消息有点奇怪。 这就是为什么您会收到此奇怪的错误消息的原因。

但是请注意，即使使用此修复程序，您也需要一个相当旧的keras版本（0.3）才能运行此功能，因此，也许您应该尝试另一种方法（如果可用）。