Question

多年来我没有编码，请原谅我。我正在尝试做一些可能不可能完成的事情。我有38个人的视频执行相同的基本动作。我想训练模型，以确定那些正确的做法v不正确。我现在正在使用颜色，因为灰度也不起作用，我想像我使用的例子一样进行测试。我使用了an example, link中定义的模型。

Keras， Anaconda 64中的Python3.5， Tensorflow后端，在Windows 10（64位）上

我希望在这个问题上尝试不同的模型并使用灰度来减少记忆，但是不能超越第一步！

感谢!!!

这是我的代码：

import time
import numpy as np
import sys
import os
import cv2
import keras
import tensorflow as tf

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization
from keras.layers import Conv3D, Conv2D, MaxPooling2D, GRU, ConvLSTM2D, TimeDistributed


y_cat = np.zeros(40,np.float)
good = "Good"
bad = "Bad"


batch_size = 32
num_classes = 1
epochs = 1
nvideos = 38
nframes = 130
nrows = 240
ncols = 320
nchan = 3

x_learn = np.zeros((nvideos,nframes,nrows,ncols,nchan),np.int32)
x_learn = np.load(".\\train\\datasetcolor.npy")

with open(".\\train\\tags.txt") as ft:
    y_learn = ft.readlines()
y_learn = [x.strip() for x in y_learn] 
ft.close()

# transform string tags to numeric.
for i in range (0,len(y_learn)):
    if (y_learn[i] == good): y_cat[i] = 1
    elif (y_learn[i]  == bad): y_cat[i] = 0


#build model 
# duplicating from https://github.com/fchollet/keras/blob/master/examples/conv_lstm.py
model = Sequential()
model.image_dim_ordering = 'tf'
model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   input_shape=(nframes,nrows,ncols,nchan),
                   padding='same', return_sequences=True))
model.add(BatchNormalization())
model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
model.add(BatchNormalization())

model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
model.add(BatchNormalization())

model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
                   padding='same', return_sequences=True))
model.add(BatchNormalization())

model.add(Conv3D(filters=1, kernel_size=(3, 3, 3),
               activation='sigmoid',
               padding='same', data_format='channels_last'))
model.compile(loss='binary_crossentropy', optimizer='adadelta')


print(model.summary())

# fit with first 3 videos because I don't have the horsepower yet
history = model.fit(x_learn[:3], y_learn[:3],
              batch_size=batch_size,
              epochs=epochs)

print (history)

结果：

Layer (type)                 Output Shape              Param #   
=================================================================
conv_lst_m2d_5 (ConvLSTM2D)  (None, 130, 240, 320, 40) 62080     
_________________________________________________________________
batch_normalization_5 (Batch (None, 130, 240, 320, 40) 160       
_________________________________________________________________
conv_lst_m2d_6 (ConvLSTM2D)  (None, 130, 240, 320, 40) 115360    
_________________________________________________________________
batch_normalization_6 (Batch (None, 130, 240, 320, 40) 160       
_________________________________________________________________
conv_lst_m2d_7 (ConvLSTM2D)  (None, 130, 240, 320, 40) 115360    
_________________________________________________________________
batch_normalization_7 (Batch (None, 130, 240, 320, 40) 160       
_________________________________________________________________
conv_lst_m2d_8 (ConvLSTM2D)  (None, 130, 240, 320, 40) 115360    
_________________________________________________________________
batch_normalization_8 (Batch (None, 130, 240, 320, 40) 160       
_________________________________________________________________
conv3d_1 (Conv3D)            (None, 130, 240, 320, 1)  1081      
=================================================================
Total params: 409,881.0
Trainable params: 409,561
Non-trainable params: 320.0
_________________________________________________________________
None
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-d909d285f474> in <module>()
     82 history = model.fit(x_learn[:3], y_learn[:3],
     83               batch_size=batch_size,
---> 84               epochs=epochs)
     85 
     86 print (history)

ValueError: Error when checking model target: expected conv3d_1 to have 5 dimensions, but got array with shape (3, 1)

Answer 1

＆＃34;目标＆＃34; 表示问题出在您的模型的输出中，而不是 y_learn 的格式。< / p>

数组y_learn应该与模型输出的形状完全相同，因为模型会输出＆＃34; guess＆＃34;，而{{1是＆＃34;正确答案＆＃34;。如果猜测具有相同的尺寸，系统只能将猜测与正确答案进行比较。

看到区别：

模型输出（见摘要）：y_learn
y_learn：(None,130,240,320,1)

凡＆＃34;无＆＃34;是批量大小。您给了y_learn [：3]，然后您的批量大小为3，用于此培训课程。

为了正确纠正它，我们需要了解y_learn是什么如果我理解得很清楚，每个视频你只有一个0或1的数字。如果是这样，你的y_learn就完全没问了，你需要的是模型输出像(None,1)这样的东西。

一种非常简单的方法（也许不是最好的，我在这里不能提供更多的帮助......）是添加一个只有一个神经元的最终Dense层：

(None,1)

现在，当您执行model.add(Flatten()) model.add(Dense(1, activation='sigmoid'))时，您会看到最终输出为model.summary()

Keras模型ValueError：检查模型目标时出错：

1 个答案: