Keras模型ValueError:检查模型目标时出错: [英] Keras model ValueError: Error when checking model target:
问题描述
我好几年没编码了，请原谅.我正在尝试做一些可能不可能的事情.我有38个视频，视频中的人们表演了相同的基本动作.我想训练模型，以找出正确的人与不正确的人. 我现在正在使用颜色，因为灰度也不起作用，因此我想像使用的示例一样进行测试.我使用了示例链接中定义的模型.
I have not coded in years, forgive me. I am trying to do something that may be impossible. I have 38 videos of people performing the same basic movement. I want to train the model to identify those doing it correct v not correct. I am using color now, because the grayscale did not work either and I wanted to test like the example I used. I used the model as defined in an example, link.
Keras， Anaconda 64中的Python3.5， Tensorflow后端， 在Windows 10(64位)上
Keras, Python3.5 in Anaconda 64, Tensorflow backend, on Windows 10 (64bit)
我希望对此问题尝试不同的模型，并使用灰度来减少内存，但无法走出第一步！
I was hoping to try different models on the problem and use grayscale to reduce memory, but cant get past first step!
谢谢！
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)
推荐答案
目标" 表示问题出在模型的输出，而不是 y_learn .
"Target" means that the problem is in the output of your model versus the format of y_learn.
数组y_learn应该与模型输出的形状完全相同，因为模型输出猜测"，而y_learn是正确答案".如果尺寸相同，系统只能将猜测与正确答案进行比较.
The array y_learn should be exactly the same shape of the model's output, because the model outputs a "guess", while y_learn is the "correct answer". The system can only compare the guess with the correct answer if they have the same dimensions.
查看区别:
- 模型输出(在摘要中看到):
(None,130,240,320,1) - y_learn:
(None,1)
- Model Output (seen in the summary):
(None,130,240,320,1) - y_learn:
(None,1)
其中无"是批次大小.您给了y_learn [:3]，那么此培训课程的批次大小为3.
Where "None" is the batch size. You gave y_learn[:3], then your batch size is 3 for this training session.
为了正确地进行更正，我们需要了解y_learn是什么.
据我了解，每个视频只有一个数字，即0或1.如果是这样，则您的y_learn完全可以，并且您需要的是模型输出(None,1)之类的东西.
In order to correct it properly, we need to understand what y_learn is.
If I understood well, you've got only a number, 0 or 1, for each video. If that's so, your y_learn is totally ok, and what you need is for your model to output things like (None,1).
一种非常简单的方法(也许不是最好的方法，在这里我没有更多帮助...)是仅使用一个神经元添加最后的密集层:
A very simple way to do that (perhaps not the best, and I couldn't be of more help here...) is to add a final Dense layer with just one neuron:
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
现在，当您执行model.summary()时，您将看到最终输出为(None,1)
Now, when you do model.summary(), you will see the final output as (None,1)
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