步幅的长度应为1、1或3，但应为2

本文介绍了步幅的长度应为1、1或3，但应为2的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我一直在尝试将卷积神经网络与GRU堆叠在一起，以解决图像到文本的问题. 这是我的模特:

I have been trying to stack Convolutional neural networks with GRUs for an image to text problem. Here's my model :

model=Sequential() model.add(TimeDistributed(Conv2D(16,kernel_size (3,3),data_format="channels_last",input_shape= (129,80,564,3),padding='SAME',strides=(1,1)))) model.add(TimeDistributed(Activation("relu"))) model.add(TimeDistributed(Conv2D(16,kernel_size =(3,3),strides=(1,1)))) model.add(TimeDistributed(Activation("relu"))) model.add(TimeDistributed(MaxPooling2D(pool_size=2,strides=(1,1) ))) model.add(TimeDistributed(Reshape((280*38*16,)))) model.add(TimeDistributed(Dense(32))) model.add(GRU(512)) model.add(Dense(50)) model.add(Activation("softmax")) modelpile(loss='categorical_crossentropy', optimizer='adam', metrics= ['accuracy'])

当我尝试拟合模型时，出现以下错误:

When I try to fit my model I get the following error :

------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-125-c6a3c418689c> in <module>() 1 nb_epoch = 100 ----> 2 model.fit(X2,L2, epochs=100) 10 frames /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_ops.py in _get_sequence(value, n, channel_index, name) 71 else: 72 raise ValueError("{} should be of length 1, {} or {} but was {}".format( ---> 73 name, n, n + 2, current_n)) 74 75 if channel_index == 1: ValueError: strides should be of length 1, 1 or 3 but was 2

我什至无法开始思考出现此消息的原因.我已经为所有图层指定了步幅"参数.任何帮助将不胜感激.

I cannot even begin to wrap my head around why this message appears.I have specified the "strides" parameters for all layers. Any help will be deeply appreciated.

P.S-当我尝试在没有TimeDistributed图层的情况下拟合模型时，我没有任何问题.也许与此相关的事情会引发此错误.

P.S - I did not have any problems when I tried to fit a model without TimeDistributed layers. Maybe there is something to do with this that raises this error.

推荐答案

您在代码中犯了一些错误.

You have made several mistakes in your code.

• 在第一层中，应指定TimeDistributed层的input_shape，而不是Conv2D层.
• MaxPooling2D用于下采样图像的空间大小.但使用strides=(1,1)时，图像大小将保持不变并且不会减小.
• 在第一层中使用padding='SAME'会在进行卷积时添加零填充，并且会在Reshape层中导致形状不匹配错误.相反，您可以使用Flatten层.
• Conv2D中的步幅默认值为strides=(1,1)，因此，可以不加提及.

• In the first layer you should specify the input_shape of the TimeDistributed layer, not Conv2D layer.
• MaxPooling2D is used for down-sampling the images spatial size. but with strides=(1,1) the image size will remain same and not be reduced.
• Using padding='SAME' in the first layer will add zero-padding while doing convolution and will result in a shape mismatch error in the Reshape layer. Instead you can use Flatten layer.
• Default value of stride in a Conv2D is strides=(1,1), so, it's optional to mention.

最后，工作代码应为以下内容:

Finally, the working code should be something following:

model=keras.models.Sequential() model.add(keras.layers.TimeDistributed(keras.layers.Conv2D(16, kernel_size=(3,3), data_format="channels_last"),input_shape=(129,80,564,3))) model.add(keras.layers.TimeDistributed(keras.layers.Activation("relu"))) model.add(keras.layers.TimeDistributed(keras.layers.Conv2D(16, kernel_size =(3,3)))) model.add(keras.layers.TimeDistributed(keras.layers.Activation("relu"))) model.add(keras.layers.TimeDistributed(keras.layers.MaxPooling2D(pool_size=2))) # model.add(keras.layers.TimeDistributed(keras.layers.Flatten())) model.add(keras.layers.TimeDistributed(keras.layers.Reshape((280*38*16,)))) model.add(keras.layers.TimeDistributed(keras.layers.Dense(32))) model.add(keras.layers.GRU(512)) model.add(keras.layers.Dense(50)) model.add(keras.layers.Activation("softmax")) modelpile(loss='categorical_crossentropy', optimizer='adam', metrics= ['accuracy'])