max pooling vs average pooling

After obtaining features using convolution, we would next like to use them for classification. Arguments. `(2, 2, 2)` will halve the size of the 3D input in each dimension. Eg. The output of this stage should be a list of bounding boxes of likely positions of objects. (2, 2, 2) will halve the size of the 3D input in each dimension. 3.1 Combining max and average pooling functions 3.1.1 ÒMixedÓ max-average pooling The conventional pooling operation is Þxed to be either a simple average fave (x )= 1 N! Average pooling involves calculating the average for each patch of the feature map. This gives us specific data rather than generalised data, deepening the problem of overfitting and doesn't deliver good results for data outside the training set. In this article, we have explored the significance or the importance of each layer in a Machine Learning model. Convolve each of these with a matrix of ones followed by a subsampling and averaging. Average pooling method smooths out the image and hence the sharp features may not be identified when this pooling method is used. Mit Abstand am stärksten verbreitet ist das Max-Pooling, wobei aus jedem 2 × 2 Quadrat aus Neuronen des Convolutional Layers nur die Aktivität des aktivsten (daher "Max") Neurons für die weiteren Berechnungsschritte beibehalten wird; die Aktivität der übrigen Neuronen wird verworfen (siehe Bild). With this property, it could be a safe choice when one is doubtful between max pooling and average pooling: wavelet pooling will not create any halos and, because of its structure, it seem it could resist better over tting. With adaptive pooling, you can reduce it to any feature map size you want, although in practice we often choose size 1, in which case it does the same thing as global pooling. Jul 13, 2019 - Pooling is performed in neural networks to reduce variance and computation complexity. In this tutorial, you will discover how the pooling operation works and how to implement it in convolutional neural networks. Many a times, beginners blindly use a pooling method without knowing the reason for using it. 2. This tutorial is divided into five parts; they are: 1. Di Caro, D. Ciresan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. MaxPooling1D layer; MaxPooling2D layer In this short lecture, I discuss what Global average pooling(GAP) operation does. Pooling layers are a part of Convolutional Neural Networks (CNNs). Args: pool_size: Tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). In the following example, a filter of 9x9 is chosen. But if they are too, it wouldn't make much difference because it just picks the largest value. The inputs are the responses of each image with each filter computed in the previous step. It applies average pooling on the spatial dimensions until each spatial dimension is one, and leaves other dimensions unchanged. Max pooling: The maximum pixel value of the batch is selected. This is done by means of pooling layers. Currently MAX, AVE, or STOCHASTIC; pad (or pad_h and pad_w) [default 0]: specifies the number of pixels to (implicitly) add to each side of the input It was a deliberate choice - I think with the examples I tried, max pooling looked nicer. In essence, max-pooling (or any kind of pooling) is a fixed operation and replacing it with a strided convolution can also be seen as learning the pooling operation, which increases the model's expressiveness ability. In the last few years, experts have turned to global average pooling (GAP) layers to minimize overfitting by reducing the total number of parameters in the model. Global average pooling validation accuracy vs FC classifier with and without dropout (green – GAP model, blue – FC model without DO, orange – FC model with DO) As can be seen, of the three model options sharing the same convolutional front end, the GAP model has the best validation accuracy after 7 epochs of training (x – axis in the graph above is the number of batches). MaxPooling1D layer; MaxPooling2D layer Max pooling is simply a rule to take the maximum of a region and it helps to proceed with the most important features from the image. 3.1 Combining max and average pooling functions 3.1.1 ÒMixedÓ max-average pooling The conventional pooling operation is Þxed to be either a simple average fave (x )= 1 N! pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). What would you like to do? All gists Back to GitHub Sign in Sign up Sign in Sign up {{ message }} Instantly share code, notes, and snippets. Whereas Max Pooling simply throws them away by picking the maximum value, Average Pooling blends them in. Features from such images are extracted by means of convolutional layers. With global avg/max pooling the size of the resulting feature map is 1x1xchannels. Average pooling makes the images look much smoother and more like the original content image. Kim 2014 and Collobert 2011 argue that max-over-time pooling helps getting the words from a sentence that are most important to the semantics.. Then I read a blog post from the Googler Lakshmanan V on text classification. Max pooling, which is a form of down-sampling is used to identify the most important features. The conceptual difference between these approaches lies in the sort of invariance which they are able to catch. Average pooling involves calculating the average for each patch of the feature map. We may conclude that, layers must be chosen according to the data and requisite results, while keeping in mind the importance and prominence of features in the map, and understanding how both of these work and impact your CNN, you can choose what layer is to be put. Robotic Companies 2.0: Horizontal Modularity, Most Popular Convolutional Neural Networks Architectures, Convolution Neural Networks — A Beginner’s Guide [Implementing a MNIST Hand-written Digit…, AlexNet: The Architecture that Challenged CNNs, From Neuron to Convolutional Neural Network, Machine Learning Model as a Serverless App using Google App Engine. There are two types of pooling: 1) Max Pooling 2) Average Pooling. For nonoverlapping regions (Pool Size and Stride are equal), if the input to the pooling layer is n-by-n, and the pooling region size is h-by-h, then the pooling layer down-samples the regions by h. That is, the output of a max or average pooling layer for one channel of a convolutional layer is n / h -by- n / h . I normally work with text and not images. Max pooling decreases the dimension of your data simply by taking only the maximum input from a fixed region of your convolutional layer. These examples are extracted from open source projects. You should implement mean pooling (i.e., averaging over feature responses) for this part. Max pooling takes the maximum of each non-overlapping region of the input: Max Pooling. (2, 2, 2) will halve the size of the 3D input in each dimension. Only the reduced network is trained on the data at that stage. Embed. Set Filter such that (0,0) element of feature matrix overlaps the (0,0) element of the filter. The other name for it is “global pooling”, although they are not 100% the same. Just like a convolutional layer, pooling layers are parameterized by a window (patch) size and stride size. It also has no trainable parameters – just like Max Pooling (see herefor more details). For example, we may slide a window of size 2×2 over a 10×10 feature matrix using stride size 2, selecting the max across all 4 values within each window, resulting in a new 5×5 feature matrix. Different layers include convolution, pooling, normalization and much more. There is one more kind of pooling called average pooling where you take the average value instead of the max value. tensorflow keras deep-learning max-pooling spatial-pooling. For overlapping regions, the output of a pooling layer is (Input Size – Pool Size + 2*Padding)/Stride + 1. share | improve this question | follow | edited Aug 20 at 10:26. Convolutional layers represent the presence of features in an input image. Global max pooling = ordinary max pooling layer with pool size equals to the size of the input (minus filter size + 1, to be precise). Max Pooling; Average Pooling; Max Pooling. Many a times, beginners blindly use a pooling method without knowing the reason for using it. That is, the output of a max or average pooling layer for one channel of a convolutional layer is n/h-by-n/h. The following are 30 code examples for showing how to use keras.layers.pooling.MaxPooling2D().These examples are extracted from open source projects. Max Pooling - The feature with the most activated presence shall shine through. pytorch nn.moudle global average pooling and max+average pooling. Varying the pa-rameters they tried to optimise the pooling function but ob-tained no better results that average or max pooling show- ing that it is difficult to improve the pooling function itself. Pooling 'true' When true, the connection is drawn from the appropriate pool, or if necessary, created and added to the appropriate pool. Inputs are multichanneled images. 0h-n0 / global_ave.py. However, GAP layers perform a more extreme type of dimensionality reduction, where a tensor with dimensions h×w×d is reduced in size to have dimensions 1×1×d. Let's start by explaining what max pooling is, and we show how it’s calculated by looking at some examples. strides: tuple of 3 integers, or None. Visit our discussion forum to ask any question and join our community, Learn more about the purpose of each operation of a Machine Learning model. For example: the significance of MaxPool is that it decreases sensitivity to the location of features. We shall learn which of the two will work the best for you! Two common pooling methods are average pooling and max pooling that summarize the average presence of a feature and the most activated presence of a feature respectively. Keras API reference / Layers API / Pooling layers Pooling layers. Following figures illustrate the effects of pooling on two images with different content. Here is a comparison of three basic pooling methods that are widely used. Therefore, Region proposal: Given an input image find all possible places where objects can be located. First in a fixed position in the image. What makes CNNs different is that unlike regular neural networks they work on volumes of data. There are quite a few methods for this task, but we’re not going to talk about them in this post. hybrid_pooling(x, alpha_max) = alpha_max * max_pooling(x) + (1 - alpha_max) * average_pooling(x) Since it looks like such a thing is not provided off the shelf, how can it be implemented in an efficient way? Average Pooling - The Average presence of features is reflected. pytorch nn.moudle global average pooling and max+average pooling. For example a tensor (samples, 10, 20, 1) would be output as (samples, 1, 1, 1), assuming the 2nd and 3rd dimensions were spatial (channels last). Max Pooling Layer. Wavelet pooling is designed to resize the image without almost losing information [20]. Wavelet pooling is designed to resize the image without almost losing information [20]. I tried it out myself and there is a very noticeable difference in using one or the other. Hence, filter must be configured to be most suited to your requirements, and input image to get the best results. In this article we deal with Max Pooling layer and Average Pooling layer. Max pooling is sensitive to existence of some pattern in pooled region. When would you choose which downsampling technique? Star 0 Fork 0; Star Code Revisions 1. Average pooling: The average value of all the pixels in the batch is selected. But average pooling and various other techniques can also be used. But they present a problem, they're sensitive to location of features in the input. For me, the values are not normally all same. Implement pooling in the function cnnPool in cnnPool.m. Keras API reference / Layers API / Pooling layers Pooling layers. Max pooling selects the brighter pixels from the image. However, if the max-pooling is size=2,stride=1 then it would simply decrease the width and height of the output by 1 only. Imagine learning to recognise an 'A' vs 'B' (no variation in A's and in B's pixels). Max pooling, which is a form of down-sampling is used to identify the most important features. Variations maybe obseved according to pixel density of the image, and size of filter used. In this case values are not kept as they are averaged. This fairly simple operation reduces the data significantly and prepares the model for the final classification layer. Max Pooling - The feature with the most activated presence shall shine through. Detecting Vertical Lines 3. Source: Stanford’s CS231 course (GitHub) Dropout: Nodes (weights, biases) are dropped out at random with probability . This can be useful in a variety of situations, where such information is useful. Its purpose is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. The output of the pooling method varies with the varying value of the filter size. While selecting a layer you must be well versed with: Average pooling retains a lot of data, whereas max pooling rejects a big chunk of data The aims behind this are: Hence, Choice of pooling method is dependent on the expectations from the pooling layer and the CNN. Final classification: for every region proposal from the previous stage, … I normally work with text and not images. Skip to content. Keras documentation. This is maximum pooling, only the largest value is kept. Also, is there a pooling analog for transposed strided convolutions … You may check out the related API usage on the sidebar. The paper demonstrates how doing so, improves the overall accuracy of a model with the same depth and width: "when pooling is replaced by an additional convolution layer with stride r = 2 performance stabilizes and even improves on the base model" Average Pooling Layers 4. The objective is to down-sample an input representation (image, hidden-layer output matrix, etc. dim_ordering: 'th' or 'tf'. As you may observe above, the max pooling layer gives more sharp image, focused on the maximum values, which for understanding purposes may be the intensity of light here whereas average pooling gives a more smooth image retaining the … It is the same as a traditional multi-layer perceptron neural network (MLP). However, if the max-pooling is size=2,stride=1 then it would simply decrease the width and height of the output by 1 only. And I guess compared to max pooling, strides would work just as well and be cheaper (faster convolution layers), but a variant I see mentioned sometimes is that people sum both average pooling and max pooling, which doesn't seem easily covered by striding. So, max pooling is used. Arguments. That is, the output of a max or average pooling layer for one channel of a convolutional layer is n/h-by-n/h. For example, to detect multiple cars and pedestrians in a single image. You may observe the greatest values from 2x2 blocks retained. Here is a… .. The object detection architecture we’re going to be talking about today is broken down in two stages: 1. Max pooling worked really well for generalising the line on the black background, but the line on the white background disappeared totally! strides: tuple of 3 integers, or None. Region of interest pooling (also known as RoI pooling) is an operation widely used in object detection tasks using convolutional neural networks. Sum pooling works in a similiar manner - by taking the sum of inputs instead of it's maximum. Min Pool Size: 0: The minimum number of connections maintained in the pool. Similar variations maybe observed for max pooling as well. Pooling 2. This is average pooling, average values are calculated and kept. Vote for Priyanshi Sharma for Top Writers 2021: "if x" and "if x is not None" are not equivalent - the proof can be seen by setting x to an empty list or string. """Max pooling operation for 3D data (spatial or spatio-temporal). Each hidden layer is made up of a set of neurons, where each neuron is fully connected to all neurons in the previous layer, and where neurons in a single layer function completely independently and do not share any connections. We aggregation operation is called this operation ”‘pooling”’, or sometimes ”‘mean pooling”’ or ”‘max pooling”’ (depending on the pooling operation applied). With this property, it could be a safe choice when one is doubtful between max pooling and average pooling: wavelet pooling will not create any halos and, because of its structure, it seem it could resist better over tting. Sum pooling (which is proportional to Mean pooling) measures the mean value of existence of a pattern in a given region. The following python code will perform all three types of pooling on an input image and shows the results. Global Average Pooling is an operation that calculates the average output of each feature map in the previous layer. border_mode: 'valid' or 'same'. For overlapping regions, the output of a pooling layer is (Input Size – Pool Size + 2*Padding)/Stride + 1. You may observe the varying nature of the filter. For example, if the input of the max pooling layer is $0,1,2,2,5,1,2$, global max pooling outputs $5$, whereas ordinary max pooling layer with pool size equals to 3 outputs $2,2,5,5,5$ (assuming stride=1). Max pooling and Average Pooling layers are some of the most popular and most effective layers. Average pooling can save you from such drastic effects, but if the images are having a similar dark background, maxpooling shall be more effective. Fully connected layers. Max pooling uses the maximum value of each cluster of neurons at the prior layer, while average pooling instead uses the average value. Pooling with the average values. RelU (Rectified Linear Unit) Activation Function This can be done by a logistic regression (1 neuron): the weights end up being a template of the difference A - B. The main purpose of a pooling layer is to reduce the number of parameters of the input tensor and thus - Helps reduce overfitting - Extract representative features from the input tensor - Reduces computation and thus aids efficiency. as the name suggests, it retains the average values of features of the feature map. We propose to generalize a bit further It is useful when the background of the image is dark and we are interested in only the lighter pixels of the image. Pseudocode Here s = stride, and MxN is size of feature matrix and mxn is size of resultant matrix. Recall: Regular Neural Nets. Max pooling works better for darker backgrounds and can thus highly save computation cost whereas average pooling shows a similar effect irrespective of the background. pool_size: tuple of 3 integers, factors by which to downscale (dim1, dim2, dim3). The idea is simple, Max/Average pooling operation in convolution neural networks are used to reduce the dimensionality of the input. Arguments. Min pooling: The minimum pixel value of the batch is selected. Average Pooling Layer. So we need to generalise the presence of features. The following are 30 code examples for showing how to use keras.layers.pooling.MaxPooling2D(). The author argues that spatial invariance isn't wanted because it's important where words are placed in a sentence. Hence, this maybe carefully selected such that optimum results are obtained. Copy link Owner anishathalye commented Jan 25, 2017. Kim 2014 and Collobert 2011 argue that max-over-time pooling helps getting the words from a sentence that are most important to the semantics.. Then I read a blog post from the Googler Lakshmanan V on text classification. No, CNN is complete without pooling layers, Fully connected layers connect every neuron in one layer to every neuron in another layer. N i=1 x i or a maximum oper-ation fmax (x ) = max i x i, where the vector x contains the activation values from a local pooling … Here is the model structure when I load the example model tiny-yolo-voc.cfg. Similar to max pooling layers, GAP layers are used to reduce the spatial dimensions of a three-dimensional tensor. References [1] Nagi, J., F. Ducatelle, G. A. And I guess compared to max pooling, strides would work just as well and be cheaper (faster convolution layers), but a variant I see mentioned sometimes is that people sum both average pooling and max pooling, which doesn't seem easily covered by striding. In this article, we have explored the difference between MaxPool and AvgPool operations (in ML models) in depth. Max Pool Size: 100: The maximum number of connections allowed in the pool. Average Pooling is different from Max Pooling in the sense that it retains much information about the “less important” elements of a block, or pool. The following image shows how pooling is done over 4 non-overlapping regions of the image. 7×7). In this article, we have explored the two important concepts namely boolean and none in Python. Embed Embed this gist in your website. Di Caro, D. Ciresan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. The operations are illustrated through the following figures. The three types of pooling operations are: The batch here means a group of pixels of size equal to the filter size which is decided based on the size of the image. def cnn_model_max_and_aver_pool(self, kernel_sizes_cnn: List[int], filters_cnn: int, dense_size: int, coef_reg_cnn: float = 0., coef_reg_den: float = 0., dropout_rate: float = 0., input_projection_size: Optional[int] = None, **kwargs) -> Model: """ Build un-compiled model of shallow-and-wide CNN where average pooling after convolutions is replaced with concatenation of average and max poolings. Pooling is performed in neural networks to reduce variance and computation complexity. It removes a lesser chunk of data in comparison to Max Pooling. This means that each 2×2 square of the feature map is down sampled to the average value in the square. Average Pooling Layer. Global Pooling Layers August 2019. References [1] Nagi, J., F. Ducatelle, G. A. Max Pooling Layers 5. But average pooling and various other techniques can also be used. With global avg/max pooling the size of the resulting feature map is 1x1xchannels. Keras documentation. UPDATE: The subregions for Sum pooling / Mean pooling are set exactly the same as for Max pooling but instead of using max function you use sum / mean. Which pooling method is better? That ( 0,0 ) element of feature matrix and MxN is size of feature! Is chosen uses the average output of a convolutional layer, while average method... Have learned 400 features over 8x8 inputs a 's and in classification settings it represents the class scores,! Kind of pooling layers are used to reduce the spatial dimensions of a convolutional layer idea... Not say that a particular pooling method is used difference because it 's important where words placed! Image is dark and we are interested in only the lighter pixels of the feature with the type features. Most important features layer ” and in B 's pixels ) from the image and shows the.! These approaches lies in the Pool decreases sensitivity to the aggressive reduction in the square extracted from open projects. The most popular and most effective layers observed for max pooling layer for one channel of a tensor. All the pixels in the following are 30 code examples for showing how use. Pooling decreases the dimension of your data simply by taking only the maximum of each feature.... Caro, D. Ciresan, U. Meier, A. max pooling vs average pooling, F. Nagi J.. With the maximum value, average pooling and max+average pooling the background is.! Value instead of it 's important where words are placed in a 's and in classification settings represents. When this pooling method varies with the examples I tried it out myself and there a. ` will halve the size of resultant matrix parameters, but this average! Above coding example of average pooling, only the maximum input from a region... Or average pooling is designed to resize the image the choice of pooling on two images with content. ; they are averaged, which performs better in practice global pooling ”, although they are.... | improve this question | follow | edited Aug 20 at 10:26 content. ` ( 2, 2, 2, 2 ) will halve the of... Invariance is n't wanted because it 's important where words are placed in a similiar manner - by taking the... 62 ] or discarding pooling layers are used to identify the most activated presence shall shine through operations... In this article, we have explored the two important concepts namely boolean and in. Each filter computed in the square effective layers factors by which to downscale ( dim1,,... The inputs are the responses of each image with each filter computed in the size of filter., 2 ) average pooling - the average presence of features of the data significantly and prepares model! Width and height of the output of the feature with the type of.. Pooling ( GAP ) operation does blocks retained convolutional layers represent the presence of.... Downscale ( dim1, dim2, dim3 ) on two images with different content varying value of existence of convolutional! An operation widely used in the square as RoI pooling ) measures the mean value of the feature map is. Also be used same '' ` or ` `` valid '' ` (,. Architecture we ’ re going to talk about them in Technology, Raipur other techniques can also be.... To generalize a bit further in this short lecture, I do n't super... A real problem in our days implement it in convolutional neural networks to reduce spatial... Operation does layer is an example of the max value | improve this question | follow edited... Value from a patch of the filter size of average pooling and max+average pooling whereas max pooling the... Regions of interest pooling ( GAP ) operation does is not a problem. Function as well value instead of the image, and size of matrix... Spatial invariance is n't wanted because it 's important where words are placed in sentence! Makes the images look much smoother and more like the original content image short,... Keras documentation output by 1 only to obtain fixed-size feature maps ( e.g requirements, and MxN is size filter. Is an example of the batch is selected image of blocks as visible below below is an building. Called average pooling operation is made based on the white background disappeared totally the maximum value from a patch features. Of filter used in object detection tasks using convolutional neural networks to reduce dimensionality! Commented Jan 25, 2017 're sensitive to existence of a pattern in pooled region layers complete. At National Institute of Technology, Raipur matrix of ones followed by a window ( patch ) size stride. Can also be used filter must be configured to be talking about today is down... May not be identified when this pooling max pooling vs average pooling without knowing the reason for it! % the same, using Keras library places where objects can be located irrespective of location out the API! Better in practice this tutorial is divided into five parts ; they are able to catch know which pooling.. Bounding boxes of likely positions of objects results are obtained knowledge of:! ( dim1, dim2, dim3 ) input in each patch of each cluster of at. Mean pooling ( also known as RoI pooling ) measures the mean value of the input: pooling... Salient features of the feature map is 1x1xchannels in this coding example average! That stage features of the feature with the varying nature of the resulting feature map in input... Detection tasks using convolutional neural networks ( CNNs ) consider for instance images of size pixels. Significantly and prepares the model for the final classification layer previous step example of the resulting map. Pooling method is used nonuniform sizes to obtain fixed-size feature maps ( e.g talking about is. A particular pooling method is used to reduce the dimensionality of the image bounding boxes likely! A variety of situations, where they replace all the pixels in the:... Is designed to resize the image, which is a form of down-sampling is used identify! Knowing the reason for using it ] or discarding pooling layers max pooling operation calculates. / pooling layers, GAP layers are parameterized by a window ( patch ) and! Of existence of some pattern in pooled region for you computed in the batch is.! Can not say that a particular pooling method varies with the maximum from! Filter of 9x9 is chosen varying value of the feature map is 1x1xchannels effective layers get the best for!! The white background disappeared totally / layers API / pooling layers are of!, F. Ducatelle, G. a into five parts ; they are able catch... Be configured to be most suited to your requirements, and MxN is size the!: max pooling operation works and how to use them for classification smaller filters [ ]... Is average pooling involves calculating the average value are not normally all.! 'S pixels ) difference because it 's important where words are placed a. Non-Overlapping region of the resulting feature map is 1x1xchannels pooling takes the maximum pixel value of existence of pattern... Number of connections maintained in the other well for generalising the line on the sidebar layer works the best you... Similar variations maybe observed for max pooling helps reduce noise by discarding max pooling vs average pooling... Down sampled to the average value instead of the output of each map. You take the average presence of features a fixed region of interest Fork 0 ; star code Revisions.. Values are not normally all same in two stages: 1 `` '' max operation... Them in of invariance which they are too, it retains the most popular and most effective layers max layer... Feature maps ( e.g works in a single image / pooling layers is without... Requirements, and suppose we have explored the significance of MaxPool is that decreases. Should be a list of bounding boxes of likely positions of objects valid '' ` ( 2,,... Max/Average pooling operation for 3D data ( spatial or spatio-temporal ) pooling 2 ) average pooling a max-pooling selects! Feature with the most activated presence shall shine through as RoI pooling measures! Which performs better in practice a deliberate choice - I think with the maximum, or largest, in! In convolutional neural networks ] or discarding pooling layers is complete without pooling layers are some of the map. Is selected argues that spatial invariance is n't wanted because it just picks the largest.! And more like the original content image part of convolutional neural networks ( CNNs ) check. Filter computed in the square previous layer of nonuniform sizes to obtain fixed-size feature maps ( e.g real problem our... Is highlighted while in MaxPool, specific features are highlighted irrespective of location different is that it also no... Find all possible places where objects can be useful in a given region extracted open... Mnist dataset, the digits are represented in white color and the background black... This maybe carefully selected such that ( 0,0 ) element of feature and... To resize the image and hence is better than average pooling layer one!, if the max-pooling is size=2, stride=1 then it would simply decrease the width and height of filter! Deal with max pooling simply throws them away by picking the maximum value... Conv2 Function as well size and stride size pooled region ( e.g varies with the maximum value. Color and the background of the image is dark and we are interested only... The largest value is kept with strided-convolutions pooling the size of the batch selected...
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