What is the role of Flatten in Keras

Question

I am trying to understand the role of the Flatten function in Keras  Below is my code  which is a simple two-layer network  It takes in 2-dimensional data of shape  3  2   and outputs 1-dimensional data of shape  1  4    model   Sequential   model add Dense 16  input shape  3  2    model add Activation  relu    model add Flatten    model add Dense 4   model compile loss  mean squared error   optimizer  SGD    x   np array    1  2    3  4    5  6      y   model predict x   print y shape   This prints out that y has shape  1  4   However  if I remove the Flatten line  then it prints out that y has shape  1  3  4    I don t understand this  From my understanding of neural networks  the model add Dense 16  input shape  3  2    function is creating a hidden fully-connected layer  with 16 nodes  Each of these nodes is connected to each of the 3x2 input elements  Therefore  the 16 nodes at the output of this first layer are already  flat   So  the output shape of the first layer should be  1  16   Then  the second layer takes this as an input  and outputs data of shape  1  4    So if the output of the first layer is already  flat  and of shape  1  16   why do I need to further flatten it

User · Answer

Flatten make explicit how you serialize a multidimensional tensor  tipically the input one   This allows the mapping between the  flattened  input tensor and the first hidden layer  If the first hidden layer is  dense  each element of the  serialized  input tensor will be connected with each element of the hidden array  If you do not use Flatten  the way the input tensor is mapped onto the first hidden layer would be ambiguous

User · Answer

If you read the Keras documentation entry for Dense  you will see that this call  Dense 16  input shape  5 3    would result in a Dense network with 3 inputs and 16 outputs which would be applied independently for each of 5 steps  So  if D x  transforms 3 dimensional vector to 16-d vector  what you ll get as output from your layer would be a sequence of vectors   D x 0      D x 1          D x 4      with shape  5  16   In order to have the behavior you specify you may first Flatten your input to a 15-d vector and then apply Dense  model   Sequential   model add Flatten input shape  3  2    model add Dense 16   model add Activation  relu    model add Dense 4   model compile loss  mean squared error   optimizer  SGD    EDIT  As some people struggled to understand - here you have an explaining image

User · Answer

This is how Flatten works converting Matrix to single array

User · Answer

It is rule of thumb that the first layer in your network should be the same shape as your data  For example our data is 28x28 images  and 28 layers of 28 neurons would be infeasible  so it makes more sense to  flatten  that 28 28 into a 784x1  Instead of wriitng all the code to handle that ourselves  we add the Flatten   layer at the begining  and when the arrays are loaded into the model later  they ll automatically be flattened for us

User · Answer

Here I would like to present another alternative to Flatten function  This may help to understand what is going on internally  The alternative method adds three more code lines  Instead of using                                            Build a Model model   tf keras models Sequential    model add keras layers Flatten input shape  28  28  3    reshapes to  2352  28x28x3 model add layers experimental preprocessing Rescaling 1  255   normalize model add keras layers Dense 128 activation tf nn relu   model add keras layers Dense 2 activation tf nn softmax    model build   model summary    summary of the model  we can use                                                Build a Model     tensor   tf keras backend placeholder dtype tf float32  shape  None  28  28  3            model   tf keras models Sequential            model add keras layers InputLayer input tensor tensor       model add keras layers Reshape  2352    model add layers experimental preprocessing Rescaling 1  255   normalize     model add keras layers Dense 128 activation tf nn relu       model add keras layers Dense 2 activation tf nn softmax            model build       model summary    summary of the model  In the second case  we first create a tensor  using a placeholder  and then create an Input layer  After  we reshape the tensor to flat form  So basically  Create tensor- gt Create InputLayer- gt Reshape    Flatten  Flatten is a convenient function  doing all this automatically  Of course both ways has its specific use cases  Keras provides enough flexibility to manipulate the way you want to create a model

User · Answer

I came across this recently  it certainly helped me understand  https   www cs ryerson ca  aharley vis conv  So there s an input  a Conv2D  MaxPooling2D etc  the Flatten layers are at the end and show exactly how they are formed and how they go on to define the final classifications  0-9

User · Answer

short read      Flattening a tensor means to remove all of the dimensions except for one  This is exactly what the Flatten layer do    long read   If we take the original model  with the Flatten layer  created in consideration we can get the following model summary   Layer  type                  Output Shape              Param                                                                        D16  Dense                    None  3  16              48                                                                           A  Activation                 None  3  16              0                                                                            F  Flatten                    None  48                 0                                                                            D4  Dense                     None  4                  196                                                                          Total params  244 Trainable params  244 Non-trainable params  0   For this summary the next image will hopefully provide little more sense on the input and output sizes for each layer   The output shape for the Flatten layer as you can read is  None  48   Here is the tip  You should read it  1  48  or  2  48  or     or  16  48      or  32  48        In fact  None on that position means any batch size  For the inputs to recall  the first dimension means the batch size and the second means the number of input features   The role of the Flatten layer in Keras is super simple    A flatten operation on a tensor reshapes the tensor to have the shape that is equal to the number of elements contained in tensor non including the batch dimension        Note  I used the model summary   method to provide the output shape and parameter details

[machine-learning] What is the role of "Flatten" in Keras?

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