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 sh

Answer 1

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.

Answer 2

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:

Answer 3

This is how Flatten works converting Matrix to single array.

Answer 4

_{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.

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Note: I used the model.summary() method to provide the output shape and parameter details.

Answer 5

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).

Answer 6

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->Create InputLayer->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.