For instance, here's a model with two separate input branches getting merged: from keras.layers import Merge The output is a layer that can be added as first layer in a new Sequential model. Multiple Sequential instances can be merged into a single output via a Merge layer. # so the model will be able to process batches of any size.Īnd so are the following three snippets: model = Sequential() # note that batch dimension is 'None' here,

some 2D layers, such as Dense, support the specification of their input shape via the argument input_dim, and some 3D temporal layers support the arguments input_dim and input_length.Īs such, the following three snippets are strictly equivalent: model = Sequential().

This is useful for specifying a fixed batch size (e.g.

pass instead a batch_input_shape argument, where the batch dimension is included.

In input_shape, the batch dimension is not included.

This is a shape tuple (a tuple of integers or None entries, where None indicates that any positive integer may be expected). pass an input_shape argument to the first layer.There are several possible ways to do this: For this reason, the first layer in a Sequential model (and only the first, because following layers can do automatic shape inference) needs to receive information about its input shape. The model needs to know what input shape it should expect. You can create a Sequential model by passing a list of layer instances to the constructor: from keras.models import Sequentialįrom keras.layers import Dense, Activation

The Sequential model is a linear stack of layers. Getting started with the Keras Sequential model

Keras sequential model