![]() The Functional API, which is an easy-to-use, fully-featured API that supports arbitrary model architectures. compile ( optimizer = 'sgd', loss = 'mse' ) # This builds the model for the first time: model. There are three ways to create Keras models: The Sequential model, which is very straightforward (a simple list of layers), but is limited to single-input, single-output stacks of layers (as the name gives away). ![]() weights ) # Returns "4" # Note that when using the delayed-build pattern (no input shape specified), # the model gets built the first time you call `fit`, `eval`, or `predict`, # or the first time you call the model on some input data. weights ) # Returns "4" # When using the delayed-build pattern (no input shape specified), you can # choose to manually build your model by calling # `build(batch_input_shape)`: model = tf. To build a model with the Keras Sequential API, the first step is to import the required class and instantiate a model using this class: from tf. Dense ( 8, input_shape = ( 16 ,))) model. ![]() def buildgenerator(self): model Sequential() model.add(Dense(128 7 7, activationrelu. Lets say you start with defining a simple MLP using Keras: from keras.models import Sequential. This page shows Python examples of keras.models. Dense ( 4 )) # model.weights not created yet # Whereas if you specify the input shape, the model gets built # continuously as you are adding layers: model = tf. Getting started with importing Keras Sequential models. # In that case the model doesn't have any weights until the first call # to a training/evaluation method (since it isn't yet built): model = tf. Dense ( 8 )) # Note that you can also omit the `input_shape` argument. ![]() Dense ( 4 )) # This is identical to the following: model = tf. Dense ( 8, input_shape = ( 16 ,))) # Afterwards, we do automatic shape inference: model. # Optionally, the first layer can receive an `input_shape` argument: model = tf. ![]()
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