Personally, I would get used to the Keras perspective :-) This model is not suited when any of the layer in the stack. It is best for simple stack of layers which have 1 input tensor and 1 output tensor. It is a linear stack of methods that groups a linear stack of layers into a tf.keras.Model. In the latter case, the default parameters for the optimizer will be used. But it does not allow us to create models that have multiple inputs or outputs. An optimizer is one of the two arguments required for compiling a Keras model: You can either instantiate an optimizer before passing it to pile (), as in the above example, or you can pass it by its string identifier. But "layers" in this graphical notation are bunches of circles that sit on a page doing nothing, whereas a layers in Keras transform tensors and do actual work for you. It allows us to create models layer by layer in sequential order. On the other hand, graphically, you might represent this network with 3 (graphical) layers of nodes, and two sets of lines connecting the layers of nodes.
So while there are 3 identifiable tensors here (input, outputs of the two layers), there are only 2 transformations involved corresponding to the 2 Keras layers. And this is exactly what you do have (in Keras, at least) because the "input layer" is not really a (Keras) layer at all: it's only a place to store a tensor, so it may as well be a tensor itself.Įach Keras layer is a transformation that outputs a tensor, possibly of a different size/shape to the input. You can switch to the H5 format by: Passing saveformat'h5' to save (). It is the default when you use model.save (). which makes it much more explicit that you only have 2 Keras layers. tf. () There are two formats you can use to save an entire model to disk: the TensorFlow SavedModel format, and the older Keras H5 format. Model.add(Dense(10, activation='softmax') Model.add(Dense(32, activation='relu', input_dim=784)) The Sequential model API is a way of creating deep learning models where an instance of the Sequential class is created and model layers are created and added to it. As a neural network, Keras sequential models take advantage of input interactions and non-linearities, but with the added benefits of an easy to implement and modifiable building block structure. If you are new to Keras or deep learning, see this step-by-step Keras tutorial. Keras sequential models may provide the 5 to 10 performance boost needed to deploy a model and achieve success.
#Keras sequential code#
Rewriting your code in line with more recent Keras tutorial examples, you would probably use: model = Sequential() As a review, Keras provides a Sequential model API.
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