12/31/2023 0 Comments Keras datagenerator![]() We then apply two more fully-connected layers on Lines 36 and 37. The outputs of x and y are both 4-dim so once we concatenate them we have a 8-dim vector. We then combine the outputs of both the x and y on Line 32. Lines 21-23 define a simple 32-8-4 network using Keras’ functional API. Here you can see we are defining two inputs to our Keras neural network: # our model will accept the inputs of the two branches and Z = Dense(2, activation="relu")(combined) # apply a FC layer and then a regression prediction on the # the second branch opreates on the second inputĬombined = concatenate() # the first branch operates on the first input To see the power of Keras’ function API consider the following code where we create a model that accepts multiple inputs: # define two sets of inputs Notice how we are no longer relying on the Sequential class. We can define the sample neural network using the functional API: inputs = Input(shape=(10,)) This network is a simple feedforward neural without with 10 inputs, a first hidden layer with 8 nodes, a second hidden layer with 4 nodes, and a final output layer used for regression. Model.add(Dense(8, input_shape=(10,), activation="relu")) Models that are both multiple input and multiple outputįor example, we may define a simple sequential neural network as: model = Sequential().The functional API, as opposed to the sequential API (which you almost certainly have used before via the Sequential class), can be used to define much more complex models that are non-sequential, including: Learn more about 3 ways to create a Keras model with TensorFlow 2.0 (Sequential, Functional, and Model Subclassing). Keras is able to handle multiple inputs (and even multiple outputs) via its functional API. In this blog post we use the functional API to support our goal of creating a model with multiple inputs and mixed data for house price prediction. How can Keras accept multiple inputs?įigure 2: As opposed to its Sequential API, Keras’ functional API allows for much more complex models. We’ll be working with mixed data in today’s tutorial to help you get a feel for some of the challenges associated with it. Working with mixed data is still very much an open area of research and is often heavily dependent on the specific task/end goal. You will see the term “mixed data” in machine learning literature when working with multiple data modalities.ĭeveloping machine learning systems capable of handling mixed data can be extremely challenging as each data type may require separate preprocessing steps, including scaling, normalization, and feature engineering. Image data, such as any MRI, X-ray, etc.Īll of these values constitute different data types however, our machine learning model must be able to ingest this “mixed data” and make (accurate) predictions on it. ![]()
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