Import layers from Keras network
imports the layers of a TensorFlow™-Keras network from a model file. The function returns the layers defined in
the HDF5 (layers
= importKerasLayers(modelfile
).h5
) or JSON (.json
) file given by the file
name modelfile
.
This function requires the Deep Learning Toolbox™ Converter for TensorFlow Models support package. If this support package is not installed, then the function provides a download link.
imports the layers from a TensorFlow-Keras network with additional options specified by one or more name-value pair
arguments.layers
= importKerasLayers(modelfile
,Name,Value
)
For example, importKerasLayers(modelfile,'ImportWeights',true)
imports the network layers and the weights from the model file
modelfile
.
importKerasLayers
supports TensorFlow-Keras versions as follows:
The function fully supports TensorFlow-Keras versions up to 2.2.4.
The function offers limited support for TensorFlow-Keras versions 2.2.5 to 2.4.0.
If the network contains a layer that Deep Learning Toolbox Converter for TensorFlow Models does not support (see Supported Keras Layers), then
importKerasLayers
inserts a placeholder layer in place of the unsupported
layer. To find the names and indices of the unsupported layers in the network, use the
findPlaceholderLayers
function. You then can replace a placeholder layer
with a new layer that you define. To replace a layer, use replaceLayer
.
You can replace a placeholder layer with a new layer that you define.
If the network is a series network, then replace the layer in the array
directly. For example, layer(2) = newlayer;
.
If the network is a DAG network, then replace the layer using replaceLayer
. For an example, see Assemble Network from Pretrained Keras Layers.
You can import a Keras network with multiple inputs and multiple outputs (MIMO). Use
importKerasNetwork
if the network includes input size information for the
inputs and loss information for the outputs. Otherwise, use
importKerasLayers
. The importKerasLayers
function inserts placeholder layers for the inputs and outputs. After importing, you can
find and replace the placeholder layers by using findPlaceholderLayers
and replaceLayer
,
respectively. The workflow for importing MIMO Keras networks is the same as the workflow
for importing MIMO ONNX™ networks. For an example, see Import and Assemble ONNX Network with Multiple Outputs. To learn about a deep
learning network with multiple inputs and multiple outputs, see Multiple-Input and Multiple-Output Networks.
To use a pretrained network for prediction or transfer learning on new images, you must preprocess your images in the same way the images that were used to train the imported model were preprocessed. The most common preprocessing steps are resizing images, subtracting image average values, and converting the images from BGR images to RGB.
For more information on preprocessing images for training and prediction, see Preprocess Images for Deep Learning.
Use importKerasNetwork
or importKerasLayers
to
import a TensorFlow-Keras network in HDF5 or JSON format. If the TensorFlow network is in the saved model format, use
importTensorFlowNetwork
or
importTensorFlowLayers
.
[1] Keras: The Python Deep Learning library. https://keras.io.
assembleNetwork
| exportONNXNetwork
| findPlaceholderLayers
| importCaffeLayers
| importCaffeNetwork
| importKerasNetwork
| importONNXLayers
| importONNXNetwork
| importTensorFlowLayers
| importTensorFlowNetwork
| replaceLayer