DAGNetwork
Directed acyclic graph (DAG) network for deep learning
Description
A DAG network is a neural network for deep learning with layers arranged as a directed acyclic graph. A DAG network can have a more complex architecture in which layers have inputs from multiple layers and outputs to multiple layers.
Creation
There are several ways to create a DAGNetwork
object:
Load a pretrained network such as
squeezenet
,googlenet
,resnet50
,resnet101
, orinceptionv3
. For an example, see Load SqueezeNet Network. For more information about pretrained networks, see Pretrained Deep Neural Networks.Train or fine-tune a network using
trainNetwork
. For an example, see Train Deep Learning Network to Classify New Images.Import a pretrained network from TensorFlow™-Keras, TensorFlow 2, Caffe, or the ONNX™ (Open Neural Network Exchange) model format.
For a Keras model, use
importKerasNetwork
. For an example, see Import and Plot Keras Network.For a TensorFlow model in the saved model format, use
importTensorFlowNetwork
. For an example, see Import TensorFlow Network as DAGNetwork to Classify Image.For a Caffe model, use
importCaffeNetwork
. For an example, see Import Caffe Network.For an ONNX model, use
importONNXNetwork
. For an example, see Import ONNX Network as DAGNetwork.
Assemble a deep learning network from pretrained layers using the
assembleNetwork
function.
Note
To learn about other pretrained networks, see Pretrained Deep Neural Networks.
Properties
Object Functions
activations | Compute deep learning network layer activations |
classify | Classify data using trained deep learning neural network |
predict | Predict responses using trained deep learning neural network |
plot | Plot neural network architecture |
Examples
Extended Capabilities
Version History
Introduced in R2017b
See Also
trainNetwork
| trainingOptions
| importKerasNetwork
| layerGraph
| classify
| predict
| plot
| googlenet
| resnet18
| resnet50
| resnet101
| inceptionv3
| inceptionresnetv2
| squeezenet
| SeriesNetwork
| analyzeNetwork
| assembleNetwork