Directed acyclic graph (DAG) network for deep learning
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.
There are several ways to create a DAGNetwork
object:
Load a pretrained network such as squeezenet
, googlenet
, resnet50
, resnet101
, or inceptionv3
. 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, 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 Caffe model, use importCaffeNetwork
. For an
example, see Import Caffe Network.
For an ONNX model, use importONNXNetwork
. For an example, see Import ONNX Network.
Note
To learn about other pretrained networks, see Pretrained Deep Neural Networks.
activations | Compute deep learning network layer activations |
classify | Classify data using a trained deep learning neural network |
predict | Predict responses using a trained deep learning neural network |
plot | Plot neural network layer graph |
analyzeNetwork
| assembleNetwork
| classify
| googlenet
| importKerasNetwork
| inceptionresnetv2
| inceptionv3
| layerGraph
| plot
| predict
| resnet101
| resnet18
| resnet50
| SeriesNetwork
| squeezenet
| trainingOptions
| trainNetwork