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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, 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.

`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 |

`trainNetwork`

| `trainingOptions`

| `importKerasNetwork`

| `layerGraph`

| `classify`

| `predict`

| `plot`

| `googlenet`

| `resnet18`

| `resnet50`

| `resnet101`

| `inceptionv3`

| `inceptionresnetv2`

| `squeezenet`

| `SeriesNetwork`

| `analyzeNetwork`

| `assembleNetwork`