### Resource retrieval

Retrieve the pre-trained net:

Out[1]= | |

### Basic usage

Apply the trained net to a set of inputs:

Out[2]= | |

Give class probabilities for a single input:

Out[3]= | |

### Feature extraction

Create a subset of the MNIST dataset:

Out[4]= | |

Remove the last linear layer of the net, which will be used as a feature extractor:

Out[5]= | |

Visualize the features of a subset of the MNIST dataset:

Out[6]= | |

### Visualization of net operation

Extract the convolutional features from the first layer:

Out[7]= | |

Visualize the features:

Out[8]= | |

### Training the uninitialized architecture

Retrieve the uninitialized architecture:

Out[9]= | |

Retrieve the MNIST dataset:

Out[10]= | |

Use the training dataset provided:

Out[11]= | |

Use the test dataset provided:

Out[12]= | |

Train the net:

Out[13]= | |

Generate a ClassifierMeasurementsObject of the net with the test set:

Out[14]= | |

Evaluate the accuracy on the validation set:

Out[15]= | |

Visualize the confusion matrix:

Out[16]= | |

### Net information

Inspect the number of parameters of all arrays in the net:

Out[17]= | |

Obtain the total number of parameters:

Out[18]= | |

Obtain the layer type counts:

Out[19]= | |

Display the summary graphic:

Out[20]= | |

### Export to MXNet

Export the net into a format that can be opened in MXNet:

Out[21]= | |

Export also creates a net.params file containing parameters:

Out[22]= | |

Get the size of the parameter file:

Out[23]= | |

The size is similar to the byte count of the resource object:

Out[24]= | |

Represent the MXNet net as a graph:

Out[25]= | |