The Knowledge

MNIST Pixel Knowledge

The primary dataset employed right here is the same old MNIST pixel information, comprised by hand-written numbers. Right here, the background is black and the digits are white.

Anomalous MNIST Pixel Knowledge

To check the brand new process and examine it to the same old one, I created 4 easy varieties of anomalous information.

The aim was to check every methodology’s detection capabilities throughout a small spectrum of noise variations, incrementally intensified from one anomaly sort to the following.

The noise charge will increase from the primary to the fourth sort of anomalous information. As you’ll be able to see within the determine under, within the first and second varieties of information, the noise will not be even detectable to the bare eye, whereas within the third sort, you’ll be able to already spot some white pixels.

Mannequin-state information

Whereas MNIST pixel information, with its hand-written digits towards a stark backdrop, offers a basic basis for anomaly detection, we’re attempting one thing else. It’s a little bit of a leap, taking us proper into the core of the skilled ANN to see what the neurons are as much as. This might give us an entire unique approach on recognizing anomalies.

As talked about, this mannequin state information is comprised by the state of the neurons in an ANN when skilled with MNIST information. As such, to generate this information, we begin with coaching a easy ANN with MNIST pixel information, each with regular and anomalous information (the anomalous are comprised by the noisy information confirmed earlier than in Determine 2).

We then carry out the same old: cut up the information into coaching and testing, after which we match the ANN mannequin:

mannequin.match(X_train,Y_cat_train,epochs=8, validation_data=(X_test, y_cat_test))

After that, we need to retrieve the names of the layers in mannequin and retailer them in a listing:

checklist(map(lambda x: x.identify, mannequin.layers))

Lastly, we create a brand new mannequin that takes the identical enter as the unique mannequin however produces output from a selected layer referred to as “dense”:

intermediate_layer_model=Mannequin(inputs=mannequin.enter, outputs=mannequin.get_layer("dense").output)

That is helpful for extracting data from intermediate layers of a neural community.

Let’s check out this model-state information:

model_state_data_layer1=pd.read_csv("first_layer_dense.csv",header=None)
model_state_data_layer2=pd.read_csv("second_layer_dense.csv",header=None)
model_state_data_layer1.head(4)

The model-state information of the primary neural layer is comprised by 32 columns and 4 rows.

With only a few strains of code, we’re capable of extract information from the intermediate layers of a neural community.

To check the effectiveness of the brand new methodology, I’ll be utilizing information from each the first and second layers of the neural community.