Orion
path: orion.primitives.aer.AER
orion.primitives.aer.AER
description: this an autoencoder-based model capable of creating both prediction-based and reconstruction-based anomaly scores.
see json.
argument
type
description
parameters
X
numpy.ndarray
n-dimensional array containing the input sequences for the model
y
n-dimensional array containing the target sequences we want to reconstruct. Typically y is a signal from a selected set of channels from X.
hyperparameters
epochs
int
number of epochs to train the model. An epoch is an iteration over the entire X data provided
input_shape
tuple
tuple denoting the shape of an input sample
optimizer
str
string (name of optimizer) or optimizer instance. Default is keras.optimizers.Adam
keras.optimizers.Adam
learning_rate
float
float denoting the learning rate of the optimizer. Default is 0.001
batch_size
number of samples per gradient update. Default is 64
layers_encoder
list
list containing layers of encoder
layers_generator
list containing layers of generator
output
ry_hat
n-dimensional array containing the regression for each input sequence (reverse)
y_hat
n-dimensional array containing the reconstructions for each input sequence
fy_hat
n-dimensional array containing the regression for each input sequence (forward)
In [1]: import numpy as np In [2]: from mlstars import load_primitive In [3]: X = np.ones((64, 100, 1)) In [4]: y = X[:,:, [0]] # signal to reconstruct from X (channel 0) In [5]: primitive = load_primitive('orion.primitives.aer.AER', ...: arguments={"X": X, "y": y, "epochs": 1, "batch_size": 1}) ...: In [6]: primitive.fit() 1/51 [..............................] - ETA: 3:23 - loss: 0.7630 - tf.__operators__.getitem_loss: 0.8866 - tf.__operators__.getitem_1_loss: 0.7140 - tf.__operators__.getitem_2_loss: 0.7374 4/51 [=>............................] - ETA: 1s - loss: 0.5434 - tf.__operators__.getitem_loss: 0.6793 - tf.__operators__.getitem_1_loss: 0.4592 - tf.__operators__.getitem_2_loss: 0.5761 7/51 [===>..........................] - ETA: 1s - loss: 0.3834 - tf.__operators__.getitem_loss: 0.5130 - tf.__operators__.getitem_1_loss: 0.2912 - tf.__operators__.getitem_2_loss: 0.4383 10/51 [====>.........................] - ETA: 0s - loss: 0.2869 - tf.__operators__.getitem_loss: 0.3847 - tf.__operators__.getitem_1_loss: 0.2170 - tf.__operators__.getitem_2_loss: 0.3290 13/51 [======>.......................] - ETA: 0s - loss: 0.2448 - tf.__operators__.getitem_loss: 0.2987 - tf.__operators__.getitem_1_loss: 0.2124 - tf.__operators__.getitem_2_loss: 0.2557 16/51 [========>.....................] - ETA: 0s - loss: 0.2168 - tf.__operators__.getitem_loss: 0.2451 - tf.__operators__.getitem_1_loss: 0.2058 - tf.__operators__.getitem_2_loss: 0.2105 18/51 [=========>....................] - ETA: 0s - loss: 0.1995 - tf.__operators__.getitem_loss: 0.2215 - tf.__operators__.getitem_1_loss: 0.1926 - tf.__operators__.getitem_2_loss: 0.1913 21/51 [===========>..................] - ETA: 0s - loss: 0.1781 - tf.__operators__.getitem_loss: 0.1993 - tf.__operators__.getitem_1_loss: 0.1694 - tf.__operators__.getitem_2_loss: 0.1745 24/51 [=============>................] - ETA: 0s - loss: 0.1628 - tf.__operators__.getitem_loss: 0.1868 - tf.__operators__.getitem_1_loss: 0.1491 - tf.__operators__.getitem_2_loss: 0.1663 27/51 [==============>...............] - ETA: 0s - loss: 0.1514 - tf.__operators__.getitem_loss: 0.1785 - tf.__operators__.getitem_1_loss: 0.1329 - tf.__operators__.getitem_2_loss: 0.1615 30/51 [================>.............] - ETA: 0s - loss: 0.1419 - tf.__operators__.getitem_loss: 0.1709 - tf.__operators__.getitem_1_loss: 0.1201 - tf.__operators__.getitem_2_loss: 0.1567 33/51 [==================>...........] - ETA: 0s - loss: 0.1335 - tf.__operators__.getitem_loss: 0.1627 - tf.__operators__.getitem_1_loss: 0.1102 - tf.__operators__.getitem_2_loss: 0.1508 36/51 [====================>.........] - ETA: 0s - loss: 0.1261 - tf.__operators__.getitem_loss: 0.1540 - tf.__operators__.getitem_1_loss: 0.1033 - tf.__operators__.getitem_2_loss: 0.1440 39/51 [=====================>........] - ETA: 0s - loss: 0.1200 - tf.__operators__.getitem_loss: 0.1456 - tf.__operators__.getitem_1_loss: 0.0987 - tf.__operators__.getitem_2_loss: 0.1370 42/51 [=======================>......] - ETA: 0s - loss: 0.1148 - tf.__operators__.getitem_loss: 0.1379 - tf.__operators__.getitem_1_loss: 0.0953 - tf.__operators__.getitem_2_loss: 0.1306 45/51 [=========================>....] - ETA: 0s - loss: 0.1101 - tf.__operators__.getitem_loss: 0.1315 - tf.__operators__.getitem_1_loss: 0.0919 - tf.__operators__.getitem_2_loss: 0.1252 48/51 [===========================>..] - ETA: 0s - loss: 0.1059 - tf.__operators__.getitem_loss: 0.1264 - tf.__operators__.getitem_1_loss: 0.0881 - tf.__operators__.getitem_2_loss: 0.1209 51/51 [==============================] - ETA: 0s - loss: 0.1021 - tf.__operators__.getitem_loss: 0.1224 - tf.__operators__.getitem_1_loss: 0.0842 - tf.__operators__.getitem_2_loss: 0.1177 51/51 [==============================] - 6s 44ms/step - loss: 0.1021 - tf.__operators__.getitem_loss: 0.1224 - tf.__operators__.getitem_1_loss: 0.0842 - tf.__operators__.getitem_2_loss: 0.1177 - val_loss: 0.0419 - val_tf.__operators__.getitem_loss: 0.0627 - val_tf.__operators__.getitem_1_loss: 0.0178 - val_tf.__operators__.getitem_2_loss: 0.0691 In [7]: ry, y, fy = primitive.produce(X=X) 1/2 [==============>...............] - ETA: 0s 2/2 [==============================] - 0s 10ms/step 1/2 [==============>...............] - ETA: 0s 2/2 [==============================] - 0s 12ms/step In [8]: print("Reverse Prediction: {}\nReconstructed Values: {}, Forward Prediction: {}".format(ry, y, fy)) Reverse Prediction: [[0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013] [0.74963013]] Reconstructed Values: [[[0.85026911] [0.92584395] [0.98223541] ... [1.02513585] [0.96338957] [0.87170058]] [[0.85026911] [0.92584395] [0.98223541] ... [1.02513585] [0.96338957] [0.87170058]] [[0.85026911] [0.92584395] [0.98223541] ... [1.02513585] [0.96338957] [0.87170058]] ... [[0.85026911] [0.92584395] [0.98223541] ... [1.02513585] [0.96338957] [0.87170058]] [[0.85026911] [0.92584395] [0.98223541] ... [1.02513585] [0.96338957] [0.87170058]] [[0.85026911] [0.92584395] [0.98223541] ... [1.02513585] [0.96338957] [0.87170058]]], Forward Prediction: [[0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127] [0.73705127]]