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: 3:08 - loss: 0.9175 - tf.__operators__.getitem_loss: 1.0453 - tf.__operators__.getitem_1_loss: 0.9086 - tf.__operators__.getitem_2_loss: 0.8074 4/51 [=>............................] - ETA: 1s - loss: 0.6055 - tf.__operators__.getitem_loss: 0.7853 - tf.__operators__.getitem_1_loss: 0.5376 - tf.__operators__.getitem_2_loss: 0.5615 7/51 [===>..........................] - ETA: 1s - loss: 0.4014 - tf.__operators__.getitem_loss: 0.5800 - tf.__operators__.getitem_1_loss: 0.3243 - tf.__operators__.getitem_2_loss: 0.3771 10/51 [====>.........................] - ETA: 0s - loss: 0.3085 - tf.__operators__.getitem_loss: 0.4307 - tf.__operators__.getitem_1_loss: 0.2689 - tf.__operators__.getitem_2_loss: 0.2655 13/51 [======>.......................] - ETA: 0s - loss: 0.2683 - tf.__operators__.getitem_loss: 0.3377 - tf.__operators__.getitem_1_loss: 0.2653 - tf.__operators__.getitem_2_loss: 0.2047 16/51 [========>.....................] - ETA: 0s - loss: 0.2334 - tf.__operators__.getitem_loss: 0.2829 - tf.__operators__.getitem_1_loss: 0.2417 - tf.__operators__.getitem_2_loss: 0.1674 19/51 [==========>...................] - ETA: 0s - loss: 0.2047 - tf.__operators__.getitem_loss: 0.2518 - tf.__operators__.getitem_1_loss: 0.2098 - tf.__operators__.getitem_2_loss: 0.1475 22/51 [===========>..................] - ETA: 0s - loss: 0.1844 - tf.__operators__.getitem_loss: 0.2339 - tf.__operators__.getitem_1_loss: 0.1822 - tf.__operators__.getitem_2_loss: 0.1390 25/51 [=============>................] - ETA: 0s - loss: 0.1697 - tf.__operators__.getitem_loss: 0.2218 - tf.__operators__.getitem_1_loss: 0.1608 - tf.__operators__.getitem_2_loss: 0.1357 28/51 [===============>..............] - ETA: 0s - loss: 0.1580 - tf.__operators__.getitem_loss: 0.2110 - tf.__operators__.getitem_1_loss: 0.1439 - tf.__operators__.getitem_2_loss: 0.1330 31/51 [=================>............] - ETA: 0s - loss: 0.1476 - tf.__operators__.getitem_loss: 0.1998 - tf.__operators__.getitem_1_loss: 0.1308 - tf.__operators__.getitem_2_loss: 0.1292 34/51 [===================>..........] - ETA: 0s - loss: 0.1386 - tf.__operators__.getitem_loss: 0.1881 - tf.__operators__.getitem_1_loss: 0.1212 - tf.__operators__.getitem_2_loss: 0.1241 37/51 [====================>.........] - ETA: 0s - loss: 0.1311 - tf.__operators__.getitem_loss: 0.1767 - tf.__operators__.getitem_1_loss: 0.1146 - tf.__operators__.getitem_2_loss: 0.1184 40/51 [======================>.......] - ETA: 0s - loss: 0.1248 - tf.__operators__.getitem_loss: 0.1664 - tf.__operators__.getitem_1_loss: 0.1100 - tf.__operators__.getitem_2_loss: 0.1129 43/51 [========================>.....] - ETA: 0s - loss: 0.1193 - tf.__operators__.getitem_loss: 0.1575 - tf.__operators__.getitem_1_loss: 0.1057 - tf.__operators__.getitem_2_loss: 0.1082 46/51 [==========================>...] - ETA: 0s - loss: 0.1144 - tf.__operators__.getitem_loss: 0.1504 - tf.__operators__.getitem_1_loss: 0.1013 - tf.__operators__.getitem_2_loss: 0.1045 49/51 [===========================>..] - ETA: 0s - loss: 0.1099 - tf.__operators__.getitem_loss: 0.1447 - tf.__operators__.getitem_1_loss: 0.0966 - tf.__operators__.getitem_2_loss: 0.1017 51/51 [==============================] - 6s 52ms/step - loss: 0.1073 - tf.__operators__.getitem_loss: 0.1416 - tf.__operators__.getitem_1_loss: 0.0936 - tf.__operators__.getitem_2_loss: 0.1003 - val_loss: 0.0418 - val_tf.__operators__.getitem_loss: 0.0678 - val_tf.__operators__.getitem_1_loss: 0.0169 - val_tf.__operators__.getitem_2_loss: 0.0658 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 11ms/step In [8]: print("Reverse Prediction: {}\nReconstructed Values: {}, Forward Prediction: {}".format(ry, y, fy)) Reverse Prediction: [[0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936] [0.73955936]] Reconstructed Values: [[[0.86043838] [0.94514086] [1.00418548] ... [0.98961667] [0.93373583] [0.85496693]] [[0.86043838] [0.94514086] [1.00418548] ... [0.98961667] [0.93373583] [0.85496693]] [[0.86043838] [0.94514086] [1.00418548] ... [0.98961667] [0.93373583] [0.85496693]] ... [[0.86043838] [0.94514086] [1.00418548] ... [0.98961667] [0.93373583] [0.85496693]] [[0.86043838] [0.94514086] [1.00418548] ... [0.98961667] [0.93373583] [0.85496693]] [[0.86043838] [0.94514086] [1.00418548] ... [0.98961667] [0.93373583] [0.85496693]]], Forward Prediction: [[0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699] [0.74350699]]