TadGANΒΆ

path: orion.primitives.tadgan.TadGAN

description: this is a reconstruction model, namely Generative Adversarial Networks (GAN), containing multiple neural networks and cycle consistency loss. the proposed model is described in the related paper.

see json.

argument

type

description

parameters

X

numpy.ndarray

n-dimensional array containing the input sequences for the model

y

numpy.ndarray

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

target_shape

tuple

tuple denoting the shape of an reconstructed sample

optimizer

str

string (name of optimizer) or optimizer instance. Default is keras.optimizers.Adam

learning_rate

float

float denoting the learning rate of the optimizer. Default is 0.005

latent_dim

int

integer denoting dimension of latent space. Default is 20

batch_size

int

number of samples per gradient update. Default is 64

iterations_critic

int

number of critic training steps per generator/encoder training steps. Default is 5

layers_encoder

list

list containing layers of encoder

layers_generator

list

list containing layers of generator

layers_critic_x

list

list containing layers of critic_x

layers_critic_z

list

list containing layers of critic_z

output

y

numpy.ndarray

n-dimensional array containing the reconstructions for each input sequence

critic

numpy.ndarray

n-dimensional array containing the critic score for each input sequence

In [1]: import numpy as np

In [2]: from mlprimitives import load_primitive

In [3]: X = np.array([1] * 100).reshape(1, -1, 1)

In [4]: y = X[:,:, [0]] # signal to reconstruct from X (channel 0)

In [5]: primitive = load_primitive('orion.primitives.tadgan.TadGAN',
   ...:     arguments={"X": X, "y":X, "epochs": 5, "batch_size": 1,
   ...:                "iterations_critic": 1})
   ...: 

In [6]: primitive.fit()
Epoch: 1/5, Losses: {'cx_loss': 8.3967, 'cz_loss': 0.2943, 'eg_loss': 10.009}
Epoch: 2/5, Losses: {'cx_loss': 7.8843, 'cz_loss': 6.6261, 'eg_loss': 9.1935}
Epoch: 3/5, Losses: {'cx_loss': 7.0615, 'cz_loss': 0.9188, 'eg_loss': 7.9756}
Epoch: 4/5, Losses: {'cx_loss': 7.0099, 'cz_loss': 4.307, 'eg_loss': 5.271}
Epoch: 5/5, Losses: {'cx_loss': 5.7711, 'cz_loss': 4.5474, 'eg_loss': 3.3933}

In [7]: y, critic = primitive.produce(X=X, y=y)

In [8]: print("average reconstructed value: {:.2f}, critic score {:.2f}".format(
   ...:     y.mean(), critic[0][0]))
   ...: 
average reconstructed value: 0.23, critic score 0.55