VAEΒΆ

path: orion.primitives.vae.VAE

description: this is a reconstruction model using Variational AutoEncoder.

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

output_shape

tuple

tuple denoting the shape of an output sample

latent_dim

int

integer denoting dimension of latent space. Default is 20.

learning_rate

float

float denoting the learning rate of the optimizer. Default is 0.001

optimizer

str

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

batch_size

int

number of samples per gradient update. Default is 64

shuffle

bool

whether to shuffle the training data before each epoch. Default is True.

verbose

int

verbosity mode where 0 = silent, 1 = progress bar, 2 = one line per epoch. Default is 0.

lstm_units

int

number of neurons (dimensionality of the output space).

length

int

equal to input_shape[0].

callbacks

list

list of keras.callbacks.Callback instances. List of callbacks to apply during training.

validation_split

float

fraction of the training data to be used as validation data. Default 0.

output_dim

int

equal to output_shape[-1]

layers_encoder

list

list containing layers of encoder

layers_generator

list

list containing layers of generator

output

y

numpy.ndarray

predicted values

In [1]: import numpy as np

In [2]: from mlstars import load_primitive

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

In [4]: primitive = load_primitive('orion.primitives.vae.VAE',
   ...:     arguments={"X": X, "y": X, "input_shape":(100, 1), "output_shape":(100, 1),
   ...:                "validation_split": 0, "batch_size": 1, "epochs": 5})
   ...: 

In [5]: primitive.fit()
Epoch 1/5

1/1 [==============================] - ETA: 0s - loss: 1.1259
1/1 [==============================] - 2s 2s/step - loss: 1.1259
Epoch 2/5

1/1 [==============================] - ETA: 0s - loss: 1.0192
1/1 [==============================] - 0s 23ms/step - loss: 1.0192
Epoch 3/5

1/1 [==============================] - ETA: 0s - loss: 0.8823
1/1 [==============================] - 0s 22ms/step - loss: 0.8823
Epoch 4/5

1/1 [==============================] - ETA: 0s - loss: 0.8256
1/1 [==============================] - 0s 23ms/step - loss: 0.8256
Epoch 5/5

1/1 [==============================] - ETA: 0s - loss: 0.6663
1/1 [==============================] - 0s 23ms/step - loss: 0.6663

In [6]: pred = primitive.produce(X=X)

1/1 [==============================] - ETA: 0s
1/1 [==============================] - 1s 518ms/step

In [7]: pred.mean()
Out[7]: 0.21746342403144783