neural network - Tensorflow autoencoders with one vector per input neuron -


i'm new tensorflow , deep neural networks. i'm trying anomaly detection on trajectories using autoencoders , i'm having issue model.

i'm not able right weight matrix / not sure how it.

here model:

  • each input neuron of encoder receive vector 4 features (this vector correspond observation part of trajectory).
  • the number of input neurons correspond number of observation (which 289).
  • i have total of 336 trajectories correspond batch

therefore input data shape (336,289,4)

  • i have 2 hidden layers; on each 1 divide number of previous neurons 2 h1 have 144 neurons , h2 72 neurons

for weights, have:

weights = {     'encoder_h1': tf.variable(tf.random_normal([336, n_hidden_1, 289])),     'encoder_h2': tf.variable(tf.random_normal([336, n_hidden_2, n_hidden_1])),     'decoder_h1': tf.variable(tf.random_normal([336, n_hidden_1, n_hidden_2])),     'decoder_h2': tf.variable(tf.random_normal([336, n_input, n_hidden_1 ])), }

and activation function sigmoid 

tf.nn.sigmoid(tf.add(tf.matmul(weights['encoder_h1'],x),                                    biases['encoder_b1'])

but i'm afraid gives wheight matrix trajectory or want weight matrix trajectories, should 2d tensor don't know how proceed.

i tried many thing such removing 336 part weight shape tensorflow says tha not possible matmul on 3d , 2d tensor.

do have idea on how do?

thanks in advance help


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