I have a neural network with the following structure: class myNetwork(nn.Module): def __init__(self): super(myNetwork, self).__init__() self.bigru = nn.GRU(input_size=2, hidden_size=100, batch_f...

Why the number of parameters of the GRU layer is 9600? Shouldn't it be ((16+32)*32 + 32) * 3 * 2 = 9,408 ? or, rearranging, 32*(16 + 32 + 1)*3*2 = 9408 model = tf.keras.Sequential([ tf.keras.l...

I have created a stacked keras decoder model using the following loop: # Create the encoder # Define an input sequence. encoder_inputs = keras.layers.Input(shape=(None, num_input_features)) # Cre...

i was just testing this model from kaggle post this model suppose to predict 1 day ahead from given set of last stocks. After tweaking few parameters i got surprisingly good result, as you can see....

This is the API I am looking at, https://pytorch.org/docs/stable/nn.html#gru It outputs: output of shape (seq_len, batch, num_directions * hidden_size) h_n of shape (num_layers * num_directions,...

Has anyone been able to mix feedforward layers and recurrent layers in Tensorflow? For example: input->conv->GRU->linear->output I can imagine one can define his own cell with feedforward layers ...

with tf.variable_scope('forward'): cell_img_fwd = tf.nn.rnn_cell.GRUCell(hidden_state_size, hidden_state_size) img_init_state_fwd = rnn_img_mapped[:, 0, :] img_init_state_fwd = tf.multiply( img...

Binary Classification Problem in Tensorflow: I have gone through the online tutorials and trying to apply it on a real-time problem using gated-recurrent unit (GRU). I have tried all the possibili...

I am trying RNN on a variable length multivariate sequence classification problem. I have defined following function to get the output of the sequence (i.e. the output of RNN cell after the final ...

Is there a canonical way to maintain a stateful LSTM, etc. with Tensorflow Serving? Using the Tensorflow API directly this is straightforward - but I'm not certain how best to accomplish persistin...