gated-recurrent-unit

I am trying to use tensorflow to create a recurrent neural network. My code is something like this: import tensorflow as tf rnn_cell = tf.nn.rnn_cell.GRUCell(3) inputs = [tf.constant([[0, 1]], dtype=tf.float32), tf.constant([[2, 3]], dtype=tf.float32)] outputs, end = tf.nn.rnn(rnn_cell, inputs, dtype=tf.float32) Now, everything runs just fine. However, I am rather confused by what is actually going on. The output dimensions are always the batch size x the size of the rnn cell's hidden state - how can they be completely independent of the input size? If my understanding is correct, the inputs

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 and no state which can then be stacked using the MultiRNNCell function, something like: cell = tf.nn.rnn_cell.MultiRNNCell([conv_cell,GRU_cell,linear_cell]) This would make life a whole lot easier... can't you just do the following: rnnouts, _ = rnn(grucell, inputs) linearout = [tf.matmul(rnnout, weights) + bias for rnnout in rnnouts] etc. This tutoria l gives an example of how to use convolutional

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. mean squared error was 5.193.so overall it looks good at predicting future stocks right? well it turned out to be horrible when i take a look closely on the results. as you can see that this model is predicting last value of the given stocks which is our current last stock. so i did adjusted predictions to one step back.. so now you can clearly see that model is predicting one step backward or last

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_init_state_fwd, tf.zeros([batch_size, hidden_state_size])) rnn_outputs2, final_state2 = tf.nn.dynamic_rnn( cell_img_fwd, rnn_img_mapped, initial_state=img_init_state_fwd, dtype=tf.float32) This is my code for a GRU for input of dimension 100x196x50, it should be unpacked along the second dimension (that is 196). hidden_state_size is 50, batch_size is 100. However I get the following error: ValueError: