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I have a code like this:

lstm_cell = tf.contrib.rnn.BasicLSTMCell(256, state_is_tuple = True)
c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c], "c_in")
h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h], "h_in")
rnn_state_in = (c_in, h_in)
rnn_in = tf.expand_dims(previous_layer, [0])
sequence_length = #size of my batch
rnn_state_in = tf.contrib.rnn.LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(lstm_cell,
                                                    rnn_in,
                                                    initial_state = rnn_state_in,
                                                    sequence_length = sequence_length,
                                                    time_major = False)
lstm_c, lstm_h = lstm_state
rnn_out = tf.reshape(lstm_outputs, [-1, 256])

Here, I use dynamic_rnn to simulate the time steps from the batch. While each forward pass, I can get lstm_c, lstm_h which I can store anywhere outside.

So, suppose I have done a forward pass for N items in a sequence in my network and have the final cell state and hidden state provided from the dynamic_rnn. Now, to perform back propagation, what should be my input to the LSTMs?

By default, does backprop happen across time steps in dynamic_rnn?

(say, no. of time steps = batch_size=N)

So is it enough for me to provide the input as:

sess.run(_train_op, feed_dict = {_state: np.vstack(batch_states),
                                            ...
                                            c_in: prev_rnn_state[0],
                                            h_in: prev_rnn_state[1]
})

(where prev_rnn_state is a tuple of cell state, hidden state, which I got from the dynamic_rnn from forward propagation for the previous batch.)

Or do I have unroll the LSTM layer across time series explicitly and train it by providing a vector of the cell states and hidden gathered across the previous time series?

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