CS:4980:006 Deep Learning Assignment 7: Due 10/30/2018
In this assignment, we will study a small recurrent neural network for echoing a sequence of binary strings. The Python program is
rnnecho.py. The same function can be implemented using a LSTM cell from Tensorflow and the code is
rnnLSTMecho.py.
Here are your tasks:
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For state_size = 3, 4, 5,
for truncated_backprop_length = 10, 15, 20,
run rnnecho.py several times, to find the minimal number of num_epochs so that the program can correctly return the echoed information (accuracy is 100%) for the last N output bits, where N = truncated_backprop_length, and collect the running times for each case.
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Repeat Task 1 for the code rnnLSTMecho.py, and compare the running times of the two tasks.
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Add one more recurrent layer (W1, b1) between the layer (W, b) and (W2, b2) in rnnecho.py, where the shape of W1 is (2*state_size, state_size) and the shape of b1 is
(1, state_size). Repeat Task 1 for the stacked rnn and compare
the running times with those obtained in Task 1.
-
Add one more LSTM recurrent layer in
rnnLSTMecho.py,
using
. A complete example
of using tf.nn.rnn_cell.MultiRNNCell
can be found
here. Repeat
Task 2 and compare
the running times with those obtained in Task 2.
Please submit everything required, including the changed code and output of a sample run, the accuracy reports, running times in the ICON dropbox for Assignment 7 before the deadline.
Thank you!