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copy_task.py
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copy_task.py
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import tensorflow as tf
from tensorflow.models.rnn import rnn, rnn_cell
import numpy as np
import random
import ntm
# Define loss and optimizer
def var_seq_loss(preds, y, nsteps):
seq_len = (nsteps[0] - 1) / 2
start = seq_len + 1
output_seq = tf.slice(
preds,
tf.pack([0, start, 0]),
tf.pack([-1, seq_len, -1]),
)
return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output_seq, y))
def predict(out, nsteps):
pred = tf.sigmoid(out)
seq_len = (nsteps[0] - 1) / 2
start = seq_len + 1
rel_pred = tf.slice(
pred,
tf.pack([0, start, 0]),
tf.pack([-1, seq_len, -1]),
)
return rel_pred
def bits_err_per_seq(out, expected, nsteps):
rel_pred = predict(out, nsteps)
rel_pred = tf.Print(
rel_pred,
[tf.slice(rel_pred, [0, 0, 0], [1, -1, 1])],
"predicted",
summarize=20,
)
expected = tf.Print(
expected,
[tf.slice(expected, [0, 0, 0], [1, -1, 1])],
"expected",
summarize=20,
)
diff = rel_pred - expected
return tf.reduce_mean(tf.reduce_sum(tf.abs(diff), [1, 2]))
def create_rnn(max_steps, n_input, mem_nrow, mem_ncol):
# Batch size, max_steps, n_input
x = tf.placeholder("float", [None, None, n_input])
y = tf.placeholder("float", [None, None, n_input])
nsteps = tf.placeholder("int32")
ntm_cell = ntm.NTMCell(
n_inputs=n_input,
n_outputs=n_input,
n_hidden=100,
mem_nrows=mem_nrow,
mem_ncols=mem_ncol,
n_heads=1,
)
outputs, _ = rnn.dynamic_rnn(
ntm_cell,
x,
dtype=tf.float32,
sequence_length=nsteps,
)
# Loss measures
cost = var_seq_loss(outputs, y, nsteps)
err = bits_err_per_seq(outputs, y, nsteps)
# Optimizer params as described in paper.
opt = tf.train.RMSPropOptimizer(
learning_rate=1e-4,
momentum=0.9,
)
# Gradient clipping as described in paper.
gvs = opt.compute_gradients(cost)
clipped_gvs = []
for g, v in gvs:
clipped_gvs.append((tf.clip_by_value(g, -10, 10), v))
optimizer = opt.apply_gradients(clipped_gvs)
return {
'x': x,
'y': y,
'steps': nsteps,
'cost': cost,
'err': err,
'optimizer': optimizer,
'pred': predict(outputs, nsteps),
}
def gen_seq(nseqs, max_steps, seq_len, nbits):
nsteps = 2*seq_len + 1
assert nsteps <= max_steps
zeros = [0] * nbits
GO = [0] * (nbits - 1) + [1]
xs = []
ys = []
for _ in xrange(nseqs):
# Note that we reserve the 0 and 1 in binary as the
# padding and delimeter symbols
seq = [random.randint(2, 2**nbits - 1) for _ in xrange(seq_len)]
# Convert each int to binary
seq = [
[int(digit) for digit in ('{0:0' + str(nbits) + 'b}').format(num)]
for num in seq
]
npad = max_steps - nsteps
pad = [zeros] * npad
# Dummy inputs after the delimeter / outputs before the delimiter
dummies = [zeros] * seq_len
x = seq + [GO] + dummies + pad
xs.append(x)
ys.append(seq)
return np.array(xs), np.array(ys), np.tile(nsteps, nseqs)
def train(
model,
n_input,
max_steps,
training_iters=1e8,
batch_size=128,
display_step=10,
seq_len_min=1,
seq_len_max=20,
):
sess = tf.Session()
# Initializing the variables
init = tf.initialize_all_variables()
sess.run(init)
step = 1
saver = tf.train.Saver()
print "Training commencing..."
while step * batch_size < training_iters:
(xs, ys, nsteps) = gen_seq(
nseqs=batch_size,
max_steps=max_steps,
seq_len=random.randint(seq_len_min, seq_len_max),
nbits=n_input,
)
sess.run(
model['optimizer'],
feed_dict={
model['x']: xs,
model['y']: ys,
model['steps']: nsteps,
},
)
if step % display_step == 0:
err, loss = sess.run(
[model['err'], model['cost']],
feed_dict={
model['x']: xs,
model['y']: ys,
model['steps']: nsteps,
},
)
print "Iter " + str(step*batch_size) + (
", Minibatch Loss= " + "{:.6f}".format(loss) +
", Average bit errors= " + "{:.6f}".format(err)
)
if step % 1000 == 0:
save_path = saver.save(sess, "checkpoints/model" + str(step) + ".ckpt")
print "Model saved in file: %s" % save_path
step += 1
print "Optimization Finished!"
# Training Parameters
max_steps = 11
seq_len_min = 1
seq_len_max = 5
batch_size = 1
n_input = 3
mem_nrow = 50
mem_ncol = 5
model = create_rnn(
max_steps=max_steps,
n_input=n_input,
mem_nrow=mem_nrow,
mem_ncol=mem_ncol,
)
if __name__ == "__main__":
train(
model=model,
n_input=n_input,
max_steps=max_steps,
seq_len_min=seq_len_min,
seq_len_max=seq_len_max,
batch_size=batch_size,
)