def animation_update(frame_no, test_data, xf, im, text_fields):
"""Update an animation frame."""
steps, inpt, out_raw = test_data
length = len(steps)
batch = frame_no / (fps * (l + 4 * xf))
index = int((frame_no % (fps * (l + 4 * xf))) / fps)
# Cut output after first padding.
out = [out_raw[i][batch] for i in xrange(len(text_fields))]
if 0 in out:
i = out.index(0)
out = out[0:i] + [0 for _ in xrange(len(out) - i)]
# Show the state after the first frames.
if index >= 2 * xf:
im.set_array(steps[min(length - 1, index - 2 * xf)][batch])
for i, t in enumerate(text_fields):
if index - 2 * xf < length:
t.set_text("")
else:
t.set_text(data.to_symbol(out[i]))
else:
for i, t in enumerate(text_fields):
t.set_text(
data.to_symbol(inpt[i][batch]) if index < xf else "")
if index < xf:
im.set_array(np.zeros_like(steps[0][0]))
else:
im.set_array(steps[0][batch])
return im,
def animation_update(frame_no, test_data, xf, im, text_fields):
"""Update an animation frame."""
steps, inpt, out_raw = test_data
length = len(steps)
batch = frame_no / (fps * (l+4*xf))
index = int((frame_no % (fps * (l+4*xf))) / fps)
# Cut output after first padding.
out = [out_raw[i][batch] for i in xrange(len(text_fields))]
if 0 in out:
i = out.index(0)
out = out[0:i] + [0 for _ in xrange(len(out) - i)]
# Show the state after the first frames.
if index >= 2*xf:
im.set_array(steps[min(length - 1, index - 2*xf)][batch])
for i, t in enumerate(text_fields):
if index - 2*xf < length:
t.set_text("")
else:
t.set_text(data.to_symbol(out[i]))
else:
for i, t in enumerate(text_fields):
t.set_text(data.to_symbol(inpt[i][batch]) if index < xf else "")
if index < xf:
im.set_array(np.zeros_like(steps[0][0]))
else:
im.set_array(steps[0][batch])
return im,
def interactive():
"""Interactively probe an existing model."""
with tf.Session() as sess:
model, _, _, _, _, _ = initialize(sess)
sys.stdout.write("Input to Neural GPU, e.g., 0 1. Use -1 for PAD.\n")
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
while inpt:
ids = [data.to_id(s) for s in inpt.strip().split()]
inpt, target = data.get_batch(len(ids), 1, False, "",
preset=(ids, [0 for _ in ids]))
_, res, _, _ = model.step(sess, inpt, target, False)
res = [np.argmax(o, axis=1) for o in res]
res = [o for o in res[:len(ids)] if o > 0]
print " " + " ".join([data.to_symbol(output[0]) for output in res])
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
def interactive():
"""Interactively probe an existing model."""
with tf.Session() as sess:
model, _, _, _, _, _ = initialize(sess)
sys.stdout.write("Input to Neural GPU, e.g., 0 1. Use -1 for PAD.\n")
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
while inpt:
ids = [data.to_id(s) for s in inpt.strip().split()]
inpt, target = data.get_batch(len(ids), 1, False, "",
preset=(ids, [0 for _ in ids]))
_, res, _, _ = model.step(sess, inpt, target, False)
res = [np.argmax(o, axis=1) for o in res]
res = [o for o in res[:len(ids)] if o > 0]
print(" " + " ".join([data.to_symbol(output[0]) for output in res]))
sys.stdout.write("> ")
sys.stdout.flush()
inpt = sys.stdin.readline()
