def sample(args):
# Need to load the model from somewhere
params = pickle.load(open('./save/{0}.model_param'.format(args.params)))
model = RegressionModel(params, infer=True)
model.inference(model.data_placeholder)
with tf.Session() as sess:
# TODO: This loads the most recent checkpoint not
tf.initialize_all_variables().run()
saver = tf.train.Saver(tf.all_variables())
checkpoints = glob.glob("./save/model_{0}.ckpt-*".format(params.id))
if len(checkpoints) > 0:
# We have worked on training this model before. Resume work rather than
# starting from scratch
# Get the iteration number for all of them
iterations = np.array([int(c.split('-')[1]) for c in checkpoints])
# Index of the checkpoint with the most iterations
idx = np.argmax(iterations)
restore_path = checkpoints[idx]
saver.restore(sess, restore_path)
print "restoring {0}".format(restore_path)
print model.sample(sess, args.n, args.prime)
else:
print "Unable to restore - no checkpoints found"
def sample(args):
# Need to load the model from somewhere
params = pickle.load(open("./save/{0}.model_param".format(args.params)))
model = RegressionModel(params, infer=True)
model.inference(model.data_placeholder)
with tf.Session() as sess:
# TODO: This loads the most recent checkpoint not
tf.initialize_all_variables().run()
saver = tf.train.Saver(tf.all_variables())
checkpoints = glob.glob("./save/model_{0}.ckpt-*".format(params.id))
if len(checkpoints) > 0:
# We have worked on training this model before. Resume work rather than
# starting from scratch
# Get the iteration number for all of them
iterations = np.array([int(c.split("-")[1]) for c in checkpoints])
# Index of the checkpoint with the most iterations
idx = np.argmax(iterations)
restore_path = checkpoints[idx]
saver.restore(sess, restore_path)
print "restoring {0}".format(restore_path)
print model.sample(sess, args.n, args.prime)
else:
print "Unable to restore - no checkpoints found"
