def __init__(self, save, sess):
self.sess = sess
# load the saved config
with open(os.path.join(save, 'config.json')) as f:
config = read_config(json.load(f), chars_vocab=True)
self.model = Model(config, True)
# restore the saved model
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(save)
saver.restore(self.sess, ckpt.model_checkpoint_path)
def plot(clargs):
sess = tf.InteractiveSession()
predictor = BayesianPredictor(clargs.save, sess)
with open(os.path.join(clargs.save, 'config.json')) as f:
config = read_config(json.load(f), chars_vocab=True)
# Plot for indicidual evidences
for ev in config.evidence:
print(ev.name)
with open(clargs.input_file[0], 'rb') as f:
deriveAndScatter(f, predictor, [ev])
# Plot with all Evidences
with open(clargs.input_file[0], 'rb') as f:
deriveAndScatter(f, predictor, [ev for ev in config.evidence])
with open(clargs.input_file[0], 'rb') as f:
useAttributeAndScatter(f, 'b2')
def __init__(self, save, sess):
with open(os.path.join(save, 'config.json')) as f:
config = read_config(json.load(f), chars_vocab=True)
assert config.model == 'lle', 'Trying to load different model implementation: ' + config.model
config.batch_size = 5
self.config = config
self.sess = sess
infer = True
self.inputs = [ev.placeholder(config) for ev in self.config.evidence]
self.nodes = tf.placeholder(
tf.int32, [config.batch_size, config.decoder.max_ast_depth])
self.edges = tf.placeholder(
tf.bool, [config.batch_size, config.decoder.max_ast_depth])
targets = tf.concat([
self.nodes[:, 1:],
tf.zeros([config.batch_size, 1], dtype=tf.int32)
],
axis=1) # shifted left by one
ev_data = self.inputs
nodes = tf.transpose(self.nodes)
edges = tf.transpose(self.edges)
###########################3
with tf.variable_scope('Embedding'):
emb = tf.get_variable(
'emb', [config.decoder.vocab_size, config.decoder.units])
with tf.variable_scope("Encoder"):
self.encoder = BayesianEncoder(config, ev_data, infer)
samples_1 = tf.random_normal(
[config.batch_size, config.latent_size],
mean=0.,
stddev=1.,
dtype=tf.float32)
self.psi_encoder = self.encoder.psi_mean + tf.sqrt(
self.encoder.psi_covariance) * samples_1
# setup the reverse encoder.
with tf.variable_scope("Reverse_Encoder"):
embAPI = tf.get_variable('embAPI', [
config.reverse_encoder.vocab_size, config.reverse_encoder.units
])
embRT = tf.get_variable(
'embRT',
[config.evidence[4].vocab_size, config.reverse_encoder.units])
embFS = tf.get_variable(
'embFS',
[config.evidence[5].vocab_size, config.reverse_encoder.units])
self.reverse_encoder = BayesianReverseEncoder(
config, embAPI, nodes, edges, ev_data[4], embRT, ev_data[5],
embFS)
samples_2 = tf.random_normal(
[config.batch_size, config.latent_size],
mean=0.,
stddev=1.,
dtype=tf.float32)
self.psi_reverse_encoder = self.reverse_encoder.psi_mean + tf.sqrt(
self.reverse_encoder.psi_covariance) * samples_2
# setup the decoder with psi as the initial state
with tf.variable_scope("Decoder"):
lift_w = tf.get_variable(
'lift_w', [config.latent_size, config.decoder.units])
lift_b = tf.get_variable('lift_b', [config.decoder.units])
initial_state = tf.nn.xw_plus_b(self.psi_reverse_encoder,
lift_w,
lift_b,
name="Initial_State")
self.decoder = BayesianDecoder(config, emb, initial_state, nodes,
edges)
with tf.variable_scope("RE_Decoder"):
## RE
emb_RE = config.evidence[
4].emb * 0.0 #tf.get_variable('emb_RE', [config.evidence[4].vocab_size, config.evidence[4].units])
lift_w_RE = tf.get_variable(
'lift_w_RE', [config.latent_size, config.evidence[4].units])
lift_b_RE = tf.get_variable('lift_b_RE',
[config.evidence[4].units])
initial_state_RE = tf.nn.xw_plus_b(self.psi_reverse_encoder,
lift_w_RE,
lift_b_RE,
name="Initial_State_RE")
input_RE = tf.transpose(
tf.reverse_v2(tf.zeros_like(ev_data[4]), axis=[1]))
output = SimpleDecoder(config, emb_RE, initial_state_RE, input_RE,
config.evidence[4])
projection_w_RE = tf.get_variable(
'projection_w_RE',
[config.evidence[4].units, config.evidence[4].vocab_size])
projection_b_RE = tf.get_variable('projection_b_RE',
[config.evidence[4].vocab_size])
logits_RE = tf.nn.xw_plus_b(output.outputs[-1], projection_w_RE,
projection_b_RE)
labels_RE = tf.one_hot(tf.squeeze(ev_data[4]),
config.evidence[4].vocab_size,
dtype=tf.int32)
loss_RE = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=labels_RE, logits=logits_RE)
cond = tf.not_equal(tf.reduce_sum(self.encoder.psi_mean, axis=1),
0)
# cond = tf.reshape( tf.tile(tf.expand_dims(cond, axis=1) , [1,config.evidence[5].max_depth]) , [-1] )
self.loss_RE = tf.reduce_mean(
tf.where(cond, loss_RE, tf.zeros(cond.shape)))
with tf.variable_scope("FS_Decoder"):
#FS
emb_FS = config.evidence[
5].emb #tf.get_variable('emb_FS', [config.evidence[5].vocab_size, config.evidence[5].units])
lift_w_FS = tf.get_variable(
'lift_w_FS', [config.latent_size, config.evidence[5].units])
lift_b_FS = tf.get_variable('lift_b_FS',
[config.evidence[5].units])
initial_state_FS = tf.nn.xw_plus_b(self.psi_reverse_encoder,
lift_w_FS,
lift_b_FS,
name="Initial_State_FS")
input_FS = tf.transpose(tf.reverse_v2(ev_data[5], axis=[1]))
self.decoder_FS = SimpleDecoder(config, emb_FS, initial_state_FS,
input_FS, config.evidence[5])
output = tf.reshape(tf.concat(self.decoder_FS.outputs, 1),
[-1, self.decoder_FS.cell1.output_size])
logits_FS = tf.matmul(output, self.decoder_FS.projection_w_FS
) + self.decoder_FS.projection_b_FS
# logits_FS = output
targets_FS = tf.reverse_v2(tf.concat(
[tf.zeros_like(ev_data[5][:, -1:]), ev_data[5][:, :-1]],
axis=1),
axis=[1])
# self.gen_loss_FS = tf.contrib.seq2seq.sequence_loss(logits_FS, target_FS,
# tf.ones_like(target_FS, dtype=tf.float32))
cond = tf.not_equal(tf.reduce_sum(self.encoder.psi_mean, axis=1),
0)
cond = tf.reshape(
tf.tile(tf.expand_dims(cond, axis=1),
[1, config.evidence[5].max_depth]), [-1])
cond = tf.where(cond, tf.ones(cond.shape), tf.zeros(cond.shape))
self.gen_loss_FS = seq2seq.sequence_loss(
[logits_FS], [tf.reshape(targets_FS, [-1])], [cond])
# get the decoder outputs
with tf.name_scope("Loss"):
output = tf.reshape(tf.concat(self.decoder.outputs, 1),
[-1, self.decoder.cell1.output_size])
logits = tf.matmul(
output, self.decoder.projection_w) + self.decoder.projection_b
ln_probs = tf.nn.log_softmax(logits)
# 1. generation loss: log P(Y | Z)
cond = tf.not_equal(tf.reduce_sum(self.encoder.psi_mean, axis=1),
0)
cond = tf.reshape(
tf.tile(tf.expand_dims(cond, axis=1),
[1, config.decoder.max_ast_depth]), [-1])
cond = tf.where(cond, tf.ones(cond.shape), tf.zeros(cond.shape))
self.gen_loss = seq2seq.sequence_loss([logits],
[tf.reshape(targets, [-1])],
[cond])
#KL_cond = tf.not_equal(tf.reduce_sum(self.encoder.psi_mean, axis=1) , 0)
self.loss = self.gen_loss + 1 / 32 * self.loss_RE + 8 / 32 * self.gen_loss_FS
probY = -1 * self.loss + self.get_multinormal_lnprob(
self.psi_reverse_encoder) - self.get_multinormal_lnprob(
self.psi_reverse_encoder, self.reverse_encoder.psi_mean,
self.reverse_encoder.psi_covariance)
EncA, EncB = self.calculate_ab(self.encoder.psi_mean,
self.encoder.psi_covariance)
RevEncA, RevEncB = self.calculate_ab(
self.reverse_encoder.psi_mean, self.reverse_encoder.psi_covariance)
###############################
countValid = tf.cast(
tf.count_nonzero(tf.not_equal(tf.reduce_sum(self.nodes, axis=1),
0)), tf.float32)
cond = tf.not_equal(tf.reduce_sum(self.nodes, axis=1), 0)
self.RevEncA = tf.reduce_sum(tf.where(cond, RevEncA,
tf.zeros_like(RevEncA)),
axis=0,
keepdims=True) / countValid
self.RevEncB = tf.reduce_sum(tf.where(cond, RevEncB,
tf.zeros_like(RevEncB)),
axis=0,
keepdims=True) / countValid
self.EncA = tf.reduce_mean(EncA, axis=0, keepdims=True)
self.EncB = tf.reduce_mean(EncB, axis=0, keepdims=True)
self.probY = tf.reduce_mean(probY, axis=0, keepdims=True)
# restore the saved model
tf.global_variables_initializer().run()
all_vars = tf.global_variables()
saver = tf.train.Saver(all_vars)
ckpt = tf.train.get_checkpoint_state(save)
saver.restore(self.sess, ckpt.model_checkpoint_path)
return
def train(clargs):
config_file = clargs.config if clargs.continue_from is None \
else os.path.join(clargs.continue_from, 'config.json')
with open(config_file) as f:
config = read_config(json.load(f), chars_vocab=clargs.continue_from)
reader = Reader(clargs, config)
jsconfig = dump_config(config)
print(clargs)
print(json.dumps(jsconfig, indent=2))
with open(os.path.join(clargs.save, 'config.json'), 'w') as f:
json.dump(jsconfig, fp=f, indent=2)
model = Model(config)
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables(), max_to_keep=None)
tf.train.write_graph(sess.graph_def, clargs.save, 'model.pbtxt')
tf.train.write_graph(sess.graph_def,
clargs.save,
'model.pb',
as_text=False)
# restore model
if clargs.continue_from is not None:
ckpt = tf.train.get_checkpoint_state(clargs.continue_from)
saver.restore(sess, ckpt.model_checkpoint_path)
# training
for i in range(config.num_epochs):
reader.reset_batches()
avg_loss = avg_evidence = avg_latent = avg_generation = 0
for b in range(config.num_batches):
start = time.time()
# setup the feed dict
ev_data, n, e, y = reader.next_batch()
feed = {model.targets: y}
for j, ev in enumerate(config.evidence):
feed[model.encoder.inputs[j].name] = ev_data[j]
for j in range(config.decoder.max_ast_depth):
feed[model.decoder.nodes[j].name] = n[j]
feed[model.decoder.edges[j].name] = e[j]
# run the optimizer
loss, evidence, latent, generation, mean, covariance, _ \
= sess.run([model.loss,
model.evidence_loss,
model.latent_loss,
model.gen_loss,
model.encoder.psi_mean,
model.encoder.psi_covariance,
model.train_op], feed)
end = time.time()
avg_loss += np.mean(loss)
avg_evidence += np.mean(evidence)
avg_latent += np.mean(latent)
avg_generation += generation
step = i * config.num_batches + b
if step % config.print_step == 0:
print(
'{}/{} (epoch {}), evidence: {:.3f}, latent: {:.3f}, generation: {:.3f}, '
'loss: {:.3f}, mean: {:.3f}, covariance: {:.3f}, time: {:.3f}'
.format(step,
config.num_epochs * config.num_batches, i,
np.mean(evidence), np.mean(latent), generation,
np.mean(loss), np.mean(mean),
np.mean(covariance), end - start))
checkpoint_dir = os.path.join(clargs.save,
'model{}.ckpt'.format(i))
saver.save(sess, checkpoint_dir)
print(
'Model checkpointed: {}. Average for epoch evidence: {:.3f}, latent: {:.3f}, '
'generation: {:.3f}, loss: {:.3f}'.format(
checkpoint_dir, avg_evidence / config.num_batches,
avg_latent / config.num_batches,
avg_generation / config.num_batches,
avg_loss / config.num_batches))
def index(clargs):
#set clargs.continue_from = True while testing, it continues from old saved config
clargs.continue_from = None
model = bayou.models.low_level_evidences.infer.BayesianPredictor
# load the saved config
with open(os.path.join(clargs.save, 'config.json')) as f:
config = read_config(json.load(f), chars_vocab=True)
config.batch_size = 500
reader = Reader(clargs, config, infer=True)
# Placeholders for tf data
nodes_placeholder = tf.placeholder(reader.nodes.dtype, reader.nodes.shape)
edges_placeholder = tf.placeholder(reader.edges.dtype, reader.edges.shape)
targets_placeholder = tf.placeholder(reader.targets.dtype,
reader.targets.shape)
evidence_placeholder = [
tf.placeholder(input.dtype, input.shape) for input in reader.inputs
]
# reset batches
feed_dict = {fp: f for fp, f in zip(evidence_placeholder, reader.inputs)}
feed_dict.update({nodes_placeholder: reader.nodes})
feed_dict.update({edges_placeholder: reader.edges})
feed_dict.update({targets_placeholder: reader.targets})
dataset = tf.data.Dataset.from_tensor_slices(
(nodes_placeholder, edges_placeholder, targets_placeholder,
*evidence_placeholder))
batched_dataset = dataset.batch(config.batch_size)
iterator = batched_dataset.make_initializable_iterator()
jsp = reader.js_programs
with tf.Session() as sess:
predictor = model(clargs.save, sess, config,
iterator) # goes to infer.BayesianPredictor
sess.run(iterator.initializer, feed_dict=feed_dict)
infer_vars = {}
allEvSigmas = predictor.get_ev_sigma()
print(allEvSigmas)
programs = []
k = 69
for j in range(config.num_batches):
prob_Y, a1, b1, a2, b2 = predictor.get_all_params_inago()
for i in range(config.batch_size):
infer_vars = jsp[i]
prog_json = deepcopy(jsp[j * config.batch_size + i])
prog_json['a2'] = "%.3f" % a2[i].item()
prog_json['b2'] = ["%.3f" % val.item() for val in b2[i]]
prog_json['ProbY'] = "%.3f" % prob_Y[i].item()
programs.append(prog_json)
if (j + 1) % 200 == 0 or (j + 1) == config.num_batches:
fileName = "Program_output_" + str(k) + ".json"
k += 1
print('\nWriting to {}...'.format(fileName), end='')
with open(fileName, 'w') as f:
json.dump({'programs': programs}, fp=f, indent=2)
for item in programs:
del item
del programs
gc.collect()
programs = []
print('Batch Processing Completed')
return infer_vars, config
def reload(clargs):
config_file = 'savedFocusModel/config.json'
clargs.continue_from = True
with open(config_file) as f:
config = read_config(json.load(f), chars_vocab=True)
reader = Reader(clargs, config, dataIsThere=True)
# Placeholders for tf data
prog_ids_placeholder = tf.placeholder(reader.prog_ids.dtype,
reader.prog_ids.shape)
js_prog_ids_placeholder = tf.placeholder(reader.js_prog_ids.dtype,
reader.js_prog_ids.shape)
nodes_placeholder = tf.placeholder(reader.nodes.dtype, reader.nodes.shape)
edges_placeholder = tf.placeholder(reader.edges.dtype, reader.edges.shape)
targets_placeholder = tf.placeholder(reader.targets.dtype,
reader.targets.shape)
evidence_placeholder = [
tf.placeholder(input.dtype, input.shape) for input in reader.inputs
]
# reset batches
feed_dict = {fp: f for fp, f in zip(evidence_placeholder, reader.inputs)}
feed_dict.update({prog_ids_placeholder: reader.prog_ids})
feed_dict.update({js_prog_ids_placeholder: reader.js_prog_ids})
feed_dict.update({nodes_placeholder: reader.nodes})
feed_dict.update({edges_placeholder: reader.edges})
feed_dict.update({targets_placeholder: reader.targets})
dataset = tf.data.Dataset.from_tensor_slices(
(prog_ids_placeholder, js_prog_ids_placeholder, nodes_placeholder,
edges_placeholder, targets_placeholder, *evidence_placeholder))
batched_dataset = dataset.batch(config.batch_size)
iterator = batched_dataset.make_initializable_iterator()
model = Model(config, iterator, bayou_mode=False)
i = 0
with tf.Session(config=tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True)) as sess:
writer = tf.summary.FileWriter(clargs.save)
writer.add_graph(sess.graph)
tf.global_variables_initializer().run()
tf.train.write_graph(sess.graph_def, clargs.save, 'model.pbtxt')
tf.train.write_graph(sess.graph_def,
clargs.save,
'model.pb',
as_text=False)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=3)
# restore model
if clargs.continue_from is not None:
bayou_vars = get_var_list()['bayou_vars']
old_saver = tf.train.Saver(bayou_vars, max_to_keep=None)
ckpt = tf.train.get_checkpoint_state('savedFocusModelReDone')
old_saver.restore(sess, ckpt.model_checkpoint_path)
reverse_encoder_vars = get_var_list()['rev_encoder_vars']
old_saver = tf.train.Saver(reverse_encoder_vars, max_to_keep=None)
ckpt = tf.train.get_checkpoint_state('savedFocusModel')
old_saver.restore(sess, ckpt.model_checkpoint_path)
checkpoint_dir = os.path.join(clargs.save,
'model{}.ckpt'.format(i + 1))
saver.save(sess, checkpoint_dir)
def forward_pass(clargs):
#set clargs.continue_from = True while testing, it continues from old saved config
clargs.continue_from = True
with open(os.path.join(clargs.save, 'config.json')) as f:
model_type = json.load(f)['model']
if model_type == 'lle':
model = bayou.models.low_level_evidences.infer.BayesianPredictor
else:
raise ValueError('Invalid model type in config: ' + model_type)
# load the saved config
with open(os.path.join(clargs.save, 'config.json')) as f:
config = read_config(json.load(f), chars_vocab=True)
reader = Reader(clargs, config, infer=True)
# Placeholders for tf data
nodes_placeholder = tf.placeholder(reader.nodes.dtype, reader.nodes.shape)
edges_placeholder = tf.placeholder(reader.edges.dtype, reader.edges.shape)
targets_placeholder = tf.placeholder(reader.targets.dtype,
reader.targets.shape)
evidence_placeholder = [
tf.placeholder(input.dtype, input.shape) for input in reader.inputs
]
# reset batches
feed_dict = {fp: f for fp, f in zip(evidence_placeholder, reader.inputs)}
feed_dict.update({nodes_placeholder: reader.nodes})
feed_dict.update({edges_placeholder: reader.edges})
feed_dict.update({targets_placeholder: reader.targets})
dataset = tf.data.Dataset.from_tensor_slices(
(nodes_placeholder, edges_placeholder, targets_placeholder,
*evidence_placeholder))
batched_dataset = dataset.batch(config.batch_size)
iterator = batched_dataset.make_initializable_iterator()
jsp = reader.js_programs
with tf.Session() as sess:
predictor = model(clargs.save, sess, config,
iterator) # goes to infer.BayesianPredictor
# testing
sess.run(iterator.initializer, feed_dict=feed_dict)
infer_vars = {}
allEvSigmas = predictor.get_ev_sigma()
print(allEvSigmas)
for j in range(config.num_batches):
prob_Y, a1, b1, a2, b2 = predictor.get_all_params_inago()
for i in range(config.batch_size):
prog_id = j * config.batch_size + i
infer_vars[prog_id] = {}
infer_vars[prog_id]['a1'] = a1[i].round(decimals=2)
infer_vars[prog_id]['a2'] = a2[i].round(decimals=2)
infer_vars[prog_id]['b1'] = b1[i].round(decimals=2)
infer_vars[prog_id]['b2'] = b2[i].round(decimals=2)
infer_vars[prog_id]['ProbY'] = prob_Y[i].round(decimals=2)
infer_vars[prog_id]['count_prog_ids'] = 1
infer_vars[prog_id]['JS'] = jsp[prog_id]
if (j + 1) % 1000 == 0:
print('Completed Processing {}/{} batches'.format(
j + 1, config.num_batches))
return infer_vars, config
