def evaluate_addition():
"""
Load NPI Model from Checkpoint, and initialize REPL, for interactive carry-addition.
"""
with tf.Session() as sess:
# Load Data
with open(DATA_PATH_TEST, 'rb') as f:
data = pickle.load(f)
# Initialize Addition Core
core = AdditionCore()
# Initialize NPI Model
npi = NPI(core, CONFIG, LOG_PATH)
# Restore from Checkpoint
saver = tf.train.Saver()
saver.restore(sess, CKPT_PATH)
# with gfile.FastGFile("/tmp/tf/log/graph.pb", 'rb') as f:
# graph_def = tf.GraphDef()
# graph_def.ParseFromString(f.read())
# sess.graph.as_default()
# tf.import_graph_def(graph_def)
# print("map variables")
# Run REPL
for x in range(0, 20):
res = ""
# try:
repl(sess, npi, data, x)
# except:
print("--------------------------")
def evaluate_addition():
"""
Load NPI Model from Checkpoint, and initialize REPL, for interactive carry-addition.
"""
with tf.Session() as sess:
# Load Data
with open(DATA_PATH_TEST, 'rb') as f:
data = pickle.load(f)
# Initialize Addition Core
core = AdditionCore()
# Initialize NPI Model
npi = NPI(core, CONFIG, LOG_PATH)
# Restore from Checkpoint
saver = tf.train.Saver()
saver.restore(sess, CKPT_PATH)
# with gfile.FastGFile("/tmp/tf/log/graph.pb", 'rb') as f:
# graph_def = tf.GraphDef()
# graph_def.ParseFromString(f.read())
# sess.graph.as_default()
# tf.import_graph_def(graph_def)
# print("map variables")
# Run REPL
predict = {};
predict["ncw"] = 0;
predict["ncr"] = 0;
predict["cw"] = 0;
predict["cr"] = 0;
count = 0;
#f = open('log/numbers.txt', 'r+')
#f.truncate()
limit = EVAL_LIMIT
r = list(range(1000))
# random.shuffle(r)
for x in r:
limit -= 1
if limit > 0:
res = ""
print(repl(sess, npi, data, x, predict))
print("predict_connect_right " + str(predict["cr"]) + " predict_connect_wrong " + str(predict["cw"]) + " predict_not_connect_right " + str(predict["ncr"]) + " predict_not_connect_wrong " + str(predict["ncw"]))
print (str(limit)+"--------------------------")
def __init__(self):
with open("/root/ContextToCode/predictor/log/1class/expect_to_prog", 'r') as handle:
self.sessions = json.load(handle)
for key, value in self.sessions.items():
value['session'] = tf.Session()
# Initialize Addition Core
core = AdditionCore(CONFIG)
# Initialize NPI Model
self.npi = NPI(core, CONFIG, LOG_PATH)
# Restore from Checkpoint
saver = tf.train.Saver()
for key, value in self.sessions.items():
saver.restore(value['session'], CKPT_PATH+value['dir']+"/models/model-0006.ckpt")
print(value)
def __init__(self):
self.sess1 = tf.Session()
self.sess2 = tf.Session()
self.sess3 = tf.Session()
self.sess4 = tf.Session()
self.sess5 = tf.Session()
# Initialize Addition Core
core = AdditionCore(CONFIG)
# Initialize NPI Model
self.npi = NPI(core, CONFIG, LOG_PATH)
# Restore from Checkpoint
saver = tf.train.Saver()
saver.restore(self.sess1, CKPT_PATH_CLASS1)
saver.restore(self.sess5, CKPT_PATH_CLASS5)
saver.restore(self.sess3, CKPT_PATH_CLASS3)
saver.restore(self.sess2, CKPT_PATH_CLASS2)
saver.restore(self.sess4, CKPT_PATH_CLASS4)
def multiclass_eval():
with open(DATA_PATH_TEST, 'rb') as f:
data = pickle.load(f)
sess1 = tf.Session()
sess2 = tf.Session()
sess3 = tf.Session()
# Initialize Addition Core
core = AdditionCore()
# Initialize NPI Model
npi = NPI(core, CONFIG, LOG_PATH)
# Restore from Checkpoint
saver = tf.train.Saver()
# saver.restore(sess1, CKPT_PATH_CLASS1)
# saver.restore(sess2, CKPT_PATH_CLASS2)
# saver.restore(sess3, CKPT_PATH_CLASS3)
predict = {};
predict["ncw"] = 0;
predict["ncr"] = 0;
predict["cw"] = 0;
predict["cr"] = 0;
f = open('log/prog_produced.txt', 'w+')
f.truncate()
limit = EVAL_LIMIT
r = list(range(1000))
# random.shuffle(r)
for x in r:
limit -= 1
if limit > 0:
with open("/root/ContextToCode/predictor/log/prog_produced.txt", "a") as myfile:
myfile.write(str(repl(sess1, npi, data, x, predict))+"\n")
myfile.write(str(repl(sess2, npi, data, x, predict))+"\n")
myfile.write(str(repl(sess3, npi, data, x, predict))+"\n")
print (str(limit)+"--------------------------")
def train_addition(epochs, verbose=0):
"""
Instantiates an Addition Core, NPI, then loads and fits model to data.
:param epochs: Number of epochs to train for.
"""
# Load Data
with open(DATA_PATH_TRAIN, 'rb') as f:
data = pickle.load(f)
# Initialize Addition Core
print ('Initializing Addition Core!')
core = AdditionCore()
# Initialize NPI Model
print ('Initializing NPI Model!')
npi = NPI(core, CONFIG, LOG_PATH, verbose=verbose)
# Initialize TF Saver
saver = tf.train.Saver()
# Initialize TF Session
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Start Training
for ep in range(1, epochs + 1):
for i in range(len(data)):
# Reset NPI States
npi.reset_state()
# Setup Environment
steps = data[i]
# print(data[i])
x, y = steps[:-1], steps[1:]
# Run through steps, and fit!
step_def_loss, step_arg_loss, term_acc, prog_acc, = 0.0, 0.0, 0.0, 0.0
arg0_acc, arg1_acc, arg2_acc, num_args = 0.0, 0.0, 0.0, 0
# dsl = DSL([], [])
for j in range(len(x)):
# {'program': {'program': 'check'}, 'environment': {'terminate': False, 'answer': 1, 'is_redirect': 2},'args': {'id': 0}}
# print(y[j])
prog_name, prog_in_id, arg, term = x[j]["program"]["program"], x[j]["program"]["id"], x[j]["args"]["id"], x[j]["environment"]["terminate"]
prog_name_out, prog_out_id, arg_out, term_out = y[j]["program"]["program"], y[j]["program"]["id"], y[j]["args"]["id"], y[j]["environment"]["terminate"]
# Get Environment, Argument Vectors
env_in = [get_env(x[j]["environment"])]
arg_in, arg_out = [get_args(arg, arg_in=True)], get_args(arg_out, arg_in=False)
term_out = [1] if term_out else [0]
# if prog_name_out=="WRITE":
# prog_out_id = dsl.get_code(y[j]["prog"]["arg"][1])
# os._exit()
prog_in, prog_out = [[prog_in_id]], [prog_out_id]
# Fit!
if True:
t_acc, p_acc, _, loss = sess.run(
[npi.t_metric, npi.p_metric, npi.default_train_op, npi.default_loss],
feed_dict={npi.env_in: env_in, npi.arg_in: arg_in, npi.prg_in: prog_in,
npi.y_prog: prog_out, npi.y_term: term_out})
# print({npi.env_in: env_in, npi.arg_in: arg_in, npi.prg_in: prog_in, npi.y_prog: prog_out, npi.y_term: term_out})
# print({npi.y_args[0]: [arg_out[0]], npi.y_args[1]: [arg_out[1]], npi.y_args[2]: [arg_out[2]]})
# step_arg_loss += loss
term_acc += t_acc
prog_acc += p_acc
step_def_loss += loss
# arg0_acc += a_acc[0]
# arg1_acc += a_acc[1]
# arg2_acc += a_acc[2]
# num_args += 1
# else:
# loss, t_acc, p_acc, _ = sess.run(
# [npi.default_loss, npi.t_metric, npi.p_metric, npi.default_train_op],
# feed_dict={npi.env_in: env_in, npi.arg_in: arg_in, npi.prg_in: prog_in,
# npi.y_prog: prog_out, npi.y_term: term_out})
# step_def_loss += loss
# term_acc += t_acc
# prog_acc += p_acc
print ("Epoch {0:02d} Step {1:03d} Loss: {2:03f} Term: {3:03f}, Prog: {4:03f}" \
.format(ep, i, step_def_loss / len(x), term_acc / len(x), prog_acc / len(x)))
# Save Model
saver.save(sess, CKPT_PATH)
# !!!!
tf.train.write_graph(sess.graph_def, '/tmp/tf/log', 'graph.pb', as_text=False)
# tf.train.write_graph(my_graph, path_to_model_pb, 'saved_model.pb', as_text=False)
# !!!!
def train_addition(epochs, start_epoch, start_step, sub, verbose=0):
"""
Instantiates an Addition Core, NPI, then loads and fits model to data.
:param epochs: Number of epochs to train for.
"""
if not os.path.exists("log/" + sub + "/models/"):
os.mkdir("log/" + sub + "/models/")
# Load Data
with open(DATA_PATH_TRAIN, 'rb') as f:
data = pickle.load(f)
# f = open('log/log_train.txt', 'r+')
#f.truncate()
# Initialize Addition Core
print('Initializing Addition Core!')
core = AdditionCore()
# Initialize NPI Model
print('Initializing NPI Model!')
npi = NPI(core, CONFIG, LOG_PATH, verbose=verbose)
# Initialize TF Saver
saver = tf.train.Saver()
# Initialize TF Session
sess = tf.Session()
if start_epoch > 0 or start_step > 0:
saver = tf.train.Saver()
if start_step > 0:
saver.restore(
sess,
"log/" + sub + "/models/model-{0:04d}-{1:06d}.ckpt".format(
start_epoch + 1, start_step))
print("log/" + sub + "/models/model-{0:04d}-{1:06d}.ckpt".format(
start_epoch + 1, start_step))
else:
saver.restore(
sess, "log/" + sub +
"/models/model-{0:04d}.ckpt".format(start_epoch))
print("log/" + sub +
"/models/model-{0:04d}.ckpt".format(start_epoch))
else:
sess.run(tf.global_variables_initializer())
# Reset NPI States
npi.reset_state()
tf.get_default_graph().finalize()
# Start Training
for ep in range(start_epoch + 1, epochs + 1):
sum = 0
for i in range(len(data)):
if i > start_step:
# Setup Environment
steps = data[i]
# print(data[i])
x, y = steps[:-1], steps[1:]
# Run through steps, and fit!
step_def_loss, step_arg_loss, term_acc, prog_acc, = 0.0, 0.0, 0.0, 0.0
arg0_acc, arg1_acc, arg2_acc, num_args = 0.0, 0.0, 0.0, 0
# dsl = DSL([], [])
if len(x) > 0:
for j in range(len(x)):
# {'program': {'program': 'check'}, 'environment': {'terminate': False, 'answer': 1, 'is_redirect': 2},'args': {'id': 0}}
#print(x[j]['addinfo'])
prog_name, prog_in_id, arg, term = x[j]["program"][
"program"], x[j]["program"]["id"], x[j]["args"][
"id"], x[j]["environment"]["terminate"]
prog_name_out, prog_out_id, arg_out, term_out = y[j][
"program"]["program"], y[j]["program"]["id"], y[j][
"args"]["id"], y[j]["environment"]["terminate"]
# Get Environment, Argument Vectors
env_in = [get_env(x[j]["environment"])]
arg_in, arg_out = [get_args(arg, arg_in=True)
], get_args(arg_out, arg_in=False)
term_out = [1] if term_out else [0]
# if prog_name_out=="WRITE":
# prog_out_id = dsl.get_code(y[j]["prog"]["arg"][1])
# os._exit()
prog_in, prog_out = [[prog_in_id]], [prog_out_id]
# Fit!
if True:
t_acc, p_acc, _, loss = sess.run(
[
npi.t_metric, npi.p_metric,
npi.default_train_op, npi.default_loss
],
feed_dict={
npi.env_in: env_in,
npi.arg_in: arg_in,
npi.prg_in: prog_in,
npi.y_prog: prog_out,
npi.y_term: term_out
})
# print({npi.env_in: env_in, npi.arg_in: arg_in, npi.prg_in: prog_in, npi.y_prog: prog_out, npi.y_term: term_out})
# print({npi.y_args[0]: [arg_out[0]], npi.y_args[1]: [arg_out[1]], npi.y_args[2]: [arg_out[2]]})
# step_arg_loss += loss
term_acc += t_acc
prog_acc += p_acc
step_def_loss += loss
# arg0_acc += a_acc[0]
# arg1_acc += a_acc[1]
# arg2_acc += a_acc[2]
# num_args += 1
# else:
# loss, t_acc, p_acc, _ = sess.run(
# [npi.default_loss, npi.t_metric, npi.p_metric, npi.default_train_op],
# feed_dict={npi.env_in: env_in, npi.arg_in: arg_in, npi.prg_in: prog_in,
# npi.y_prog: prog_out, npi.y_term: term_out})
# step_def_loss += loss
# term_acc += t_acc
# prog_acc += p_acc
sum += prog_acc / len(x)
#with open('log/log_train.txt', "a") as myfile:
if i % 1000 == 0:
message = "Epoch "+sub+" {0:02d} Step {1:03d} Loss: {2:03f} Term: {3:03f}, Prog: {4:03f} AVG: {5:03f}" \
.format(ep, i, step_def_loss / len(x), term_acc / len(x), prog_acc / len(x), sum / (i - start_step))
print(message)
with open("log/" + sub + "/info", "a") as myfile:
myfile.write(message + "\n")
if i % chunk == 0:
saver.save(
sess, "log/" + sub +
"/models/model-{0:04d}-{1:06d}.ckpt".format(ep, i))
#if os.path.exists("log/model-{0:04d}-{1:06d}.ckpt.meta".format(ep, i-chunk)):
# os.remove("log/model-{0:04d}-{1:06d}.ckpt.meta".format(ep, i-chunk))
# os.remove("log/model-{0:04d}-{1:06d}.ckpt.index".format(ep, i-chunk))
# os.remove("log/model-{0:04d}-{1:06d}.ckpt.data-00000-of-00001".format(ep, i-chunk))
print ("Epoch {0:02d} Step {1:03d} AVG: {2:03f}" \
.format(ep, len(data), sum / len(data)))
# Save Model
saver.save(sess,
"log/" + sub + "/models/model-{0:04d}.ckpt".format(ep))
# !!!!
tf.train.write_graph(sess.graph_def,
'/tmp/tf/log',
'graph.pb',
as_text=False)