def runAgent(x_train, x_test):
# checkpoint run model folder
ckpt_dir_run = FLAGS.ckpt_dir + 'model_' + FLAGS.model
log_dir_run = FLAGS.logging_dir + 'model_' + FLAGS.model
if not (tf.gfile.Exists(ckpt_dir_run)):
tf.gfile.MakeDirs(ckpt_dir_run)
if not (tf.gfile.Exists(log_dir_run)):
tf.gfile.MakeDirs(log_dir_run)
with tf.Session() as sess:
if FLAGS.do_training:
# create new model instance
nnet = NNet(
lr=FLAGS.learning_rate,
optimizer=FLAGS.optimizer,
nonlinearity=FLAGS.nonlinearity,
n_hidden=FLAGS.n_hidden,
n_latent=FLAGS.n_latent,
beta=FLAGS.beta,
)
# initialize all variables
nnet.init_graph_vars(sess, log_dir=log_dir_run)
# tell researcher what's going on ...
print("{} Now training my model, LR: {} , EPs: {}, BS: {}".format(
datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
FLAGS.learning_rate, FLAGS.n_training_episodes,
FLAGS.batch_size))
# .. and begin with training
results = trainModel(sess,
nnet,
x_train,
x_test,
n_episodes=FLAGS.n_training_episodes,
n_batches=FLAGS.n_training_batches,
batch_size=FLAGS.batch_size,
model_dir=ckpt_dir_run)
evalModel(sess, nnet, x_train)
else:
nnet = myModel(is_trained=True)
print("Now evaluating my model")
ops = loadMyModel(sess, ['nnet', 'input'], ckpt_dir_run)
print(ops)
nnet.session = sess
nnet.y_hat = ops[0]
nnet.x = ops[1]
results = evalModel(sess, nnet, x_train)
return results
def runAgent(data_train,data_test):
# checkpoint run model folder
ckpt_dir_run = FLAGS.ckpt_dir
log_dir_run = FLAGS.log_dir
if not(tf.gfile.Exists(ckpt_dir_run)):
tf.gfile.MakeDirs(ckpt_dir_run)
if not(tf.gfile.Exists(log_dir_run)):
tf.gfile.MakeDirs(log_dir_run)
with tf.Session() as sess:
if FLAGS.do_training:
# import graph from trained model
# ckpt = tf.train.get_checkpoint_state(FLAGS.import_dir)
# if ckpt and ckpt.model_checkpoint_path:
# saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path + '.meta')
# myGraph = tf.get_default_graph()
myGraph = importModelGraph(FLAGS.import_dir)
for ii in myGraph.get_operations():
print(ii.name)
nnet = myModel(lr = FLAGS.learning_rate,
optimizer = FLAGS.optimizer,
nonlinearity = FLAGS.nonlinearity,
imported_graph = myGraph
)
print("Now training the Trees Agent, " + FLAGS.model + ', ' + FLAGS.exp_curriculum + ', ' + FLAGS.exp_boundary + ', ' + FLAGS.exp_taskorder + ', ' + FLAGS.exp_rewards)
# initialize all variables
nnet.init_graph_vars(sess,log_dir=log_dir_run)
# train model
results = trainModel(sess,nnet,data_train,data_test,
model_dir = ckpt_dir_run)
else:
nnet = myModel(is_trained=True)
print("Now evaluating Trees Agent")
ops = loadMyModel(sess,['nnet','input'],ckpt_dir_run)
print(ops)
nnet.session = sess
nnet.y_hat = ops[0]
nnet.x = ops[1]
results = evalModel(sess,nnet)
return results
def runCAE(x_train, x_test):
# checkpoint run model folder
ckpt_dir_run = FLAGS.ckpt_dir + 'model_' + FLAGS.model
log_dir_run = FLAGS.log_dir + 'model_' + FLAGS.model
if not (tf.gfile.Exists(ckpt_dir_run)):
tf.gfile.MakeDirs(ckpt_dir_run)
if not (tf.gfile.Exists(log_dir_run)):
tf.gfile.MakeDirs(log_dir_run)
with tf.Session() as sess:
if FLAGS.do_training:
nnet = myModel(
lr=FLAGS.learning_rate,
optimizer=FLAGS.optimizer,
nonlinearity=FLAGS.nonlinearity,
)
print(
"{} Now training Convolutional Autoencoder, LR: {} , EPs: {}, BS: {}"
.format(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
FLAGS.learning_rate, FLAGS.n_training_episodes,
FLAGS.batch_size))
# initialize all variables
nnet.init_graph_vars(sess, log_dir=log_dir_run)
# train model
results = trainModel(sess,
nnet,
x_train,
x_test,
n_episodes=FLAGS.n_training_episodes,
n_batches=FLAGS.n_training_batches,
batch_size=FLAGS.batch_size,
model_dir=ckpt_dir_run)
evalModel(sess, nnet, x_train)
else:
nnet = myModel(is_trained=True)
print("Now evaluating Convolutional Autoencoder")
ops = loadMyModel(sess, ['nnet'], ckpt_dir_run)
print(ops)
nnet.y_hat = ops[0]
results = evalModel(sess, nnet, x_train)
return results
def runAgent(data_train, data_test):
# checkpoint run model folder
ckpt_dir_run = FLAGS.ckpt_dir
log_dir_run = FLAGS.log_dir
if not (tf.gfile.Exists(ckpt_dir_run)):
tf.gfile.MakeDirs(ckpt_dir_run)
if not (tf.gfile.Exists(log_dir_run)):
tf.gfile.MakeDirs(log_dir_run)
with tf.Session() as sess:
if FLAGS.do_training:
nnet = myModel(
lr=FLAGS.learning_rate,
optimizer=FLAGS.optimizer,
nonlinearity=FLAGS.nonlinearity,
)
print("Now training the Trees Agent, " + FLAGS.model + ', ' +
FLAGS.exp_curriculum + ', ' + FLAGS.exp_boundary + ', ' +
FLAGS.exp_taskorder + ', ' + FLAGS.exp_rewards)
# initialize all variables
nnet.init_graph_vars(sess, log_dir=log_dir_run)
# train model
results = trainModel(sess,
nnet,
data_train,
data_test,
model_dir=ckpt_dir_run)
else:
nnet = myModel(is_trained=True)
print("Now evaluating Trees Agent")
ops = loadMyModel(sess, ['nnet', 'input'], ckpt_dir_run)
print(ops)
nnet.session = sess
nnet.y_hat = ops[0]
nnet.x = ops[1]
results = evalModel(sess, nnet)
return results