def main():
para = params_setup()
logging_config_setup(para)
logging.info('Creating graph')
graph, model, data_generator = create_graph(para)
with tf.Session(config=config_setup(), graph=graph) as sess:
sess.run(tf.global_variables_initializer())
logging.info('Loading weights')
load_weights(para, sess, model)
print_num_of_trainable_parameters()
try:
if para.mode == 'train':
logging.info('Started training')
train(para, sess, model, data_generator)
if para.save_final_model_path != '':
save_weights(sess, model, para.save_final_model_path)
elif para.mode == 'validation':
logging.info('Started validation')
test(para, sess, model, data_generator)
elif para.mode == 'test':
logging.info('Started testing')
test(para, sess, model, data_generator)
elif para.mode == 'predict':
logging.info('Predicting')
predict(para, sess, model, data_generator,
'./data/solar-energy3/solar_predict.txt', para.samples)
except KeyboardInterrupt:
print('KeyboardInterrupt')
finally:
print('Stop')
def main():
para = params_setup()
logging_config_setup(para)
print("Creating graph...")
graph, model, data_generator = create_graph(para)
print("Done creating graph.")
with tf.Session(config=config_setup(), graph=graph) as sess:
sess.run(tf.global_variables_initializer())
print("Loading weights...")
load_weights(para, sess, model)
print_num_of_trainable_parameters()
# PRINT NAMES OF TENSORS THAT ARE ALPHAS
# example name: "model/rnn/cond/rnn/multi_rnn_cell/cell_0/cell_0/temporal_pattern_attention_cell_wrapper/attention/Sigmoid:0"
# for item in [n.name for n in tf.get_default_graph().as_graph_def().node
# if (n.name.find("temporal_pattern_attention_cell_wrapper/attention")!=-1 and
# n.name.find("Sigmoid")!=-1)]:
# print(item)
# Print names of ops
# for op in tf.get_default_graph().get_operations():
# if(op.name.find("ben_multiply")!=-1):
# print(str(op.name))
# PRINT REG KERNEL AND BIAS
# reg_weights = [v for v in tf.global_variables() if v.name == "model/dense_2/kernel:0"][0]
# reg_bias = [v for v in tf.global_variables() if v.name == "model/dense_2/bias:0"][0]
# print("Reg Weights:", sess.run(reg_weights))
# print("Reg Bias:", sess.run(reg_bias) * data_generator.scale[0])
try:
if para.mode == 'train':
train(para, sess, model, data_generator)
elif para.mode == 'test':
print("Evaluating model...")
test(para, sess, model, data_generator)
except KeyboardInterrupt:
print('KeyboardInterrupt')
finally:
print('Stop')
def main():
para = params_setup()
logging_config_setup(para)
graph, model, data_generator = create_graph(para)
with tf.Session(config=config_setup(), graph=graph) as sess:
sess.run(tf.global_variables_initializer())
load_weights(para, sess, model)
print_num_of_trainable_parameters()
try:
if para.mode == 'train':
train(para, sess, model, data_generator)
elif para.mode == 'test':
test(para, sess, model, data_generator)
except KeyboardInterrupt:
print('KeyboardInterrupt')
finally:
print('Stop')
def main():
para = sess_params_setup()
logging_config_setup(para)
graph, model, data_generator = create_graph(para)
with tf.Session(config=config_setup(), graph=graph) as sess:
sess.run(tf.global_variables_initializer())
load_weights(para, sess, model)
print_num_of_trainable_parameters()
try:
all_inputs, all_labels, all_outputs = train(
para, sess, model, data_generator)
return all_inputs, all_labels, all_outputs, model
# all_inputs = all_inputs.permute(0,2,1)
#
# modelSize = 500
# sampleSize = 500
#
# model_input = [tf.convert_to_tensor(x, dtype=tf.float32) for x in all_inputs[:modelSize]]
# model_output = tf.convert_to_tensor(all_outputs[:modelSize], dtype=tf.float32)
#
# data = [x for x in all_inputs[:sampleSize]]
# X = [x for x in all_inputs[:modelSize]]
#
# model2 = (model_input, model_output)
# explainer = shap.DeepExplainer(model2, data, sess)
# shap_values = explainer.shap_values(X)
#
# return shap_values
except KeyboardInterrupt:
print('KeyboardInterrupt')
finally:
print('Stop')
def main():
para = params_setup()
logging_config_setup(para)
graph, model, data_generator = create_graph(para)
with tf.Session(config=config_setup(), graph=graph) as sess:
sess.run(tf.global_variables_initializer())
load_weights(para, sess, model)
print_num_of_trainable_parameters()
try:
# EXTRACT WEIGHTS HERE
for variable in tf.global_variables(): # tf.trainable_variables():
print(variable)
# VIEW FETCHABLE OPS
graph = tf.get_default_graph()
print([op for op in parent_ops if graph.is_fetchable(op)])
except KeyboardInterrupt:
print('KeyboardInterrupt')
finally:
print('Weights extracted. Stop.')
def main():
para = params_setup()
logging_config_setup(para)
graph, model, data_generator = create_graph(para)
with tf.Session(config=config_setup(), graph=graph) as sess:
sess.run(tf.global_variables_initializer())
load_weights(para, sess, model)
print_num_of_trainable_parameters()
try:
if para.mode == 'train':
train(para, sess, model, data_generator)
elif para.mode == 'test':
obs, predicted = test(para, sess, model, data_generator)
obs = obs * data_generator.scale + data_generator.min_value
predicted = predicted * data_generator.scale + data_generator.min_value
print("MSE: ", mean_squared_error(obs[:, 0], predicted[:, 0]))
idx = pd.DatetimeIndex(start='2016-10-16',
end='2018-11-04',
freq='W')
obs_df = pd.DataFrame(data=obs[:, 0],
columns=['Observed'],
index=idx)
pred_df = pd.DataFrame(data=predicted[:, 0],
columns=['Predicted'],
index=idx)
df = pd.concat([obs_df, pred_df], axis=1)
df.plot()
plt.show()
except KeyboardInterrupt:
print('KeyboardInterrupt')
finally:
print('Stop')
parser.add_argument('--num_epochs', type=int, default=40)
parser.add_argument('--num_layers', type=int, default=3)
parser.add_argument('--num_units', type=int, default=338)
parser.add_argument('--custom', type=bool, default=True)
parser.add_argument('--split_date',
type=list,
default=['20181201', '20190320'])
parser.add_argument('--dataset_address',
type=str,
default='./data/raw_time_series.parquet')
parser.add_argument('--output_dir', type=str, default='./output')
#%%
para = parser.parse_args(args=[])
para.logging_level = logging.INFO
logging_config_setup(para)
#%%
create_dir(para.model_dir)
create_dir(para.output_dir)
json_path = para.model_dir + '/parameters.json'
json.dump(vars(para), open(json_path, 'w'), indent=4)
# %%
graph = tf.Graph()
# %%
graph, model, data_generator = create_graph(para)
# %%
with tf.Session(config=config_setup(), graph=graph) as sess:
sess.run(tf.global_variables_initializer())
from lib.config import params_setup
from lib.utils import print_parameters
from lib.model_utils import create_model_dir, load_weights, create_graph
from lib.setup import config_setup, logging_config_setup
from lib.pretrain import pretrain
from lib.rl import policy_gradient
from lib.test import test
if __name__ == "__main__":
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
PARA = params_setup()
create_model_dir(PARA)
logging_config_setup(PARA)
print_parameters(PARA)
GRAPH, MODEL = create_graph(PARA)
with tf.Session(config=config_setup(), graph=GRAPH) as sess:
sess.run(tf.global_variables_initializer())
load_weights(PARA, sess, MODEL)
COORD = tf.train.Coordinator()
THREADS = tf.train.start_queue_runners(sess=sess, coord=COORD)
try:
if PARA.mode == 'pretrain':
pretrain(PARA, sess, MODEL)
elif PARA.mode == 'rl':
policy_gradient(PARA, sess, MODEL)