/
run_rnn_model.py
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run_rnn_model.py
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from datetime import datetime
import os
import numpy as np
import tensorflow as tf
from sklearn.model_selection import KFold
from models.rnn.lstm_tf_decov_mlp_init import LSTM_TF_DeCov_MLP_init
from models.rnn.lstm_tf_l1 import LSTM_TF_L1
from util.common import normalized, ensure_dir, fill_pred_lstm_batch, fill_lstm_batch, date2str
from util.loader import load_data_as_numpy
from util.common import loss as mse
# TODO: track best epoch number for early stopping and retrain on whole data?
def run_rnn_model(session, configs, learning_curves, log_dir,
save_dir, model_class, n_input, n_test,
normalize, train_epochs, batch_size, eval_every, params,
early_stopping=False, patience=50, n_folds=3, model_desc=None,
tf_seed=1123, numpy_seed=1123, verbose=True, reload_existing=False,
post_fold_func=None):
tf.set_random_seed(tf_seed)
if n_test is None:
n_test = [5, 10, 20, 30]
elif not isinstance(n_test, list):
n_test = [n_test]
input_tensor = tf.placeholder(tf.float32, [None, None, 6])
target = tf.placeholder(tf.float32, [None, None, 1])
c1 = tf.placeholder(tf.float32, [None, 64])
h1 = tf.placeholder(tf.float32, [None, 64])
c2 = tf.placeholder(tf.float32, [None, 64])
h2 = tf.placeholder(tf.float32, [None, 64])
initial_state = (tf.nn.rnn_cell.LSTMStateTuple(c1, h1),
tf.nn.rnn_cell.LSTMStateTuple(c2, h2))
phase = tf.placeholder(tf.bool, name='phase')
rnn = model_class(input_tensor, target, initial_state, phase, **params)
saver = tf.train.Saver()
if model_desc is None:
model_desc = '{0}_{1}_{2}'.format(
model_class.__name__,
'_'.join(['{0}={1}'.format(a, b) for a, b in zip(params.keys(), params.values())]),
date2str(datetime.now())
)
if log_dir is not None:
train_summary_writer = tf.summary.FileWriter(
os.path.join(log_dir, model_desc),
session.graph
)
x = np.zeros((batch_size, n_input if n_input is not None else 20, 6), dtype=np.float32)
y = np.zeros((batch_size, n_input if n_input is not None else 20, 1), dtype=np.float32)
k_fold = KFold(n_splits=n_folds, shuffle=True, random_state=1)
performances_valid = -np.ones((n_folds, 4))
performances_test = np.zeros((n_folds, 4))
stopped_early = np.zeros(n_folds)
fold_num_epochs = np.zeros(n_folds)
current_fold = 0
rs_ = np.random.RandomState(numpy_seed)
for train_indices, test_indices in k_fold.split(configs):
if log_dir is not None:
t_loss_summary = tf.summary.scalar('losses/TrainingLoss_fold:{0}'.format(current_fold),
rnn.loss)
v_loss_summary = tf.summary.scalar('losses/ValidationLoss_fold:{0}'.format(current_fold),
rnn.loss_pure)
session.run(tf.global_variables_initializer())
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# not counting the validation set being taken out to keep this constant with/without ES
num_train_samples = train_indices.shape[0]
epoch_steps = int(np.ceil(num_train_samples / batch_size))
if early_stopping:
rs_.shuffle(train_indices)
valid_length = int(np.ceil(train_indices.shape[0] * 0.2))
valid_indices = train_indices[:valid_length]
train_indices = train_indices[valid_length:]
train_configs = configs[train_indices]
train_curves = learning_curves[train_indices]
valid_configs = configs[valid_indices]
valid_curves = learning_curves[valid_indices]
best_valid = float('inf')
counter = 0
else:
train_configs = configs[train_indices]
train_curves = learning_curves[train_indices]
valid_configs = None
valid_curves = None
test_configs = configs[test_indices]
test_curves = learning_curves[test_indices]
if normalize:
train_configs, mean, std = normalized(train_configs)
test_configs, _, _ = normalized(test_configs, mean, std)
if early_stopping:
valid_configs, _, _ = normalized(valid_configs, mean, std)
fold_save_path = os.path.join(save_dir, '{0}_fold_{1}.ckpt'.format(model_desc, current_fold))
total_epochs = 0
curr_steps = 0
if reload_existing and os.path.exists(fold_save_path):
saver.restore(session, fold_save_path)
else:
while total_epochs < int(np.round(train_epochs)):
loss_data = np.zeros(epoch_steps)
for step_ in range(epoch_steps):
x, y = fill_lstm_batch(x, y, n_input if n_input is not None else 20,
train_configs, train_curves, rs_)
n_input_step = n_input if n_input is not None else rs_.randint(5, 21)
loss, _, _, state = session.run(
[rnn.loss, rnn.optimize, update_ops, rnn.lstm_final_state],
{rnn.input_tensor: x[:, :n_input_step, :],
rnn.target: y[:, :n_input_step, :],
phase: 1,
c1: np.zeros((batch_size, 64), dtype=np.float32),
h1: np.zeros((batch_size, 64), dtype=np.float32),
c2: np.zeros((batch_size, 64), dtype=np.float32),
h2: np.zeros((batch_size, 64), dtype=np.float32)}
)
loss_data[step_] = loss
curr_steps += 1
total_epochs += 1
if total_epochs % eval_every == 0:
test_mse = -1
# test_mse = loss_for_several_n_input(initial_state, n_test, 40,
# phase, rnn, session,
# test_configs, test_curves).mean()
if early_stopping:
valid_losses, valid_pred = loss_and_predictions_for_several_n_input(
initial_state, n_test, 40,
phase, rnn, session,
valid_configs, valid_curves
)
valid_mse = valid_losses.mean()
if verbose:
print('Epoch {0} test loss: {1:.5f}, valid loss: {3:.5f} train_loss: {2:.5f}'.format(
total_epochs,
test_mse,
loss_data.mean(),
valid_mse)
)
if valid_mse < best_valid:
best_valid = valid_mse
counter = 0
if save_dir is not None:
if verbose:
print('saved model')
saver.save(session, fold_save_path)
else:
counter += 1
if counter > patience:
if verbose:
print('restored model')
saver.restore(session, fold_save_path)
stopped_early[current_fold] = 1
break
else:
if verbose:
print('Epoch {0} test loss: {1:.5f}, train_loss: {2:.5f}'.format(
total_epochs,
test_mse,
loss_data.mean())
)
if log_dir is not None: # TODO: broken
if total_epochs % eval_every == 0:
sm, t_loss = session.run([t_loss_summary, rnn.loss],
{rnn.input_tensor: train_configs,
rnn.target: train_curves[:, -1].reshape(-1, 1),
phase: 0})
train_summary_writer.add_summary(sm, total_epochs)
sm, ev_loss, pure_loss = session.run([v_loss_summary, rnn.loss, rnn.loss_pure],
{rnn.input_tensor: test_configs,
rnn.target: test_curves[:, -1].reshape(-1, 1),
phase: 0})
train_summary_writer.add_summary(sm, total_epochs)
fold_num_epochs[current_fold] = total_epochs
performances_test[current_fold, :], predictions_test = \
loss_and_predictions_for_several_n_input(
initial_state, [5, 10, 20, 30], 40,
phase, rnn, session,
test_configs, test_curves
)
if early_stopping:
performances_valid[current_fold, :], predictions_valid = \
loss_and_predictions_for_several_n_input(
initial_state, [5, 10, 20, 30], 40,
phase, rnn, session,
valid_configs, valid_curves
)
if post_fold_func is not None:
post_fold_func(predictions_test,
initial_state, phase, rnn, session, test_configs, test_curves)
current_fold += 1
if verbose:
if early_stopping:
print('mean cross-validation valid loss: {0}'.format(performances_valid.mean(axis=0)))
print('mean cross-validation test loss: {0}, params: {1}'.format(performances_test.mean(axis=0), params))
return performances_test.mean(axis=0), performances_valid.mean(axis=0), \
dict(stopped_early=np.all(stopped_early), num_epochs=fold_num_epochs)
def predict_curve(initial_state, n_input, n_output, phase, rnn, session, configs, curves):
pred_x = np.zeros((configs.shape[0], n_input, 6), dtype=np.float32)
pred_x = fill_pred_lstm_batch(pred_x, n_input, configs, curves)
pred, pred_state \
= session.run([rnn.prediction,
rnn.lstm_final_state],
{rnn.input_tensor: pred_x, phase: 0,
initial_state[0].c: np.zeros((pred_x.shape[0], 64), dtype=np.float32),
initial_state[0].h: np.zeros((pred_x.shape[0], 64), dtype=np.float32),
initial_state[1].c: np.zeros((pred_x.shape[0], 64), dtype=np.float32),
initial_state[1].h: np.zeros((pred_x.shape[0], 64), dtype=np.float32)})
pred_next_list = []
extra_x = np.zeros((pred_x.shape[0], 1, 6))
extra_x[:, 0, :5] = 0
extra_x[:, 0, 5] = pred[:, -1, 0]
for extra_step in range(n_input, n_output): # TODO: re-check
pred_next, pred_state = session.run([rnn.prediction, rnn.lstm_final_state],
{rnn.input_tensor: extra_x,
phase: 0,
initial_state[0].c: pred_state[0].c,
initial_state[0].h: pred_state[0].h,
initial_state[1].c: pred_state[1].c,
initial_state[1].h: pred_state[1].h})
pred_next_list.append(pred_next)
extra_x[:, 0, 5] = pred_next[:, 0, 0]
pred_full = np.concatenate(
(pred, np.concatenate(pred_next_list, axis=1)), axis=1
)
return pred_full
def loss_and_predictions_for_several_n_input(initial_state, n_input, n_output,
phase, rnn, session, configs, curves):
if not isinstance(n_input, list):
n_input = [n_input]
res = np.zeros(len(n_input))
predictions = []
for l, in_length in enumerate(n_input):
pred_full = predict_curve(initial_state, in_length, n_output,
phase, rnn, session, configs, curves)
res[l] = mse(pred_full[:, -1].reshape(-1), curves[:, -1])
predictions.append(pred_full)
return res, predictions
if __name__ == '__main__':
log_dir = os.path.join(os.path.dirname(__file__), 'logs')
ensure_dir(log_dir)
save_dir = os.path.join(os.path.dirname(__file__), 'checkpoints')
ensure_dir(save_dir)
res_dir = os.path.join(os.path.dirname(__file__), 'optimization_results')
ensure_dir(res_dir)
configs, learning_curves = load_data_as_numpy()
batch_size = 32
n_input_train = None # if None - randomized between 5 and 20
n_input_test = None # 30 # if None - average of results for [5, 10, 20, 30] is used
train_epochs = 1500
eval_every = 1
normalize = True
early_stopping = True
patience = 250
# model = LSTM_TF_DeCov
# model = LSTM_TF_Dropout
model = LSTM_TF_L1
# model = LSTM_TF_L2
# model = LSTM_TF_DeCov_MLP_init
with tf.Session() as session:
params = {'learning_rate': 0.00350212567264791, 'reg_weight': 2.063759204812831e-05}
# params = {
# 'learning_rate': 0.005,
# 'reg_weight': 0.1,
# # 'drop_rate': 0.00,
# 'batch_size': batch_size,
# 'exponential_decay': False,
# 'decay_rate': 0.1,
# 'decay_steps': 200 * 176 / batch_size # hacky
# }
training_start = date2str(datetime.now())
perf_test, perf_valid, _ = \
run_rnn_model(session, configs, learning_curves, None, save_dir,
model, n_input_train, n_input_test, normalize,
train_epochs, batch_size, eval_every, params,
early_stopping=early_stopping, patience=patience,
n_folds=3, tf_seed=1123, numpy_seed=1123, verbose=True)
with open(os.path.join(res_dir, 'rnn_results.txt'), 'a') as f:
f.write('{0}, started {1}, finished {2}\n'.format(
model.__name__, training_start, date2str(datetime.now())
))
f.write('cv_loss_valid: {0}\ncv_loss_test: {1}\n'.format(
np.array2string(perf_valid, precision=6, floatmode='maxprec_equal'),
np.array2string(perf_test, precision=6, floatmode='maxprec_equal')
))
f.write('training params: {0} \nmodel params: {1}\n'.format(
dict(
batch_size=batch_size,
n_input_train=n_input_train,
n_input_test=n_input_test,
train_epochs=train_epochs,
eval_every=eval_every,
normalize=normalize,
early_stopping=early_stopping,
patience=patience
),
params
))
f.write('------------------------------------------------------\n')