def get_data():
x_train_batch, y_train_batch = f.inputs([f.TRAIN_TF_RECORDS_FILE], name="train")
x_test_batch, y_test_batch = f.inputs([f.TEST_TF_RECORDS_FILE], name="test")
out = {
'traX' : x_train_batch,
'traY' : y_train_batch,
'tstX' : x_train_batch,
'tstY' : y_train_batch
}
return out
def get_data(constants):
x_train_batch, y_train_batch = f.inputs([f.TRAIN_TF_RECORDS_FILE],
shuffle=constants['SHUFFLE'],
name="train")
x_test_batch, y_test_batch = f.inputs([f.TEST_TF_RECORDS_FILE],
shuffle=constants['SHUFFLE'],
name="test")
out = {
'traX': x_train_batch,
'traY': y_train_batch,
'tstX': x_train_batch,
'tstY': y_train_batch
}
return out
def train():
c = constants()
p = params()
x, y = f.inputs([f.TRAIN_TF_RECORDS_FILE], shuffle=True, name='inputs')
u.shape_log(x)
u.shape_log(y)
logits = model(x, c)
logits = tf.reshape(logits, [-1, c.MAX_TIME, f.OUTPUT_DIM])
labels = tf.reshape(y, [-1, c.MAX_TIME, f.OUTPUT_DIM])
loss = tf.reduce_mean(tf.square(logits - labels))
train_op = tf.train.AdamOptimizer(learning_rate=0.05).minimize(
loss, global_step=p.global_step)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
# initialize global variables
sess.run(init_op)
# enable batch fetchers
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
#setup saver
saver = u.saver_ops(sess=sess,
max_to_keep=3,
global_step=p.global_step)
eng = mo.matlab_connection(c.MATLAB_SESSION)
# run the training operation
for indx in range(20000):
_, log = sess.run([train_op, logits])
# logi.append(log)
u.display_update(indx, 25)
if ((indx + 1) % 2000 == 0):
saver.save()
if ((indx) % 5 == 0):
# CHECK EMERGENCY STOP
if eng.should_stop():
print('stopping')
break
# connect to matlab
# eng = mo.matlab_connection(c.MATLAB_SESSION)
# eng.put_var('logits', logi)
saver.save()
coord.request_stop()
coord.join(threads)
def train():
c = constants()
p = params()
x, y = f.inputs([f.TRAIN_TF_RECORDS_FILE], name='inputs')
u.shape_log(x)
u.shape_log(y)
logits = model(x, c)
labels = tf.reshape(y, [c.MAX_TIME])
loss = tf.reduce_mean(tf.square(logits - labels))
train_op = tf.train.AdamOptimizer(learning_rate=0.05).minimize(
loss, global_step=p.global_step)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
# initialize global variables
sess.run(init_op)
# enable batch fetchers
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
#setup saver
saver = u.saver_ops(sess=sess,
max_to_keep=3,
global_step=p.global_step)
logi = []
# run the training operation
for indx in range(10000):
_, log = sess.run([train_op, logits])
logi.append(log)
u.display_update(indx, 50)
# connect to matlab
eng = mo.matlab_connection(c.MATLAB_SESSION)
eng.put_var('logits', logi)
saver.save()
coord.request_stop()
coord.join(threads)
def test():
c = constants()
p = params()
# connect to matlab
eng = mo.matlab_connection(c.MATLAB_SESSION)
x, y = f.inputs([f.TEST_TF_RECORDS_FILE], shuffle=True, name='inputs')
u.shape_log(x)
u.shape_log(y)
logits = model(x, c)
logits = tf.reshape(logits, [-1, c.MAX_TIME, f.OUTPUT_DIM])
labels = tf.reshape(y, [-1, c.MAX_TIME, f.OUTPUT_DIM])
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
# initialize global variables
sess.run(init_op)
# enable batch fetchers
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
#setup saver
saver = u.saver_ops(sess=sess,
max_to_keep=3,
global_step=p.global_step)
logi = []
labs = []
# run the training operation
for indx in range(6):
log, lab = sess.run([logits, labels])
logi.append(log)
labs.append(lab)
u.display_update(indx, 250)
eng.put_var('logits', logi)
eng.put_var('labels', labs)
coord.request_stop()
coord.join(threads)
