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train_net_24.py
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train_net_24.py
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import tensorflow as tf
import numpy as np
from image_inputs import inputs
from models import fcn_24_detect
import h5py
def train():
f = h5py.File('net_24_neg_for_train.hdf5','r')
imgs_neg = f['imgs'][:]
neg_len = len(imgs_neg)
lists = ['net_pos_list.txt','net_pos_flip_list.txt']
image_train, label_train = inputs(lists, [24, 24, 3], 12)
net_output = fcn_24_detect(0.2)
global_step = tf.Variable(0, tf.int32)
starter_learning_rate = 0.005
learning_rate = tf.train.exponential_decay(
learning_rate=starter_learning_rate,
global_step=global_step,
decay_steps=1000,
decay_rate=0.99,
staircase=True,
name=None)
opt_vars_24 = [v for v in tf.trainable_variables() if v.name.startswith('net_24')]
train_step = tf.train.AdamOptimizer(learning_rate).minimize(net_output['cost'], var_list=opt_vars_24, global_step=global_step)
sess = tf.Session()
opt_vars_12 = [v for v in tf.trainable_variables() if v.name.startswith('net_12')]
saver_12 = tf.train.Saver(opt_vars_12)
saver_24 = tf.train.Saver(tf.trainable_variables())
# import pdb; pdb.set_trace()
sess.run(tf.initialize_all_variables())
coord = tf.train.Coordinator()
# tf.get_default_graph().finalize()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver_12.restore(sess, 'model/model_net_12-400000')
try:
for i in range(400000):
sn_rand = np.random.random_integers(0, neg_len-1, 50)
imgs_pos = sess.run(image_train)
imgs = np.vstack([imgs_pos,imgs_neg[sn_rand]])
imgs_12 = []
for img in imgs:
im = img.copy()
im.resize((12, 12, 3))
imgs_12.append(im)
imgs_12 = np.array(imgs_12)
labels = np.vstack([np.ones((12,1)), np.zeros((50,1))])
sn_shf = np.array(random.sample(range(62), 62))
imgs = imgs[sn_shf]
imgs_12 = imgs_12[sn_shf]
labels = labels[sn_shf]
if i%10000==0 and i!=0:
saver_24.save(sess, 'model/model_net_24', global_step=global_step, write_meta_graph=False)
if i%1==0:
feed_dict = {
net_output['imgs']: imgs,
net_output['labels']: labels,
net_output['imgs_12']: imgs_12,
net_output['labels_12']: labels,
net_output['keep_prob']: 1.0,
net_output['keep_prob_12']: 1.0
}
cost, accuracy, recall, lr = sess.run(
[net_output['cost'], net_output['accuracy'], net_output['recall'], learning_rate],
feed_dict=feed_dict)
print("Step %d, cost: %f, acc: %f, recall: %f, lr: %f"%(i, cost, accuracy, recall, lr))
# print("target: ", target)
# print("pred: ", pred)
# train
feed_dict = {
net_output['imgs']: imgs,
net_output['labels']: labels,
net_output['imgs_12']: imgs_12,
net_output['labels_12']: labels,
net_output['keep_prob']: 0.8,
net_output['keep_prob_12']: 0.8
}
sess.run(train_step, feed_dict=feed_dict)
coord.request_stop()
except Exception as e:
coord.request_stop(e)
finally:
print('Done training.')
saver_24.save(sess, 'model/model_net_24', global_step=global_step, write_meta_graph=False)
coord.request_stop()
coord.join(threads)
sess.close()
if __name__ == '__main__':
train()