def train_network(training_image_num):
global_step = tf.Variable(0, trainable=False)
image_inputs = tf.placeholder(
tf.float32,
(ct.BATCH_SIZE, ct.INPUT_SIZE, ct.INPUT_SIZE, ct.IMAGE_CHANNEL * 2),
'inputs')
label_inputs = tf.placeholder(tf.float32, (ct.BATCH_SIZE, ct.CLASS_NUM),
'outputs')
nn_output = foward_propagation(image_inputs)
# nn_output = tf.clip_by_value(nn_output,1e-15,1.0)
# nn_output = nn_output/50000
# nn_softmax = tf.nn.softmax(nn_output)
cross_entropy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=nn_output, labels=tf.argmax(label_inputs, 1))
cross_entropy_loss_mean = tf.reduce_mean(cross_entropy_loss)
loss_func = cross_entropy_loss_mean
learning_rate = tf.train.exponential_decay(
ct.LEARNING_RATE_INIT, global_step, training_image_num / ct.BATCH_SIZE,
ct.LEARNING_DECAY_RATE)
train_step = tf.train.AdamOptimizer(learning_rate).minimize(
loss_func, global_step)
image_batch_tensor, label_batch_tensor = readImageBatchFromTFRecord(
ct.CATELOGS[0])
saver = tf.train.Saver()
with tf.Session() as sess:
tf.local_variables_initializer().run()
tf.global_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(ct.STEPS + 1):
image_batch, label_batch = sess.run(
[image_batch_tensor, label_batch_tensor])
_, loss_val, step = sess.run(
[train_step, cross_entropy_loss_mean, global_step],
feed_dict={
image_inputs: image_batch,
label_inputs: label_batch
})
# output = sess.run(nn_output,
# feed_dict= {image_inputs:image_batch,label_inputs:label_batch})
# print(output)
if not (i % 100):
print('after %d iteration, loss is %g' % (step, loss_val))
if not (i % 1000):
saver.save(sess,
os.path.join(ct.MODEL_SAVE_PATH, ct.MODEL_NAME),
global_step)
coord.request_stop()
coord.join(threads)
def train_network(training_image_num):
global_step = tf.Variable(0, trainable=False)
curr_image_inputs = tf.placeholder(
tf.float32,
(ct.BATCH_SIZE, ct.INPUT_SIZE, ct.INPUT_SIZE, ct.IMAGE_CHANNEL),
'curr_inputs')
hist_image_inputs = tf.placeholder(
tf.float32,
(ct.BATCH_SIZE, ct.INPUT_SIZE, ct.INPUT_SIZE, ct.IMAGE_CHANNEL),
'hist_inputs')
label_inputs = tf.placeholder(tf.float32, (ct.BATCH_SIZE, ct.CLASS_NUM),
'outputs')
nn_output, end_points = forward_propagation(curr_image_inputs,
hist_image_inputs)
# print(end_points)
# output_max = tf.reduce_max(nn_output, axis=1)
# nn_output = tf.clip_by_value(nn_output,1e-8,1.0)
# nn_output = nn_output/50000
# temp =tf.nn.softmax(nn_output)
# nn_softmax = tf.clip_by_value(tf.nn.softmax(nn_output),1e-10,1.0)
# cross_entropy_loss = label_inputs * tf.log(nn_softmax)
cross_entropy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=nn_output, labels=tf.argmax(label_inputs, 1))
cross_entropy_loss_mean = tf.reduce_mean(cross_entropy_loss)
# loss_func = cross_entropy_loss_mean
learning_rate = tf.train.exponential_decay(
ct.LEARNING_RATE_INIT, global_step, training_image_num / ct.BATCH_SIZE,
ct.LEARNING_DECAY_RATE)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies([tf.group(*update_ops)]):
train_step = slim.learning.create_train_op(cross_entropy_loss_mean,
optimizer,
global_step=global_step)
# train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss_func, global_step)
curr_img_batch_tensor, hist_img_batch_tensor, label_batch_tensor = readImageBatchFromTFRecord(
ct.CATELOGS[0])
saver = tf.train.Saver()
isFileExist(ct.MODEL_SAVE_PATH)
with tf.Session() as sess:
tf.local_variables_initializer().run()
tf.global_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
start_time = time.time()
for i in range(ct.STEPS + 1):
curr_img_batch, hist_img_batch, label_batch = sess.run([
curr_img_batch_tensor, hist_img_batch_tensor,
label_batch_tensor
])
_, loss_val, step = sess.run(
[train_step, cross_entropy_loss_mean, global_step],
feed_dict={
curr_image_inputs: curr_img_batch,
hist_image_inputs: hist_img_batch,
label_inputs: label_batch
})
if not (i % 100):
print('after %d iteration, loss is %g' % (step, loss_val))
duration = time.time() - start_time
print('duration is %g' % duration)
if not (i % 1000):
saver.save(sess,
os.path.join(ct.MODEL_SAVE_PATH, ct.MODEL_NAME),
global_step)
# a,b = cv2.split(image_batch[0])
# cv2.namedWindow('1',0)
# cv2.namedWindow('2',0)
# cv2.imshow('1',a)
# cv2.imshow('2',b)
# cv2.waitKey()
# print(label_batch)
# output = sess.run(nn_softmax,
# feed_dict= {image_inputs:image_batch,label_inputs:label_batch})
# print(output)
# output = sess.run(temp,
# feed_dict= {image_inputs:image_batch,label_inputs:label_batch})
# print(output)
coord.request_stop()
coord.join(threads)