def run_training():
# you need to change the directories to yours.
#train_dir = '/home/kevin/tensorflow/cats_vs_dogs/data/train/'
train_dir = 'D:/tensorflow/mydata/cat_dog2/' #My dir--20170727-csq
#logs_train_dir = '/home/kevin/tensorflow/cats_vs_dogs/logs/train/'
logs_train_dir = 'D:/tensorflow/mylog/cat_dog2/'
train, train_label = input_data.get_files(train_dir)
print(train)
print(train_label)
train_batch, train_label_batch = input_data.get_batch(
train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
print(train_batch)
print(train_label_batch)
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train__acc = model.evaluation(train_logits, train_label_batch)
summary_op = tf.summary.merge_all()
print(summary_op)
with tf.Session() as sess:
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
try:
for step in np.arange(MAX_STEP):
print(step)
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run(
[train_op, train_loss, train__acc])
if step % cnt_summary == 0:
print(
'Step %d, train loss = %.2f, train accuracy = %.2f%%' %
(step, tra_loss, tra_acc * 100.0))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % cnt_cache == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(logs_train_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
import input_data1 import model1 N_CLASSES = 2 IMG_W = 208 IMG_H = 208 BATCH_SIZE = 16 CAPACITY = 2000 MAX_STEP = 10000 learning_rate = 0.0001 train_dir = 'E:\Jupyter\catanddog\ALLPetImages' # train_dir = 'E:\PyCharmProject\mycatvsdog\PetImages' logs_train_dir = 'E:\PyCharmProject\mycatvsdog\log' train, train_label = input_data1.get_file(train_dir) dataset = input_data1.get_batch(train, train_label, BATCH_SIZE) # iterator = dataset.make_initializable_iterator() iterator = dataset.make_one_shot_iterator() next_element = iterator.get_next() train_batch = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_H, IMG_W, 3]) train_label_batch = tf.placeholder(tf.int32, shape=[BATCH_SIZE]) train_logits = model1.mynn_inference(train_batch, BATCH_SIZE, N_CLASSES) train_loss = model1.losses(train_logits, train_label_batch) train_op = model1.training(train_loss, learning_rate) train_acc = model1.evaluation(train_logits, train_label_batch) summary_op = tf.compat.v1.summary.merge_all() # 折线图