def run_training():
train_dir = 'E:/Code/Dog vs Cat/train/'
logs_train_dir = 'E:/Code/Dog vs Cat/log/'
train, train_label = input_data.get_file(train_dir)
train_batch, train_label_batch = input_data.get_batch(
train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.training(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)
summary_op = tf.summary.merge_all()
sess = tf.Session()
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)
time_start = time.time()
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
if step % 50 == 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 % 2000 == 0:
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()
time_end = time.time()
train_time = time_end - time_start
print("train time:", train_time)
coord.join(threads)
sess.close()
def train():
image_list, label_list = input_data.get_file(TRAIN_DIR)
image_batch, label_batch = input_data.get_batch(image_list, label_list,
IMG_H, IMG_W, BATCH_SIZE,
CAPACITY)
logits = model_fcn.inference(image_batch, True)
train_loss = model_fcn.loss(logits, label_batch)
train_op = model_fcn.backpropagation(train_loss, LEARNING_RATE)
accuracy = model_fcn.accuracy(logits, label_batch)
summary_op = tf.summary.merge_all()
with tf.Session() as sess:
train_writer = tf.summary.FileWriter(LOGS_DIR, sess.graph)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator() # 多线程
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
_, loss, tra_acc = sess.run(
[train_op, train_loss, accuracy * 100.0])
if step % 50 == 0:
print('step %d, train loss = %.2f, accuracy = %.2f%%' %
(step, loss, tra_acc))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(LOGS_DIR, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
coord.request_stop()
coord.join(threads)
def train():
pre_trained_weights = './vgg_weights/vgg16.npy'
train_dir = 'E:/Code/Dog vs Cat/train/'
train_log_dir = './logs_vgg/'
with tf.name_scope('input'):
image_list, label_list = input_data.get_file(train_dir)
image_batch,label_batch = input_data.get_batch(image_list, label_list, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMG_H, IMG_W, 3])
y_ = tf.placeholder(tf.int32, shape=[BATCH_SIZE])
logits = VGG.VGG16N(x, N_CLASSES, IS_PRETRAIN)
loss = tools.loss(logits, y_)
accuracy = tools.accuracy(logits, y_)
my_global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = tools.optimize(loss, learning_rate, my_global_step)
saver = tf.train.Saver(tf.global_variables())
summary_op = tf.summary.merge_all()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
# load the parameter file, assign the parameters, skip the specific layers
tools.load_with_skip(pre_trained_weights, sess, ['fc6', 'fc7', 'fc8'])
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
tra_summary_writer = tf.summary.FileWriter(train_log_dir, sess.graph)
# val_summary_writer = tf.summary.FileWriter(val_log_dir, sess.graph)
try:
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
tra_images, tra_labels = sess.run([image_batch, label_batch])
_, tra_loss, tra_acc = sess.run([train_op, loss, accuracy],
feed_dict={x: tra_images, y_: tra_labels})
if step % 50 == 0 or (step + 1) == MAX_STEP:
print('Step: %d, loss: %.4f, accuracy: %.4f%%' % (step, tra_loss, tra_acc))
summary_str = sess.run(summary_op)
tra_summary_writer.add_summary(summary_str, step)
# if step % 200 == 0 or (step + 1) == MAX_STEP:
# val_images, val_labels = sess.run([val_image_batch, val_label_batch])
# val_loss, val_acc = sess.run([loss, accuracy],
# feed_dict={x: val_images, y_: val_labels})
# print('** Step %d, val loss = %.2f, val accuracy = %.2f%% **' % (step, val_loss, val_acc))
#
# summary_str = sess.run(summary_op)
# val_summary_writer.add_summary(summary_str, step)
if step % 2000 == 0 or (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(train_log_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()
tf.summary.scalar('loss', cost),
tf.summary.scalar('accuracy', accuracy),
])
init = tf.global_variables_initializer()
# Launch the graph
writer = tf.summary.FileWriter(tf_logs)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
writer.add_graph(sess.graph)
# Keep training until reach max iterations
progress = tqdm(range(training_iters))
train_images, train_labels = input_data.get_file(train_dir)
for batch_idx in progress:
batch_x, batch_y = input_data.get_batch(train_images, train_labels,
image_w, image_h, 10, 50)
# Run optimization op (backprop)
sess.run(optimizer,
feed_dict={
x: batch_x.eval(session=sess),
y: batch_y.eval(session=sess),
keep_prob: dropout
})
if batch_idx % display_step == 0:
summary, loss, acc = sess.run(
[summary_op, cost, accuracy],
feed_dict={
import input_data
import model
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_data.get_file(train_dir)
train_batch, train_label_batch = input_data.get_batch(train, train_label,
IMG_W, IMG_H, BATCH_SIZE,
CAPACITY)
train_logits = model.mynn_inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.training(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)
summary_op = tf.compat.v1.summary.merge_all()
# 折线图
step_list = list(range(100))
cnn_list1 = []
cnn_list2 = []
import input_data as data
train_dir = r'/Volumes/learn/learning-package/project/code_git/data_preprocessing/Training/'
IMG_W = 28
IMG_H = 28
TRAIN_BATCH_SIZE = 32
# 遍历训练集文件夹下的图片，输出图片list和label的list
train,train_labels = data.get_file(train_dir)
# 输出预训练的batch数据
train_batch,train_label_batch = data.get_batch(train,train_labels,IMG_W,IMG_H,TRAIN_BATCH_SIZE)
print(train_label_batch)
print('\n')
print(train_batch)