def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
image_lists = dog_cat.create_image_lists(FLAGS.image_dir,
FLAGS.testing_percentage,
FLAGS.validation_percentage)
class_count = len(image_lists.keys())
if class_count == 0:
tf.logging.error('No valid folders of images found at ' +
FLAGS.image_dir)
return -1
if class_count == 1:
tf.logging.error('Only one valid folder of images found at ' +
FLAGS.image_dir +
' - multiple classes are needed for classification.')
return -1
with tf.Session() as sess:
(test_cached_tensor, test_ground_truth,
_) = get_random_cached_bottlenecks(sess, image_lists,
FLAGS.test_batch_size, 'testing',
FLAGS.cache_dir, FLAGS.image_dir)
logits = VGG16.vgg16_net(tf.convert_to_tensor(test_cached_tensor),
class_count)
correct = num_correct_prediction(logits, test_ground_truth)
saver = tf.train.Saver(tf.global_variables())
print("Reading checkpoints...")
ckpt = tf.train.get_checkpoint_state('model')
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
print('Loading success, global_step is %s' % global_step)
else:
print('No checkpoint file found')
return
print('\nEvaluating......')
batch_correct = sess.run(correct)
print('Total testing samples: %d' % len(test_ground_truth))
print('Total correct predictions: %d' % batch_correct)
print('Average accuracy: %.2f%%' %
(100 * batch_correct / len(test_ground_truth)))
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
image_lists = create_image_lists(FLAGS.image_dir, FLAGS.testing_percentage,
FLAGS.validation_percentage)
class_count = len(image_lists.keys())
if class_count == 0:
tf.logging.error('No valid folders of images found at ' + FLAGS.image_dir)
return -1
if class_count == 1:
tf.logging.error('Only one valid folder of images found at ' +
FLAGS.image_dir +
' - multiple classes are needed for classification.')
return -1
x = tf.placeholder(tf.float32, shape=[None, 224, 224, 3])
y_ = tf.placeholder(tf.float32, shape=[None, class_count])
logits = VGG16.vgg16_net(x,class_count)
loss = caculateloss(logits, y_)
acc = accuracy(logits, y_)
my_global_step = tf.Variable(0, name='global_step', trainable=False)
train_step = optimize(loss, FLAGS.learning_rate,my_global_step)
#with graph.as_default():
# (train_step, cross_entropy,bottleneck_input,input_groud_truth) = train(output_tensor)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
#print(tf.trainable_variables())
load_with_skip('vgg16.npy', sess, ['fc6', 'fc7', 'fc8'])
jpeg_data_tensor, decoded_image_tensor = add_jpeg_decoding()
cache_bottleneck(sess, image_lists, FLAGS.image_dir,
FLAGS.cache_dir, jpeg_data_tensor,
decoded_image_tensor, x)
# 评估预测准确率
#evaluation_step, _ = add_evaluation_step(output_tensor, ground_truth_input)
saver = tf.train.Saver(tf.global_variables())
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
sess.graph)
validation_writer = tf.summary.FileWriter(
FLAGS.summaries_dir + '/validation')
train_saver = tf.train.Saver()
for i in range(FLAGS.how_many_training_steps):
(train_cached_tensor,
train_ground_truth, _) = get_random_cached_bottlenecks(
sess, image_lists, FLAGS.train_batch_size, 'training',
FLAGS.cache_dir, FLAGS.image_dir, jpeg_data_tensor,
decoded_image_tensor, x)
_, tra_loss, tra_acc = sess.run([train_step,loss,acc],feed_dict={x:train_cached_tensor, y_:train_ground_truth})
print('Step: %d, loss: %.4f, accuracy: %.4f%%' % (i, tra_loss, tra_acc))
is_last_step = (i + 1 == FLAGS.how_many_training_steps)
# 训练完成或每完成eval_step_interval各batch训练，打印准确率和交叉熵
if (i % FLAGS.eval_step_interval) == 0 or is_last_step:
(validation_cached_tensor,
validation_ground_truth, _) = get_random_cached_bottlenecks(
sess, image_lists, FLAGS.validation_batch_size, 'validation',
FLAGS.cache_dir, FLAGS.image_dir, jpeg_data_tensor,
decoded_image_tensor, x)
val_loss, val_acc = sess.run([loss, acc],
feed_dict={x: validation_cached_tensor, y_: validation_ground_truth})
print('** Step %d, val loss = %.2f, val accuracy = %.2f%% **' % (i, val_loss, val_acc))
if i % 2000 == 0 or is_last_step:
checkpoint_path = os.path.join('model', 'model.ckpt')
saver.save(sess, checkpoint_path,global_step=i)