当我使用VGG16模型训练分类器时，我遇到了问题。
当我在转换层之后使用批量标准化时，训练精度似乎在增加，但是，验证准确性总是不会增加。
我也做了一个比较实验。当我删除代码with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params):时，性能正常，验证准确性也在提高。
def VGG16Model(input_tensor, weight_decay, is_training):
batch_norm_params = {
'decay': 0.997,
'epsilon': 1e-5,
'scale': True,
'is_training': is_training
}
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay)):
with slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params):
input_tensor = tf.image.resize_images(input_tensor, [224, 224])
input_tensor = tf.concat([input_tensor, input_tensor, input_tensor], axis=-1)
_, end_points = vgg.vgg_16(input_tensor, is_training=is_training)
logits = end_points['vgg_16/pool5']
feature = tf.reduce_mean(logits, reduction_indices=[1, 2])
fc1 = tf.contrib.layers.fully_connected(feature, num_outputs=1024)
fc2 = tf.contrib.layers.fully_connected(fc1, num_outputs=512)
fc3 = tf.contrib.layers.fully_connected(fc2, num_outputs=10)
return fc3
def train():
batch_size = 64
train_total = 1920000
epoch_num = 5
input_image_tensor = tf.placeholder(dtype=tf.float32, shape=[None, 40, 40, 1], name='image-input')
input_label_tensor = tf.placeholder(dtype=tf.int32, shape=[None, 10], name='label-input')
global_step = tf.Variable(initial_value=0, trainable=False)
predicted_tensor = VGG16Model(input_image_tensor, weight_decay=1e-5, is_training=True)
cross_entropy_loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=predicted_tensor, labels=input_label_tensor))
optimizer = tf.train.AdamOptimizer(learning_rate=0.000005)
train_op = optimizer.minimize(cross_entropy_loss, global_step=global_step)
accuracy = tf.contrib.metrics.accuracy(labels=tf.argmax(input_label_tensor, axis=1),
predictions=tf.argmax(tf.nn.softmax(predicted_tensor), axis=1))
saver = tf.train.Saver(tf.global_variables(), max_to_keep=10)
reader = Reader(
'/home/give/homework/cv/dataset/affiNist/training_and_validation_batches',
'/home/give/homework/cv/dataset/affiNist/test.mat',
batch_size=batch_size
)
print 'reader operation finished!'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
if FLAGS.pretrained_flag:
variable_restore_op = slim.assign_from_checkpoint_fn(FLAGS.pretrained_model_path,
slim.get_trainable_variables(),
ignore_missing_vars=True)
with tf.Session(config=config) as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
if FLAGS.pretrained_flag:
print variable_restore_op
variable_restore_op(sess)
if FLAGS.restore_flag:
ckpt = tf.train.latest_checkpoint(FLAGS.restore_model_path)
print('continue training from previous checkpoint from %s' % ckpt)
start_step = int(os.path.basename(ckpt).split('-')[1])
variable_restore_op = slim.assign_from_checkpoint_fn(ckpt,
slim.get_trainable_variables(),
ignore_missing_vars=True)
variable_restore_op(sess)
sess.run(tf.assign(global_step, start_step))
step = 0
for epoch_id in range(epoch_num):
start = 0
while start < train_total:
step += 1
end = start + batch_size
if end > train_total:
end = train_total
cur_batch_images = reader.train_images[start:end]
cur_batch_labels = reader.train_labels[start:end]
feed_dict = {
input_image_tensor: np.expand_dims(cur_batch_images, axis=3),
input_label_tensor: keras.utils.to_categorical(cur_batch_labels, num_classes=10)
}
_, train_loss, train_acc = sess.run([train_op, cross_entropy_loss, accuracy], feed_dict=feed_dict)
print '[%d --> %d] / %d, training loss: %.4f, training acc: %.4f' % (
start, end, train_total, train_loss, train_acc)
start = end
if step % 100 == 0:
print 'save model at ', FLAGS.save_model_path + 'model.ckpt'
saver.save(sess, FLAGS.save_model_path + 'model.ckpt', global_step=global_step)
def test():
# tensor
input_image_tensor = tf.placeholder(dtype=tf.float32, shape=[None, 40, 40, 1], name='image-input')
predicted_tensor = VGG16Model(input_image_tensor, is_training=False, weight_decay=1e-5)
predicted_tensor = tf.nn.softmax(predicted_tensor)
global_step = tf.Variable(initial_value=0, trainable=False)
# hyper-parameters
test_total = 320000
batch_size = 70
epoch_num = 1
reader = Reader(
'/home/give/homework/cv/dataset/affiNist/training_and_validation_batches',
'/home/give/homework/cv/dataset/affiNist/test.mat',
batch_size=batch_size
)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
ckpt = tf.train.latest_checkpoint(FLAGS.restore_model_path)
print('continue training from previous checkpoint from %s' % ckpt)
start_step = int(os.path.basename(ckpt).split('-')[1])
variable_restore_op = slim.assign_from_checkpoint_fn(ckpt,
slim.get_trainable_variables(),
ignore_missing_vars=True)
variable_restore_op(sess)
sess.run(tf.assign(global_step, start_step))
start = 0
predicted = []
while start < test_total:
end = start + batch_size
if end > test_total:
end = test_total
cur_batch_images = reader.test_images[start:end]
predicted_array = sess.run(tf.argmax(predicted_tensor, axis=1), feed_dict={
input_image_tensor: np.expand_dims(cur_batch_images, axis=3)
})
predicted.extend(predicted_array)
print 'Batch Accuracy[%d, %d] : %.4f' % (
start, test_total, np.mean(np.asarray(predicted_array == reader.test_labels[start:end], np.float32)))
start = end
predicted = np.array(predicted)
print 'Total Accuracy: ', np.mean(np.asarray(predicted == reader.test_labels, np.float32))
calculate_acc_error(predicted, reader.test_labels)