训练集的准确度很高，但测试集的准确度不是很高。我曾经尝试过辍学和L2正规化，训练集的准确率可以达到90％以上，但是测试设置达到70％，不知道问题在哪里？参数是否未调整？或其他什么？
答案 0 :(得分：0)
#input.py
# ---------------------
import tensorflow as tf
import os
import numpy as np
import math
def get_files(file_dir, ratio):
'''
Args:
file_dir: file directory
ratio: take ratio % of dataset as validation data
Returns:
list of train_images and train_labels, val_images and val_labels
'''
all_image_list = []
for root, dirs, files in os.walk(file_dir):
for file in files:
all_image_list.append(os.path.join(root, file))
all_label_list = [0] * 640 + [1] * 640
temp = np.array([all_image_list, all_label_list])
temp = temp.transpose()
np.random.shuffle(temp)
np.random.shuffle(temp)
all_image_list = temp[:, 0]
all_label_list = temp[:, 1]
n_sample = len(all_label_list)
n_val = math.ceil(n_sample * ratio)
n_train = n_sample - n_val
tra_images = all_image_list[0:n_train]
tra_labels = all_label_list[0:n_train]
tra_labels = [int(float(i)) for i in tra_labels]
val_images = all_image_list[n_train:-1]
val_labels = all_label_list[n_train:-1]
val_labels = [int(float(i)) for i in val_labels]
return tra_images, tra_labels, val_images, val_labels
def get_batch(image, label, image_W, image_H, batch_size):
'''
Args:
image: list type
label: list type
image_W: image width
image_H: image height
batch_size: batch size
shuffle：shuffle data Bool
Returns:
image_batch: 4D tensor [batch_size, width, height, 3], dtype=tf.float32
label_batch: 1D tensor [batch_size], dtype=tf.int32
'''
image = tf.cast(image, tf.string)
label = tf.cast(label, tf.int32)
input_queue = tf.train.slice_input_producer([image, label])
label = input_queue[1]
labels = tf.one_hot(label, 2, dtype=tf.uint8)
image_contents = tf.read_file(input_queue[0])
image = tf.image.decode_jpeg(image_contents, channels=3)
######################################
# data argumentation should go to here
######################################
image = tf.image.resize_images(image, [image_W, image_H], method=1)
# image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
# image = tf.image.random_flip_left_right(image)
# image = tf.image.random_flip_up_down(image)
# image = tf.subtract(image, VGG_MEAN)
image = tf.image.per_image_standardization(image)
image_batch, label_batch = tf.train.shuffle_batch([image, labels],
batch_size=batch_size,
capacity=88,
min_after_dequeue=64)
# label_batch = tf.reshape(label_batch, [batch_size])
image_batch = tf.cast(image_batch, tf.float32)
return image_batch, label_batch
#tools.py
# ---------------------
import tensorflow as tf
import numpy as np
def conv(layer_name, x, out_channels, kernel_size=[3, 3], stride=[1, 1, 1, 1], is_trainable=True):
'''Convolution op wrapper, use RELU activation after convolution
Args:
layer_name: e.g. conv1, pool1...
x: input tensor, [batch_size, height, width, channels]
out_channels: number of output channels (or comvolutional kernels)
kernel_size: the size of convolutional kernel, VGG paper used: [3,3]
stride: A list of ints. 1-D of length 4. VGG paper used: [1, 1, 1, 1]
is_trainable: if load pretrained parameters, freeze all conv layers.
Depending on different situations, you can just set part of conv layers to be freezed.
the parameters of freezed layers will not change when training.
Returns:
4D tensor
'''
in_channels = x.get_shape()[-1]
with tf.variable_scope(layer_name):
w = tf.get_variable(name='weights',
trainable=is_trainable,
shape=[kernel_size[0], kernel_size[1], in_channels, out_channels],
initializer=tf.contrib.layers.xavier_initializer()) # default is uniform distribution initialization
b = tf.get_variable(name='biases',
trainable=is_trainable,
shape=[out_channels],
initializer=tf.constant_initializer(0.0))
x = tf.nn.conv2d(x, w, stride, padding='SAME', name='conv')
x = tf.nn.bias_add(x, b, name='bias_add')
x = tf.nn.relu(x, name='relu')
return x
def pool(layer_name, x, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], is_max_pool=True):
'''Pooling op
Args:
x: input tensor
kernel: pooling kernel, VGG paper used [1,2,2,1], the size of kernel is 2X2
stride: stride size, VGG paper used [1,2,2,1]
padding:
is_max_pool: boolen
if True: use max pooling
else: use avg pooling
'''
if is_max_pool:
x = tf.nn.max_pool(x, kernel, strides=stride, padding='SAME', name=layer_name)
else:
x = tf.nn.avg_pool(x, kernel, strides=stride, padding='SAME', name=layer_name)
return x
def batch_norm(x):
'''
Batch normlization(I didn't include the offset and scale)
'''
epsilon = 1e-3
batch_mean, batch_var = tf.nn.moments(x, [0])
x = tf.nn.batch_normalization(x,
mean=batch_mean,
variance=batch_var,
offset=None,
scale=None,
variance_epsilon=epsilon)
return x
def FC_layer(layer_name, x, out_nodes):
'''
Wrapper for fully connected layers with RELU activation as default
Args:
layer_name: e.g. 'FC1', 'FC2'
x: input feature map
out_nodes: number of neurons for current FC layer
'''
shape = x.get_shape()
if len(shape) == 4:
size = shape[1].value * shape[2].value * shape[3].value
else:
size = shape[-1].value
with tf.variable_scope(layer_name):
w = tf.get_variable('weights',
shape=[size, out_nodes],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable('biases',
shape=[out_nodes],
initializer=tf.constant_initializer(0.0))
flat_x = tf.reshape(x, [-1, size]) # flatten into 1D
x = tf.nn.bias_add(tf.matmul(flat_x, w), b)
# x = tf.nn.relu(x)
return x
def loss(logits, labels):
'''Compute loss
Args:
logits: logits tensor, [batch_size, n_classes]
labels: one-hot labels
'''
with tf.name_scope('loss') as scope:
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=labels, name='cross-entropy')
# reg = tf.contrib.layers.apply_regularization(tf.contrib.layers.l2_regularizer(0.5), tf.trainable_variables())
# loss = tf.reduce_mean(cross_entropy + reg, name='loss')
loss = tf.reduce_mean(cross_entropy, name='loss')
# tf.summary.scalar(scope+'/loss', loss)
return loss
def accuracy(logits, labels):
"""Evaluate the quality of the logits at predicting the label.
Args:
logits: Logits tensor, float - [batch_size, NUM_CLASSES].
labels: Labels tensor,
"""
with tf.name_scope('accuracy') as scope:
correct = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
correct = tf.cast(correct, tf.float32)
accuracy = tf.reduce_mean(correct) * 100.0
# tf.summary.scalar(scope+'/accuracy', accuracy)
return accuracy
def optimize(loss, learning_rate):
'''
optimization, use Gradient Descent as default
'''
with tf.name_scope('optimizer'):
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
# optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate, momentum=0.9)
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9)
# optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss)
return train_op
def load_with_skip(data_path, session, skip_layer):
data_dict = np.load(data_path, encoding='latin1').item()
keys = sorted(data_dict.keys())
for key in keys:
if key not in skip_layer:
with tf.variable_scope(key, reuse=True):
for subkey, data in zip(('weights', 'biases'), data_dict[key]):
session.run(tf.get_variable(subkey).assign(data))
# vgg.py
# import tensorflow as tf
import tools
def VGG16(x, n_classes, is_trainable=True, keep_prob=1):
x = tools.conv('conv1_1', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=False)
x = tools.conv('conv1_2', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=False)
x = tools.pool('pool1', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv2_1', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.conv('conv2_2', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.pool('pool2', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv3_1', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.conv('conv3_2', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.conv('conv3_3', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv4_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.conv('conv4_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.conv('conv4_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv5_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.conv('conv5_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.conv('conv5_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_trainable=is_trainable)
x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.FC_layer('fc6', x, out_nodes=4096)
x = tools.batch_norm(x)
x = tf.nn.relu(x)
relu6 = tf.nn.dropout(x, keep_prob=keep_prob)
x = tools.FC_layer('fc7', relu6, out_nodes=4096)
x = tools.batch_norm(x)
x = tf.nn.relu(x)
relu7 = tf.nn.dropout(x, keep_prob=keep_prob)
x = tools.FC_layer('fc8', relu7, out_nodes=n_classes)
relu8 = tf.nn.relu(x)
return relu8, x
# train.py
import time
import tensorflow as tf
import input
import vgg
import tools
# import os
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
num_class = 2
img_w = 224
img_h = 224
batch_size = 8
n_epoch = 100
learning_rate = 0.00001
is_Trainable = True
ratio = 0.3
num_images = 1280
n_step_epoch = int(num_images * (1 - ratio) / batch_size)
n_step = n_epoch * n_step_epoch
def run_training():
pre_trained_weights = r'F:\DrProject\vgg16_pretrain\vgg16.npy'
image_dir = r'G:\DR数据集\Messidor\png\\'
logs_train_dir = r'D:\logs\log\log1\train\\'
logs_val_dir = r'D:\logs\log\log1\test\\'
logs_model_dir = r'D:\logs\log\log1\model\\'
train, train_label, val, val_label = input.get_files(image_dir, ratio)
train_batch, train_label_batch = input.get_batch(train, train_label, img_w, img_h, batch_size)
val_batch, val_label_batch = input.get_batch(val, val_label, img_w, img_h, batch_size)
x = tf.placeholder(tf.float32, shape=[batch_size, img_w, img_h, 3])
y_ = tf.placeholder(tf.int32, shape=[batch_size, num_class])
keep_prob = tf.placeholder(tf.float32)
loss_mean = tf.placeholder(tf.float32)
acc_mean = tf.placeholder(tf.float32)
logits, score = vgg.VGG16(x, num_class, is_trainable=is_Trainable)
loss = tools.loss(logits, y_)
acc = tools.accuracy(logits, y_)
train_op = tools.optimize(loss, learning_rate)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# loss_scalar_train = tf.summary.scalar('train/loss', loss)
# accuracy_scalar_train = tf.summary.scalar('train/accuracy', acc)
loss_mean_scalar_train = tf.summary.scalar('train/loss_mean', loss_mean)
acc_mean_scalar_train = tf.summary.scalar('train/acc_mean', acc_mean)
# accuracy_scalar_test = tf.summary.scalar('test/accuracy', acc)
acc_mean_scalar_test = tf.summary.scalar('test/acc_mean', acc_mean)
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
validation_writer = tf.summary.FileWriter(logs_val_dir, sess.graph)
saver = tf.train.Saver()
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)
step = 0
step_test = 0
try:
for epoch in range(n_epoch):
start_time = time.time()
if coord.should_stop():
break
train_loss, train_acc, n_batch = 0, 0, 0
for i in range(n_step_epoch):
tra_images, tra_labels = sess.run([train_batch, train_label_batch])
_, loss1, acc1 = sess.run([train_op, loss, acc], feed_dict={x: tra_images, y_: tra_labels,
keep_prob: 0.5})
step += 1
train_loss += loss1
train_acc += acc1
n_batch += 1
# sum_loss_train, sum_accuracy_train, mean_loss_train, mean_acc_train = sess.run(
# [loss_scalar_train, accuracy_scalar_train, loss_mean_scalar_train, acc_mean_scalar_train],
# feed_dict={x: tra_images, y_: tra_labels, loss_mean: train_loss / n_batch,
# acc_mean: train_acc / n_batch})
mean_loss_train, mean_acc_train = sess.run([loss_mean_scalar_train, acc_mean_scalar_train],
feed_dict={x: tra_images, y_: tra_labels,
loss_mean: train_loss / n_batch,
acc_mean: train_acc / n_batch})
# train_writer.add_summary(sum_loss_train, step)
# train_writer.add_summary(sum_accuracy_train, step)
train_writer.add_summary(mean_acc_train, step)
train_writer.add_summary(mean_loss_train, step)
print("\nEpoch %d : Step %d-%d of %d took %fs" % (epoch + 1, step, step + n_step_epoch, n_step,
time.time() - start_time))
print(" train loss: %f" % (train_loss / n_batch))
print(" train acc: %f %%" % (train_acc / n_batch))
test_loss, test_acc, n_batch = 0, 0, 0
for j in range(int(num_images * ratio / batch_size)):
val_images, val_labels = sess.run([val_batch, val_label_batch])
err, ac = sess.run([loss, acc], feed_dict={x: val_images, y_: val_labels, keep_prob: 1})
step_test += 1
test_loss += err
test_acc += ac
n_batch += 1
# sum_accuracy_test, mean_acc_test = sess.run(
# [accuracy_scalar_test, acc_mean_scalar_test], feed_dict={x: val_images, y_: val_labels,
# loss_mean: test_loss / n_batch, acc_mean: test_acc / n_batch})
mean_acc_test = sess.run(acc_mean_scalar_test, feed_dict={x: val_images, y_: val_labels,
acc_mean: test_acc / n_batch})
# validation_writer.add_summary(sum_accuracy_test, step_test)
validation_writer.add_summary(mean_acc_test, step_test)
print(' ------------------')
print(" test loss: %f" % (test_loss / n_batch))
print(" test acc: %f %%" % (test_acc / n_batch))
if (epoch + 1) % 5 == 0 or (epoch + 1) == n_epoch:
print("Save model !")
saver.save(sess, logs_model_dir+'model.ckpt', global_step=epoch + 1)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
train_writer.close()
validation_writer.close()
coord.join(threads)
sess.close()
if __name__ == '__main__':
run_training()
enter code here