tensorboard

# 指定目录
LOG_DIR = '.'
run_label = 'run_vgg_tensorboard_fine_tune'
run_dir = os.path.join(LOG_DIR, run_label)
if not os.path.join(run_dir):os.mkdir(run_dir)
train_log_dir = os.path.join(run_dir, 'train')
test_log_dir = os.path.join(run_dir, 'test')# 命名name
conv1_1 = tf.layers.conv2d(x_image,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv1_1')def variable_summary(var, name):"""Constructs summary for statistics of a variable"""with tf.name_scope(name):mean = tf.reduce_mean(var)with tf.name_scope('stddev'):stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))tf.summary.scalar('mean', mean)tf.summary.scalar('stddev', stddev)tf.summary.scalar('min', tf.reduce_min(var))tf.summary.scalar('max', tf.reduce_max(var))tf.summary.histogram('histogram', var)with tf.name_scope('summary'):variable_summary(conv1_1, 'conv1_1')variable_summary(conv1_2, 'conv1_2')variable_summary(conv2_1, 'conv2_1')variable_summary(conv2_2, 'conv2_2')variable_summary(conv3_1, 'conv3_1')variable_summary(conv3_2, 'conv3_2')
...
# 写入train_writer = tf.summary.FileWriter(train_log_dir, sess.graph)test_writer = tf.summary.FileWriter(test_log_dir)train_summary_str = eval_ops_results[-1]train_writer.add_summary(train_summary_str, i+1)test_summarys_str = sess.run([megred_summary_test],feed_dict={x: fixed_test_batch_data,y: fixed_test_batch_labels,    })[0]test_writer.add_summary(test_summarys_str, i+1)

3.tensorboard --host=*** --logdir=文件目录

fine-tune(微改其他人的模型)

1.save models

model_dir = os.path.join(run_dir, 'model')
if not os.path.exists(model_dir):os.mkdir(model_dir)
saver = tf.train.Saver()
...
if (i+1) % output_model_every_steps == 0:saver.save(sess, os.path.join(model_dir, 'ckp-{}'.format(i+1)))print('model saved to ckp-{}'.format(i+1))

2.restore models

model_name = 'ckp-1000'
model_path = os.path.join(model_dir, model_name)
....if os.path.exists(model_path + '.index'):saver.restore(sess, model_path)print('model restore from {}'.format(model_path))else:print('model {} does not exist'.format(model_path))

activation:relu, sigmoid,tanh
weight initializer: he,xavier,normal
optimzier:Adam,Momentum,Gradient Descent

def convent(inputs, activation, kernel_initializer):conv1_1 = tf.layers.conv2d(inputs,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,kernel_initializer = kernel_initializername = 'conv1_1')
# activation :tf.nn.relu,tf.nn.sigmoid,tf.nn.tanh
# kernel_initializer:默认为tf.glorot_normal_initializer,
# tf.truncated_normal_initializer(stddev=0.02)
sigmoid:53.39% vs relu:73.35% on 10k train
tf.glorot_normal_initializer 76.53%, 100k train.
tf.truncated_normal_initializer(stddev=0.02) :78.04% 100k train
tf.keras.initializers.he_normal: 71.52%         100k train (deep net)

with tf.name_scope('train_op'):train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)    # 78.04%tf.train.GradientDescentOptimizer(1e-4).minimize(loss)     # 12.35%# reason: 1. initializer incorrect. 2.不充分的训练tf.train.MomentumOptimizer(learning_rate=1e-4, momentum=0.9).minimize(loss) #35.75%

数据增强api

resize (缩放图像)
crop (裁剪)
flip (翻转)
brightness (改变光照) & contrast (改变对比度)

基本库

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import  imshow

resize api

tf.image.resize_area
tf.image.resize_bicubic # 图像缩放,双线性算法插入
tf.image.resize_nearest_neighbor # 图像放大,使用相近的像素点插入放大的像素点

# 图片放大
img_string = tf.read_file(picture1)
# img_string2 = tf.read_file(picture2)
img_decoded = tf.image.decode_image(img_string)
# sess多图片,使用四维矩阵,[index, 宽, 长, 通道数]
# resizeimg_decoded = tf.reshape(img_decoded, [1, 1200, 1400, 3])
# 缩小
resize_img = tf.image.resize_bicubic(img_decoded, [2400, 2800])
sess = tf.Session()
img_decoded_val = sess.run(resize_img)
img_decoded_val = img_decoded_val.reshape((2400, 2800, 3))
img_decoded_val = np.asarray(img_decoded_val, np.uint8)
print(img_decoded_val.shape)imshow(img_decoded_val)
# imshow(img_decoded_val2)pylab.show()

# crop 裁剪
tf.image.pad_to_bounding_box()
tf.image.crop_to_bounding_box()
tf.random_crop()img_decoded = tf.image.decode_image(img_string)img_decoded = tf.reshape(img_decoded, [1, 1200, 1400, 3])
# crop
padded_img = tf.image.pad_to_bounding_box(img_decoded, 100, 200, 1500, 1600) # arg:[0:2]画布中的位置,[2:4]画布大小
sess = tf.Session()
img_decoded_val = sess.run(padded_img)
img_decoded_val = img_decoded_val.reshape((1500, 1600, 3))
img_decoded_val = np.asarray(img_decoded_val, np.uint8)
print(img_decoded_val.shape)imshow(img_decoded_val)
# imshow(img_decoded_val2)pylab.show()

flip

tf.image.flip_up_down()
tf.image.flip_left_right()
tf.image.random_flip_left_right()
tf.image.random_flip_up_down()ex:
padded_img = tf.image.random_flip_left_right(img_decoded)

brightness

tf.image.adjust_brightness()
tf.image.random_brightness()
tf.image.adjust_contrast()
tf.image.random_contrast()

ex:
padded_img = tf.image.adjust_brightness(img_decoded, -0.5) # -0.5 为光强减少50%

实战

# train 100k: 78.04%  -> 82.6%
x_image = tf.reshape(x, [-1, 3, 32, 32])
x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])
data_aug_1 = tf.image.random_flip_left_right(x_image)
data_aug_2 = tf.image.random_brightness(data_aug_1, max_delta=63)
data_aug_3 = tf.image.random_contrast(data_aug_2, lower=0.2, upper=1.8)normal_data_aug_3 = data_aug_3 / 127.5 - 1

继续加深网络层次

conv1_1 = tf.layers.conv2d(normal_data_aug_3,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv1_1')
conv1_2 = tf.layers.conv2d(conv1_1,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv1_2')
conv1_3 = tf.layers.conv2d(conv1_2,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv1_3')# 16 * 16
pooling1 = tf.layers.max_pooling2d(conv1_3,(2, 2), # kernel size(2, 2), # stridename = 'pool1')conv2_1 = tf.layers.conv2d(pooling1,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv2_1')
conv2_2 = tf.layers.conv2d(conv2_1,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv2_2')
conv2_3 = tf.layers.conv2d(conv2_2,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv2_3')
# 8 * 8
pooling2 = tf.layers.max_pooling2d(conv2_3,(2, 2), # kernel size(2, 2), # stridename = 'pool2')conv3_1 = tf.layers.conv2d(pooling2,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv3_1')
conv3_2 = tf.layers.conv2d(conv3_1,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv3_2')
conv3_3 = tf.layers.conv2d(conv3_2,32, # output channel number(3,3), # kernel sizepadding = 'same',activation = tf.nn.relu,name = 'conv3_3')
# 4 * 4 * 32
pooling3 = tf.layers.max_pooling2d(conv3_2,(2, 2), # kernel size(2, 2), # stridename = 'pool3')

归一化

train 100K :85.6%


def conv_wrapper(inputs, name, is_training,output_channel=32, kernel_size=(3,3),activation=tf.nn.relu,padding = 'same'):"""wrapper of tf.laysers.conv2d"""# without bn: conv  -> activation# with batch normalization: conv -> bn -> activationwith tf.name_scope(name):conv2d = tf.layers.conv2d(inputs,output_channel,kernel_size,padding= padding,activation= None,name= name+'/conv2d')bn = tf.layers.batch_normalization(conv2d,training= is_training)return activation(bn)def pooling_warpper(inputs, name):return tf.layers.max_pooling2d(inputs,(2,2),(2,2),name=name)
# conv1: 神经元图， feature_map, 输出图像conv1_1 = conv_wrapper(normal_data_aug_3, name = 'conv1_1', is_training=True)
conv1_2 = conv_wrapper(conv1_1, name = 'conv1_2', is_training=True)
conv1_3 = conv_wrapper(conv1_2, name = 'conv1_3', is_training=True)
pooling1 = pooling_warpper(conv1_3, name = 'pool1')conv2_1 = conv_wrapper(pooling1, name = 'conv2_1', is_training=True)
conv2_2 = conv_wrapper(conv2_1, name = 'conv2_2', is_training=True)
conv2_3 = conv_wrapper(conv2_2, name = 'conv2_3', is_training=True)
pooling2 = pooling_warpper(conv2_3, name = 'pool2')conv3_1 = conv_wrapper(normal_data_aug_3, name = 'conv3_1', is_training=True)
conv3_2 = conv_wrapper(conv3_1, name = 'conv3_2', is_training=True)
conv3_3 = conv_wrapper(conv3_2, name = 'conv3_3', is_training=True)
pooling3 = pooling_warpper(conv3_3, name = 'pool1')

使用resnet train acc 67% after fine-tune -> 94%

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