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 size padding = '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,tanhweight initializer: he,xavier,normaloptimzier:Adam,Momentum,Gradient Descent

---

def convent(inputs, activation, kernel_initializer): conv1_1 = tf.layers.conv2d(inputs, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, kernel_initializer = kernel_initializer name = '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] # resize img_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 size padding = 'same', activation = tf.nn.relu, name = 'conv1_1') conv1_2 = tf.layers.conv2d(conv1_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_2') conv1_3 = tf.layers.conv2d(conv1_2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv1_3') # 16 * 16 pooling1 = tf.layers.max_pooling2d(conv1_3, (2, 2), # kernel size (2, 2), # stride name = 'pool1') conv2_1 = tf.layers.conv2d(pooling1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_1') conv2_2 = tf.layers.conv2d(conv2_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_2') conv2_3 = tf.layers.conv2d(conv2_2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv2_3') # 8 * 8 pooling2 = tf.layers.max_pooling2d(conv2_3, (2, 2), # kernel size (2, 2), # stride name = 'pool2') conv3_1 = tf.layers.conv2d(pooling2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_1') conv3_2 = tf.layers.conv2d(conv3_1, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_2') conv3_3 = tf.layers.conv2d(conv3_2, 32, # output channel number (3,3), # kernel size padding = 'same', activation = tf.nn.relu, name = 'conv3_3') # 4 * 4 * 32 pooling3 = tf.layers.max_pooling2d(conv3_2, (2, 2), # kernel size (2, 2), # stride name = '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 -> activation with 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%