from skimage import io,transform

import glob

import os

import tensorflow as tf

import numpy as np

import time

import os

os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

#读取花朵图片

def read_img(path):

cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]

imgs=[]

labels=[]

for idx,folder in enumerate(cate):

print('reading the dirs :%s' % (folder))

for im in glob.glob(folder+'/*.jpg'):

img=io.imread(im)

img=transform.resize(img,(w,h))

imgs.append(img)

labels.append(idx)

return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)

def inference(input_tensor, train, regularizer):

with tf.variable_scope('layer1-conv1'):

conv1_weights = tf.get_variable("weight",[5,5,3,32],initializer=tf.truncated_normal_initializer(stddev=0.1))

conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))

conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')

relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))

with tf.name_scope("layer2-pool1"):

pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="VALID")

with tf.variable_scope("layer3-conv2"):

conv2_weights = tf.get_variable("weight",[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.1))

conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))

conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')

relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))

with tf.name_scope("layer4-pool2"):

pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

with tf.variable_scope("layer5-conv3"):

conv3_weights = tf.get_variable("weight",[3,3,64,128],initializer=tf.truncated_normal_initializer(stddev=0.1))

conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))

conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')

relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))

with tf.name_scope("layer6-pool3"):

pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

with tf.variable_scope("layer7-conv4"):

conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1))

conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))

conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')

relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))

with tf.name_scope("layer8-pool4"):

pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

nodes = 6*6*128

reshaped = tf.reshape(pool4,[-1,nodes])

with tf.variable_scope('layer9-fc1'):

fc1_weights = tf.get_variable("weight", [nodes, 1024],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))

fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))

fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)

if train: fc1 = tf.nn.dropout(fc1, 0.5)

with tf.variable_scope('layer10-fc2'):

fc2_weights = tf.get_variable("weight", [1024, 512],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))

fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))

fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)

if train: fc2 = tf.nn.dropout(fc2, 0.5)

with tf.variable_scope('layer11-fc3'):

fc3_weights = tf.get_variable("weight", [512, 5],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))

fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))

logit = tf.matmul(fc2, fc3_weights) + fc3_biases

return logit

def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):

assert len(inputs) == len(targets)

if shuffle:

indices = np.arange(len(inputs))

np.random.shuffle(indices)

for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):

if shuffle:

excerpt = indices[start_idx:start_idx + batch_size]

else:

excerpt = slice(start_idx, start_idx + batch_size)

yield inputs[excerpt], targets[excerpt]

def semantic_alignment(src_feature, tgt_feature, src_label, tgt_label, num_classes=2):

''' input: src_feature: feature from source domain tgt_feature: feature from target somain src_label: source label(one-hot encoding) tgt_label: target label(one-hot encoding) num_classes : the number of class(e.g., 2) output: semantic_loss : the semantic loss between domains. '''

source_result = tf.argmax(src_label, 1) # source label

target_result = tf.argmax(tgt_label, 1) # target label

ones = tf.ones_like(src_feature)#得到一个与源域数据格式一致的全1的张量

print('ones',ones.shape)

print('source_result', source_result.shape)

print('target_result', target_result.shape)

current_source_count = tf.unsorted_segment_sum(ones, source_result, num_classes)#计算出当前源域数据

current_target_count = tf.unsorted_segment_sum(ones, target_result, num_classes)#计算出当前目标域数据

current_positive_source_count = tf.maximum(current_source_count, tf.ones_like(current_source_count))#返回当前源域数据与之间的最大值

current_positive_target_count = tf.maximum(current_target_count, tf.ones_like(current_target_count))#返回当前目标域数据与之间的最大值

current_source_centroid = tf.divide(tf.unsorted_segment_sum(data=src_feature, segment_ids= \

source_result, num_segments=num_classes), current_positive_source_count)

current_target_centroid = tf.divide(tf.unsorted_segment_sum(data=tgt_feature, segment_ids= \

target_result, num_segments=num_classes), current_positive_target_count)

semantic_loss = tf.reduce_mean((tf.square(current_source_centroid - current_target_centroid)))

return semantic_loss

if __name__ == '__main__':

# 数据集地址

path = 'D:/python/workspace/flower/data/flowers/'

# 模型保存地址

model_path = 'D:/python/workspace/flower/model/model.ckpt'

#测试集地址

path1= 'D:/python/workspace/flower/test1/'

# 将所有的图片resize成100*100

w = 100

h = 100

c = 3

data, label = read_img(path)

newlabel=[]

# 打乱顺序

num_example = data.shape[0]

arr = np.arange(num_example)

np.random.shuffle(arr)

data = data[arr]

label = label[arr]

# 将所有数据分为训练集和验证集

ratio = 0.8

s = np.int(num_example * ratio)

x_train = data[:s]

y_train = label[:s]

x_val = data[s:]

y_val = label[s:]

# -----------------构建网络----------------------

# 占位符

x = tf.placeholder(tf.float32, shape=[None, w, h, c], name='x')

y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_')

def inference(input_tensor, train, regularizer):

with tf.variable_scope('layer1-conv1'):

conv1_weights = tf.get_variable("weight", [5, 5, 3, 32],

initializer=tf.truncated_normal_initializer(stddev=0.1))

conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))

conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')

relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))

with tf.name_scope("layer2-pool1"):

pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")

with tf.variable_scope("layer3-conv2"):

conv2_weights = tf.get_variable("weight", [5, 5, 32, 64],

initializer=tf.truncated_normal_initializer(stddev=0.1))

conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))

conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')

relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))

with tf.name_scope("layer4-pool2"):

pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

with tf.variable_scope("layer5-conv3"):

conv3_weights = tf.get_variable("weight", [3, 3, 64, 128],

initializer=tf.truncated_normal_initializer(stddev=0.1))

conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))

conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')

relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))

with tf.name_scope("layer6-pool3"):

pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

with tf.variable_scope("layer7-conv4"):

conv4_weights = tf.get_variable("weight", [3, 3, 128, 128],

initializer=tf.truncated_normal_initializer(stddev=0.1))

conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))

conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')

relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))

with tf.name_scope("layer8-pool4"):

pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')

nodes = 6 * 6 * 128

reshaped = tf.reshape(pool4, [-1, nodes])

with tf.variable_scope('layer9-fc1'):

fc1_weights = tf.get_variable("weight", [nodes, 1024],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))

fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))

fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)

if train: fc1 = tf.nn.dropout(fc1, 0.5)

with tf.variable_scope('layer10-fc2'):

fc2_weights = tf.get_variable("weight", [1024, 512],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))

fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))

fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)

if train: fc2 = tf.nn.dropout(fc2, 0.5)

with tf.variable_scope('layer11-fc3'):

fc3_weights = tf.get_variable("weight", [512, 5],

initializer=tf.truncated_normal_initializer(stddev=0.1))

if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))

fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))

logit = tf.matmul(fc2, fc3_weights) + fc3_biases

return logit

# ---------------------------网络结束---------------------------

regularizer = tf.contrib.layers.l2_regularizer(0.0001)

logits = inference(x, False, regularizer)

# (小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensor

b = tf.constant(value=1, dtype=tf.float32)

logits_eval = tf.multiply(logits, b, name='logits_eval')

loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)

train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)

acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 定义一个函数，按批次取数据

def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):

assert len(inputs) == len(targets)

if shuffle:

indices = np.arange(len(inputs))

np.random.shuffle(indices)

for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):

if shuffle:

excerpt = indices[start_idx:start_idx + batch_size]

else:

excerpt = slice(start_idx, start_idx + batch_size)

yield inputs[excerpt], targets[excerpt]

# 训练和测试数据，可将n_epoch设置更大一些

n_epoch = 10

batch_size = 64

saver = tf.train.Saver()

sess = tf.Session()

sess.run(tf.global_variables_initializer())

for epoch in range(n_epoch):

start_time = time.time()

# training

train_loss, train_acc, n_batch = 0, 0, 0

for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):

_, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})

train_loss += err;

train_acc += ac;

n_batch += 1

print("%d epoch" % epoch)

print(" train loss: %f" % (np.sum(train_loss) / n_batch))

print(" train acc: %f" % (np.sum(train_acc) / n_batch))

# validation

val_loss, val_acc, n_batch = 0, 0, 0

for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):

err, ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a})

val_loss += err;

val_acc += ac;

n_batch += 1

print(" validation loss: %f" % (np.sum(val_loss) / n_batch))

print(" validation acc: %f" % (np.sum(val_acc) / n_batch))

print("============================================================ ")

saver.save(sess, model_path)

sess.close()