from skimage import io,transform

import glob

import os

import tensorflow as tf

import numpy as np

import time

#数据集地址

path='E:/data/datasets/flower_photos/'

#模型保存地址

model_path='E:/data/model/flower/model.ckpt'

#将所有的图片resize成100*100

w=100

h=100

c=3

#读取图片

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):

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

            print('reading the images:%s'%(im))

            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)

data,label=read_img(path)

#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("   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))

saver.save(sess,model_path)

sess.close()

from skimage import io,transform

import tensorflow as tf

import numpy as np

path1 = "E:/data/datasets/flower_photos/daisy/5547758_eea9edfd54_n.jpg"

path2 = "E:/data/datasets/flower_photos/dandelion/7355522_b66e5d3078_m.jpg"

path3 = "E:/data/datasets/flower_photos/roses/394990940_7af082cf8d_n.jpg"

path4 = "E:/data/datasets/flower_photos/sunflowers/6953297_8576bf4ea3.jpg"

path5 = "E:/data/datasets/flower_photos/tulips/10791227_7168491604.jpg"

flower_dict = {0:'dasiy',1:'dandelion',2:'roses',3:'sunflowers',4:'tulips'}

w=100

h=100

c=3

def read_one_image(path):

    img = io.imread(path)

    img = transform.resize(img,(w,h))

    return np.asarray(img)

with tf.Session() as sess:

    data = []

    data1 = read_one_image(path1)

    data2 = read_one_image(path2)

    data3 = read_one_image(path3)

    data4 = read_one_image(path4)

    data5 = read_one_image(path5)

    data.append(data1)

    data.append(data2)

    data.append(data3)

    data.append(data4)

    data.append(data5)

    saver = tf.train.import_meta_graph('E:/data/model/flower/model.ckpt.meta')

    saver.restore(sess,tf.train.latest_checkpoint('E:/data/model/flower/'))

    graph = tf.get_default_graph()

    x = graph.get_tensor_by_name("x:0")

    feed_dict = {x:data}

    logits = graph.get_tensor_by_name("logits_eval:0")

    classification_result = sess.run(logits,feed_dict)

    #打印出预测矩阵

    print(classification_result)

    #打印出预测矩阵每一行最大值的索引

    print(tf.argmax(classification_result,1).eval())

    #根据索引通过字典对应花的分类

    output = []

    output = tf.argmax(classification_result,1).eval()

    for i in range(len(output)):

        print("第",i+1,"朵花预测:"+flower_dict[output[i]])

[[  5.76620245   3.18228579  -3.89464641  -2.81310582   1.40294015]

 [ -1.01490593   3.55570269  -2.76053429   2.93104005  -3.47138596]

 [ -8.05292606  -7.26499033  11.70479774   0.59627819   2.15948296]

 [ -5.12940931   2.18423128  -3.33257103   9.0591135    5.03963232]

 [ -4.25288343  -0.95963973  -2.33347392   1.54485476   5.76069307]]

[0 1 2 3 4]

第 1 朵花预测:dasiy

第 2 朵花预测:dandelion

第 3 朵花预测:roses

第 4 朵花预测:sunflowers

第 5 朵花预测:tulips