tensorflow花朵分类

数据集地址:

链接：#####https://pan.baidu.com/s/1ZXVb7M5p0JtS1edYRCJG2w
#####提取码：1xhz

代码如下：

#coding=utf-8
import os
#图像读取库
from PIL import Image
#矩阵运算库
import numpy as np
import tensorflow as tf#数据集文件夹
train_dir = r"D:/Modeling_2/train/"
test_dir = r"D:/Modeling_2/test/"#是否训练
train = True
#模型文件路径
model_path = "D:/Modeling_2/models/image_model"def read_data(train_dir):file_paths = []datas = []labels = []# 读取 dir路径下的 文件的名称for file_name in os.listdir(train_dir):# 将该路径下的文件 及其路径一同打印file_path = os.path.join(train_dir, file_name)file_paths.append(file_path)# 返回（图片类型JPEG，大小32*32，RGB）image = Image.open(file_path)# 归一化处理data = np.array(image) / 255.0# 取出标签label = int(file_name.split("_")[0])datas.append(data)labels.append(label)datas = np.array(datas) labels = np.array(labels) print("shape of datas: {}\tshape of labels: {}".format(datas.shape, labels.shape)) return file_paths, datas, labels#定义权重
def weight_variable(shape):initial = tf.truncated_normal(shape, stddev=0.1)return tf.Variable(initial)#定义偏置
def bias_variable(shape):initial = tf.constant(0.1, shape=shape)return tf.Variable(initial)#卷积层
def conv2d(x, W):return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding="SAME")#池化层 2x2池化层 步长为2
def max_2x2_pool(x):return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")if __name__ == "__main__":#返回文件路径，数据，标签file_paths, datas, labels = read_data(train_dir)# 分类数num_classes = len(set(labels))# 数据与标签x = tf.placeholder(tf.float32, [None, 32, 32, 3])y = tf.placeholder(tf.int32, [None])# 5x5x3 32个卷积核W_conv1 = weight_variable([5, 5, 3, 32])b_conv1 = bias_variable([32])# relu函数，卷积，池化h_conv1 = tf.nn.relu(conv2d(x, W_conv1)+ b_conv1)h_pool1 = max_2x2_pool(h_conv1)# 4x4x32 64个卷积核W_conv2 = weight_variable([5,5,32,64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2)+ b_conv2)h_pool2 = max_2x2_pool(h_conv2)# 5x5x3 32个卷积核W_conv3 = weight_variable([5, 5, 64, 128])b_conv3 = bias_variable([128])# relu函数，卷积，池化h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)h_pool3 = max_2x2_pool(h_conv3)# 4x4x32 64个卷积核W_conv4 = weight_variable([5, 5, 128, 256])b_conv4 = bias_variable([256])h_conv4 = tf.nn.relu(conv2d(h_pool3, W_conv4) + b_conv4)h_pool4 = max_2x2_pool(h_conv4)# 4次卷积池化过后 32/2/2/2/2 = 2  2*2*256# 通过计算得到 神经元个数 2*2*256# 扁平化处理h_pool2_flat = tf.reshape(h_pool4, [-1, 2 * 2 * 256])W_fc1 = weight_variable([2 * 2 * 256, num_classes])b_fc1 = bias_variable([num_classes])h_fc1 = tf.matmul(h_pool2_flat, W_fc1) + b_fc1# dropout优化keep_prob = tf.placeholder(tf.float32)h_fc1_dropout = tf.nn.dropout(h_fc1, keep_prob)logits = h_fc1# 返回最大值的标签prediction_label = tf.argmax(logits, 1)# 交叉熵函数 tf.one_hot(输入(一维)，深度)losses = tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(y, num_classes), logits=logits)mean_loss =tf.reduce_mean(losses)# 最速梯度下降法train_step = tf.train.AdamOptimizer(1e-4).minimize(losses)#交叉熵代价函数correct_predition = tf.equal(tf.argmax(tf.one_hot(y, num_classes), -1), tf.argmax(logits, -1))# 求准确率accuracy = tf.reduce_mean(tf.cast(correct_predition, tf.float32))Saver = tf.train.Saver()with tf.Session() as sess:if train:print("训练模式！")sess.run(tf.global_variables_initializer())train_feed_dict = {x:datas, y:labels, keep_prob:0.5}for step in range(1001):_, mean_loss_val  = sess.run([train_step, mean_loss], feed_dict=train_feed_dict)if step%20 ==0:print("step = {}\tmean loss = {}".format(step, mean_loss_val))train_acc =  sess.run(accuracy, feed_dict={x:datas, y:labels})print("准确率：", train_acc)Saver.save(sess, model_path)print("训练结束，保存模型到{}".format(model_path))else:print("测试模式")Saver.restore(sess, model_path)print("从{}载入模型".format(model_path))label_name_dict = {0:"百合花",1:"白玉兰",2:"茉莉花",3:"栀子花"}test_feed_dict ={x:datas, y:labels, keep_prob:0}prediction_val = sess.run(prediction_label, feed_dict=test_feed_dict)for file_paths, real_label, predicted_label in zip(file_paths, labels, prediction_val):# 将label id转换为label名real_label_name = label_name_dict[real_label]predicted_label_name = label_name_dict[predicted_label]print("{}\t{} => {}".format(file_paths, real_label_name, predicted_label_name))

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tensorflow花朵分类

数据集地址:

代码如下：

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