基于tensorflow的猫狗图片的识别分类_深度学习CNN网络alexnet模型

import os import numpy as np import tensorflow as tf from alexnet import alexnet from datagenerator import ImageDataGenerator from datetime import datetime import glob from tensorflow.python.data import Iterator def main(): # 初始参数设置 learning_rate = 1e-3 num_epochs = 17 # 代的个数 之前是10 train_batch_size = 200 # 之前是1024 test_batch_size = 100 dropout_rate = 0.5 num_classes = 2 # 类别标签 display_step = 2 # display_step个train_batch_size训练完了就在tensorboard中写入loss和accuracy # need: display_step <= train_dataset_size / train_batch_size filewriter_path = "./tmp/tensorboard" # 存储tensorboard文件 checkpoint_path = "./tmp/checkpoints" # 训练好的模型和参数存放目录 image_format = 'jpg' # 数据集的数据类型 file_name_of_class = ['cat', 'dog'] # cat对应标签0,dog对应标签1。默认图片包含独特的名词，比如类别 train_dataset_paths = ['G:/python/猫狗大战/train/cat/', 'G:/python/猫狗大战/train/dog'] # 指定训练集数据路径（根据实际情况指定训练数据集的路径） test_dataset_paths = ['G:/python/猫狗大战/test/cat/', 'G:/python/猫狗大战/test/dog/'] # 指定测试集数据路径（根据实际情况指定测试数据集的路径） # 注意：默认数据集中的样本文件名称中包含其所属类别标签的名称，即file_name_of_class中的名称 # 初始参数设置完毕 print(train_dataset_paths) # 训练数据集数据处理 train_image_paths = [] train_labels = [] # 打开训练数据集目录，读取全部图片，生成图片路径列表 for train_dataset_path in train_dataset_paths: length = len(train_image_paths) train_image_paths[length:length] = np.array(glob.glob(train_dataset_path + '*.' + image_format)).tolist() for train_dataset_path in train_dataset_paths: length = len(train_image_paths) train_image_paths[length:length] = np.array(glob.glob(train_dataset_path + '*.' + image_format)).tolist() for image_path in train_image_paths: image_file_name = image_path.split('/')[-1] for i in range(num_classes): if file_name_of_class[i] in image_file_name: train_labels.append(i) break # 测试数据集数据处理 test_image_paths = [] test_labels = [] # 打开测试数据集目录，读取全部图片，生成图片路径列表 for test_dataset_path in test_dataset_paths: length = len(test_image_paths) test_image_paths[length:length] = np.array(glob.glob(test_dataset_path + '*.' + image_format)).tolist() print(test_image_paths) for image_path in test_image_paths: image_file_name = image_path.split('/')[-1] for i in range(num_classes): if file_name_of_class[i] in image_file_name: test_labels.append(i) break print(test_labels) '''# get Datasets # 调用图片生成器，把训练集图片转换成三维数组 train_data = ImageDataGenerator( images=train_image_paths, labels=train_labels, batch_size=train_batch_size, num_classes=num_classes, image_format=image_format, shuffle=True) # 调用图片生成器，把测试集图片转换成三维数组 test_data = ImageDataGenerator( images=test_image_paths, labels=test_labels, batch_size=test_batch_size, num_classes=num_classes, image_format=image_format, shuffle=False) # get Iterators with tf.name_scope('input'): # 定义迭代器 train_iterator = Iterator.from_structure(train_data.data.output_types, train_data.data.output_shapes) training_initalizer = train_iterator.make_initializer(train_data.data) test_iterator = Iterator.from_structure(test_data.data.output_types, test_data.data.output_shapes) testing_initalizer = test_iterator.make_initializer(test_data.data) # 定义每次迭代的数据 train_next_batch = train_iterator.get_next() test_next_batch = test_iterator.get_next() x = tf.placeholder(tf.float32, [None, 227, 227, 3]) y = tf.placeholder(tf.float32, [None, num_classes]) keep_prob = tf.placeholder(tf.float32) # alexnet fc8 = alexnet(x, keep_prob, num_classes) # loss with tf.name_scope('loss'): loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=fc8, labels=y)) # optimizer with tf.name_scope('optimizer'): optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # accuracy with tf.name_scope("accuracy"): correct_pred = tf.equal(tf.argmax(fc8, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) init = tf.global_variables_initializer() # Tensorboard tf.summary.scalar('loss', loss_op) tf.summary.scalar('accuracy', accuracy) merged_summary = tf.summary.merge_all() writer = tf.summary.FileWriter(filewriter_path) # saver saver = tf.train.Saver() # 定义一代的迭代次数 train_batches_per_epoch = int(np.floor(train_data.data_size / train_batch_size)) test_batches_per_epoch = int(np.floor(test_data.data_size / test_batch_size)) # Start training with tf.Session() as sess: sess.run(init) # saver.restore(sess, "./tmp/checkpoints/model_epoch18.ckpt") # Tensorboard writer.add_graph(sess.graph) print("{}: Start training...".format(datetime.now())) print("{}: Open Tensorboard at --logdir {}".format(datetime.now(), filewriter_path)) for epoch in range(num_epochs): sess.run(training_initalizer) print("{}: Epoch number: {} start".format(datetime.now(), epoch + 1)) # train for step in range(train_batches_per_epoch): img_batch, label_batch = sess.run(train_next_batch) loss, _ = sess.run([loss_op, train_op], feed_dict={x: img_batch, y: label_batch, keep_prob: dropout_rate}) if step % display_step == 0: # loss print("{}: loss = {}".format(datetime.now(), loss)) # Tensorboard s = sess.run(merged_summary, feed_dict={x: img_batch, y: label_batch, keep_prob: 1.}) writer.add_summary(s, epoch * train_batches_per_epoch + step) # accuracy print("{}: Start validation".format(datetime.now())) sess.run(testing_initalizer) test_acc = 0. test_count = 0 for _ in range(test_batches_per_epoch): img_batch, label_batch = sess.run(test_next_batch) acc = sess.run(accuracy, feed_dict={x: img_batch, y: label_batch, keep_prob: 1.0}) test_acc += acc test_count += 1 try: test_acc /= test_count except: print('Z