return h_conv2 + input ClassNum = 2 #ImagePath='F:/kaggle_cat_dog_dataset/train' ImagePath = 'F:/kaggle_cat_dog_dataset/test1' LabelPath = 'train_label.txt' SavePath = './model/AlexNetModel.ckpt' BatchSize = 64 training = True trainingFc = True w = 224 h = 224 dataset = kaggleCatDogLoad.ImageNetDataSet(ImagePath, BatchSize) #加载图片根目录 dataset.get_labels() #dataset = readImageNet.ImageNetDataSet(ImagePath,ClassNum,BatchSize)#加载图片根目录 #dataset.get_labels(LabelPath) image_batch, label_batch = dataset.get_batch_data() x = tf.placeholder("float", [None, w * h * 3]) y_ = tf.placeholder("float", [None, ClassNum]) #input_d=tf.get_variable("input_data0",initializer=tf.truncated_normal([1,224,224,3],stddev=0.0001)) input_d = tf.reshape(x, [-1, w, h, 3]) input_dd = tf.get_variable("input_data0", initializer=tf.truncated_normal([1, w, h, 3], stddev=0.0001)) #input_dd=tf.get_variable("input_data0",initializer=tf.truncated_normal([1,224,224,3],stddev=0.0001))
#saver.save(sess, self.model_save_path) def evaluate(self, test_features, test_labels, name='test '): tf.reset_default_graph() x = tf.placeholder(tf.float32, [None, 64, 64, 3]) y_ = tf.placeholder(tf.int64, [None, self.classnum]) logits, keep_prob, train_mode = self.deepnn(x) accuracy = self.accuracy(logits, y_) saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, self.model_save_path) accu = sess.run(accuracy, feed_dict={ x: test_features, y_: test_labels, keep_prob: 1.0, train_mode: False }) print('%s accuracy %g' % (name, accu)) reader = kaggleCatDogLoad.ImageNetDataSet("F:/kaggle_cat_dog_dataset/train", 32) reader.get_labels() images, labels = reader.get_batch_data() resnet = Resnet(2) resnet.train(images, labels)
import kaggleCatDogLoad import tensorflow as tf import numpy as np import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" np.set_printoptions(threshold=np.inf) reader = kaggleCatDogLoad.ImageNetDataSet( "C:/Users/25285/Desktop/testdataset/train", 1) reader.get_labels() image_v, label_v = reader.get_batch_data() sess = tf.Session() sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) image_v_out, label_v_out = sess.run([image_v, label_v]) print(image_v_out) print(label_v_out) coord.request_stop() coord.join(threads)