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)