[train_op, loss, global_step, accuracy],
feed_dict={
x: batch[0],
y_: batch[1]
})
if i % 100 == 0:
if i == 0:
continue
print("step %d, loss %g, training accuracy %g" %
(i, loss_value, train_accuracy))
print("Training Success!")
saver.save(sess, os.path.join(model_dir, model_name), global_step=0)
print("Save success！")
sess.close()
if __name__ == "__main__":
backward(mnist)
dir_name = "./test_num"
files = os.listdir(dir_name)
cnt = len(files)
for i in range(cnt):
#print(files[i].split('_')[0])
print(files[i])
files[i] = dir_name + "/" + files[i]
test_images1 = reader.pre_img(files[i])
mnist_demo.restore_model_ckpt(test_images1)
def img2class(imgFile):
img = reader.pre_img(imgFile)
res = remnist.restore_model_ckpt(img)
return res