image captioning tensorflow tutorial

). The course content is basically code-based programming, there will be a small amount of deep learning theoretical content. The course starts with some of the most basic knowledge from TensorFlow's most basic diagrams (graphs), sessions (session), tensor (tensor), variables (Variable), and gradually talks about the basics of TensorFlow, And the use of CNN and LSTM in TensorFlow. After the course, we will

); return 0;}intMainintargcChar*argv[]) { if(-1==Read_lables ()) { return-1; } if(-1==read_images ()) { return-1; } return 0;}Download and extract the dataset files Train-images-idx3-ubyte and train-labels-idx1-ubyte into the directory where the source code is located, compile and execute:gcc-o read_images read_images.c. /read_imagesThe results shown are as follows:A total of 60,000 pictures, from the code can be seen in the data set is stored in the actual

Installation Environment Win10 Python3.6.4 More than 3.5 version can be, currently tensorflow only support 64-bit python3.5 above version NumPy After installing Python, open the terminal cmd input PIP3 install NumPy Specific ProcessDownload installation Cuda8.0， must be 8.0 version. Download the address and follow the image below to download the local installation package. If the installation is wrong

Session:with Tf.device ("/gpu:0"): Session.run (init) coord=tf.train.Coordinator () Threads= Tf.train.start_queue_runners (coord=coord) Max_iter=10000ITER=0ifOs.path.exists (Os.path.join ("Model",'model.ckpt')) isTrue:tf.train.Saver (Max_to_keep=none). Restore (Session, Os.path.join ("Model",'model.ckpt')) whileiterMax_iter:#Loss_np,_,label_np,image_np,inf_np=session.run ([Loss,opti,batch_image,batch_label,inf])B_batch_image,b_batch_label =Session.run ([Batch_image,batch_label]) l

TensorFlow Official Tutorial: The last layer of the retraining model to cope with the new classification This article mainly includes the following content: TensorFlow Official Tutorial re-training the final layer of the model to cope with the new classification flowers the inception model for the dataset re-training

Reprint: Https://mp.weixin.qq.com/s/J6eo4MRQY7jLo7P-b3nvJg Li Lin compiled from PyimagesearchAuthor Adrian rosebrockQuantum bit Report | Public number Qbitai OpenCV is a 2000 release of the open-source computer vision Library, with object recognition, image segmentation, face recognition, motion recognition and other functions, can be run on Linux, Windows, Android, Mac OS and other operating systems, with lightweight, efficient known, and provides