tensorflow

问题 I want to create VAE(variational autoencoder). During model creating it throws exception. When subclassing the Model class, you should implement a call method. I am using Tensorflow 2.0 def vae(): models ={} def apply_bn_and_dropout(x): return l.Dropout(dropout_rate)(l.BatchNormalization()(x)) input_image = l.Input(batch_shape=(batch_size,28,28,1)) x = l.Flatten()(input_image) x = l.Dense(256,activation="relu")(x) x = apply_bn_and_dropout(x) x = l.Dense(128,activation="relu")(x) x = apply_bn

问题 How do I split my data into 3 folds using ImageDataGenerator of Keras? ImageDataGenerator only gives validation_split argument so if I use it, I wont be having my test set for later purpose. My data is in the form of >input_data_dir >class_1_dir > image_1.png > image_2.png > class_2_dir > class_3_dir 回答1: As you rightly mentioned, splitting the Data into 3 Folds is not possible in one line of code using Keras ImageDataGenerator . Work around would be to store the Images corresponding to Test

问题 I installed latest version of tensorflow that is 2.3 and when i try to import tensorflow i get below error runfile('C:/Users/Sriram/untitled1.py', wdir='C:/Users/Sriram') Traceback (most recent call last): File "<ipython-input-7-ae532bb97ae9>", line 1, in <module> runfile('C:/Users/Sriram/untitled1.py', wdir='C:/Users/Sriram') File "C:\Users\Sriram\anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 435, in runfile __umr__.run() File "C:\Users\Sriram\anaconda3\lib

问题 I am new to CNN and machine learning in general and have been trying to follow Image Classification tutorial of TensorFlow. Now, the Google Colab can be found here. I've followed the this official tutorial of TensorFlow. And I've changed it slightly so it saves the model as h5 instead of tf format so I can use the Keras' model.predict_classes . Now, I have the model trained and the model reloaded from the saved model alright. But I'm repeatedly getting list index out of range error whenever I

问题 When starting a TensorFlow server ( tf.distribute.Server ), you must pass a ClusterSpec which specifies all tasks/workers in the cluster. Why? What does it use that information for? Is this just that device names (for tf.device ) can uniquely identify on what worker they run in the cluster? E.g. for with tf.device("/job:ps/task:0") or with tf.device("/job:worker/task:7") ? 来源： https://stackoverflow.com/questions/62004631/why-does-a-tf-server-needs-to-know-about-all-other-tasks-workers-in-the

问题 I'm using Tensorflow 2.0 / Keras to generate building footprints for a procedural city. I would like to feed in a source image representing a city 'block', and have the network return the predicted building foundations. Input / Output I've had some success using a pix2pix-like GAN architecture, but the output would ideally be a vector image rather than a bitmap. I've therefore been trying to implement a different model, where the input image is convolved down to a 32x32 tensor, converted to a

问题 I am attempting to predict the ideal move in a game using a keras sequential network. The network is fairly simple with (what I think to be) an input shape of (3216,). The code for defining the network is attached bellow. self.model = keras.Sequential() self.model.add(Dense(2000, activation='relu', input_dim=(2 * 7 + 2 + Cfg.tiles_x * Cfg.tiles_y))) self.model.add(Dense(1000, activation='relu')) self.model.add(Dense(100, activation='relu')) self.model.add(Dense(9)) self.model.compile(loss

问题 I trying yolo model in python. To process the data and annotation I'm taking the data in batches. batchsize = 50 #boxList= [] #boxArr = np.empty(shape = (0,26,5)) for i in range(0, len(box_list), batchsize): boxList = box_list[i:i+batchsize] imagesList = image_list[i:i+batchsize] #to convert the annotation from VOC format convertedBox = np.array([np.array(get_boxes_for_id(box_l)) for box_l in boxList]) #pre-process on image and annotaion image_data, boxes = process_input_data(imagesList,max

问题 in my neural network, I want to normalize words of a text. It means words get converted to infinitives and gender independent. Example: "Unternehmensgruppenführer" (german) gets 3 words: unternehmen gruppe fahren. This requires a lot of "startsWith" processing. I built a table that gives me word=>infinitive and loads it into a sorted(!) dictionary of length 400k. However, the processing of that dictionary by finding words in words got very slow. I optimized that with an implementation of

问题 Suppose I have an existing Windows application (hello1.exe). Suppose I also have a tf.Keras neural net model trained and classifying test data. How do I take the latter "out of the lab" and deploy it in the "real world" by combining the two components something like this: > hello2.exe cat.jpg Hello World. BTW: the file 'cat.jpg' contains a cat That is, hello2.exe extends my existing hello1.exe by using the trained model to infer the contents of the input file. I don't mind if the input data