tensorflow

问题 I am trying to visualize the prediction of VGG-16 for the cat image, Compute the top-5 scores (the 5 classes with the maximum probability), For each of these 5 scores, print the corresponding label and the corresponding probability. from keras.applications.vgg16 import preprocess_input from keras.preprocessing import image # load the image from cat class and resize it img = image.load_img('cat.jpg', target_size=(224, 224)) # convert to numpy array of (224, 224, 3) x = image.img_to_array(img)

问题 I have a tensorflow graph (stored in a protobuffer file) with placeholder operations as inputs. I want to wrap this graph as a keras layer or model. Here is an example: with tf.Graph().as_default() as gf: x = tf.placeholder(tf.float32, shape=(None, 123), name='x') c = tf.constant(100, dtype=tf.float32, name='C') y = tf.multiply(x, c, name='y') with tf.gfile.GFile("test_graph/y.pb", "wb") as f: raw = gf.as_graph_def().SerializeToString() f.write(raw) Load back as a tensorflow graph: persisted

问题 Tensorflow beginner here. This is my first project and I am working with pre-defined estimators. I have an extremely unbalanced dataset where positive outcomes represent roughly 0.1% of the total data and I suspect this imbalance to considerably affect the performance of my model. As a first attempt to solve the issue, since I have tons of data, I would like to throw away most of my negatives in order to create a balanced dataset. I can see two ways of doing it: preprocess the data to keep

问题 I am having trouble with Keras Custom loss function. I want to be able to access truth as a numpy array. Because it is a callback function, I think I am not in eager execution , which means I can't access it using the backend.get_value() function. i also tried different methods, but it always comes back to the fact that this 'Tensor' object doesn't exist. Do I need to create a session inside the custom loss function ? I am using Tensorflow 2.2, which is up to date. def custom_loss(y_true, y

问题 I am having trouble with Keras Custom loss function. I want to be able to access truth as a numpy array. Because it is a callback function, I think I am not in eager execution , which means I can't access it using the backend.get_value() function. i also tried different methods, but it always comes back to the fact that this 'Tensor' object doesn't exist. Do I need to create a session inside the custom loss function ? I am using Tensorflow 2.2, which is up to date. def custom_loss(y_true, y

问题 I am using tf.keras to build my network. And I am doing all the augmentation in tensor_wise level since my data in tfrecords file. Then I needed to do shearing and zca for augmentation but couldn't find a proper implementation in tensor flow. And I can't use the DataImageGenerator that did both operation I needed because as I said my data doesn't fit in memory and it is in tfrecord format. So all my augmentations process should be tesnorwise. @fchollet here suggested a way to use

问题 I am using tf.keras to build my network. And I am doing all the augmentation in tensor_wise level since my data in tfrecords file. Then I needed to do shearing and zca for augmentation but couldn't find a proper implementation in tensor flow. And I can't use the DataImageGenerator that did both operation I needed because as I said my data doesn't fit in memory and it is in tfrecord format. So all my augmentations process should be tesnorwise. @fchollet here suggested a way to use

问题 I've read related threads but not been able to solve my problem. I'm currently trying to get my model to run in order to classify 5000 different events, which all currently fall under the same category (so my "labels" dataset consists of 5000 1s). I'm using one hot encoding for my labels data set: labels = np.loadtxt("/content/drive/My Drive/5000labels1.csv") from keras.utils import to_categorical labels=to_categorical(labels) # convert labels to one-hot encoding I then define my model like

问题 I'm trying to import tensorflow in PyCharm, however I get an error that the module is not found. I used pip install to install tensorflow. Also when I look at the interpreter in PyCharm it says I have pip version 9.0.1 and the latest is 10.0.1. I have upgraded to 10.0.1 using the pip commands, and when I run pip --version it says I got 10.0.1. I have tried both reinstalling pycharm and making new projects with no luck. 回答1: Go to Files -> Settings -> Project:projectname -> Project Interpreter

问题 Tensorflow Version: 2.2.0 OS: Windows 10 I am trying to convert a saved_model.pb to a tflite file. Here is the code I am running: import tensorflow as tf # Convert converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir='C:\Data\TFOD\models\ssd_mobilenet_v2_quantized') tflite_model = converter.convert() fo = open("model.tflite", "wb") fo.write(tflite_model) fo.close This code gives an error while converting: File "C:\Users\Mr.Ace\AppData\Roaming\Python\Python38\site-packages