tensorflow | 易学教程

Tensorflow: How can I assign numpy pre-trained weights to subsections of graph?

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问题 This is a simple thing which I just couldn't figure out how to do. I converted a pre-trained VGG caffe model to tensorflow using the github code from https://github.com/ethereon/caffe-tensorflow and saved it to vgg16.npy... I then load the network to my sess default session as "net" using: images = tf.placeholder(tf.float32, [1, 224, 224, 3]) net = VGGNet_xavier({'data': images, 'label' : 1}) with tf.Session() as sess: net.load("vgg16.npy", sess) After net.load, I get a graph with a list of

Changing the scale of a tensor in tensorflow

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问题 Sorry if I messed up the title, I didn't know how to phrase this. Anyways, I have a tensor of a set of values, but I want to make sure that every element in the tensor has a range from 0 - 255, (or 0 - 1 works too). However, I don't want to make all the values add up to 1 or 255 like softmax, I just want to down scale the values. Is there any way to do this? Thanks! 回答1: You are trying to normalize the data. A classic normalization formula is this one: normalize_value = (value − min_value) /

Changing the scale of a tensor in tensorflow

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Add DropOut after loading the weights in Keras

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问题 I am doing king of transfer learning. What I have done is First train the model with the big datasets and save the weights. Then I train the model with my dataset by freezing the layers. But I see there was some overfitting. So I try to change the dropout of the model and load the weights since the numbers are changing while drop out are changing. I find difficulties to change the dropout. Directly my question is, Is it possible to change the model's dropout while loading the weights? my

How to save trained model in tensorflow?

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问题 I wrote a convolutional neural network in tensorflow to perform on the mnist dataset. Everything works just fine, but i want to save the model with the tf.train.Saver(). How am i gonna do it? Here is my code: from __future__ import print_function import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Parameters learning_rate = 0.001 training_iters = 200000 batch_size = 128 display

How to save trained model in tensorflow?

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Training a simple model in Tensorflow GPU slower than CPU

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问题 I have set up a simple linear regression problem in Tensorflow, and have created simple conda environments using Tensorflow CPU and GPU both in 1.13.1 (using CUDA 10.0 in the backend on an NVIDIA Quadro P600). However, it looks like the GPU environment always takes longer time than the CPU environment. The code I'm running is below. import time import warnings import numpy as np import scipy import tensorflow as tf import tensorflow_probability as tfp from tensorflow_probability import

How to add additional classes to a pre-trained object detection model and train it to detect all of the classes (pre-trained + new)?

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问题 I had followed this blog --> https://medium.com/@teyou21/training-your-object-detection-model-on-tensorflow-part-2-e9e12714bdf , and built a SSD Mobilenet model which is pre-trained on the COCO Dataset called "ssd_mobilenet_v2_quantized_coco". What happens here is that it perfectly detects my new classes, but I want to include the pre-trained classes as well. I tried changing the number of classes to 96 ( 90 pre-trained + 6 new ) and edited the "labelmap.pbtxt" with the name and corresponding

How to add additional classes to a pre-trained object detection model and train it to detect all of the classes (pre-trained + new)?

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小白学PyTorch | 15 TF2实现一个简单的服装分类任务

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