deep-learning

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

问题 I am working in a unit test system for my tensorflow workspace and I would like to know if there is any method or attribute, given a graph with an optimizer operation (after calling .minimize()), to obtain the final loss tensor that it is optimizing and the variables that it controls. For example if I call train_op = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) I would like to retrieve cross_entropy only having access to train_op. I have access to the train_op object, I only want to

问题 In a default Conv2D layer with kernel_size=3 the weights of a slice of one of the filters could be named like this: A B C D E F G H I With kernel_size=5 like this: A B C D E F G H I J K L M N O P Q R S T U V W X Y Now I'd like to build (and train/test) a model based on conv layers with kernels like that: A A B C C A A B C C D D E F F G G H I I G G H I I How could the implementation of such a custom layer look like? 回答1: Maybe like this? class CustomConv2D(Layer): def __init__(self, filters, *

问题 I want to ask a question about how to monitor validation loss in the training process of estimators in TensorFlow. I have checked a similar question (validation during training of Estimator) asked before, but it did not help much. If I use estimators to build a model, I will give an input function to the Estimator.train() function. But there is no way to add another validation_x, and validation_y data in the training process. Therefore, when the training started, I can only see the training

问题 I am trying to have a lambda layer in keras that performs a vector matrix multiplication, before passing it to another layer. The matrix is fixed (I don't want to learn it). Code below: model.add(Dropout(0.1)) model.add(Lambda(lambda x: x.dot(A))) model.add(Dense(output_shape, activation='softmax')) model.compile(<stuff here>)} A is the fixed matrix, and I want to do x.dot(A) WHen I run this, I get the following error: 'Tensor' object has no attribute 'dot' Same Error when I replace dot with

问题 I am trying to have a lambda layer in keras that performs a vector matrix multiplication, before passing it to another layer. The matrix is fixed (I don't want to learn it). Code below: model.add(Dropout(0.1)) model.add(Lambda(lambda x: x.dot(A))) model.add(Dense(output_shape, activation='softmax')) model.compile(<stuff here>)} A is the fixed matrix, and I want to do x.dot(A) WHen I run this, I get the following error: 'Tensor' object has no attribute 'dot' Same Error when I replace dot with

问题 I am trying to extract all text regions from an image using opencv python. I have successfully detected the text regions but could not extract it. I extracted the smaller sub-matrices of a text region but I cannot aggregrate them into a bigger matrix that we see as the text region in the image. import numpy as np import cv2 from imutils.object_detection import non_max_suppression import matplotlib.pyplot as plt %matplotlib inline from PIL import Image # pip install imutils image1 = cv2.imread

问题 I have followed the TensorFlow Layers tutorial to create a CNN for MNIST digit classification using TensorFlow's tf.layers module. Now I'm trying to learn how to use TensorBoard from TensorBoard: Visualizing Learning. Perhaps this tutorial hasn't been updated recently, because it says its example code is a modification of that tutorial's and links to it, but the code is completely different: it manually defines a single-hidden-layer fully-connected network. The TensorBoard tutorial shows how

问题 I have followed the TensorFlow Layers tutorial to create a CNN for MNIST digit classification using TensorFlow's tf.layers module. Now I'm trying to learn how to use TensorBoard from TensorBoard: Visualizing Learning. Perhaps this tutorial hasn't been updated recently, because it says its example code is a modification of that tutorial's and links to it, but the code is completely different: it manually defines a single-hidden-layer fully-connected network. The TensorBoard tutorial shows how