conv-neural-network

问题 The bounty expires in 4 days . Answers to this question are eligible for a +100 reputation bounty. Galen wants to draw more attention to this question: I'm putting this question up for bounty because it has received little attention despite its potential use for myself and other researchers. I'm willing to work those attempting to answer the question both in terms of clarifying and refining the question as we find issues. I'm interested in using the Networkx Python package to perform network

问题 Short question: Is the difference between validation and training loss at the beginning of the training (first epochs) a good indicator for the amount of data that should be used? E.g would it be a good method to increase the amount of data until the difference at the beginning is as small as possible? It would save me time and computation. backround: I am working on a neuronal network that overfits very fast. The best result after applying many different techniques like dropouts, batch

问题 I would like if possible how to make this Threshold for Evaluation and validation of created R-CNN object Detector, i tried to make it in the attached scripts but it does not work, I want to make Threshold for score that like below 0.58 that score and bboxes should not be appeared Herein the code:- load('gTruth.mat') output = selectLabels(gTruth,'signal'); if ~isfolder(fullfile('EvaluationData')) mkdir EvaluationData addpath('EvaluationData'); evaluationData = objectDetectorTrainingData

问题 I would like if possible how to make this Threshold for Evaluation and validation of created R-CNN object Detector, i tried to make it in the attached scripts but it does not work, I want to make Threshold for score that like below 0.58 that score and bboxes should not be appeared Herein the code:- load('gTruth.mat') output = selectLabels(gTruth,'signal'); if ~isfolder(fullfile('EvaluationData')) mkdir EvaluationData addpath('EvaluationData'); evaluationData = objectDetectorTrainingData

问题 I am implementing the CNN as below, but I got this error: InvalidArgumentError (see above for traceback): logits and labels must be broadcastable I have attached my partial code below. I suspect the error is coming from the shapes and dimensions of my weight and biases. What I'm trying to implement - I want to reduce the CNN layers from two fully connected layers to just one fully connected layer, meaning, out=tf.add(tf.add(fc1....) and stop it there. nInput = 32 nChannels = 3 nClasses = 10 #

问题 I have a problem of applying masking layer to CNNs in RNN/LSTM model. My data is not original image, but I converted into a shape of (16, 34, 4)(channels_first). The data is sequential, and the longest step length is 22. So for invariant way, I set the timestep as 22. Since it may be shorter than 22 steps, I fill others with np.zeros. However, for 0 padding data, it's about half among all dataset, so with 0 paddings, the training cannot reach a very good result with so much useless data. Then

问题 I am trying to load a Keras model which was trained on an Azure VM (NC promo). But I am getting the following error. TypeError: Unexpected keyword argument passed to optimizer:learning_rate EDIT: Here is the code snippet that I am using to load my model: from keras.models import load_model model = load_model('my_model_name.h5') 回答1: Did you use a custom optimizer? If so, you can load like this: model = load_model('my_model_name.h5', custom_objects={ 'Adam': lambda **kwargs: hvd

问题 I have this error Error when checking input: expected input_13 to have 4 dimensions, but got array with shape (7, 100, 100) For the following code how should I reshape array to fit with 4-dimensions, I searched for it but didn't understand the previous solutions. Please ask if not clear its very common issue in convolution neural network. inputs=Input(shape=(100,100,1)) x=Conv2D(16,(3,3), padding='same')(inputs) x=Activation('relu')(x) x=Conv2D(8,(3,3))(x) x=Activation('relu')(x) x

问题 I have this error Error when checking input: expected input_13 to have 4 dimensions, but got array with shape (7, 100, 100) For the following code how should I reshape array to fit with 4-dimensions, I searched for it but didn't understand the previous solutions. Please ask if not clear its very common issue in convolution neural network. inputs=Input(shape=(100,100,1)) x=Conv2D(16,(3,3), padding='same')(inputs) x=Activation('relu')(x) x=Conv2D(8,(3,3))(x) x=Activation('relu')(x) x

问题 I am new to Keras and I was trying to build a text-classification CNN model using Python 3.6 when I encountered this error : Traceback (most recent call last): File "model.py", line 94, in <module> model.fit([x1, x2], y_label, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_split=0.2) # starts training File "/../../anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 955, in fit batch_size=batch_size) File "/../../anaconda3/lib/python3.6