Questions tagged [convnet]

For questions regarding "Convolutional Neural Networks" (CNN)

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Tensorflow Conv3D with variable input size

I have a hypotethical question: Is it possible to train Conv3D with variable input size? Sample dim = Length x Width x Depth ; Depth are fixed per each samples, let's say 500. However Length x Width ...
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9 views

Modifying network to handle images consistently

I'm modifying IRNet to work with the Cityscapes dataset. This network takes images as input and is supposed to output images that can be used as instance segmentation labels. IRNet originally uses ...
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1answer
27 views

Improve performances of a convolutional neural network

I am doing image classificaition, and to do this I have built the following neural network: ...
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1answer
13 views

Validity of PU learning while using character-level encoding using CNNs for classifying text data

I'm trying to classify a large set of documents (~100M) as valid or invalid, based upon a small given set of labeled valid documents (~3k). I'd like to know if the PU learning approach described in ...
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0answers
11 views

Model not learning when using transfer learning

I am working on a personal project on image classification (two classes) and am trying to see how the MobileNet v2 structure would perform. While training the training accuracy is already quite high ...
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4 views

if two convolution layer connected in tandem follow associative property of convolution?

Two Convolution filter follow the associative property as follows :- I want to ask whether this property will hold for two convolution layer with no operation in between them?
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1answer
25 views

To calculate my confusion matrix with recall and precision, my test set need to be equal(balanced)?

In my CNN, I have 200 'negative' images and 50 'positive' images in my test set and I want to make a confusion matrix. My doubt is if I have to equalize the samples in the dataset because if I keep ...
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81 views

Python Keras model performs much worse than R Keras model

I got this R Keras model from GitHub that performs really well. GitHub repo ...
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1answer
45 views

Why/When should I use VGG16 to do fine-tuning? [closed]

Why or When should I use VGG16 in my cnn? what is the pros and cons to use this model? I search but not found this answer. If you have references, I appreciate
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4answers
102 views

How to combine GridSearchCV with Early Stopping?

I'm a beginner in machine learning and want to train a CNN (for image recognition) with optimized hyperparameter like dropout rate, learning rate and number of epochs. The optimal hyperparameter I ...
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0answers
30 views

Why when I apply GaussianBlur in my images, my model overfit? CNN KERAS

I have 1400 images (700 each class), and i'm using vgg-16 to classificate between one and other class. But when I apply the preprocess method Gaussian Blur (which seem to be very much clear to see the ...
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39 views

Combining 2D Detection with Disparity Maps to Learn 3D Object Geometry

Since the disparity map above is a representation of the object's distance from the camera's origin, is it reasonable to assume that a network (perhaps a convolutional LSTM) could be trained to ...
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Did I do the right thing in my CNN Keras (class imbalance - augmentation)

To implement my Binary CNN in keras, I had a dataset of ~~35000 images but only 700 is from one class and all the others are from the other class, so what I did: I get the 700 unique images from class ...
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7 views

How do I augment data after spliting traininng datset into train and validation set for CIFAR10 using PyTorch?

When classifying the CIFAR10 in PyTorch, there are normally 50,000 training samples and 10,000 testing samples. However, if I need to create a validation set, I can do it by splitting the training set ...
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1answer
27 views

What is a latent space vector?

I do not understand this about GANs. Apparently the Generator is supposed to receive a latent space vector as its input. Yet I couldn't find an example of how I can implement it in Pytorch. This is a ...
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16 views

CNN model with transfer learning not performing, training loss is still high, test accuracy is very low

Hi I'm trying to train a cnn model with transfer learning, and I am not able to get a good test accuracy (14%) - I don't know why it doesn't work for me. ...
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2answers
139 views

Why are my predictions bad, if my accuracy in train is roughly 100% (Keras CNN)

In my CNN i have to handle 2 classes in a binary system, I have 700 images each class to train, and others to validation. This is my train.py: ...
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0answers
14 views

What is the difference between these 2 training scenarios?

I have a very large dataset and due to computational constraints, I have to divide the data into 20 parts (each part is around 1.5GB). I constructed a deep CNN model using Keras for this dataset. The ...
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7 views

Process with which ImageDataGenerator with flow_from_directory() method fetches and augments images from a directory in Keras 2

When using Keras 2.0 ImageDataGenerator and in particular the flow_from_directory() method to perform data augmentation, how many images are generated and in what order? Do we have a way to control ...
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0answers
9 views

Shuffling the images with DataImageGenerator and flow_from_directory in a Medical application (tensorflow 2/Keras)

I am facing the following situation: I have CT images (scans) of patients where 10 images describe each patient. These images are stored in separate directories based on what is the focus of each ...
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0answers
23 views

ResNet50 Model is not learning with transfer learning in keras

I am trying to perform transfer learning on ResNet50 model pretrained on Imagenet weights for PASCAL VOC 2012 dataset. As it is a multi label dataset, I am using ...
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2answers
18 views

What does Conv1d do in a sentiment analysis?

I am doing some study on https://www.kaggle.com/anshulrai/cudnnlstm-implementation-93-7-accuracy I understand we need LSTM to capture the sequence of words in the sentience, but I am not quite ...
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1answer
34 views

MobileNet and MobileNetV2: Bad Inference Results

unfortunately I am having subjectively bad results in inference with pre-trained models of both MobileNet v1 and v2: ...
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0answers
11 views

What is the use of having shared weights in later layers of a CNN?

In a CNN, all the neurons in a single layer use the same weights and bias. As a result, all the neurons detect the same feature. The early layers of a CNN detect simple features like edges and hence ...
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1answer
51 views

Are CNNs indeed translation invariant?

I read that in a hidden layer of a convolutional neural network, all neurons share the same weights and bias. As a result, all the neurons detect the same feature and hence ConvNets become invariant ...
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1answer
85 views

How to increase the accuracy of my predictions (CNN fine tuning VGG16 KERAS)

In my VGG16 fine-tuning, I have to classify retinal images in 2 classes (or 4th stage or not 4th stage) and I have 700 images each class to train. This is my code now: ...
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3answers
48 views

Problem with overfitting

I make small CNN from scratch to classify barcodes. I have two classes: one for images with barcodes and second for all what isn't barcodes (items, animals, landscape, furniture, people). I got good ...
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2answers
19 views

Choosing a set of CNNs for paper

There are so many CNNs out there and im trying to do a comparison between some of them in my paper which networks should I use? Resnet, vgg and inception are obvious but I need 3 or 4 others. which ...
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0answers
17 views

Keras model with second to last sigmoid activated Conv1D layer followed by globalMaxPool outputs values outside [0,1]. Why?

I am trying to train a binary classifier. It is a residual network with skip layers etc. but ultimately, the bottom two layers are a 1D convolution with sigmoid activation followed by a global max ...
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13 views

MASK RCNN with multicalss classification

I want to create a model which solve a multiclass classification problem. The main concept is: every picture contain only one object the background is very simple all object is coming from the same ...
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3answers
131 views

Model Validation accuracy stuck at 0.65671 Keras

I am using conv1d to classify EEG signals, but my val_accuracy stuck at 0.65671. No matter what changes i do, it never go beyond 0.65671. Here is the architecture ...
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0answers
16 views

What transforms do we need to apply to masks of images in segmentation tasks

I am trying a semantic segmentation task for multi-class segmentation. I am wondering what transforms are applied to both images and masks in it. Following are my transforms- ...
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0answers
22 views

When do I use Multiply and Add

I want to know the effect of Add and Multiply in keras by functionality. The dumb way of thinking is that they are meant to add ...
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2answers
133 views

Finding Feature Importance in CNN's?

Let's say I have images of cars. For each image in the dataset, I have let's say 3 pictures of the same car but in different angles. 1) The first image is the picture of the car from the front. 2) ...
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1answer
99 views

Tensorboard with pytorch dont display a graph

I am trying to visualize a model I created using Tensorboard with Pytorch but when running tensorboard and going to the graph tab nothing is shown, im adding my code for reference, also im adding a ...
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1answer
156 views

Transfer learning on new image size

Transfer learning: Take a trained neural network and use it for a new classification task. When we want to use transfer learning with a convolutional neural network, we don't have to use the same ...
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0answers
19 views

How to backpropogate error from convolutional layer with respect to the input when using multiple channels

I have been attempting to implement a Convolutional Neural Network in python and have run into a bit of a roadblock. When backpropogating the error in a convolutional layer let us say that we receive ...
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1answer
108 views

Why is training and validation loss steadily rising (eventually to NaN) in this CNN of mine?

Dear ML and data scientists: I have 4 layers of gray scale images for every single biological specimen in my dataset. I am trying to train a 4-convolution CNN (see pytorch architecture below) to ...
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2answers
20 views

ConvNet with concatenated data

I have a basic question regarding convolutional neural network. Assume I have a set of 1000 RGB images and I train a CNN from this set. I can obviously split each of my RGB images into 3 different ...
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1answer
48 views

CNN for unsupervised anomaly detection

I'm wondering if the following strategy has been already used and could work Let's says you have a CNN which work well to classify image data, dog and cat. You only have cat and dog image as training ...
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14 views

How to train a model on image + text features?

So I have a dataset of 6 different dermatology disease pictures along with data of the age, gender, etc other data for each picture. I want to train a model that combines the image and the ...
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0answers
137 views

How to use PCA in CNN for image recognition using Keras?

I created a CNN model for image classification and I want to use Principal Component Analysis (PCA) but when I run pca.fit() code, the code still running for hours ...
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0answers
19 views

Computing derivatives for backpropagation across a convolution step

This will be a long post, but I hope it'll be instructive to anyone else in my position. I'm trying to find how the derivatives of the loss function are calculated with respect to the kernels and ...
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2answers
24 views

What is the benefit of using Max pooling in convnets as opposed to just using convolution layers? (from Francois Chollet's Deep Learning with Python)

I am reading Francois Chollet's Deep learning with python, and I came across a section about max pooling that's really giving me trouble. I am unable to copy paste the content, so I've included ...
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1answer
178 views

How to merge two CNN deep learning model using weighted sum and weighted product in Keras?

I am using Keras to create a deep learning model and I want to merge two CNNs by using weighted sum or weighted product. How can I merge two CNNs using weighted sum and weighted product?
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29 views

Does a max-pooling layer in a ConvNet contribute to the “vanishing gradient” problem?

I would answer no, but am not sure if I'm missing something and hope you can help me out: The derivative of a max-pooling layer in a ConvNet is one w.r.t. the maximum value and zero for all others. A ...
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1answer
454 views

What is the difference between multiply and dot functions that is used to merge layer in Keras?

I want to merge two CNN deep learning model using Keras and would like to know what is the difference multiply and dot functions that is used to merge layer? ...
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0answers
548 views

Input 0 is incompatible with layer conv1d_40: expected ndim=3, found ndim=2

i am working on computer vision using deep learning. my training data contains (x,128) shape. i am passing the same to conv1d layer but facing issues below is my model ...
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1answer
56 views

Triplet loss function for face recognition?

In the Andrew-NG coursera course on Convnets he talked about triplet loss function for one shot face recognition. The formula given in the video is, $$\to \small \small \small ||f(A)-f(P)||^2 \;+\;\...
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1answer
43 views

How many parameters in a Conv2d Layer?

I was following andrew-ng coursera course on deep learning and there's a question that has been asked there which I couldn't figure out the answer for? Suppose your input is a 300 by 300 color (RGB) ...