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Results tagged with deep-learning
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user 56922
a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.
1
vote
1
answer
1k
views
Confused about false positive and false negative in confusion matrix?
I am working on binary classification for classifying cancer=1 and no-cancer=0, I use confusion matrix from sklearn, this is my confusion matrix on test set:
# confusion matrix
[[18 0]
[ 7 15]]
…
- 1,177
8
votes
3
answers
45k
views
Is a large number of epochs good or bad idea in CNN
In my CNN model, by using large number of epochs like 400 or above, the validations accuracy and some times test accuracy gets better, but I think this large number of epochs is not good idea? I am ri …
- 1,177
-1
votes
2
answers
31
views
two dimensional list takes huge amount of memory
I have a 2d list which is created from lung CT image data and a label (the first item is a 3d array(image data) and the second item is a label(0 or 1)), I need this to data to train CNN model, the lis …
- 1,177
0
votes
Why there is no exact picture of softmax activation function?
The softmax function is used in the last layer of CNN network. Softmax is an activation function like tanh and ReLU, the difference is that this technique can interpret the incoming inputs as output p …
- 1,177
0
votes
1
answer
3k
views
how to save deep learning model and test it after training?
I have a CNN model written using tensorflow for python, the model is for classifying lung CT images (cancer/no-cancer), after training the model with training and validation data and get a reasonable …
- 1,177
10
votes
2
answers
21k
views
Using Cross Validation technique for a CNN model
I am working on a CNN model. As always, I used batches with epochs to train my model. When it completed training and validation, finally I used a test set to measure the model performance and generate …
- 1,177
4
votes
Accepted
What exactly is BatchNormalization() in Keras?
Batch Normalization is a layer that is put in between convolution and activation layers or sometimes after activation layers. It is used to normalize layer’s input to reduce the internal covariate shi …
- 1,177
2
votes
Accepted
CNN output shape explanation
In the convolution layer, the filter (3x3 in your case) is applied to the images in order to produce the output (feature map) and is slid to the right and bottom by a parameter called stride (in your …
- 1,177
2
votes
1
answer
1k
views
Is there a way to get y_pred values from saved Keras model?
I have a Keras model saved in a .h5 file. As you know there are a y_pred and a y_act that confusion matrix creates from, at run time, it's easy to get y_pred values but my model is saved and now I nee …
- 1,177
1
vote
1
answer
2k
views
How to save and test CNN model on test set after training
My CNN model is trained on the training set and validated on the validation set, now I want to test it on test set, here is my code:
x_img = tf.placeholder(tf.float32, name='x_img')
y_label = tf.plac …
- 1,177
3
votes
1
answer
695
views
storing a huge dataset in h5py file format
I work on preparing the luna16 dataset for feeding into the CNN model, after reading all '.mhd' files and the labels(0, 1) in the annotated CSV file, I get a memory error, I know the problem is becaus …
- 1,177
1
vote
1
answer
8k
views
Dealing with pre-trained model for grayscale images
I would like to do Transfer Learning using one of the novel networks such as VGG, ResNet, Inception, etc.
The problem is that my images are grayscale (1 channel) since all the above mentioned models w …
- 1,177
5
votes
Accepted
Over fitting in Transfer Learning with small dataset
First of all:
I think you should reduce the number of FC layers and number of nodes of FC layers, for example, one FC with 256 or 512, or 2 FC with 256 and 512. Try this.
Try to make your batch size …
- 1,177
4
votes
2
answers
323
views
How is Stochastic Gradient Descent(SGD) used like Mini Batch Gradient Descent(MBGD)?
As I know, Gradient Descent(GD) has three variants which are:
1- Batch Gradient Descent(BGD): processes all the training examples for each iteration of gradient descent.
2- Stochastic Gradient Descent …
- 1,177