My question may sound easy, however, I have difficulty in creating an Hdf5 dataset from my 3d medical images that have been saved in nii format for both images and manual segmentation (label files). My questions are:
The blob shape in pycaffe is N*C*W*H, is it different order in matcaffe? for example in pycaffe the data blob shape will be 1*1*60*320*320 for a grayscale volume with width and height 320-by-320 and 60 slices. I tried to use a Matlab code to create HDF5 dataset for the 3D data, and the order of blob in hdf5info file is 320*320*60*1*1 for both data and label. How should I change the orders in the Matlab code to be readable in Pycaffe?
Is there any python code for creating the hdf5 database for 3D data?
if I create the hdf5 data in Matlab and use the list the pycaffe, will it raise issue?
Thanks
Matlab arranges elements in ND arrays from the first dimension to the last (like fortran), while caffe and hdf5 stores the arrays from last dimension to first ("col-major").
This answer explains how to simply modify Matlab's code to write "col-major" arrays to HDF5 files.
You might also find this answer useful for your problem.
Related
I have a question about variational autoencoders (VAE),
I need to generate new data from my dataset which contains just numerical data, so i want to use VAE for that task, but all the available tutorials and articles use images as input data for the variatioanl autoencoder.
My question is: can i use VAE for generating new data from my datasets eventhough my data is not images ??
Thank you.
Short answer is yes. You should read up a bit on the basics of neural nets if this wasn't obvious already - an image is just a Channel X Height X Width dimensional vector. You might use different kinds of layers in your network to suit the kind of data that you have to give a better inductive bias, but otherwise nothing changes. Follow those tutorials!
I have a dataset of 78 variables which the input data of all the variables are binary (0 and 1). I want to plot the data in one graph. originally I plan to plot in PCA, but I think it won't work since PCA required numerical input data (is it?). Any suggestions what kind of data visualization to be used for this type of data? Thank you very much.
I do python and R.
I'm new in Deep Learning and I started with the TenserFlow tutorials (The beginner one and the expert one).In both of them, the data is imported at the beginning with these 2 lines :
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
I would like to use this neural network on my own images. I have 100 000 images et a fileLabel.txt giving the labels for each image in order by column. Is there a way to change these two lines or a few others to import my images without breaking all the code ? I really don't see how to do that, I have the impression that the structure mnist is specific to the images of the tutorial.
Thanks in advance for your help
Short answer to your question is yes - its possible. You don't need to break any code IF your data is also similar to MNIST data with 10 labels and well organized.
Assuming that is not the case, then you need to organize your input data so that you can define (create) your model.
Organizing of your input data includes
Having consistent image sizes (for example MNIST is 28x28 pixel images)
Labeling of your images (for example MNIST has 10 labels - 0 to 9)
Finally how you intend to split your data (for example MNIST data is split into three parts: 55,000 data points of training data (mnist.train), 10,000 points of test data (mnist.test), and 5,000 points of validation data (mnist.validation).
Once you organize your input data, then you read your data by writing a small function like read_images that does something like
reader = tf.WholeFileReader()
key, value = reader.read(filename_queue)
....
Then assuming you want to "label" ahead of time similar to MNIST data, you can store them in a file and read them in your program.
After that, you would have to populate the tf.train.string_input_producer() with a list of strings containing the filename and the label.
....
I'm totally new in caffe and I'm try to convert a tensorflow model to caffe.
I have a tuple which's shape is a little complex for it's stored some word vector.
This is the shape of the tuple data——
data[0]: a list, [684, 84], stores the sentence vector;
data[1]: a list, [684, 84], stores the position vector;
data[2]: a matrix, [684, 10], stores the aspects of the sentence;
data[3]: a matrix, [1, 684], stores the label of each sentence;
data[4]: a number, stores the max length of sentences;
Each row represents a sentences, which is also a sample of the dataset.
In tf, I return the whole tuple from a function which is wrote by myself.
train_data = read_data(FLAGS.train_data, source_count, source_word2idx)
I noticed that caffe always requires a data layer before training the data, but I don't have ideas how to convert my data to lmdb type or just sent them as a tuple or matrix into the model.
By the way, I'm using pycaffe.
Counld anyone help?
Thanks a lot!
There's no particular magic; all you need to do is to write an input routine that reads the file and returns the data in the format expected for train_data. You do not need to pre-convert your data to LMDB or any other format; just write read data to accept your current input format, and give the model the format it requires.
We can't help you from there: you haven't specified the model's format at all, and you've given us only the shape for the input data (no internal structure or semantics). Simply treat the data as if you were figuring out how to organize the input data for a given output format.
I would like to modify the ImageNet caffe model as described bellow:
As the input channel number for temporal nets is different from that
of spatial nets (20 vs. 3), we average the ImageNet model filters of
first layer across the channel, and then copy the average results 20
times as the initialization of temporal nets.
My question is how can I achive the above results? How can I open the caffe model to be able to do those changes to it?
I read the net surgery tutorial but it doesn't cover the procedure needed.
Thank you for your assistance!
AMayer
The Net Surgery tutorial should give you the basics you need to cover this. But let me explain the steps you need to do in more detail:
Prepare the .prototxt network architectures: You need two files: the existing ImageNet .prototxt file, and your new temporal network architecture. You should make all layers except the first convolutional layers identical in both networks, including the names of the layers. That way, you can use the ImageNet .caffemodel file to initialize the weights automatically.
As the first conv layer has a different size, you have to give it a different name in your .prototxt file than it has in the ImageNet file. Otherwise, Caffe will try to initialize this layer with the existing weights too, which will fail as they have different shapes. (This is what happens in the edit to your question.) Just name it e.g. conv1b and change all references to that layer accordingly.
Load the ImageNet network for testing, so you can extract the parameters from the model file:
net = caffe.Net('imagenet.prototxt', 'imagenet.caffemodel', caffe.TEST)
Extract the weights from this loaded model.
conv_1_weights = old_net.params['conv1'][0].data
conv_1_biases = old_net.params['conv1'][1].data
Average the weights across the channels:
conv_av_weights = np.mean(conv_1_weights, axis=1, keepdims=True)
Load your new network together with the old .caffemodel file, as all layers except for the first layer directly use the weights from ImageNet:
new_net = caffe.Net('new_network.prototxt', 'imagenet.caffemodel', caffe.TEST)
Assign your calculated average weights to the new network
new_net.params['conv1b'][0].data[...] = conv_av_weights
new_net.params['conv1b'][1].data[...] = conv_1_biases
Save your weights to a new .caffemodel file:
new_net.save('new_weights.caffemodel')