dilation | Dilated Convolution for Semantic Image Segmentation | Machine Learning library

I am working with a data set that is comprised of three columns: patient ID (ID), TIME, and cervical dilation (CD). I apologize in advance for being unable to share my data, as it is confidential, but I have included a sample table below. Each patient CD was recorded in time as they progressed through labor. Time is measured in hours and CD can be 1-10cm. The number of time points/CD scores vary from patient to patient. In this model t is set in reverse, where 10 cm (fully dilated) is set as t=0 for all patients. This is done so that all patients can be aligned at time of full dilation. My dataset has no NA's and all patients have 2 or more time points.

I know for this problem I need to use nonlinear mixed effects model. I know from literature that the function that defines this biological process is modeled best as a biexponential function of the form CD= Cexp(-At)+(10-C)exp(-Lt), where A is the active labor rate [cm/hour], L is the latent labor rate [cm/hour], C is the diameter of the cervix [cm] at the point where the patient transitions from latent to active labor, and t is time in hours.

I have tried using both nlmer() and nlme() to fit this data, and I have used both the self-start biexponential function SSbiexp() as well as created my own function and its deriv(). Each parameter C, A, and L should have a random effect based on ID. Previous work has shown that C~4.98cm, A~0.41cm/hr, and L~0.07cm/hr. When using the SSbiexp(), there is a term for the second exponential component that is labeled here as C2, but should be the same as the (10-C) component of my self-made biexponential function.

When using nlme() with SSbiexp() I receive the error: Singularity in backsolve at level 0, block 1

I'm new to tensorflow and I'm trying to create a cnn and got this error ValueError: Shape must be rank 4 but is rank 2 for '{{node Conv2D}} = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], explicit_paddings=[], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](concat, Variable_6/read)' with input shapes: [?,1568], [1568,784]. this error is related to weight or input, how can i solved this thank you . My code :

I’m trying to swap resNet blocks with resNext blocks in my current model. All worked and I even trained the model for 1000+ epochs with the resNet blocks but when I added the following class to the model, it returned this error. (ran without errors in my local CPU but got the error when running in colab)

Added Class :

I've been trying to get tesseract OCR to extract some digits from a pre-cropped image and it's not working well at all even though the images are fairly clear. I've tried looking around for solutions but all the other questions I've seen on here involve a problem with cropping or skewed text.

Here's an example of my code which tries to read the image and output to the command line.

I have a resnet model which I am working with. I originally trained the model using batches of images. Now that it is trained, I want to do inference on a single image (224x224 with 3 color channels). However, when I pass the image to my model via model(imgs[:, :, :, 2]) I get:

I'm trying to use VGG16 with transfer learning, but getting errors:

I am trying to understand an example snippet that makes use of the PyTorch transposed convolution function, with documentation here, where in the docs the author writes:

"The padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input."

Consider the snippet below where a [1, 1, 4, 4] sample image of all ones is input to a ConvTranspose2D operation with arguments stride=2 and padding=1 with a weight matrix of shape (1, 1, 4, 4) that has entries from a range between 1 and 16 (in this case dilation=1 and added_padding = 1*(4-1)-1 = 2)

Thanks for reading my question!

I was just learning about custom grad functions in Jax, and I found the approach JAX took with defining custom functions is quite elegant.

One thing troubles me though.

I created a wrapper to make lax convolution look like PyTorch conv2d.

Is it possible to remove those black dots without hampering the image text using OpenCV?

So far, I have tried dilation, morphologyEx, erode etc.