Keras is a high level api which works on top of Tensorflow. It is very easy to learn and implement. Tensorflow allows us to change every minute detail in the network. Thus making it useful in scenarios where flexibility is essential. But building a network and debugging it in Tensorflow is more time consuming than … Read more
import os import dicom import matplotlib.image as image os.chdir(“path/to/dicom.dcm”) dicom_file = dicom.read_file(“dicom.dcm”) dicom_array = dicom_file.pixel_array image.imsave(“dicom.jpg”, dicom_array) 1 solved Converting DICOM files to JPEG files for use in Inception V3
This is a very large question. And a hard task. I think, the easy way is to extract one frame by second if the source is a video stream. Then use OpenCV to make a face detection. Once you got the faces, feed a NN for recognitions. Some links for face recognition in Deep Learning: … Read more
Well, I got result that was need for me. I found him in another question from Stack Overflow: How to show graph in Visual Studio Code itself? Use #%% for creating cells in that you will run independent fragments of code. It seems like Python Shell(this is Jupyter Notebook that I love). Result that I … Read more
This problems is a binary segmentation problem. In a sense you learn a mapping from satellite images and predict for each pixel, iff this pixel is part of the road. A simple algorithm to do this would be to check if the pixel color is part of some range. A CNN naturally will learn you … Read more
A little google search would already help or just reading the Readme… You forgot to run this: #From tensorflow/models/ protoc object_detection/protos/*.proto –python_out=. 2 solved Object detection realtime using tensorflow
That print statement will give you an error in Python 3.X because it is no longer a statement, it is a function call. print(‘{0} {1} {2} {3}’.format(step, sess.run(cost), sess.run(W), sess.run(b))) See the python documentation or here for more information. EDIT: As per late comments above, try changing xrange() to range(), since xrange does not exist … Read more
the error has resolved just replace bgr_image to augmented_image predictions = sess.run(prob_tensor, {input_node: [bgr_image] }) to predictions = sess.run(prob_tensor, {input_node: [augmented_image] }) solved can anybody help i have got an error NameError: name ‘bgr_image’ is not defined [closed]
I got the same error today. In previous version of tf, I need to install a Nvidia toolkit to get the file. Here is the right toolkit for the cudart64_110.dll file: https://developer.nvidia.com/cuda-11.3.0-download-archive Then just follow the installation guide. If you need more help or it doesnt work, just write it. solved tensorflow: Could not load … Read more
What you are trying to do is called time series prediction (given data at time t-n, t-(n+1) … t-1: predict the state at time t) and is generally a task for a recurrent neural network. Here is the great blog post by Andrej Karpathy about the topic that you should have a look at. About … Read more
First of all, there seems to be a mistake in your code. Both if and else conditions set your mat1[i,j] to 1… Supposing your code is actually: for i in range(0,4): for j in range(0,4): if edges[i,j]==1 or (edges[i,0]==1 and edges[0,j]==1): mat1[i,j]=1 Then a Tensorflow solution is: import tensorflow as tf edges = tf.constant([[0, 0, … Read more
I think you need to do something like this # Print out predictions y = regressor.predict(input_fn=lambda: input_fn(prediction_set)) # .predict() returns an iterator; convert to a list and print predictions predictions = list(itertools.islice(y, 6)) print(“Predictions: {}”.format(str(predictions))) I found it from the boston tutorial: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/input_fn/boston.py 0 solved Race Condition when reading from generator