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I have the below code, I have the account, username, pw, etc, but I'm still seeing the below error:

raise error_class( sqlalchemy.exc.ProgrammingError: (snowflake.connector.errors.ProgrammingError) 251001: Account must be specified

I've also tried by changing the engine variable in my created_db_engine function like below, but I see the same error:

I am trying to use the redisgraph-bulk-loader to load the dataset into the RedisGraph instance running in a docker container. However, it returns with IndexError: list index out of range which I do not understand. I have read the documentation and my CSV files are valid.

I have opened an issue here.

Command : $ redisgraph-bulk-loader DemoGraph --enforce-schema --nodes-with-label TBox import/nodes.csv --relations-with-type relations import/relationships.csv

Log :

I have a table named movierating consisting of the following fields:

I am using the classic titanic dataset to build a decision tree. However, I am not sure what goes wrong with the edges or branches that are almost invisible.

Here is the code for building the decision tree

I am a beginner at working with CNNs.

So, I am building a 2D convolutional neural network that predicts brain tumor type and have a question about NumPy arrays. The input-shape of my model is (1, 512, 512) as (channels, img_height, img_width). The 4th dimension is num_images which seems to be automatically defined by TensorFlow. This is just a quick background. I have 3064 ".mat" extension files with MRI scans of brain tumors. Everything is setup. I converted ".mat" files into numpy matrices and appended the entire list of matrices in a single numpy array to pass as input for the CNN. I also have the corresponding labels (index-linked to the images when passing input into the model) as a numpy array. All the numbers are of float type in both images and labels.

Again, my input shape is (1, 512, 512). However, when fitting my model I get the following error:

ValueError: Error when checking input: expected conv2d_130_input to have shape (1, 512, 512) but got array with shape (79, 512, 512)

So, I am slicing my NumPy arrays to create train_images, train_labels, test_images, test_labels. I have verified the length of each both train and test sets with there labels match. They are also arrays, I checked multiple times. And this is a value error. So, how do I fix this?

I don't even know where the input shape became (79,512,512). I have a loop to convert f"{n}.mat" images to a matrix. I am using 100 images to test and have 80 train and 20 test. I think the mistake is here, the input shape is (channels, img-hght, img-wdth), but the number of images left to train is being placed in the channel's value instead. So, the input is being placed as (num_images, img-hght, img-wdth). This is wrong and should be changed, but I don't know how to do it. Or, I could be wrong and what I said might not make sense. I am providing all the code, running it on Colab. Make sure to change the image paths if you download the code and want to run it in order to help me out. Thanks a lot!

Dataset: https://figshare.com/articles/brain_tumor_dataset/1512427/5