Is it possible to see the progress of GridSearchCV in a Jupyter Notebook? I’m running this script in python: I can see only some warnings in the output of the cell. Answer You want the verbose parameter: An example of what I got on toy data:
Tag: machine-learning
Tensorflow/keras: “logits and labels must have the same first dimension” How to squeeze logits or expand labels?
I’m trying to make a simple CNN classifier model. For my training images (BATCH_SIZEx227x227x1) and labels (BATCH_SIZEx7) datasets, I’m using numpy ndarrays that are fed to the model in batches via ImageDataGenerator. The loss function I’m using is tf.nn.sparse_categorical_crossentropy. The problem arises when the model tries to train; the model (batch size here is 1 for my simplified experimentations) outputs
Why does this decision tree’s values at each step not sum to the number of samples?
I’m reading about decision trees and bagging classifiers, and I’m trying to show the first decision tree that is used in the bagging classifier. I’m confused about the output. Here’s a snippet out of the output It’s been my understanding that the value is supposed to show how many of the samples are classified as each category. In that case,
Macro VS Micro VS Weighted VS Samples F1 Score
In sklearn.metrics.f1_score, the f1 score has a parameter called “average”. What does macro, micro, weighted, and samples mean? Please elaborate, because in the documentation, it was not explained properly. Or simply answer the following: Why is “samples” best parameter for multilabel classification? Why is micro best for an imbalanced dataset? what’s the difference between weighted and macro? Answer The question
Split autoencoder on encoder and decoder keras
I am trying to create an autoencoder for: Train the model Split encoder and decoder Visualise compressed data (encoder) Use arbitrary compressed data to get the output (decoder) How to split train it and split with the trained weights? Answer Make encoder: Make decoder: Make autoencoder: Now you can use any of them any way you want to. train the
TypeError: train_test_split() got an unexpected keyword argument ‘test_size’
I’m trying to find the best feature set using random forest approach I need to split the dataset into test and train. here is my code parameters data,data_y are parsing correctly. But I’m getting the following error. I couldn’t figure out why this is. Answer You are using the same function name in your code same as the one from
confusion matrix error “Classification metrics can’t handle a mix of multilabel-indicator and multiclass targets”
I am getting a error when I try to use confusion matrix. I am doing my first deep learning project. I am new to it. I am using the mnist dataset provided by keras. I have trained and tested my model successfully. However, when I try to use the scikit learn confusion matrix I get the error stated above. I
OneHotEncoder categorical_features deprecated, how to transform specific column
I need to transform the independent field from string to arithmetical notation. I am using OneHotEncoder for the transformation. My dataset has many independent columns of which some are as: I have to encode the Country column like I succeed to get the desire transformation via using OneHotEncoder as Now I’m getting the depreciation message to use categories=’auto’. If I
What’s the difference between torch.stack() and torch.cat() functions?
OpenAI’s REINFORCE and actor-critic example for reinforcement learning has the following code: REINFORCE: actor-critic: One is using torch.cat, the other uses torch.stack, for similar use cases. As far as my understanding goes, the doc doesn’t give any clear distinction between them. I would be happy to know the differences between the functions. Answer stack Concatenates sequence of tensors along a
How to embed machine learning python codes in hardware platform like raspberry pi?
I want to implement machine learning on hardware platform s which can learning by itself Is there any way to by which machine learning on hardware works seamlessly? Answer Python supports wide range of platforms, including arm-based. You raspberry pi supports Linux distros, just install Python and go on.