machine-learning

问题 I have been given 2 data sets and want to perform cluster analysis for the sets using KNIME. Once I have completed the clustering, I wish to carry out a performance comparison of 2 different clustering algorithms. With regard to performance analysis of clustering algorithms, would this be a measure of time (algorithm time complexity and the time taken to perform the clustering of the data etc) or the validity of the output of the clusters? (or both) Is there any other angle one look at to

问题 I have been given 2 data sets and want to perform cluster analysis for the sets using KNIME. Once I have completed the clustering, I wish to carry out a performance comparison of 2 different clustering algorithms. With regard to performance analysis of clustering algorithms, would this be a measure of time (algorithm time complexity and the time taken to perform the clustering of the data etc) or the validity of the output of the clusters? (or both) Is there any other angle one look at to

问题 I have been given 2 data sets and want to perform cluster analysis for the sets using KNIME. Once I have completed the clustering, I wish to carry out a performance comparison of 2 different clustering algorithms. With regard to performance analysis of clustering algorithms, would this be a measure of time (algorithm time complexity and the time taken to perform the clustering of the data etc) or the validity of the output of the clusters? (or both) Is there any other angle one look at to

问题 I have been given 2 data sets and want to perform cluster analysis for the sets using KNIME. Once I have completed the clustering, I wish to carry out a performance comparison of 2 different clustering algorithms. With regard to performance analysis of clustering algorithms, would this be a measure of time (algorithm time complexity and the time taken to perform the clustering of the data etc) or the validity of the output of the clusters? (or both) Is there any other angle one look at to

问题 model.fit throws an error ValueError: Error when checking input: expected embedding_1_input to have shape (32,) but got array with shape (1,) , but there are no arrays of shape (1,) passed to model.fit . def create_model(vocabulary_size, input_word_count, embedding_dims=50): model = Sequential() model.add(Embedding(vocabulary_size, embedding_dims, input_length=input_word_count)) model.add(GlobalAveragePooling1D()) model.add(Dense(1, activation="sigmoid")) model.compile(loss="binary

问题 I'm working on a machine learning application in Python (using the sklearn module), and am currently trying to decide on a model for performing inference. A brief description of the problem: Given many instances of user data, I'm trying to classify them into various categories based on relative keyword containment. It is supervised, so I have many, many instances of pre-classified data that are already categorized. (Each piece of data is between 2 and 12 or so words.) I am currently trying to

问题 I am wondering if the filename information of the image encoded could be encoded into a TFRecord file while creating the tfrecord files, and if so, how could this information be decoded back? When decoded, is the filename a Tensor object? 回答1: Just like fabrizioM said, you have to store the sources in the tfrecords file if you want to use them. Here is an example: #!/usr/bin/env python """Example for reading and writing tfrecords.""" import tensorflow as tf from PIL import Image import numpy

问题 It's the first time I'm working with GANs and I am facing an issue regarding the Discriminator repeatedly outperforming the Generator. I am trying to reproduce the PA model from this article and I'm looking at this slightly different implementation to help me out. I have read quite a lot of papers on how GANs work and also followed some tutorials to understand them better. Moreover, I've read articles on how to overcome the major instabilities, but I can't find a way to overcome this behavior

问题 I am trying to completely understand difference between categorical and ordinal data when doing regression analysis. For now, what is clear: Categorical feature and data example: Color: red, white, black Why categorical: red < white < black is logically incorrect Ordinal feature and data example: Condition: old, renovated, new Why ordinal: old < renovated < new is logically correct Categorical-to-numeric and ordinal-to-numeric encoding methods: One-Hot encoding for categorical data Arbitrary

问题 I am trying to completely understand difference between categorical and ordinal data when doing regression analysis. For now, what is clear: Categorical feature and data example: Color: red, white, black Why categorical: red < white < black is logically incorrect Ordinal feature and data example: Condition: old, renovated, new Why ordinal: old < renovated < new is logically correct Categorical-to-numeric and ordinal-to-numeric encoding methods: One-Hot encoding for categorical data Arbitrary