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I have data with 5 output classes. The training data has the following no of samples for these 5 classes: [706326, 32211, 2856, 3050, 901] I am using the following keras (tf.keras) code:
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def fftconvolve (in1, in2, mode = "full"): """Convolve two N-dimensional arrays using FFT. Convolve `in1` and `in2` using the fast Fourier transform method, with the output size determined by the `mode` argument.
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Sep 19, 2019 · WordPunctTokenizer tokenized_corpus = [wpt. tokenize (document) for document in norm_bible] # Set values for various parameters feature_size = 100 # Word vector dimensionality window_context = 30 # Context window size min_word_count = 1 # Minimum word count sample = 1e-3 # Downsample setting for frequent words w2v_model = word2vec.
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def path_detect_frequency (point_cloud, downsample_size, frequency_threshold): """ Detects points from major paths in a graph generated from a point cloud. The detection is performed by comparing the frequency of all paths that each node is present. Nodes with frequency larger than threshold are selected as detected.
May 06, 2017 · Hey Folks, There are several python packages or libraries available to do time series forecasting Modeling for Machine learning. Python Packages that are used for Time-series Analysis Arrow - A sensible, human-friendly approach to creating, manipu... The following are 30 code examples for showing how to use sklearn.neural_network.MLPClassifier().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.Sep 10, 2015 · % matplotlib inline import numpy as np import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from bs4 import BeautifulSoup from matplotlib import pyplot as plt from sklearn.ensemble import GradientBoostingClassifier from sklearn.feature_selection import SelectKBest, chi2 from sklearn.metrics import roc_curve, auc
Jun 29, 2017 · In this post, I describe the results of implementing and training a variation of the VGG-16 convolutional neural network (convnet). The convnet is trained and evaluated on the Tiny ImageNet dataset. In random mode, “downsample” and “upsample” methods are provided. Users can set the sample parameter “fractions”, which is the sample ratio within data. In stratified mode, “downsample” and “upsample” methods are also provided.
Apr 29, 2015 · The scikit-learn modules for different models. ... To get a better picture of the day, we can downsample this time series to larger intervals, for example, 10 minutes
downSample: randomly sample a data set so that all the classes have the same frequency as the minority class. confusionMatrix: includes metrics from the confusion matrix (sensitivity, specificity, kappa, precision, and more.)
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