visualization

问题 I'm using keras in my ML project with the TensorBoard callback. I have an image autoencoder and I want to visualize its progress in reconstructing some images. So I sub-classed the TensorBoard class as such: class Monitor(TensorBoard): def on_train_begin(self, logs=None): super().on_train_begin(logs) def on_epoch_begin(self, epoch, logs=None): # 1. Get the reconstructed images reconstructions = Autoencoder.predict(validation[0]) # 2. Generate a summary summary = tf.summary.image(

问题 I am using 3d kernel of size 3x3x3 for the convoution layer and would like to plot the weights of the layer. Since plotting in 3d is not possible i tried to split the kernels into 3 3×3 for plotting. Is this approach correct? The conv layer consists of 5 layers #model.add(layers.Conv3D(5, (3, 3, 3), padding=’same’)) Please find below the code I used to plot the weights from keras.models import load_model mymodel = load_model(‘model.hdf5′) from matplotlib import pyplot as plt # load the model

问题 I like to use Plotly to visualize everything, I'm trying to visualize a confusion matrix by Plotly, this is my code: def plot_confusion_matrix(y_true, y_pred, class_names): confusion_matrix = metrics.confusion_matrix(y_true, y_pred) confusion_matrix = confusion_matrix.astype(int) layout = { "title": "Confusion Matrix", "xaxis": {"title": "Predicted value"}, "yaxis": {"title": "Real value"} } fig = go.Figure(data=go.Heatmap(z=confusion_matrix, x=class_names, y=class_names, hoverongaps=False),

问题 I like to use Plotly to visualize everything, I'm trying to visualize a confusion matrix by Plotly, this is my code: def plot_confusion_matrix(y_true, y_pred, class_names): confusion_matrix = metrics.confusion_matrix(y_true, y_pred) confusion_matrix = confusion_matrix.astype(int) layout = { "title": "Confusion Matrix", "xaxis": {"title": "Predicted value"}, "yaxis": {"title": "Real value"} } fig = go.Figure(data=go.Heatmap(z=confusion_matrix, x=class_names, y=class_names, hoverongaps=False),

问题 I am building a sorting visualization. I started with a simple bubble sort. When I sort a small array everything is fine and the visualization looks good no matter the speed, but when I visualize a large array there is a delay and the visualization seems to start a few steps ahead and not showing some of the first steps. Why is it happening? This is my Code: import React, { useContext, useState, useEffect } from 'react'; const NUMBER_OF_ELEMENTS = 10; const DEFAULT_COLOR = 'black'; const

问题 I am generating a wordcloud image for single word and that works out fine. I want to generate the image for phrases. Their documentation states in the very first question that for 2 word phrases I need to use ~ to link them. I've tried this method linking my n lenght phrases but it still takes only 2 words in to consideration. Is this the limit or there is a way to generate wordcloud with more than 2 word phrases? wordcloud = WordCloud( width=1000, height=1000, background_color='black',

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 6 years ago . I want to find some great tools or styles to visualize my RDF data so that it can give viewers a shock when they access the RDF data. The problem is that the visualization tools I get now can just generate some simple styles (node,edge...) like below: They look ugly because edges take

问题 I've found this answer to a question where an approach for a scale outside a polar plot is provided (the add_scale function there). I tried to apply this to my data as well and it almost worked out. As shown in the picture, there is an issue at the top of the scale bar. How can I remove this little too long part of the scale? Second question is: How can I round the values in this scale to two digits. I've had a look at this question, but it didn't work out. I'll re-post the code here with all

问题 lineslist, below, represents a set of lines (for some chemical spectrum, let's say), in MHz. I know the linewidth of the laser used to probe these lines to be 5 MHz. So, naively, the kernel density estimate of these lines with a bandwidth of 5 should give me the continuous distribution that would be produced in an experiment using the aforementioned laser. The following code: import seaborn as sns import numpy as np import matplotlib.pyplot as plt lineslist=np.array([-153.3048645 , -75

问题 I'm writing test visualization program based on test results. I want to run jupyter notebook via terminal and generate html page to show it to user without showing the editable scripts to user. Can I do that? Or suggest the better way to show visualized test results. 回答1: Also use --execute to get the output jupyter nbconvert --execute --to html notebook.ipynb This produces a notebook.html file. The best practice is to keep the output out of the notebook for version control, see: Using