Sigmoid cross-entropy loss is also often used in deep learning mode. In this tutorial, we will introduce how to compute sigmoid cross-entropy loss with masking in tensorflow.
We often need to process variable length sequence in deep learning. In that situation, we will need use mask in our model. In this tutorial, we will introduce how to calculate softmax cross-entropy loss with masking in TensorFlow.
TensorFlow tf.nn.conv1d() allow us to compute a 1-D convolution for a tensor. In this tutorial, we will use some examples to show you how to use this function correctly.
Leaky ReLU is an activation function in deep learning, it often is used in graph attention networks. In this tutorial, we will introduce it for deep learning beginners.
It is very easy to display popular posts in wordpress, we can use WordPress Popular Posts plugin. However, this plugin can not allow us to display popular posts by category ids dynamically.
![Fix matplotlib.rcParams['lines.color'] Has no Affect on plot()](https://www.tutorialexample.com/wp-content/uploads/2020/08/Fix-matplotlib.rcParamslines.color-Has-no-Affect-on-plot-200x140.png)
matplotlib.rcParams[‘lines.color’] allows us to change the color of the line in matplotlib. However, it has no affect on plot() function. In this tutorial, we will introduce how to fix this problem.
We can use matplotlib.rcParams[‘lines.linestyle’] to set the style of line in matplotlib. In this tutorial, we will list all the line styles to help matplotlib beginners to use.
matplotlib.rcParams is a matplotlib.RcParams object, it is a dictionary-like variable which store some rc settings in matplotlib. In this tutorial, we will introduce how to use it.
We often use CNN networks to process images in deep learning. However, how to see the effect of images processd by CNN networks? In this tutorial, we will introduce the way to use python matplotlib to display.
matplotlib.pyplot.imshow() can display data as an image file, we will use some examples to show you how to use this function correctly in this tutorial.
