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Machine Learning Articles
Page 54 of 56
How to manage image resolution of a graph in Matplotlib
An Image contains a 2-D matrix RGB data points which can be defined by the dots point per inch [ DPI ] of the image. The resolution of the image is important because a hi-resolution image will have much more clarity.We have a method ‘plt.savefig()’ in Matplotlib which determines the size of the image in terms of its pixels. Ideally it is having an ‘dpi’ parameter.Let’s see how we can manage the resolution of a graph in Matplotlib.Exampleimport matplotlib.pyplot as plt import numpy as np #Prepare the data for histogram np.random.seed(1961) nd = np.random.normal(13, 5, 1000) #Define the ...
Read MoreHow to provide shadow effect in a Plot using path_effect attribute in Matplotlib
In order to provide path effects like shadow effect in a plot or a graph, we can use the path_effect attribute.For example, let’s see how we can use the path_effect attribute in Matplotlib add a shadow effect to a sigmoid function.import matplotlib.pyplot as plt import numpy as np from matplotlib.patheffects import PathPatchEffect, SimpleLineShadow, NormalNow let us define the size of the figure and plot the sigmoid function, plt.style.use('seaborn-deep') plt.subplots(figsize=(10, 10))Let us define the datapoints for the plot, x = np.linspace(-10, 10, 50) y = 1+ np.exp(-x))Let us define the shadow property in the plot, plt.plot(x, y, linewidth=8, color='blue', path_effects= [SimpleLineShadow(), ...
Read MoreHow to plot with multiple color cycle using cycler property in Matplotlib
Matplotlib has a default color cycle for all the graphs and plots, however, in order to draw plots with multiple color cycles, we can use the cycler property of Matplotlib. It is used to plot repetitive patterns for the axis.First, we will use the Object Oriented APIs such as pyplot to plot the specific visualization.from cycler import cycler import numpy as np from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas from matplotlib.figure import Figure from IPython.core.display import displayIn this example, we will create two objects which will repeat the cycle after every four objects. Thus, after creating two objects, the last two ...
Read MoreHow to plot Exponentially Decaying Function using FuncAnimation in Matplotlib
Let us assume that we want to animate a nature of function which is exponentially decaying like y = a(b)^x where b = growth factor and a = initial value.An exponentially decay function would look like this, However, for now, we want to animate and plot the exponentially decaying tan function.First import the libraries, import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimationDefine the axes, fig, a = plt.subplots()Plotting a blank figure with axes, xdata, ydata = [], [] line, = ax.plot(xdata, ydata)Set the limit of the grids, ax.set_xlim(0, 10) ax.set_ylim(-3.0, 3.0) ax.grid() Define the function to ...
Read MoreHow to create custom markers on a plot in Matplotlib
To create a custom marker on a plot or graph, we use a list where we write the markers we want to see in the plot. The markers are nothing but symbols, emoji, character or any character which we want to see on the figure.In order to create the marker, we will first import the required libraries.import matplotlib.pyplot as plt import numpy as npFor now, we will create a marker on a sine curve. Let us create the grid with size (12, 6), x = np.arange(1, 2.6, 0.1) y = np.sin(2 * np.pi * x) plt.subplots(figsize=(12, 6))Here we will create ...
Read MoreHow can a Convolutional Neural Network be used to build learning model?
A neural network that contains at least one layer is known as a convolutional layer. A convolutional neural network would generally consist of some combination of the below mentioned layers:Convolutional layersPooling layersDense layersConvolutional Neural Networks have been used to produce great results for a specific kind of problems, such as image recognition. It is a Deep Learning algorithm that takes an image as input, assigns importance to it, i.e. the algorithm learns to assign weights and biases to values. This helps differentiate one object from the other.The amount of pre-processing required in a ConvNet is lesser than other classification algorithms. ...
Read MoreHow can Tensorflow be used to download flower dataset into the environment?
The flower dataset can be downloaded using a google API that basically links to the flower dataset. The ‘get_file’ method can be used to pass the API as a parameter. Once this is done, the data gets downloaded into the environment.Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?We will be using the flowers dataset, which contains images of several thousands of flowers. It contains 5 sub-directories, and there is one sub-directory for every class. We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code ...
Read MoreHow can Keras be used to recreate the model and check its accuracy?
Keras means ‘horn’ in Greek. Keras was developed as a part of research for the project ONEIROS (Open ended Neuro−Electronic Intelligent Robot Operating System). Keras is a deep learning API, which is written in Python. It is a high−level API that has a productive interface that helps solve machine learning problems.Tensorflow is a machine learning framework that is provided by Google. It is an open−source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes.Keras runs on top of Tensorflow framework. It was built to help ...
Read MoreHow can the 'Word2Vec' algorithm be trained using Tensorflow?
Tensorflow is a machine learning framework that is provided by Google. It is an open−source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes. It has optimization techniques that help in performing complicated mathematical operations quickly.This is because it uses NumPy and multi−dimensional arrays. These multi−dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network. It is highly scalable, and comes with many popular datasets. It uses GPU computation and automates the management of resources.The ‘tensorflow’ package can be installed ...
Read MoreHow can Logistic Regression be implemented using TensorFlow?
Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes. It has optimization techniques that help in performing complicated mathematical operations quickly. This is because it uses NumPy and multi-dimensional arrays.Multi−dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network. It is highly scalable, and comes with many popular datasets. It uses GPU computation and automates the management of resources.The ‘tensorflow’ package can be installed on ...
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