I want to see data in an easy-to-understand manner with Jupyter Notebook

I was learning TensorFlow and was investigating the API, but I thought that it would be convenient if I could see it in a graph, so I tried using matplotlib. It is an installation method and a simple usage memo.

Below are links to articles related to the current environment and TensorFlow.

-Installing TensorFlow on Windows was easy even for Python beginners -[Explanation for beginners] TensorFlow basic syntax and concept -[Explanation for beginners] TensorFlow tutorial MNIST (for beginners) -Visualize TensorFlow tutorial MNIST (for beginners) with TensorBoard -TensorFlow API memo -[Introduction to TensorBoard] Visualize TensorFlow processing to deepen understanding

Install matplotlib with pip from Anaconda

1. Start the terminal from Anaconda Navigator

Launch Anaconda Navigator from the Windows menu. Select Environment from the menu, select the virtual environment, and start the terminal with "Open Terminal". 10.open_terminal_via_anaconda.JPG

2. Install matplotlib with pip

Just install with pip.

pip install matplotlib

20.install_matplotlib.JPG

Try matplotlib

Try the contents of Wikipedia as it is.

Line graph

import matplotlib.pyplot as plt
import numpy as np
a = np.linspace(0,10,100)
b = np.exp(-a)
plt.plot(a,b)
plt.show()

30.matplotlib01.JPG

histogram

import matplotlib.pyplot as plt
from numpy.random import normal,rand
x = normal(size=200)
plt.hist(x,bins=30)
plt.show()

30.matplotlib02.JPG

Scatter plot

import matplotlib.pyplot as plt
from numpy.random import rand
a = rand(100)
b = rand(100)
plt.scatter(a,b)
plt.show()

30.matplotlib03.JPG

3D graph

amazing···

from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm)
plt.show()

30.matplotlib04.JPG

Check TensorFlow API

The original motivation for using matplotlib was TensorFlow API confirmation. As a bonus, I'm checking the functionality of TensorFlow's API truncated_normal like this.

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
x = sess.run(tf.truncated_normal([30000], stddev=0.1))
fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.hist(x, bins=100)
ax.set_title('Histogram tf.truncated_normal')
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.show()

30.matplotlib05.JPG