neural network consoleのデータを表示(jupyter)
import os
import numpy as np
from pandas import Series,DataFrame
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
input_dir = 'XXX'
input_file = os.path.join(input_dir, "iris_flower_dataset_training_delo.csv")
data = pd.read_csv(input_file,delimiter=",")
data[:4]
x1 = data[data["y:label"] == 0]["x__3:Petal width"]
x2 = data[data["y:label"] == 2]["x__3:Petal width"]
plt.hist([x1, x2], stacked=False, label=["true", "false"])
plt.title("Petal width")
plt.ylabel("Petal width")
plt.legend(loc="upper right")
x1 = data[data["y:label"] == 0]["x__3:Petal width"]
y1 = data[data["y:label"] == 0]["x__0:Sepal length"]
x2 = data[data["y:label"] == 2]["x__3:Petal width"]
y2 = data[data["y:label"] == 2]["x__0:Sepal length"]
plt.title("Sepal length/Petal width")
plt.plot(x1, y1, "o", label="true")
plt.plot(x2, y2, "x", label="false")
plt.ylim([0, 10])
plt.xlabel("Petal width")
plt.ylabel("Sepal length")
plt.legend(loc="lower right")
Author And Source
この問題について(neural network consoleのデータを表示(jupyter)), 我々は、より多くの情報をここで見つけました https://qiita.com/copacopiyon/items/538f1c6f8e8c5c6be8e4著者帰属:元の著者の情報は、元のURLに含まれています。著作権は原作者に属する。
Content is automatically searched and collected through network algorithms . If there is a violation . Please contact us . We will adjust (correct author information ,or delete content ) as soon as possible .