本体を解凍 # tar zxvf McAfeeTP-10.6.9-121-Eval-standalone.tar.gz
解凍された圧縮ファイルを解凍 # tar zxvf McAfeeTP-10.6.9-121-standalone.linux.tar.gz すべて解凍 # tar zxvf McAfeeESP-Basic-10.6.9-126-Full.linux.tar.gz # tar zxvf McAfeeESP-KernelModule-10.6.9-126-Full.linux.tar.gz
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
とするとハッピーになれるようでした。（下記サンプルコード転記）
import tensorflow.compat.v1 as tf
import numpy as np
tf.disable_v2_behavior()
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random.uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# Before starting, initialize the variables. We will 'run' this first.
init = tf.global_variables_initializer()
# Launch the graph.
sess = tf.Session()
sess.run(init)
# Fit the line.
for step in range(201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(W), sess.run(b))
# Learns best fit is W: [0.1], b: [0.3]
eto = ["申","酉","戌","亥","子","丑","寅","卯","辰","巳","午","未"]
try:
x = input("生まれた年を西暦で入力してください。: ")
y = int(x)
y = y % 12
print("あなたの干支は {} です。".format(eto[y]))
except(ValueError):
print("4桁の半角数字を入力してください。")
Titles = [
"Discopolis 2.0 - Fehrplay Remix",
"Never Alone - J-Kraken Remix",
"Golden",
"Steal Your Heart",
"I Want You To Know",
]
Artists = [
"Lifelike, Kris Menace, Fehrplay",
"Chachi, Natascha Bessez",
"Thomas Hayes, Kyler England",
"BRKLYN, Lenachka",
"ゼッド, セレーナ・ゴメス",
]
i = 0
for i, new in enumerate(zip(Titles,Artists)):
new = Artists[i]
x = input("{} {} / {} | アーティスト名を修正してください。:".format(i, Titles[i], Artists[i]))
if x == "":
continue
elif x == "q":
break
new = x
Artists[i] = new
print(i, Titles[i], "/", Artists[i])
