best python programs
<pre id="3346" class="graf graf--pre graf-after--p">%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection=’3d’)</pre>best python programs
<pre id="3346" class="graf graf--pre graf-after--p">%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.axes(projection=’3d’)</pre>best python programs
theta = 2 * np.pi * np.random.random(1000)
r = 6 * np.random.random(1000)
x = np.ravel(r * np.sin(theta))
y = np.ravel(r * np.cos(theta))
z = f(x, y)
ax = plt.axes(projection=’3d’)
ax.plot_trisurf(x, y, z,cmap=’viridis’, edgecolor=’none’);best python programs
ax = plt.axes(projection=’3d’)# Data for a three-dimensional line
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline, zline, ‘gray’)# Data for three-dimensional scattered points
zdata = 15 * np.random.random(100)
xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap=’Greens’);best python programs
import numpy as np
import tensorflow as tf
from include.data import get_data_set
from include.model import model
test_x, test_y = get_data_set("test")
x, y, output, y_pred_cls, global_step, learning_rate = model()
_BATCH_SIZE = 128
_CLASS_SIZE = 10
_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"
saver = tf.train.Saver()
sess = tf.Session()
try:
print("
Trying to restore last checkpoint ...")
last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
saver.restore(sess, save_path=last_chk_path)
print("Restored checkpoint from:", last_chk_path)
except ValueError:
print("
Failed to restore checkpoint. Initializing variables instead.")
sess.run(tf.global_variables_initializer())
def main():
i = 0
predicted_class = np.zeros(shape=len(test_x), dtype=np.int)
while i < len(test_x):
j = min(i + _BATCH_SIZE, len(test_x))
batch_xs = test_x[i:j, :]
batch_ys = test_y[i:j, :]
predicted_class[i:j] = sess.run(y_pred_cls, feed_dict={x: batch_xs, y: batch_ys})
i = j
correct = (np.argmax(test_y, axis=1) == predicted_class)
acc = correct.mean() * 100
correct_numbers = correct.sum()
print()
print("Accuracy on Test-Set: {0:.2f}% ({1} / {2})".format(acc, correct_numbers, len(test_x)))
if __name__ == "__main__":
main()
sess.close()best python programs
from mpl_toolkits import mplot3dbest python programs
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(X, Y, Z, 50, cmap='binary')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z');Copyright © 2021 Codeinu
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
