lidopypy

lidopypy

lidopypy

lidopypy

lidopypy

lidopypy

import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
#數據預處理
test_data = np.array(pd.read_csv(r'C:\\Users\\lido_lee\\Downloads\\fasion_mnist\\fashion-mnist_test.csv'), dtype='float32')
embed_count = 1600
x_test = test_data[:embed_count, 1:] / 255

lidopypy

import os
from keras.datasets import mnist
import matplotlib.pyplot as plt
from keras import backend as K
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector

batch_size = 128
num_classes = 10

lidopypy

from os.path import join
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import numpy as np

batch_size = 128

lidopypy

'''
========================
3D surface (solid color)
========================

Demonstrates a very basic plot of a 3D surface using a solid color.
'''

# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import

lidopypy

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Make data
for r in range(1,10,1):
    u = np.linspace(0, 2 * np.pi, 1000)
    v = np.linspace(0, np.pi, 1000)