maxrohleder

from tensorflow.keras import layers, models

# define the model
model = models.Sequential(name="Keras Test CNN")
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())

maxrohleder

import numpy as np

# first define our input values and all values for k
t = np.linspace(0, 1, 200) 
k = np.arange(0, 1000)
f = 10 # frequency in Hertz

# from the formula, isolate all factors in the sinus term which include k
k = 2 * np.pi * (2 * k - 1) * f

maxrohleder

import numpy as np

# some sample data in shaped cubic (100, 100, 100)
img = np.random.sample((100, 100, 100))

# set all values between 0.5 and 0.6 to zero
img[(img < 0.6) & (img > 0.5)] = 0

# in a small sub cube (10, 10, 10)..
# .. get index arrays of all elements above 0.6

maxrohleder

import numpy as np

img = np.arange(6).reshape(2, 3)

# looping over all elements like this ...
for x in range(img.shape[0]):
    for y in range(img.shape[1]):
        print(img[x, y])

# ... is easier and more readable with numpy

maxrohleder

a = np.array([[0, 1, 2],
              [3, 4, 5]])
b = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

# subsetting of the array (start is included, end is excluded)
print(a[0:2, 0:2])
#> [[0 1]
#>  [3 4]]

# striding / stepping "every second entry"