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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"

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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

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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

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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

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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())

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# taken from https://www.tensorflow.org/guide/keras/functional#a_toy_resnet_model
inputs = keras.Input(shape=(32, 32, 3), name="img")
x = layers.Conv2D(32, 3, activation="relu")(inputs)
x = layers.Conv2D(64, 3, activation="relu")(x)
block_1_output = layers.MaxPooling2D(3)(x)

x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_1_output)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x)
block_2_output = layers.add([x, block_1_output])

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# Adapted from https://www.tensorflow.org/overview
import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# 1. Define a model
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),

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# we have 1 batch of a stack of size 2 of images shape (3, 4) 
test = tf.constant(np.arange(24).reshape(1, 2, 3, 4), dtype=tf.int64)

# <tf.Tensor: shape=(1, 2, 3, 4), dtype=int64, numpy=
# array([[[[ 0,  1,  2,  3],
#          [ 4,  5,  6,  7],
#          [ 8,  9, 10, 11]],
#         [[12, 13, 14, 15],
#          [16, 17, 18, 19],
#          [20, 21, 22, 23]]]])>

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import tensorflow as tf

class DiceBCELoss(tf.keras.losses.Loss):
    def __init__(self, roi, smooth=1e-6, eps=1e-8, name='DiceBCE'):
        """ A more stable surrogate for the dice metric
        Sources:
        [1] https://github.com/Project-MONAI/MONAI/issues/807
        [2] https://www.kaggle.com/bigironsphere/loss-function-library-keras-pytorch
        Args:
            roi:        bool mask same size as y_pred and y_true

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