import tensorflow as tf
print("TensorFlow Version:", tf.__version__)
print("Is GPU available?:", tf.config.list_physical_devices('GPU'))

rank_0 = tf.constant(4)
rank_1 = tf.constant([2.0, 3.0, 4.0])
rank_2 = tf.constant([[1, 2], [3, 4], [5, 6]], dtype=tf.float16)

tensor = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
tensor.shape          # (2, 2)
tensor.dtype          # float32
tensor[0, 0]          # element access
tensor[:, 0]          # slicing

var = tf.Variable([[5.0, 6.0], [7.0, 8.0]])
var.assign([[1, 2], [3, 4]])       # replace values
var.assign_add([[1, 1], [1, 1]])   # increment in place

A = tf.constant([[2.0, 3.0], [1.0, 4.0]])
B = tf.constant([[1.0, 2.0], [3.0, 4.0]])
tf.linalg.matmul(A, B)

tf.random.normal([3, 3], mean=0.0, stddev=1.0)
tf.random.uniform([2, 2], minval=0, maxval=10)
tf.random.set_seed(42)     # reproducibility

x = tf.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.float32)
tf.reduce_mean(x)            # 3.5 (all elements)
tf.reduce_mean(x, axis=0)    # [2.5 3.5 4.5] (column means)
tf.reduce_mean(x, axis=1)    # [2. 5.] (row means)

@tf.function
def computation_graph(x1, x2):
    return (x1 + x2) - (x1 * x2)

x = tf.Variable(2.0)
with tf.GradientTape() as tape:
    y = x ** 3
dy_dx = tape.gradient(y, x)   # 12.0, since d/dx(x^3) = 3x^2