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

rank_0_tensor = tf.constant(4)
# tf.Tensor(4, shape=(), dtype=int32)

rank_1_tensor = tf.constant([2.0, 3.0, 4.0])
# tf.Tensor([2. 3. 4.], shape=(3,), dtype=float32)

rank_2_tensor = tf.constant([[1, 2], [3, 4], [5, 6]], dtype=tf.float16)
# shape=(3, 2), dtype=float16

tensor = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
print("Shape:", tensor.shape)      # (2, 2)
print("Data Type:", tensor.dtype)  # float32

tensor[0, 0]   # عنصر واحد -> 1.0
tensor[:, 0]   # كل الصفوف، العمود 0 -> [1. 3.]

var = tf.Variable([[5.0, 6.0], [7.0, 8.0]])
var.assign([[1, 2], [3, 4]])
var.assign_add([[1, 1], [1, 1]])
# Incremented Value: [[2. 3.] [4. 5.]]

x = tf.constant([2.0, 4.0, 6.0])
y = tf.constant([1.0, 2.0, 3.0])

tf.add(x, y)       # [3. 6. 9.]
tf.subtract(x, y)  # [1. 2. 3.]
tf.multiply(x, y)  # [2. 8. 18.]
tf.divide(x, y)    # [2. 2. 2.]

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)
# [[11. 16.]
#  [13. 18.]]

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)

x = tf.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.float32)
tf.reduce_mean(x)          # 3.5 (كل العناصر)
tf.reduce_mean(x, axis=0)  # [2.5 3.5 4.5] (لكل عمود)
tf.reduce_mean(x, axis=1)  # [2. 5.]        (لكل صف)

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

x1 = tf.constant(7, dtype=tf.float32)
x2 = tf.constant(3, dtype=tf.float32)
computation_graph(x1, x2)  # -11

x = tf.Variable(2.0)
with tf.GradientTape() as tape:
    y = x ** 3  # y = x^3

dy_dx = tape.gradient(y, x)
# dy/dx = 3*x^2 = 12.0

w = tf.Variable(1.0)
b = tf.Variable(2.0)
x_vals = tf.constant([1.0, 2.0, 3.0])

with tf.GradientTape() as tape:
    y_vals = w * x_vals + b
    loss = tf.reduce_mean((y_vals - tf.constant([2.0, 4.0, 6.0]))**2)

gradients = tape.gradient(loss, [w, b])
# dLoss/dw: -1.333, dLoss/db: 0.0