TF_Basics .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "975f859a",
   "metadata": {},
   "source": [
    "## TenserFlow: Мат Операции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be1e4ba4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff5addd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54fa56d",
   "metadata": {},
   "source": [
    "### Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd7adfad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[4.]], dtype=float32)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.constant(4, shape=(1, 1), dtype=tf.float32)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7ad0da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1 2 3]\n",
      " [4 5 6]], shape=(2, 3), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.constant([[1, 2, 3], [4, 5, 6]], shape=(2, 3))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4950139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]], shape=(3, 3), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.eye(3)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e4f6e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]], shape=(4, 3), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.ones((4, 3))\n",
    "print(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19d53695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[[0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0.]]\n",
      "\n",
      " [[0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0.]]\n",
      "\n",
      " [[0. 0. 0. 0. 0.]\n",
      "  [0. 0. 0. 0. 0.]]], shape=(3, 2, 5), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.zeros((3, 2, 5))\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "428fd4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[0.92388046 0.44442916]\n",
      " [0.01177216 0.74455965]], shape=(2, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.random.uniform((2, 2), minval=0, maxval=1)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47fbc903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[ 1.38212919  0.03250717 -0.53851688]\n",
      " [-1.04308891 -0.99633753 -1.1919843 ]\n",
      " [-0.71122622  2.36879873 -0.00838318]], shape=(3, 3), dtype=float64)\n"
     ]
    }
   ],
   "source": [
    "x = tf.random.normal((3, 3), mean=0, stddev=1)\n",
    "print(tf.cast(x, dtype=tf.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c7a85109",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([0 2 4 6 8], shape=(5,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.range(9)\n",
    "x = tf.range(start=0, limit=10, delta=2)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2af814f",
   "metadata": {},
   "source": [
    "### Math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f2f3f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.constant([1, 2, 3])\n",
    "y = tf.constant([9, 8, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "731ba00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([10, 10, 10], dtype=int32)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = tf.add(x, y)\n",
    "z = x + y\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0f022e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([-8, -6, -4], dtype=int32)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = tf.subtract(x, y)\n",
    "z = x - y\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2407e4cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=float64, numpy=array([0.11111111, 0.25      , 0.42857143])>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = tf.divide(x, y)\n",
    "z = x / y\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a064b654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3,), dtype=int32, numpy=array([ 9, 16, 21], dtype=int32)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = tf.multiply(x, y)\n",
    "z = x * y\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f2ecfaa1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=int32, numpy=46>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = tf.tensordot(x, y, axes=1)\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "522debb9",
   "metadata": {},
   "source": [
    "### Reshaping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e68557a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "x = tf.range(9)\n",
    "\n",
    "x = tf.reshape(x, (3, 3))\n",
    "\n",
    "x = tf.transpose(x, perm=[1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6345ca82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 3), dtype=int32, numpy=\n",
       "array([[0, 3, 6],\n",
       "       [1, 4, 7],\n",
       "       [2, 5, 8]], dtype=int32)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c906b4ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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