DL-1_TF2Basic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **1.2 PROCEDURE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
      "<function is_built_with_cuda at 0x000002A132595F70>\n",
      "/device:GPU:0\n",
      "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "print(tf.config.list_physical_devices('GPU'))\n",
    "# [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
    "\n",
    "print(tf.test.is_built_with_cuda)\n",
    "# <function is_built_with_cuda at 0x7f4f5730fbf8>\n",
    "\n",
    "print(tf.test.gpu_device_name())\n",
    "# /device:GPU:0\n",
    "\n",
    "print(tf.config.get_visible_devices())\n",
    "# [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 2), dtype=float32, numpy=\n",
       "array([[1., 2.],\n",
       "       [3., 4.]], dtype=float32)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "const_a = tf.constant([[1, 2, 3, 4]],shape=[2,2], dtype=tf.float32) #Create a 2x2 matrix with values 1, 2, 3, and 4.\n",
    "const_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "value of the constant const_a: [[1. 2.]\n",
      " [3. 4.]]\n",
      "data type of the constant const_a: <dtype: 'float32'>\n",
      "shape of the constant const_a: (2, 2)\n",
      "name of the device that is to generate the constant const_a: /job:localhost/replica:0/task:0/device:GPU:0\n"
     ]
    }
   ],
   "source": [
    "#View common attributes.\n",
    "print(\"value of the constant const_a:\", const_a.numpy())\n",
    "print(\"data type of the constant const_a:\", const_a.dtype)\n",
    "print(\"shape of the constant const_a:\", const_a.shape)\n",
    "print(\"name of the device that is to generate the constant const_a:\", const_a.device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
       "array([[0, 0, 0],\n",
       "       [0, 0, 0]])>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros_b = tf.zeros(shape=[2, 3], dtype=tf.int32)   #Create a 2x3 matrix with all values being 0.\n",
    "zeros_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros_like_c = tf.zeros_like(const_a) #View generated data.\n",
    "zeros_like_c.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 8, 8],\n",
       "       [8, 8, 8],\n",
       "       [8, 8, 8]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fill_d = tf.fill([3,3], 8) #2x3 matrix. The element value is 8. #View the data.\n",
    "fill_d.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.8113182 ,  1.4845989 ,  0.06532937, -2.4427042 ,  0.0992484 ],\n",
       "       [ 0.5912243 ,  0.59282297, -2.1229296 , -0.72289723, -0.05627037],\n",
       "       [ 0.6435448 , -0.26432407,  1.8566332 ,  0.5678417 , -0.3828359 ],\n",
       "       [-1.4853433 ,  1.2617711 , -0.02530608, -0.2646297 ,  1.5328138 ],\n",
       "       [-1.7429771 , -0.4378929 , -0.56601   ,  0.32066926,  1.132831  ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_e = tf.random.normal([5,5],mean=0,stddev=1.0, seed = 1) #View created data.\n",
    "random_e.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a list.\n",
    "list_f = [1,2,3,4,5,6]\n",
    "\n",
    "#View the data type.\n",
    "type(list_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(6,), dtype=float32, numpy=array([1., 2., 3., 4., 5., 6.], dtype=float32)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor_f = tf.convert_to_tensor(list_f, dtype=tf.float32)\n",
    "tensor_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'Variable:0' shape=(2, 3) dtype=float32, numpy=\n",
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]], dtype=float32)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a variable. Only the initial value needs to be provided.\n",
    "var_1 = tf.Variable(tf.ones([2,3]))\n",
    "var_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the variable var_1: tf.Tensor(\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]], shape=(2, 3), dtype=float32)\n",
      "Value of the variable var_1 after the assignment: tf.Tensor(\n",
      "[[1. 2. 3.]\n",
      " [4. 5. 6.]], shape=(2, 3), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#Read the variable value.\n",
    "print(\"Value of the variable var_1:\",var_1.read_value())\n",
    "\n",
    "#Assign a variable value.\n",
    "var_value_1=[[1,2,3],[4,5,6]]\n",
    "var_1.assign(var_value_1)\n",
    "print(\"Value of the variable var_1 after the assignment:\",var_1.read_value())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'Variable:0' shape=(2, 3) dtype=float32, numpy=\n",
       "array([[2., 3., 4.],\n",
       "       [5., 6., 7.]], dtype=float32)>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Variable addition\n",
    "var_1.assign_add(tf.ones([2,3]))\n",
    "var_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 100, 100, 3), dtype=float32, numpy=\n",
       "array([[[[-1.70930135e+00, -4.49918061e-02, -1.58903217e+00],\n",
       "         [ 3.12792771e-02,  2.17144251e+00, -2.62376189e+00],\n",
       "         [ 1.08832431e+00, -1.21833399e-01, -3.34820181e-01],\n",
       "         ...,\n",
       "         [ 2.54986525e-01, -2.06253398e-02, -4.48896229e-01],\n",
       "         [ 1.17051923e+00,  6.14995062e-01, -2.78890040e-03],\n",
       "         [ 2.07062078e+00,  2.72161037e-01, -2.14600325e+00]],\n",
       "\n",
       "        [[ 5.59184968e-01, -1.99044907e+00,  8.71416390e-01],\n",
       "         [ 1.91730335e-02, -8.32339466e-01,  1.64546180e+00],\n",
       "         [-1.12154019e+00, -9.41377997e-01,  6.70729637e-01],\n",
       "         ...,\n",
       "         [-2.87934184e-01, -8.55423093e-01,  1.09883618e+00],\n",
       "         [ 1.28498042e+00, -1.32767618e+00,  1.23782849e+00],\n",
       "         [ 1.12714517e+00, -1.14500356e+00, -1.25173151e+00]],\n",
       "\n",
       "        [[-1.02668419e-01, -6.69009089e-01, -8.82090211e-01],\n",
       "         [ 4.89823222e-02, -5.13644338e-01,  1.59072787e-01],\n",
       "         [ 1.50328517e-01, -1.35522461e+00,  8.61459896e-02],\n",
       "         ...,\n",
       "         [-1.14009666e+00, -1.33848345e+00, -3.75119686e-01],\n",
       "         [-4.31539237e-01,  1.52098572e+00,  1.20400083e+00],\n",
       "         [ 3.37815881e-01, -5.71363866e-01, -1.33441985e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 3.97834986e-01, -3.14389169e-01,  1.78797126e-01],\n",
       "         [ 1.11895835e+00,  1.03093636e+00,  1.68021023e-01],\n",
       "         [-5.42512536e-02, -9.41509783e-01, -9.80965734e-01],\n",
       "         ...,\n",
       "         [-8.48648906e-01,  9.64067459e-01,  2.45217055e-01],\n",
       "         [ 1.29427826e+00,  5.16352654e-01, -3.76630872e-01],\n",
       "         [-1.02236062e-01,  9.48032811e-02,  2.89034247e-01]],\n",
       "\n",
       "        [[ 7.03044832e-02,  9.01744366e-01, -1.01953506e+00],\n",
       "         [ 7.20060349e-01,  7.86616981e-01,  1.55204344e+00],\n",
       "         [-6.64023042e-01, -8.97359967e-01, -5.32017946e-01],\n",
       "         ...,\n",
       "         [ 2.70576775e-03, -9.46501195e-02,  1.26195267e-01],\n",
       "         [ 1.21402967e+00,  1.39070165e+00, -2.49737591e-01],\n",
       "         [-1.03215969e+00,  1.67834091e+00, -9.74069715e-01]],\n",
       "\n",
       "        [[-3.93523753e-01, -2.26982623e-01,  9.70505476e-01],\n",
       "         [ 8.16267133e-01, -6.07654274e-01, -1.81344199e+00],\n",
       "         [ 4.44840938e-01,  4.79898542e-01, -1.22700357e+00],\n",
       "         ...,\n",
       "         [ 5.61485231e-01, -1.58809662e+00,  2.66148657e-01],\n",
       "         [ 8.35777819e-01, -8.81994367e-02,  1.00678384e+00],\n",
       "         [-6.87266648e-01, -1.24804044e+00, -3.20472896e-01]]],\n",
       "\n",
       "\n",
       "       [[[ 8.96064758e-01,  1.28783262e+00, -2.19376040e+00],\n",
       "         [ 7.63432801e-01,  5.79136550e-01, -1.94563401e+00],\n",
       "         [ 8.65961313e-01,  4.87109780e-01,  8.44129324e-02],\n",
       "         ...,\n",
       "         [-3.30545574e-01, -3.62908766e-02,  1.31732547e+00],\n",
       "         [ 5.44765413e-01, -5.33078194e-01, -4.28906381e-01],\n",
       "         [-4.12797064e-01, -3.35832387e-01, -9.45991576e-01]],\n",
       "\n",
       "        [[-1.20157731e+00, -1.48786020e+00, -1.64138412e+00],\n",
       "         [-9.10971105e-01, -3.46064180e-01,  1.81170177e+00],\n",
       "         [ 5.83114505e-01, -2.95682281e-01, -9.41989869e-02],\n",
       "         ...,\n",
       "         [-2.51222587e+00, -1.69570476e-01,  7.74091855e-02],\n",
       "         [ 1.30304074e+00,  1.50109068e-01, -9.08915162e-01],\n",
       "         [-1.03926516e+00, -2.10158920e+00, -1.18682671e+00]],\n",
       "\n",
       "        [[-8.05404663e-01,  4.30949092e-01, -1.90790224e+00],\n",
       "         [ 1.17605090e+00,  8.67776811e-01, -3.55562776e-01],\n",
       "         [ 1.83153486e+00,  1.45608473e+00,  1.07640815e+00],\n",
       "         ...,\n",
       "         [ 7.96891332e-01, -1.20095026e+00, -5.65537989e-01],\n",
       "         [ 1.15479076e+00,  1.33165097e+00,  2.29775548e+00],\n",
       "         [-4.06537235e-01, -1.82202101e+00, -4.77740645e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 6.81141734e-01, -5.67603350e-01, -1.25882471e+00],\n",
       "         [ 2.05119061e+00,  5.01929641e-01,  4.82803971e-01],\n",
       "         [-4.88765061e-01, -1.68718016e+00, -2.69729674e-01],\n",
       "         ...,\n",
       "         [-1.45945907e-01, -5.07107317e-01, -1.83800232e+00],\n",
       "         [-1.07615244e+00,  6.11010313e-01, -8.90796661e-01],\n",
       "         [-2.34100029e-01, -1.04565620e+00,  1.37684500e+00]],\n",
       "\n",
       "        [[-4.03083265e-01,  1.83027968e-01,  6.45967484e-01],\n",
       "         [ 1.51492763e+00,  3.32741708e-01, -1.33788168e+00],\n",
       "         [ 1.20981693e+00,  6.05531037e-01,  6.20406568e-01],\n",
       "         ...,\n",
       "         [-3.56484938e+00,  1.67251945e+00, -1.52176046e+00],\n",
       "         [-1.42148459e+00, -1.04810305e-01, -1.35811555e+00],\n",
       "         [-1.07770371e+00, -1.08440089e+00, -6.52764857e-01]],\n",
       "\n",
       "        [[-9.96119156e-02, -6.40050292e-01,  1.52310503e+00],\n",
       "         [-5.44337928e-02,  5.04586697e-01, -5.14228866e-02],\n",
       "         [ 2.73815375e-02,  1.28592908e+00,  2.66702712e-01],\n",
       "         ...,\n",
       "         [-1.23916554e+00,  2.53114589e-02,  3.24541897e-01],\n",
       "         [-5.20799518e-01,  1.08634615e+00, -1.49880445e+00],\n",
       "         [-2.25754023e-01, -2.47761175e-01, -2.27872342e-01]]],\n",
       "\n",
       "\n",
       "       [[[-8.32782313e-03, -1.62171590e+00,  5.15094876e-01],\n",
       "         [-7.94800520e-01,  2.48940659e+00, -1.40761244e+00],\n",
       "         [-9.06533599e-01, -8.10927272e-01, -1.66026926e+00],\n",
       "         ...,\n",
       "         [ 9.96437848e-01, -9.70831066e-02,  2.87023216e-01],\n",
       "         [-8.88477191e-02,  1.13955474e+00, -8.13939348e-02],\n",
       "         [ 1.26069129e+00, -6.18464112e-01, -2.13964194e-01]],\n",
       "\n",
       "        [[-3.69497150e-01, -1.58038175e+00, -8.53929639e-01],\n",
       "         [-1.92075953e-01, -4.90587085e-01, -9.31818604e-01],\n",
       "         [-1.89240682e+00,  1.31771040e+00, -1.95689547e+00],\n",
       "         ...,\n",
       "         [ 1.35590005e+00,  1.40259469e+00,  7.61416018e-01],\n",
       "         [-2.74607122e-01,  1.70924914e+00, -6.20987296e-01],\n",
       "         [-3.99657995e-01,  2.43942595e+00, -4.62720841e-01]],\n",
       "\n",
       "        [[-9.34040725e-01, -2.80366033e-01, -1.25714338e+00],\n",
       "         [ 2.22977940e-02,  5.64718843e-01,  6.67270362e-01],\n",
       "         [ 1.72110483e-01, -1.80195972e-01, -8.93234551e-01],\n",
       "         ...,\n",
       "         [-8.46639216e-01,  1.14472282e+00,  1.28593326e-01],\n",
       "         [-7.97360063e-01,  9.06222761e-01, -9.01242495e-01],\n",
       "         [ 1.27888441e+00,  6.81898057e-01, -2.45869793e-02]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-1.81135923e-01, -1.02577098e-01,  9.86425340e-01],\n",
       "         [-8.98562551e-01,  1.04288943e-01,  8.08887839e-01],\n",
       "         [ 7.37638414e-01, -1.10659480e+00, -8.11675668e-01],\n",
       "         ...,\n",
       "         [-1.36479712e+00, -3.37738365e-01, -1.35746825e+00],\n",
       "         [ 1.51019788e+00,  6.83137715e-01,  3.51325758e-02],\n",
       "         [-1.35059291e-02, -8.00938904e-01, -1.14501464e+00]],\n",
       "\n",
       "        [[ 2.10730702e-01, -2.25078177e+00,  1.31448615e+00],\n",
       "         [ 1.53629386e+00, -1.38590693e+00, -1.60294354e+00],\n",
       "         [-3.15277755e-01,  1.11690211e+00, -4.03798580e-01],\n",
       "         ...,\n",
       "         [ 4.51701373e-01, -8.00107121e-01,  1.46390998e+00],\n",
       "         [-2.60519803e-01, -8.81854773e-01, -5.21289289e-01],\n",
       "         [ 8.74933124e-01,  4.73013788e-01,  1.63781703e-01]],\n",
       "\n",
       "        [[-8.43718410e-01, -8.04746300e-02, -5.22439003e-01],\n",
       "         [ 7.37058595e-02,  8.05023253e-01, -1.45290840e+00],\n",
       "         [-6.42721117e-01, -2.92341322e-01,  1.42631054e+00],\n",
       "         ...,\n",
       "         [ 1.14458668e+00,  3.90416712e-01, -9.72490251e-01],\n",
       "         [-2.02652931e-01, -6.47101700e-01,  9.97192502e-01],\n",
       "         [ 4.07282740e-01,  2.01770234e+00, -1.07702911e+00]]],\n",
       "\n",
       "\n",
       "       [[[ 8.06004584e-01,  3.51013213e-01, -7.43508041e-01],\n",
       "         [-1.88535690e+00, -5.76852202e-01, -7.21006632e-01],\n",
       "         [ 3.08038384e-01, -1.40733004e+00,  3.36514354e-01],\n",
       "         ...,\n",
       "         [ 9.84766245e-01,  1.77316403e+00,  1.46486759e-01],\n",
       "         [ 8.23367834e-01, -6.80106819e-01,  9.08959746e-01],\n",
       "         [-3.01967651e-01,  7.68677950e-01, -1.30150878e+00]],\n",
       "\n",
       "        [[ 6.99271441e-01, -1.68843722e+00, -1.16908813e+00],\n",
       "         [-6.75371647e-01,  9.99369398e-02,  1.67251334e-01],\n",
       "         [-1.30121037e-01,  1.90715611e+00, -1.30874240e+00],\n",
       "         ...,\n",
       "         [ 5.94998479e-01, -1.43332517e+00, -9.72964019e-02],\n",
       "         [ 1.89400747e-01,  1.04823971e+00,  7.94802248e-01],\n",
       "         [-8.46201777e-01, -3.28234918e-02,  1.22993827e+00]],\n",
       "\n",
       "        [[ 4.26873535e-01, -1.32984281e+00,  1.28083396e+00],\n",
       "         [ 4.44405109e-01, -1.87218904e+00,  5.51761091e-01],\n",
       "         [ 2.04190540e+00, -2.83061326e-01, -6.17134571e-01],\n",
       "         ...,\n",
       "         [-2.05719471e+00,  1.36754230e-01,  8.51878464e-01],\n",
       "         [ 2.14284635e+00,  7.89164722e-01,  2.01312140e-01],\n",
       "         [ 2.65346795e-01, -5.90703189e-01, -1.70399293e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 2.05004394e-01, -5.06796092e-02,  9.79414940e-01],\n",
       "         [ 1.23416162e+00,  1.99148667e+00, -1.36392987e+00],\n",
       "         [-4.28795427e-01, -4.68795240e-01, -5.12261748e-01],\n",
       "         ...,\n",
       "         [-2.33998790e-01,  6.93986118e-02,  3.24772298e-01],\n",
       "         [-1.02131464e-01,  4.76896733e-01,  1.23354268e+00],\n",
       "         [ 1.79207087e-01,  4.12386715e-01, -2.21119595e+00]],\n",
       "\n",
       "        [[-3.79722685e-01,  8.31899941e-01, -1.94075060e+00],\n",
       "         [ 9.34035301e-01,  1.26024127e-01, -5.48633449e-02],\n",
       "         [-1.23407185e+00,  1.42780411e+00,  1.26194596e+00],\n",
       "         ...,\n",
       "         [ 4.08723831e-01,  9.37341750e-01, -2.48553336e-01],\n",
       "         [ 1.90197754e+00, -5.63911311e-02,  3.90655160e-01],\n",
       "         [-4.82857943e-01, -9.70952034e-01,  1.39593512e-01]],\n",
       "\n",
       "        [[ 5.24867535e-01, -3.99139732e-01,  6.63599819e-02],\n",
       "         [ 7.99401045e-01,  1.37324703e+00, -9.03609931e-01],\n",
       "         [-4.58517969e-01,  1.12716115e+00, -9.80619192e-01],\n",
       "         ...,\n",
       "         [ 3.65745157e-01, -4.13613766e-01,  3.06701154e-01],\n",
       "         [-8.72449458e-01, -7.06567168e-01, -1.03959754e-01],\n",
       "         [-6.15281284e-01,  1.34257996e+00,  5.32175362e-01]]]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a 4-dimensional tensor. The tensor contains four images. The size of each image is 100 x 100 x 3.\n",
    "tensor_h = tf.random.normal([4,100,100,3])\n",
    "tensor_h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(100, 100, 3), dtype=float32, numpy=\n",
       "array([[[-1.7093014 , -0.04499181, -1.5890322 ],\n",
       "        [ 0.03127928,  2.1714425 , -2.623762  ],\n",
       "        [ 1.0883243 , -0.1218334 , -0.33482018],\n",
       "        ...,\n",
       "        [ 0.25498652, -0.02062534, -0.44889623],\n",
       "        [ 1.1705192 ,  0.61499506, -0.0027889 ],\n",
       "        [ 2.0706208 ,  0.27216104, -2.1460032 ]],\n",
       "\n",
       "       [[ 0.55918497, -1.9904491 ,  0.8714164 ],\n",
       "        [ 0.01917303, -0.83233947,  1.6454618 ],\n",
       "        [-1.1215402 , -0.941378  ,  0.67072964],\n",
       "        ...,\n",
       "        [-0.28793418, -0.8554231 ,  1.0988362 ],\n",
       "        [ 1.2849804 , -1.3276762 ,  1.2378285 ],\n",
       "        [ 1.1271452 , -1.1450036 , -1.2517315 ]],\n",
       "\n",
       "       [[-0.10266842, -0.6690091 , -0.8820902 ],\n",
       "        [ 0.04898232, -0.51364434,  0.15907279],\n",
       "        [ 0.15032852, -1.3552246 ,  0.08614599],\n",
       "        ...,\n",
       "        [-1.1400967 , -1.3384835 , -0.3751197 ],\n",
       "        [-0.43153924,  1.5209857 ,  1.2040008 ],\n",
       "        [ 0.33781588, -0.57136387, -0.13344198]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0.397835  , -0.31438917,  0.17879713],\n",
       "        [ 1.1189584 ,  1.0309364 ,  0.16802102],\n",
       "        [-0.05425125, -0.9415098 , -0.98096573],\n",
       "        ...,\n",
       "        [-0.8486489 ,  0.96406746,  0.24521706],\n",
       "        [ 1.2942783 ,  0.51635265, -0.37663087],\n",
       "        [-0.10223606,  0.09480328,  0.28903425]],\n",
       "\n",
       "       [[ 0.07030448,  0.90174437, -1.0195351 ],\n",
       "        [ 0.72006035,  0.786617  ,  1.5520434 ],\n",
       "        [-0.66402304, -0.89735997, -0.53201795],\n",
       "        ...,\n",
       "        [ 0.00270577, -0.09465012,  0.12619527],\n",
       "        [ 1.2140297 ,  1.3907017 , -0.24973759],\n",
       "        [-1.0321597 ,  1.6783409 , -0.9740697 ]],\n",
       "\n",
       "       [[-0.39352375, -0.22698262,  0.9705055 ],\n",
       "        [ 0.81626713, -0.6076543 , -1.813442  ],\n",
       "        [ 0.44484094,  0.47989854, -1.2270036 ],\n",
       "        ...,\n",
       "        [ 0.56148523, -1.5880966 ,  0.26614866],\n",
       "        [ 0.8357778 , -0.08819944,  1.0067838 ],\n",
       "        [-0.68726665, -1.2480404 , -0.3204729 ]]], dtype=float32)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract the first image.\n",
    "tensor_h[0,:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 100, 100, 3), dtype=float32, numpy=\n",
       "array([[[[-1.7093014 , -0.04499181, -1.5890322 ],\n",
       "         [ 0.03127928,  2.1714425 , -2.623762  ],\n",
       "         [ 1.0883243 , -0.1218334 , -0.33482018],\n",
       "         ...,\n",
       "         [ 0.25498652, -0.02062534, -0.44889623],\n",
       "         [ 1.1705192 ,  0.61499506, -0.0027889 ],\n",
       "         [ 2.0706208 ,  0.27216104, -2.1460032 ]],\n",
       "\n",
       "        [[ 0.55918497, -1.9904491 ,  0.8714164 ],\n",
       "         [ 0.01917303, -0.83233947,  1.6454618 ],\n",
       "         [-1.1215402 , -0.941378  ,  0.67072964],\n",
       "         ...,\n",
       "         [-0.28793418, -0.8554231 ,  1.0988362 ],\n",
       "         [ 1.2849804 , -1.3276762 ,  1.2378285 ],\n",
       "         [ 1.1271452 , -1.1450036 , -1.2517315 ]],\n",
       "\n",
       "        [[-0.10266842, -0.6690091 , -0.8820902 ],\n",
       "         [ 0.04898232, -0.51364434,  0.15907279],\n",
       "         [ 0.15032852, -1.3552246 ,  0.08614599],\n",
       "         ...,\n",
       "         [-1.1400967 , -1.3384835 , -0.3751197 ],\n",
       "         [-0.43153924,  1.5209857 ,  1.2040008 ],\n",
       "         [ 0.33781588, -0.57136387, -0.13344198]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 0.397835  , -0.31438917,  0.17879713],\n",
       "         [ 1.1189584 ,  1.0309364 ,  0.16802102],\n",
       "         [-0.05425125, -0.9415098 , -0.98096573],\n",
       "         ...,\n",
       "         [-0.8486489 ,  0.96406746,  0.24521706],\n",
       "         [ 1.2942783 ,  0.51635265, -0.37663087],\n",
       "         [-0.10223606,  0.09480328,  0.28903425]],\n",
       "\n",
       "        [[ 0.07030448,  0.90174437, -1.0195351 ],\n",
       "         [ 0.72006035,  0.786617  ,  1.5520434 ],\n",
       "         [-0.66402304, -0.89735997, -0.53201795],\n",
       "         ...,\n",
       "         [ 0.00270577, -0.09465012,  0.12619527],\n",
       "         [ 1.2140297 ,  1.3907017 , -0.24973759],\n",
       "         [-1.0321597 ,  1.6783409 , -0.9740697 ]],\n",
       "\n",
       "        [[-0.39352375, -0.22698262,  0.9705055 ],\n",
       "         [ 0.81626713, -0.6076543 , -1.813442  ],\n",
       "         [ 0.44484094,  0.47989854, -1.2270036 ],\n",
       "         ...,\n",
       "         [ 0.56148523, -1.5880966 ,  0.26614866],\n",
       "         [ 0.8357778 , -0.08819944,  1.0067838 ],\n",
       "         [-0.68726665, -1.2480404 , -0.3204729 ]]],\n",
       "\n",
       "\n",
       "       [[[-0.00832782, -1.6217159 ,  0.5150949 ],\n",
       "         [-0.7948005 ,  2.4894066 , -1.4076124 ],\n",
       "         [-0.9065336 , -0.8109273 , -1.6602693 ],\n",
       "         ...,\n",
       "         [ 0.99643785, -0.09708311,  0.28702322],\n",
       "         [-0.08884772,  1.1395547 , -0.08139393],\n",
       "         [ 1.2606913 , -0.6184641 , -0.2139642 ]],\n",
       "\n",
       "        [[-0.36949715, -1.5803818 , -0.85392964],\n",
       "         [-0.19207595, -0.4905871 , -0.9318186 ],\n",
       "         [-1.8924068 ,  1.3177104 , -1.9568955 ],\n",
       "         ...,\n",
       "         [ 1.3559    ,  1.4025947 ,  0.761416  ],\n",
       "         [-0.27460712,  1.7092491 , -0.6209873 ],\n",
       "         [-0.399658  ,  2.439426  , -0.46272084]],\n",
       "\n",
       "        [[-0.9340407 , -0.28036603, -1.2571434 ],\n",
       "         [ 0.02229779,  0.56471884,  0.66727036],\n",
       "         [ 0.17211048, -0.18019597, -0.89323455],\n",
       "         ...,\n",
       "         [-0.8466392 ,  1.1447228 ,  0.12859333],\n",
       "         [-0.79736006,  0.90622276, -0.9012425 ],\n",
       "         [ 1.2788844 ,  0.68189806, -0.02458698]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-0.18113592, -0.1025771 ,  0.98642534],\n",
       "         [-0.89856255,  0.10428894,  0.80888784],\n",
       "         [ 0.7376384 , -1.1065948 , -0.81167567],\n",
       "         ...,\n",
       "         [-1.3647971 , -0.33773836, -1.3574682 ],\n",
       "         [ 1.5101979 ,  0.6831377 ,  0.03513258],\n",
       "         [-0.01350593, -0.8009389 , -1.1450146 ]],\n",
       "\n",
       "        [[ 0.2107307 , -2.2507818 ,  1.3144861 ],\n",
       "         [ 1.5362939 , -1.3859069 , -1.6029435 ],\n",
       "         [-0.31527776,  1.1169021 , -0.40379858],\n",
       "         ...,\n",
       "         [ 0.45170137, -0.8001071 ,  1.46391   ],\n",
       "         [-0.2605198 , -0.8818548 , -0.5212893 ],\n",
       "         [ 0.8749331 ,  0.4730138 ,  0.1637817 ]],\n",
       "\n",
       "        [[-0.8437184 , -0.08047463, -0.522439  ],\n",
       "         [ 0.07370586,  0.80502325, -1.4529084 ],\n",
       "         [-0.6427211 , -0.29234132,  1.4263105 ],\n",
       "         ...,\n",
       "         [ 1.1445867 ,  0.3904167 , -0.97249025],\n",
       "         [-0.20265293, -0.6471017 ,  0.9971925 ],\n",
       "         [ 0.40728274,  2.0177023 , -1.0770291 ]]]], dtype=float32)>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract one slice at an interval of two images.\n",
    "tensor_h[::2,...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(4, 100, 100, 3), dtype=float32, numpy=\n",
       "array([[[[ 8.06004584e-01,  3.51013213e-01, -7.43508041e-01],\n",
       "         [-1.88535690e+00, -5.76852202e-01, -7.21006632e-01],\n",
       "         [ 3.08038384e-01, -1.40733004e+00,  3.36514354e-01],\n",
       "         ...,\n",
       "         [ 9.84766245e-01,  1.77316403e+00,  1.46486759e-01],\n",
       "         [ 8.23367834e-01, -6.80106819e-01,  9.08959746e-01],\n",
       "         [-3.01967651e-01,  7.68677950e-01, -1.30150878e+00]],\n",
       "\n",
       "        [[ 6.99271441e-01, -1.68843722e+00, -1.16908813e+00],\n",
       "         [-6.75371647e-01,  9.99369398e-02,  1.67251334e-01],\n",
       "         [-1.30121037e-01,  1.90715611e+00, -1.30874240e+00],\n",
       "         ...,\n",
       "         [ 5.94998479e-01, -1.43332517e+00, -9.72964019e-02],\n",
       "         [ 1.89400747e-01,  1.04823971e+00,  7.94802248e-01],\n",
       "         [-8.46201777e-01, -3.28234918e-02,  1.22993827e+00]],\n",
       "\n",
       "        [[ 4.26873535e-01, -1.32984281e+00,  1.28083396e+00],\n",
       "         [ 4.44405109e-01, -1.87218904e+00,  5.51761091e-01],\n",
       "         [ 2.04190540e+00, -2.83061326e-01, -6.17134571e-01],\n",
       "         ...,\n",
       "         [-2.05719471e+00,  1.36754230e-01,  8.51878464e-01],\n",
       "         [ 2.14284635e+00,  7.89164722e-01,  2.01312140e-01],\n",
       "         [ 2.65346795e-01, -5.90703189e-01, -1.70399293e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 2.05004394e-01, -5.06796092e-02,  9.79414940e-01],\n",
       "         [ 1.23416162e+00,  1.99148667e+00, -1.36392987e+00],\n",
       "         [-4.28795427e-01, -4.68795240e-01, -5.12261748e-01],\n",
       "         ...,\n",
       "         [-2.33998790e-01,  6.93986118e-02,  3.24772298e-01],\n",
       "         [-1.02131464e-01,  4.76896733e-01,  1.23354268e+00],\n",
       "         [ 1.79207087e-01,  4.12386715e-01, -2.21119595e+00]],\n",
       "\n",
       "        [[-3.79722685e-01,  8.31899941e-01, -1.94075060e+00],\n",
       "         [ 9.34035301e-01,  1.26024127e-01, -5.48633449e-02],\n",
       "         [-1.23407185e+00,  1.42780411e+00,  1.26194596e+00],\n",
       "         ...,\n",
       "         [ 4.08723831e-01,  9.37341750e-01, -2.48553336e-01],\n",
       "         [ 1.90197754e+00, -5.63911311e-02,  3.90655160e-01],\n",
       "         [-4.82857943e-01, -9.70952034e-01,  1.39593512e-01]],\n",
       "\n",
       "        [[ 5.24867535e-01, -3.99139732e-01,  6.63599819e-02],\n",
       "         [ 7.99401045e-01,  1.37324703e+00, -9.03609931e-01],\n",
       "         [-4.58517969e-01,  1.12716115e+00, -9.80619192e-01],\n",
       "         ...,\n",
       "         [ 3.65745157e-01, -4.13613766e-01,  3.06701154e-01],\n",
       "         [-8.72449458e-01, -7.06567168e-01, -1.03959754e-01],\n",
       "         [-6.15281284e-01,  1.34257996e+00,  5.32175362e-01]]],\n",
       "\n",
       "\n",
       "       [[[-8.32782313e-03, -1.62171590e+00,  5.15094876e-01],\n",
       "         [-7.94800520e-01,  2.48940659e+00, -1.40761244e+00],\n",
       "         [-9.06533599e-01, -8.10927272e-01, -1.66026926e+00],\n",
       "         ...,\n",
       "         [ 9.96437848e-01, -9.70831066e-02,  2.87023216e-01],\n",
       "         [-8.88477191e-02,  1.13955474e+00, -8.13939348e-02],\n",
       "         [ 1.26069129e+00, -6.18464112e-01, -2.13964194e-01]],\n",
       "\n",
       "        [[-3.69497150e-01, -1.58038175e+00, -8.53929639e-01],\n",
       "         [-1.92075953e-01, -4.90587085e-01, -9.31818604e-01],\n",
       "         [-1.89240682e+00,  1.31771040e+00, -1.95689547e+00],\n",
       "         ...,\n",
       "         [ 1.35590005e+00,  1.40259469e+00,  7.61416018e-01],\n",
       "         [-2.74607122e-01,  1.70924914e+00, -6.20987296e-01],\n",
       "         [-3.99657995e-01,  2.43942595e+00, -4.62720841e-01]],\n",
       "\n",
       "        [[-9.34040725e-01, -2.80366033e-01, -1.25714338e+00],\n",
       "         [ 2.22977940e-02,  5.64718843e-01,  6.67270362e-01],\n",
       "         [ 1.72110483e-01, -1.80195972e-01, -8.93234551e-01],\n",
       "         ...,\n",
       "         [-8.46639216e-01,  1.14472282e+00,  1.28593326e-01],\n",
       "         [-7.97360063e-01,  9.06222761e-01, -9.01242495e-01],\n",
       "         [ 1.27888441e+00,  6.81898057e-01, -2.45869793e-02]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[-1.81135923e-01, -1.02577098e-01,  9.86425340e-01],\n",
       "         [-8.98562551e-01,  1.04288943e-01,  8.08887839e-01],\n",
       "         [ 7.37638414e-01, -1.10659480e+00, -8.11675668e-01],\n",
       "         ...,\n",
       "         [-1.36479712e+00, -3.37738365e-01, -1.35746825e+00],\n",
       "         [ 1.51019788e+00,  6.83137715e-01,  3.51325758e-02],\n",
       "         [-1.35059291e-02, -8.00938904e-01, -1.14501464e+00]],\n",
       "\n",
       "        [[ 2.10730702e-01, -2.25078177e+00,  1.31448615e+00],\n",
       "         [ 1.53629386e+00, -1.38590693e+00, -1.60294354e+00],\n",
       "         [-3.15277755e-01,  1.11690211e+00, -4.03798580e-01],\n",
       "         ...,\n",
       "         [ 4.51701373e-01, -8.00107121e-01,  1.46390998e+00],\n",
       "         [-2.60519803e-01, -8.81854773e-01, -5.21289289e-01],\n",
       "         [ 8.74933124e-01,  4.73013788e-01,  1.63781703e-01]],\n",
       "\n",
       "        [[-8.43718410e-01, -8.04746300e-02, -5.22439003e-01],\n",
       "         [ 7.37058595e-02,  8.05023253e-01, -1.45290840e+00],\n",
       "         [-6.42721117e-01, -2.92341322e-01,  1.42631054e+00],\n",
       "         ...,\n",
       "         [ 1.14458668e+00,  3.90416712e-01, -9.72490251e-01],\n",
       "         [-2.02652931e-01, -6.47101700e-01,  9.97192502e-01],\n",
       "         [ 4.07282740e-01,  2.01770234e+00, -1.07702911e+00]]],\n",
       "\n",
       "\n",
       "       [[[ 8.96064758e-01,  1.28783262e+00, -2.19376040e+00],\n",
       "         [ 7.63432801e-01,  5.79136550e-01, -1.94563401e+00],\n",
       "         [ 8.65961313e-01,  4.87109780e-01,  8.44129324e-02],\n",
       "         ...,\n",
       "         [-3.30545574e-01, -3.62908766e-02,  1.31732547e+00],\n",
       "         [ 5.44765413e-01, -5.33078194e-01, -4.28906381e-01],\n",
       "         [-4.12797064e-01, -3.35832387e-01, -9.45991576e-01]],\n",
       "\n",
       "        [[-1.20157731e+00, -1.48786020e+00, -1.64138412e+00],\n",
       "         [-9.10971105e-01, -3.46064180e-01,  1.81170177e+00],\n",
       "         [ 5.83114505e-01, -2.95682281e-01, -9.41989869e-02],\n",
       "         ...,\n",
       "         [-2.51222587e+00, -1.69570476e-01,  7.74091855e-02],\n",
       "         [ 1.30304074e+00,  1.50109068e-01, -9.08915162e-01],\n",
       "         [-1.03926516e+00, -2.10158920e+00, -1.18682671e+00]],\n",
       "\n",
       "        [[-8.05404663e-01,  4.30949092e-01, -1.90790224e+00],\n",
       "         [ 1.17605090e+00,  8.67776811e-01, -3.55562776e-01],\n",
       "         [ 1.83153486e+00,  1.45608473e+00,  1.07640815e+00],\n",
       "         ...,\n",
       "         [ 7.96891332e-01, -1.20095026e+00, -5.65537989e-01],\n",
       "         [ 1.15479076e+00,  1.33165097e+00,  2.29775548e+00],\n",
       "         [-4.06537235e-01, -1.82202101e+00, -4.77740645e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 6.81141734e-01, -5.67603350e-01, -1.25882471e+00],\n",
       "         [ 2.05119061e+00,  5.01929641e-01,  4.82803971e-01],\n",
       "         [-4.88765061e-01, -1.68718016e+00, -2.69729674e-01],\n",
       "         ...,\n",
       "         [-1.45945907e-01, -5.07107317e-01, -1.83800232e+00],\n",
       "         [-1.07615244e+00,  6.11010313e-01, -8.90796661e-01],\n",
       "         [-2.34100029e-01, -1.04565620e+00,  1.37684500e+00]],\n",
       "\n",
       "        [[-4.03083265e-01,  1.83027968e-01,  6.45967484e-01],\n",
       "         [ 1.51492763e+00,  3.32741708e-01, -1.33788168e+00],\n",
       "         [ 1.20981693e+00,  6.05531037e-01,  6.20406568e-01],\n",
       "         ...,\n",
       "         [-3.56484938e+00,  1.67251945e+00, -1.52176046e+00],\n",
       "         [-1.42148459e+00, -1.04810305e-01, -1.35811555e+00],\n",
       "         [-1.07770371e+00, -1.08440089e+00, -6.52764857e-01]],\n",
       "\n",
       "        [[-9.96119156e-02, -6.40050292e-01,  1.52310503e+00],\n",
       "         [-5.44337928e-02,  5.04586697e-01, -5.14228866e-02],\n",
       "         [ 2.73815375e-02,  1.28592908e+00,  2.66702712e-01],\n",
       "         ...,\n",
       "         [-1.23916554e+00,  2.53114589e-02,  3.24541897e-01],\n",
       "         [-5.20799518e-01,  1.08634615e+00, -1.49880445e+00],\n",
       "         [-2.25754023e-01, -2.47761175e-01, -2.27872342e-01]]],\n",
       "\n",
       "\n",
       "       [[[-1.70930135e+00, -4.49918061e-02, -1.58903217e+00],\n",
       "         [ 3.12792771e-02,  2.17144251e+00, -2.62376189e+00],\n",
       "         [ 1.08832431e+00, -1.21833399e-01, -3.34820181e-01],\n",
       "         ...,\n",
       "         [ 2.54986525e-01, -2.06253398e-02, -4.48896229e-01],\n",
       "         [ 1.17051923e+00,  6.14995062e-01, -2.78890040e-03],\n",
       "         [ 2.07062078e+00,  2.72161037e-01, -2.14600325e+00]],\n",
       "\n",
       "        [[ 5.59184968e-01, -1.99044907e+00,  8.71416390e-01],\n",
       "         [ 1.91730335e-02, -8.32339466e-01,  1.64546180e+00],\n",
       "         [-1.12154019e+00, -9.41377997e-01,  6.70729637e-01],\n",
       "         ...,\n",
       "         [-2.87934184e-01, -8.55423093e-01,  1.09883618e+00],\n",
       "         [ 1.28498042e+00, -1.32767618e+00,  1.23782849e+00],\n",
       "         [ 1.12714517e+00, -1.14500356e+00, -1.25173151e+00]],\n",
       "\n",
       "        [[-1.02668419e-01, -6.69009089e-01, -8.82090211e-01],\n",
       "         [ 4.89823222e-02, -5.13644338e-01,  1.59072787e-01],\n",
       "         [ 1.50328517e-01, -1.35522461e+00,  8.61459896e-02],\n",
       "         ...,\n",
       "         [-1.14009666e+00, -1.33848345e+00, -3.75119686e-01],\n",
       "         [-4.31539237e-01,  1.52098572e+00,  1.20400083e+00],\n",
       "         [ 3.37815881e-01, -5.71363866e-01, -1.33441985e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 3.97834986e-01, -3.14389169e-01,  1.78797126e-01],\n",
       "         [ 1.11895835e+00,  1.03093636e+00,  1.68021023e-01],\n",
       "         [-5.42512536e-02, -9.41509783e-01, -9.80965734e-01],\n",
       "         ...,\n",
       "         [-8.48648906e-01,  9.64067459e-01,  2.45217055e-01],\n",
       "         [ 1.29427826e+00,  5.16352654e-01, -3.76630872e-01],\n",
       "         [-1.02236062e-01,  9.48032811e-02,  2.89034247e-01]],\n",
       "\n",
       "        [[ 7.03044832e-02,  9.01744366e-01, -1.01953506e+00],\n",
       "         [ 7.20060349e-01,  7.86616981e-01,  1.55204344e+00],\n",
       "         [-6.64023042e-01, -8.97359967e-01, -5.32017946e-01],\n",
       "         ...,\n",
       "         [ 2.70576775e-03, -9.46501195e-02,  1.26195267e-01],\n",
       "         [ 1.21402967e+00,  1.39070165e+00, -2.49737591e-01],\n",
       "         [-1.03215969e+00,  1.67834091e+00, -9.74069715e-01]],\n",
       "\n",
       "        [[-3.93523753e-01, -2.26982623e-01,  9.70505476e-01],\n",
       "         [ 8.16267133e-01, -6.07654274e-01, -1.81344199e+00],\n",
       "         [ 4.44840938e-01,  4.79898542e-01, -1.22700357e+00],\n",
       "         ...,\n",
       "         [ 5.61485231e-01, -1.58809662e+00,  2.66148657e-01],\n",
       "         [ 8.35777819e-01, -8.81994367e-02,  1.00678384e+00],\n",
       "         [-6.87266648e-01, -1.24804044e+00, -3.20472896e-01]]]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Slice data from the last element.\n",
    "tensor_h[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.31445485>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Obtain the pixel in the position [20,40] in the second channel of the first image.\n",
    "tensor_h[0][19][39][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 100, 100, 3), dtype=float32, numpy=\n",
       "array([[[[-1.70930135e+00, -4.49918061e-02, -1.58903217e+00],\n",
       "         [ 3.12792771e-02,  2.17144251e+00, -2.62376189e+00],\n",
       "         [ 1.08832431e+00, -1.21833399e-01, -3.34820181e-01],\n",
       "         ...,\n",
       "         [ 2.54986525e-01, -2.06253398e-02, -4.48896229e-01],\n",
       "         [ 1.17051923e+00,  6.14995062e-01, -2.78890040e-03],\n",
       "         [ 2.07062078e+00,  2.72161037e-01, -2.14600325e+00]],\n",
       "\n",
       "        [[ 5.59184968e-01, -1.99044907e+00,  8.71416390e-01],\n",
       "         [ 1.91730335e-02, -8.32339466e-01,  1.64546180e+00],\n",
       "         [-1.12154019e+00, -9.41377997e-01,  6.70729637e-01],\n",
       "         ...,\n",
       "         [-2.87934184e-01, -8.55423093e-01,  1.09883618e+00],\n",
       "         [ 1.28498042e+00, -1.32767618e+00,  1.23782849e+00],\n",
       "         [ 1.12714517e+00, -1.14500356e+00, -1.25173151e+00]],\n",
       "\n",
       "        [[-1.02668419e-01, -6.69009089e-01, -8.82090211e-01],\n",
       "         [ 4.89823222e-02, -5.13644338e-01,  1.59072787e-01],\n",
       "         [ 1.50328517e-01, -1.35522461e+00,  8.61459896e-02],\n",
       "         ...,\n",
       "         [-1.14009666e+00, -1.33848345e+00, -3.75119686e-01],\n",
       "         [-4.31539237e-01,  1.52098572e+00,  1.20400083e+00],\n",
       "         [ 3.37815881e-01, -5.71363866e-01, -1.33441985e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 3.97834986e-01, -3.14389169e-01,  1.78797126e-01],\n",
       "         [ 1.11895835e+00,  1.03093636e+00,  1.68021023e-01],\n",
       "         [-5.42512536e-02, -9.41509783e-01, -9.80965734e-01],\n",
       "         ...,\n",
       "         [-8.48648906e-01,  9.64067459e-01,  2.45217055e-01],\n",
       "         [ 1.29427826e+00,  5.16352654e-01, -3.76630872e-01],\n",
       "         [-1.02236062e-01,  9.48032811e-02,  2.89034247e-01]],\n",
       "\n",
       "        [[ 7.03044832e-02,  9.01744366e-01, -1.01953506e+00],\n",
       "         [ 7.20060349e-01,  7.86616981e-01,  1.55204344e+00],\n",
       "         [-6.64023042e-01, -8.97359967e-01, -5.32017946e-01],\n",
       "         ...,\n",
       "         [ 2.70576775e-03, -9.46501195e-02,  1.26195267e-01],\n",
       "         [ 1.21402967e+00,  1.39070165e+00, -2.49737591e-01],\n",
       "         [-1.03215969e+00,  1.67834091e+00, -9.74069715e-01]],\n",
       "\n",
       "        [[-3.93523753e-01, -2.26982623e-01,  9.70505476e-01],\n",
       "         [ 8.16267133e-01, -6.07654274e-01, -1.81344199e+00],\n",
       "         [ 4.44840938e-01,  4.79898542e-01, -1.22700357e+00],\n",
       "         ...,\n",
       "         [ 5.61485231e-01, -1.58809662e+00,  2.66148657e-01],\n",
       "         [ 8.35777819e-01, -8.81994367e-02,  1.00678384e+00],\n",
       "         [-6.87266648e-01, -1.24804044e+00, -3.20472896e-01]]],\n",
       "\n",
       "\n",
       "       [[[ 8.96064758e-01,  1.28783262e+00, -2.19376040e+00],\n",
       "         [ 7.63432801e-01,  5.79136550e-01, -1.94563401e+00],\n",
       "         [ 8.65961313e-01,  4.87109780e-01,  8.44129324e-02],\n",
       "         ...,\n",
       "         [-3.30545574e-01, -3.62908766e-02,  1.31732547e+00],\n",
       "         [ 5.44765413e-01, -5.33078194e-01, -4.28906381e-01],\n",
       "         [-4.12797064e-01, -3.35832387e-01, -9.45991576e-01]],\n",
       "\n",
       "        [[-1.20157731e+00, -1.48786020e+00, -1.64138412e+00],\n",
       "         [-9.10971105e-01, -3.46064180e-01,  1.81170177e+00],\n",
       "         [ 5.83114505e-01, -2.95682281e-01, -9.41989869e-02],\n",
       "         ...,\n",
       "         [-2.51222587e+00, -1.69570476e-01,  7.74091855e-02],\n",
       "         [ 1.30304074e+00,  1.50109068e-01, -9.08915162e-01],\n",
       "         [-1.03926516e+00, -2.10158920e+00, -1.18682671e+00]],\n",
       "\n",
       "        [[-8.05404663e-01,  4.30949092e-01, -1.90790224e+00],\n",
       "         [ 1.17605090e+00,  8.67776811e-01, -3.55562776e-01],\n",
       "         [ 1.83153486e+00,  1.45608473e+00,  1.07640815e+00],\n",
       "         ...,\n",
       "         [ 7.96891332e-01, -1.20095026e+00, -5.65537989e-01],\n",
       "         [ 1.15479076e+00,  1.33165097e+00,  2.29775548e+00],\n",
       "         [-4.06537235e-01, -1.82202101e+00, -4.77740645e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 6.81141734e-01, -5.67603350e-01, -1.25882471e+00],\n",
       "         [ 2.05119061e+00,  5.01929641e-01,  4.82803971e-01],\n",
       "         [-4.88765061e-01, -1.68718016e+00, -2.69729674e-01],\n",
       "         ...,\n",
       "         [-1.45945907e-01, -5.07107317e-01, -1.83800232e+00],\n",
       "         [-1.07615244e+00,  6.11010313e-01, -8.90796661e-01],\n",
       "         [-2.34100029e-01, -1.04565620e+00,  1.37684500e+00]],\n",
       "\n",
       "        [[-4.03083265e-01,  1.83027968e-01,  6.45967484e-01],\n",
       "         [ 1.51492763e+00,  3.32741708e-01, -1.33788168e+00],\n",
       "         [ 1.20981693e+00,  6.05531037e-01,  6.20406568e-01],\n",
       "         ...,\n",
       "         [-3.56484938e+00,  1.67251945e+00, -1.52176046e+00],\n",
       "         [-1.42148459e+00, -1.04810305e-01, -1.35811555e+00],\n",
       "         [-1.07770371e+00, -1.08440089e+00, -6.52764857e-01]],\n",
       "\n",
       "        [[-9.96119156e-02, -6.40050292e-01,  1.52310503e+00],\n",
       "         [-5.44337928e-02,  5.04586697e-01, -5.14228866e-02],\n",
       "         [ 2.73815375e-02,  1.28592908e+00,  2.66702712e-01],\n",
       "         ...,\n",
       "         [-1.23916554e+00,  2.53114589e-02,  3.24541897e-01],\n",
       "         [-5.20799518e-01,  1.08634615e+00, -1.49880445e+00],\n",
       "         [-2.25754023e-01, -2.47761175e-01, -2.27872342e-01]]],\n",
       "\n",
       "\n",
       "       [[[ 8.06004584e-01,  3.51013213e-01, -7.43508041e-01],\n",
       "         [-1.88535690e+00, -5.76852202e-01, -7.21006632e-01],\n",
       "         [ 3.08038384e-01, -1.40733004e+00,  3.36514354e-01],\n",
       "         ...,\n",
       "         [ 9.84766245e-01,  1.77316403e+00,  1.46486759e-01],\n",
       "         [ 8.23367834e-01, -6.80106819e-01,  9.08959746e-01],\n",
       "         [-3.01967651e-01,  7.68677950e-01, -1.30150878e+00]],\n",
       "\n",
       "        [[ 6.99271441e-01, -1.68843722e+00, -1.16908813e+00],\n",
       "         [-6.75371647e-01,  9.99369398e-02,  1.67251334e-01],\n",
       "         [-1.30121037e-01,  1.90715611e+00, -1.30874240e+00],\n",
       "         ...,\n",
       "         [ 5.94998479e-01, -1.43332517e+00, -9.72964019e-02],\n",
       "         [ 1.89400747e-01,  1.04823971e+00,  7.94802248e-01],\n",
       "         [-8.46201777e-01, -3.28234918e-02,  1.22993827e+00]],\n",
       "\n",
       "        [[ 4.26873535e-01, -1.32984281e+00,  1.28083396e+00],\n",
       "         [ 4.44405109e-01, -1.87218904e+00,  5.51761091e-01],\n",
       "         [ 2.04190540e+00, -2.83061326e-01, -6.17134571e-01],\n",
       "         ...,\n",
       "         [-2.05719471e+00,  1.36754230e-01,  8.51878464e-01],\n",
       "         [ 2.14284635e+00,  7.89164722e-01,  2.01312140e-01],\n",
       "         [ 2.65346795e-01, -5.90703189e-01, -1.70399293e-01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 2.05004394e-01, -5.06796092e-02,  9.79414940e-01],\n",
       "         [ 1.23416162e+00,  1.99148667e+00, -1.36392987e+00],\n",
       "         [-4.28795427e-01, -4.68795240e-01, -5.12261748e-01],\n",
       "         ...,\n",
       "         [-2.33998790e-01,  6.93986118e-02,  3.24772298e-01],\n",
       "         [-1.02131464e-01,  4.76896733e-01,  1.23354268e+00],\n",
       "         [ 1.79207087e-01,  4.12386715e-01, -2.21119595e+00]],\n",
       "\n",
       "        [[-3.79722685e-01,  8.31899941e-01, -1.94075060e+00],\n",
       "         [ 9.34035301e-01,  1.26024127e-01, -5.48633449e-02],\n",
       "         [-1.23407185e+00,  1.42780411e+00,  1.26194596e+00],\n",
       "         ...,\n",
       "         [ 4.08723831e-01,  9.37341750e-01, -2.48553336e-01],\n",
       "         [ 1.90197754e+00, -5.63911311e-02,  3.90655160e-01],\n",
       "         [-4.82857943e-01, -9.70952034e-01,  1.39593512e-01]],\n",
       "\n",
       "        [[ 5.24867535e-01, -3.99139732e-01,  6.63599819e-02],\n",
       "         [ 7.99401045e-01,  1.37324703e+00, -9.03609931e-01],\n",
       "         [-4.58517969e-01,  1.12716115e+00, -9.80619192e-01],\n",
       "         ...,\n",
       "         [ 3.65745157e-01, -4.13613766e-01,  3.06701154e-01],\n",
       "         [-8.72449458e-01, -7.06567168e-01, -1.03959754e-01],\n",
       "         [-6.15281284e-01,  1.34257996e+00,  5.32175362e-01]]]],\n",
       "      dtype=float32)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract the first, second, and fourth images from tensor_h ([4,100,100,3]).\n",
    "indices = [0,1,3]\n",
    "tf.gather(tensor_h,axis=0,indices=indices,batch_dims=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.01917303, 1.8315349 ], dtype=float32)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract the pixel in [1,1] from the first dimension of the first image and the pixel in [2,2] from the first dimension of the second image in tensot_h ([4,100,100,3]).\n",
    "indices = [[0,1,1,0],[1,2,2,0]]\n",
    "tf.gather_nd(tensor_h,indices=indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2)\n",
      "(2, 2)\n",
      "tf.Tensor([2 2], shape=(2,), dtype=int32)\n",
      "tf.Tensor([2 2], shape=(2,), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "const_d_1 = tf.constant([[1, 2, 3, 4]],shape=[2,2], dtype=tf.float32)\n",
    "\n",
    "#Three common methods for viewing a dimension:\n",
    "print(const_d_1.shape)\n",
    "print(const_d_1.get_shape())\n",
    "print(tf.shape(const_d_1))\n",
    "print(tf.shape(const_d_1)) #The output is a tensor. The value of the tensor indicates the size of the tensor dimension to be viewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[1 2 3]\n",
      " [4 5 6]], shape=(2, 3), dtype=int32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(3, 2), dtype=int32, numpy=\n",
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshape_1 = tf.constant([[1,2,3],[4,5,6]])\n",
    "print(reshape_1)\n",
    "tf.reshape(reshape_1, (3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of the original data: (100, 100, 3)\n",
      "add a dimension before the first dimension (axis=0):  (1, 100, 100, 3)\n",
      "add a dimension before the second dimension (axis=1):  (100, 1, 100, 3)\n",
      "add a dimension after the last dimension (axis=-1):  (100, 100, 3, 1)\n"
     ]
    }
   ],
   "source": [
    "#Generate a 100 x 100 x 3 tensor to represent a 100 x 100 three-channel color image.\n",
    "expand_sample_1 = tf.random.normal([100,100,3], seed=1)\n",
    "print(\"size of the original data:\",expand_sample_1.shape)\n",
    "print(\"add a dimension before the first dimension (axis=0): \",tf.expand_dims(expand_sample_1, axis=0).shape)\n",
    "print(\"add a dimension before the second dimension (axis=1): \",tf.expand_dims(expand_sample_1, axis=1).shape)\n",
    "print(\"add a dimension after the last dimension (axis=-1): \",tf.expand_dims(expand_sample_1, axis=-1).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of the original data: (1, 100, 100, 3)\n",
      "data size after dimension squeezing: (100, 100, 3)\n"
     ]
    }
   ],
   "source": [
    "#Generate a 100 x 100 x 3 tensor to represent a 100 x 100 three-channel color image.\n",
    "squeeze_sample_1 = tf.random.normal([1,100,100,3])\n",
    "print(\"size of the original data:\",squeeze_sample_1.shape)\n",
    "squeezed_sample_1 = tf.squeeze(expand_sample_1)\n",
    "print(\"data size after dimension squeezing:\",squeezed_sample_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of the original data: (2, 3)\n",
      "size of transposed data: (3, 2)\n"
     ]
    }
   ],
   "source": [
    "#The transposition problem of the low dimension is simple. The input needs to be called by transposing tensors.\n",
    "tf.transpose\n",
    "trans_sample_1 = tf.constant([1,2,3,4,5,6],shape=[2,3])\n",
    "print(\"size of the original data:\",trans_sample_1.shape)\n",
    "transposed_sample_1 = tf.transpose(trans_sample_1)\n",
    "print(\"size of transposed data:\",transposed_sample_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of the original data: (4, 100, 200, 3)\n",
      "size of transposed data: (4, 200, 100, 3)\n"
     ]
    }
   ],
   "source": [
    "#perm is required for high-dimensional data transposition, and indicates the dimension sequence of the input tensor. The original dimension sequence of a three-dimensional tensor is [0, 1, 2] (perm), indicating the length, width, and height of high-dimensional data, respectively.Deep Learning Lab Guide-Teacher Version Page 12 Data dimensions can be transposed by changing the sequence of values in perm.\n",
    "#Generate an $ x 100 x 200 x 3 tensor to represent four 100 x 200 three-channel color images.\n",
    "trans_sample_2 = tf.random.normal([4,100,200,3])\n",
    "print(\"size of the original data:\",trans_sample_2.shape)\n",
    "\n",
    "#Exchange the length and width for the four images. The original perm value is [0,1,2,3], and the new perm value is [0,2,1,3].\n",
    "transposed_sample_2 = tf.transpose(trans_sample_2,[0,2,1,3])\n",
    "print(\"size of transposed data:\",transposed_sample_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original data: [1 2 3 4 5 6]\n",
      "broadcasted data: [[1 2 3 4 5 6]\n",
      " [1 2 3 4 5 6]\n",
      " [1 2 3 4 5 6]\n",
      " [1 2 3 4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "broadcast_sample_1 = tf.constant([1,2,3,4,5,6])\n",
    "print(\"original data:\",broadcast_sample_1.numpy())\n",
    "broadcasted_sample_1 = tf.broadcast_to(broadcast_sample_1,shape=[4,6])\n",
    "print(\"broadcasted data:\",broadcasted_sample_1.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[ 1  2  3]\n",
      " [11 12 13]\n",
      " [21 22 23]\n",
      " [31 32 33]], shape=(4, 3), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "#During the operation, if two arrays have different shapes, TensorFlow automatically triggers the broadcast mechanism as NumPy does.\n",
    "a = tf.constant([[ 0, 0, 0], [10,10,10], [20,20,20], [30,30,30]])\n",
    "b = tf.constant([1,2,3])\n",
    "print(a + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[ 4 11]\n",
      " [ 6 17]], shape=(2, 2), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "a = tf.constant([[3, 5], [4, 8]])\n",
    "b = tf.constant([[1, 6], [2, 9]])\n",
    "print(tf.add(a, b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 2), dtype=int32, numpy=\n",
       "array([[13, 63],\n",
       "       [20, 96]])>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.matmul(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input tensor: [[1 3 2]\n",
      " [2 5 8]\n",
      " [7 5 9]]\n",
      "locate the maximum value by column: [2 1 2]\n",
      "locate the maximum value by row: [1 2 2]\n"
     ]
    }
   ],
   "source": [
    "argmax_sample_1 = tf.constant([[1,3,2],[2,5,8],[7,5,9]])\n",
    "print(\"input tensor:\",argmax_sample_1.numpy())\n",
    "max_sample_1 = tf.argmax(argmax_sample_1, axis=0)\n",
    "max_sample_2 = tf.argmax(argmax_sample_1, axis=1)\n",
    "print(\"locate the maximum value by column:\",max_sample_1.numpy())\n",
    "print(\"locate the maximum value by row:\",max_sample_2.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original data [[1 2 3]\n",
      " [4 5 6]]\n",
      "calculate the sum of all elements in the tensor (axis=None): 21\n",
      "calculate the sum of elements in each column by column (axis=0): [5 7 9]\n",
      "calculate the sum of elements in each column by row (axis=1): [ 6 15]\n"
     ]
    }
   ],
   "source": [
    "reduce_sample_1 = tf.constant([1,2,3,4,5,6],shape=[2,3])\n",
    "print(\"original data\",reduce_sample_1.numpy())\n",
    "print(\"calculate the sum of all elements in the tensor (axis=None):\",tf.reduce_sum(reduce_sample_1,axis=None).numpy())\n",
    "print(\"calculate the sum of elements in each column by column (axis=0):\",tf.reduce_sum(reduce_sample_1,axis=0).numpy())\n",
    "print(\"calculate the sum of elements in each column by row (axis=1):\",tf.reduce_sum(reduce_sample_1,axis=1).numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sizes of original data:  (4, 100, 100, 3) (40, 100, 100, 3)\n",
      "size of the concatenated data: (44, 100, 100, 3)\n"
     ]
    }
   ],
   "source": [
    "concat_sample_1 = tf.random.normal([4,100,100,3])\n",
    "concat_sample_2 = tf.random.normal([40,100,100,3])\n",
    "print(\"sizes of original data: \",concat_sample_1.shape, concat_sample_2.shape)\n",
    "concated_sample_1 = tf.concat([concat_sample_1,concat_sample_2],axis=0)\n",
    "print(\"size of the concatenated data:\",concated_sample_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sizes of original data:  (100, 100, 3) (100, 100, 3)\n",
      "size of the concatenated data: (2, 100, 100, 3)\n"
     ]
    }
   ],
   "source": [
    "stack_sample_1 = tf.random.normal([100,100,3])\n",
    "stack_sample_2 = tf.random.normal([100,100,3])\n",
    "print(\"sizes of original data: \",stack_sample_1.shape, stack_sample_2.shape)\n",
    "\n",
    "#Dimensions increase after the concatenation. If axis is set to 0, a dimension is added before the first dimension.\n",
    "stacked_sample_1 = tf.stack([stack_sample_1, stack_sample_2],axis=0)\n",
    "print(\"size of the concatenated data:\",stacked_sample_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tf.Tensor: shape=(100, 100, 3), dtype=float32, numpy=\n",
       " array([[[-0.03299728, -0.1775733 ,  0.25025854],\n",
       "         [ 0.50594115,  0.02866779, -0.2389727 ],\n",
       "         [-1.7778921 , -0.37175632,  0.29791516],\n",
       "         ...,\n",
       "         [ 0.04447179,  1.0483598 ,  1.2940078 ],\n",
       "         [ 0.5280574 ,  0.27182984, -0.49930203],\n",
       "         [-0.39468908,  1.283789  , -1.4101322 ]],\n",
       " \n",
       "        [[-0.11600413, -0.78924435, -0.8406448 ],\n",
       "         [-1.3690124 , -1.7435253 , -0.7023674 ],\n",
       "         [-1.1477895 ,  0.2786808 , -0.90994096],\n",
       "         ...,\n",
       "         [-0.52972406, -1.904917  , -0.24199174],\n",
       "         [ 0.6177599 , -1.3840274 ,  1.9006202 ],\n",
       "         [-0.6254984 , -2.0439138 , -1.0701884 ]],\n",
       " \n",
       "        [[-0.18974133,  1.093628  ,  1.0882305 ],\n",
       "         [-0.99843556,  0.5457577 ,  0.9167265 ],\n",
       "         [-1.1324621 , -1.0126121 , -0.19320475],\n",
       "         ...,\n",
       "         [-1.1459612 ,  1.9901415 , -0.8263735 ],\n",
       "         [ 0.69589025, -1.7395126 ,  1.6910956 ],\n",
       "         [ 0.06461692, -1.9591635 ,  0.6231125 ]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[ 0.93873894,  0.74551916,  0.1078174 ],\n",
       "         [-1.3016871 ,  0.6304462 , -0.2957877 ],\n",
       "         [ 0.8874592 ,  0.8288319 ,  0.5613799 ],\n",
       "         ...,\n",
       "         [-1.3475097 ,  1.4009444 , -1.4255561 ],\n",
       "         [-1.3156437 , -1.1441089 ,  1.5345409 ],\n",
       "         [ 0.7503989 , -0.32807282, -0.48308453]],\n",
       " \n",
       "        [[ 0.81079286,  0.83350134, -0.19388495],\n",
       "         [-1.3687497 ,  0.13661735,  1.2915549 ],\n",
       "         [ 0.948935  ,  0.9252142 ,  0.48945138],\n",
       "         ...,\n",
       "         [-0.446844  , -1.0779573 ,  0.47932667],\n",
       "         [ 0.12921944, -0.50434965, -0.597663  ],\n",
       "         [-1.9645085 , -0.37538624,  1.9775668 ]],\n",
       " \n",
       "        [[ 0.876474  , -0.2719896 ,  0.14188938],\n",
       "         [ 1.5261027 ,  0.30477774, -0.5088504 ],\n",
       "         [-0.63750863, -0.13700303, -1.2206507 ],\n",
       "         ...,\n",
       "         [-1.9653457 , -2.271305  , -1.2711818 ],\n",
       "         [ 0.10536229, -0.9475883 ,  0.21554275],\n",
       "         [-0.20003143,  1.1137782 ,  0.48421448]]], dtype=float32)>,\n",
       " <tf.Tensor: shape=(100, 100, 3), dtype=float32, numpy=\n",
       " array([[[ 1.1988084 , -0.63137263,  0.57183564],\n",
       "         [-0.9825188 ,  0.33909166, -0.6398105 ],\n",
       "         [-1.1568812 ,  0.90976906,  2.485065  ],\n",
       "         ...,\n",
       "         [ 0.02694275, -0.35052544,  0.5525615 ],\n",
       "         [-0.02490192,  1.3866749 , -0.7006998 ],\n",
       "         [ 0.28569582,  0.45249534,  1.3933775 ]],\n",
       " \n",
       "        [[-0.9665145 , -0.10347729,  1.3477457 ],\n",
       "         [-0.24345112, -0.8086766 , -1.0677764 ],\n",
       "         [-0.03651553,  1.4240452 , -0.32201743],\n",
       "         ...,\n",
       "         [ 1.0952753 ,  1.5462039 , -1.2850261 ],\n",
       "         [-0.32818627, -0.62660944, -0.2899277 ],\n",
       "         [ 1.7424006 , -0.20950745, -0.84309787]],\n",
       " \n",
       "        [[-0.84475034, -0.48252144, -1.67505   ],\n",
       "         [-0.72216195, -0.12126294,  1.2444439 ],\n",
       "         [-0.5910181 ,  1.6256766 ,  0.06515833],\n",
       "         ...,\n",
       "         [-0.32095012, -0.50486815, -1.9209414 ],\n",
       "         [-1.7550522 ,  0.89247495,  1.6711957 ],\n",
       "         [-0.11923177, -0.5227358 , -0.02708759]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[-0.1984952 ,  0.9123824 , -0.6983737 ],\n",
       "         [ 0.59649956, -0.4743638 , -1.0231757 ],\n",
       "         [ 1.6763462 ,  0.12567487, -0.5413716 ],\n",
       "         ...,\n",
       "         [-0.22456014,  1.2543058 ,  0.6010976 ],\n",
       "         [ 0.27795127,  1.1155229 ,  0.2608086 ],\n",
       "         [-1.9952122 ,  0.19133694, -1.9857436 ]],\n",
       " \n",
       "        [[-0.37542552,  0.99843454, -0.99047273],\n",
       "         [-0.14533018,  0.41374195,  0.9695135 ],\n",
       "         [-0.18731645,  0.93556815,  1.5979782 ],\n",
       "         ...,\n",
       "         [ 0.657331  , -0.17921153,  0.39837742],\n",
       "         [-1.2615666 ,  0.6747831 ,  0.33380172],\n",
       "         [-2.3599405 ,  0.6217667 , -1.3017267 ]],\n",
       " \n",
       "        [[-0.03416264,  0.28172544,  0.02011067],\n",
       "         [ 1.3926578 , -0.08317165,  0.603494  ],\n",
       "         [ 2.1058316 ,  0.5414351 ,  0.47598782],\n",
       "         ...,\n",
       "         [-0.73913395,  0.5845367 ,  0.56675476],\n",
       "         [-0.22267611,  0.21538225, -1.1280848 ],\n",
       "         [-2.6386657 , -0.71507895, -1.2012554 ]]], dtype=float32)>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split data based on the first dimension and output the split data in a list.\n",
    "tf.unstack(stacked_sample_1,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size of the original data: (10, 100, 100, 3)\n",
      "size of the split data when m_or_size_splits is set to 10:  (5, 2, 100, 100, 3)\n",
      "When num_or_size_splits=[3,5,2], the size of the split data is: (3, 100, 100, 3) (5, 100, 100, 3) (2, 100, 100, 3)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "split_sample_1 = tf.random.normal([10,100,100,3])\n",
    "print(\"size of the original data:\",split_sample_1.shape)\n",
    "\n",
    "splited_sample_1 = tf.split(split_sample_1, num_or_size_splits=5,axis=0)\n",
    "print(\"size of the split data when m_or_size_splits is set to 10: \",np.shape(splited_sample_1))\n",
    "\n",
    "splited_sample_2 = tf.split(split_sample_1, num_or_size_splits=[3,5,2],axis=0)\n",
    "print(\"When num_or_size_splits=[3,5,2], the size of the split data is:\", np.shape(splited_sample_2[0]),\n",
    "\n",
    "np.shape(splited_sample_2[1]), np.shape(splited_sample_2[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input tensor: [3 4 1 2 6 7 8 0 9 5]\n",
      "tensor sorted in ascending order: [0 1 2 3 4 5 6 7 8 9]\n",
      "indexes of elements of the tensors in ascending order: [7 2 3 0 1 9 4 5 6 8]\n"
     ]
    }
   ],
   "source": [
    "sort_sample_1 = tf.random.shuffle(tf.range(10))\n",
    "print(\"input tensor:\",sort_sample_1.numpy())\n",
    "\n",
    "sorted_sample_1 = tf.sort(sort_sample_1, direction=\"ASCENDING\")\n",
    "print(\"tensor sorted in ascending order:\",sorted_sample_1.numpy())\n",
    "\n",
    "sorted_sample_2 = tf.argsort(sort_sample_1,direction=\"ASCENDING\")\n",
    "print(\"indexes of elements of the tensors in ascending order:\",sorted_sample_2.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input tensor: [3 4 1 2 6 7 8 0 9 5]\n",
      "first five values in ascending order: [9 8 7 6 5]\n",
      "indexes of the first five values in ascending order: [8 6 5 4 9]\n"
     ]
    }
   ],
   "source": [
    "values, index = tf.nn.top_k(sort_sample_1,5)\n",
    "print(\"input tensor:\",sort_sample_1.numpy())\n",
    "print(\"first five values in ascending order:\", values.numpy())\n",
    "print(\"indexes of the first five values in ascending order:\", index.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[4. 6.]\n",
      " [4. 6.]], shape=(2, 2), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "x = tf.ones((2, 2), dtype=tf.dtypes.float32)\n",
    "y = tf.constant([[1, 2], [3, 4]], dtype=tf.dtypes.float32)\n",
    "z = tf.matmul(x, y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4. 6.]\n",
      " [4. 6.]]\n"
     ]
    }
   ],
   "source": [
    "#Use the syntax of TensorFlow 1.x in TensorFlow 2. You can install the v1 compatibility package in TensorFlow 2.0 to inherit the TensorFlow 1.x code and disable the eager execution mode.\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_eager_execution()\n",
    "\n",
    "#Create a graph and define it as a computational graph.\n",
    "a = tf.ones((2, 2), dtype=tf.dtypes.float32)\n",
    "b = tf.constant([[1, 2], [3, 4]], dtype=tf.dtypes.float32)\n",
    "c = tf.matmul(a, b)\n",
    "\n",
    "#Enable the drawing function, and perform the multiplication operation to obtain data.\n",
    "with tf.Session() as sess:\n",
    "   print(sess.run(c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"random_uniform/RandomUniform:0\", shape=(), dtype=float32)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Tensor' object has no attribute 'numpy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 6\u001b[0m\n\u001b[0;32m      4\u001b[0m x \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mreshape(tf\u001b[39m.\u001b[39mrange(\u001b[39m0\u001b[39m, \u001b[39m4\u001b[39m), [\u001b[39m2\u001b[39m, \u001b[39m2\u001b[39m])\n\u001b[0;32m      5\u001b[0m \u001b[39mprint\u001b[39m(thre_1)\n\u001b[1;32m----> 6\u001b[0m \u001b[39mif\u001b[39;00m thre_1\u001b[39m.\u001b[39;49mnumpy() \u001b[39m>\u001b[39m \u001b[39m0.5\u001b[39m:\n\u001b[0;32m      7\u001b[0m    y \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mmatmul(x, x)\n\u001b[0;32m      8\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\Lintang\\anaconda3\\envs\\MS20_1\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py:446\u001b[0m, in \u001b[0;36mTensor.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m    437\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m {\u001b[39m\"\u001b[39m\u001b[39mT\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mastype\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mravel\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtranspose\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mreshape\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mclip\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39msize\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m    438\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mtolist\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m}:\n\u001b[0;32m    439\u001b[0m   \u001b[39m# TODO(wangpeng): Export the enable_numpy_behavior knob\u001b[39;00m\n\u001b[0;32m    440\u001b[0m   \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[0;32m    441\u001b[0m       \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m object has no attribute \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mname\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m. \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m \u001b[39m\"\"\"\u001b[39m\n\u001b[0;32m    442\u001b[0m \u001b[39m    If you are looking for numpy-related methods, please run the following:\u001b[39m\n\u001b[0;32m    443\u001b[0m \u001b[39m    from tensorflow.python.ops.numpy_ops import np_config\u001b[39m\n\u001b[0;32m    444\u001b[0m \u001b[39m    np_config.enable_numpy_behavior()\u001b[39m\n\u001b[0;32m    445\u001b[0m \u001b[39m  \u001b[39m\u001b[39m\"\"\"\u001b[39m)\n\u001b[1;32m--> 446\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m__getattribute__\u001b[39;49m(name)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'numpy'"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "thre_1 = tf.random.uniform([], 0, 1)\n",
    "x = tf.reshape(tf.range(0, 4), [2, 2])\n",
    "print(thre_1)\n",
    "if thre_1.numpy() > 0.5:\n",
    "   y = tf.matmul(x, x)\n",
    "else:\n",
    "   y = tf.add(x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"b:0\", shape=(), dtype=float32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'PartitionedCall:0' shape=(3, 3) dtype=float32>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@tf.function\n",
    "def simple_nn_layer(w,x,b):\n",
    "   print(b)\n",
    "   return tf.nn.relu(tf.matmul(w, x)+b)\n",
    "w = tf.random.uniform((3, 3))\n",
    "x = tf.random.uniform((3, 3))\n",
    "b = tf.constant(0.5, dtype='float32')\n",
    "simple_nn_layer(w,x,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time required for performing the computation of one convolutional neural network (CNN) layer in eager execution mode: 0.021845400000000126\n",
      "time required for performing the computation of one CNN layer in graph mode: 0.009503900000002119\n"
     ]
    }
   ],
   "source": [
    "#Use the timeit module to measure the execution time of a small code segment.\n",
    "import timeit\n",
    "\n",
    "#Create a convolutional layer.\n",
    "CNN_cell = tf.keras.layers.Conv2D(filters=100,kernel_size=2,strides=(1,1))\n",
    "\n",
    "#Use @tf.function to convert the operation into a graph.\n",
    "@tf.function\n",
    "def CNN_fn(image):\n",
    "   return CNN_cell(image)\n",
    "image = tf.zeros([100, 200, 200, 3])\n",
    "\n",
    "#Compare the execution time of the two modes.\n",
    "CNN_cell(image)\n",
    "CNN_fn(image)\n",
    "\n",
    "#Call timeit.timeit to measure the time required for executing the code 10 times.\n",
    "print(\"time required for performing the computation of one convolutional neural network (CNN) layer in eager execution mode:\", timeit.timeit(lambda: CNN_cell(image), number=10))\n",
    "print(\"time required for performing the computation of one CNN layer in graph mode:\", timeit.timeit(lambda: CNN_fn(image), number=10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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