Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'BreakoutDeterministic-v4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env = gym.make('BreakoutDeterministic-v4')\n",
    "str(env)[20:-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3218d1a650>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "s1,_,_,_ = env.step(1)\n",
    "s2,_,_,_ = env.step(2)\n",
    "s3,_,_,i = env.step(2)\n",
    "s4,_,_,i = env.step(3)\n",
    "s5,_,_,i = env.step(3)\n",
    "s6,_,_,i = env.step(3)\n",
    "s7,_,_,i = env.step(3)\n",
    "s8,_,_,i = env.step(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(s1 == s2).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3218fd7110>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(1,4,1)\n",
    "plt.imshow(s1)\n",
    "plt.subplot(1,4,2)\n",
    "plt.imshow(s2)\n",
    "plt.subplot(1,4,3)\n",
    "plt.imshow(s3)\n",
    "plt.subplot(1,4,4)\n",
    "plt.imshow(s4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        # Currently, memory growth needs to be the same across GPUs\n",
    "        for gpu in gpus:\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "        logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "        print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n",
    "    except RuntimeError as e:\n",
    "        # Memory growth must be set before GPUs have been initialized\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.random.normal(0.1, 1, 100)\n",
    "b = np.random.normal(0.2, 1.1, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h1 = plt.hist(a,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2 = plt.hist(b,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f373c340790>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h1[1][:-1],h1[0])\n",
    "plt.plot(h2[1][:-1],h2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = tf.convert_to_tensor(np.array([[1,2,3,4,5],\n",
    "              [1,2,3,4,5],\n",
    "              [1,2,3,4,5],\n",
    "              [1,2,3,4,5],\n",
    "              [1,2,3,4,5]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.0'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.array([[1,2,3,4,5],\n",
    "              [1,2,3,4,5],\n",
    "              [1,2,3,4,5],\n",
    "              [1,2,3,4,5],\n",
    "              [1,2,3,4,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([0,1,2,3,4])\n",
    "b.reshape(5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(5, 5), dtype=uint8, numpy=\n",
       "array([[1, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 0, 1]], dtype=uint8)>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.one_hot(b, 5, dtype='uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "`indices` and `arr` must have the same number of dimensions",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-66-e3fffb2615a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake_along_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mtake_along_axis\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/shape_base.py\u001b[0m in \u001b[0;36mtake_along_axis\u001b[0;34m(arr, indices, axis)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;31m# use the fancy index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_make_along_axis_idx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/shape_base.py\u001b[0m in \u001b[0;36m_make_along_axis_idx\u001b[0;34m(arr_shape, indices, axis)\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr_shape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         raise ValueError(\n\u001b[0;32m---> 37\u001b[0;31m             \"`indices` and `arr` must have the same number of dimensions\")\n\u001b[0m\u001b[1;32m     38\u001b[0m     \u001b[0mshape_ones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0mdest_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: `indices` and `arr` must have the same number of dimensions"
     ]
    }
   ],
   "source": [
    "np.take_along_axis(c, b, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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