introductory/Intro_Plotting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Intro: Plotting.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "6zF1zr4ODp60",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Plotting with matplotlib, more numpy\n",
        "\n",
        "We will use [matplotlib](https://matplotlib.org/) to\n",
        "\n",
        "* make scatter plots\n",
        "* plot of (training) curves\n",
        "* show images.\n",
        "\n",
        "There are many good tutorials for these on the internet, so we keep it short here with annotated full examples.\n"
      ]
    },
    {
      "metadata": {
        "id": "vKv9b44EEAqJ",
        "colab_type": "code",
        "colab": {},
        "cellView": "both"
      },
      "cell_type": "code",
      "source": [
        "#@title Setup and Imports. Run this cell.\n",
        "\n",
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import random\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from google.colab import files\n",
        "from scipy.stats import multivariate_normal\n",
        "\n",
        "sns.set_style('ticks')\n",
        "\n",
        "\n",
        "def get_data(num_examples):\n",
        "  inputs = 2*np.random.random((num_examples, 2)) - 1\n",
        "  labels = np.prod(inputs, axis=1)\n",
        "  labels[labels <= 0] = -1 \n",
        "  labels[labels > 0] = 1 \n",
        "  return inputs, labels\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fBoiMrH1eHLo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The import corresponding to matplotlib is `from matplotlib import pyplot as plt` and it will be used as `plt.some_function()`.\n"
      ]
    },
    {
      "metadata": {
        "id": "ighZ0qCBUuWm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We start by **recreating the visualization of the NXOR function** introduced in the numpy colab.\n",
        "\n",
        "In the same colab we wrote a function `get_data()` to generate numpy arrays with inputs and outputs of the NXOR function. In the setup cell above we redefined `get_data()`. Use it to generate a dataset of 100 examples."
      ]
    },
    {
      "metadata": {
        "id": "fyUeXhkoQOQ_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "inputs, labels = get_data(100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pqG2GV04UTty",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Scatter Plot"
      ]
    },
    {
      "metadata": {
        "id": "W5eFm0T1vZ2i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "28fcd83b-5671-477a-81c8-e9d0b813ce79"
      },
      "cell_type": "code",
      "source": [
        "MARKER_COLORS = np.array([\n",
        "    [1.0, 0.0, 0.0],  # red for -1 \n",
        "    [0.0, 1.0, 0.0],  # green for +1\n",
        "])\n",
        "# Convert the labels to colors.\n",
        "class_idx = (labels + 1 / 2.0).astype(np.int)\n",
        "\n",
        "# Visualize the data.\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Visualizing the labels assigned by NXOR')\n",
        "plt.scatter(\n",
        "    x=inputs[:, 0], y=inputs[:, 1], c=MARKER_COLORS[class_idx], alpha=0.8)\n",
        "\n",
        "plt.savefig('nxor_data.png', bbox_inches='tight')  # Can save it, or\n",
        "plt.show()  # More often just display it."
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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16+saU0pKCnPmzCEwMFDXOIR4HGmuFyKH/Oc//+HgwYO0adMGg8HAa6+9lunH6ET6Xn75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\nEMJNSZIXQggh3NT/AzpOMNko9q1zAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f20076ed0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ZMLU-Ek0PPIa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Downloading matplotlib plots from colab"
      ]
    },
    {
      "metadata": {
        "id": "_m_3P-oxVoc9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# If you would like to do this, uncomment and run the line below.\n",
        "# files.download('nxor_data.png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6_rVo6Zwg2Sm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Exercise: Make markers class dependent\n",
        "\n",
        "Create a scatter plot like above, but use an 'o' marker for negative examples and 'x' marker for positive ones.\n",
        "\n",
        "*Note:* it is not possible to pass markers like colors to `scatter`, instead you will have to call `scatter` twice, independently for each class. In this case you will also have to look up how to provide labels for data plotted."
      ]
    },
    {
      "metadata": {
        "id": "pB-GsWvvq_Ji",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Code"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kNcymy8QrA48",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "cellView": "form",
        "outputId": "d7be20e7-63db-48a8-c2bd-c0b4691b2d77"
      },
      "cell_type": "code",
      "source": [
        "#@title Solution\n",
        "\n",
        "neg_inputs = inputs[labels == -1]\n",
        "pos_inputs = inputs[labels == 1]\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Visualizing the labels assigned by NXOR')\n",
        "plt.scatter(\n",
        "    x=neg_inputs[:, 0], y=neg_inputs[:, 1], c='r',\n",
        "    marker='o', alpha=0.8, label='neg')\n",
        "plt.scatter(\n",
        "    x=pos_inputs[:, 0], y=pos_inputs[:, 1], c='b',\n",
        "    marker='x', alpha=0.8, label='pos')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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llltafQ2R07VUe/byAn7/HbCWWQoIEDrEOVr37kIWwRpnnZNkhyvSkSPYVZMf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\nFkG/pgRn1YUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f1ffd9d50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8ghfTnlCMGtB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Plotting Curves\n",
        "\n",
        "Curves assign y-values to a sequence of x-values. Here we will show how to plot two different curves onto the same plot, so they can be compared."
      ]
    },
    {
      "metadata": {
        "id": "BaNH30tIWCgq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "683f011d-286b-4536-ef66-7ec0dd4519eb"
      },
      "cell_type": "code",
      "source": [
        "# Create [0, ..., 1] evenly spaced with 50 elements:\n",
        "xs = np.linspace(start=0, stop=1, num=50)\n",
        "\n",
        "y_real = np.sin(xs*2*np.pi)\n",
        "\n",
        "# Pretend there is some noise at each timesteps.\n",
        "err = np.random.normal(loc=0.0, scale=0.08, size=y_real.shape)\n",
        "err = np.cumsum(err)  # Which adds up over time.\n",
        "y_measured = y_real + err  # Yielding the final measured signal.\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Sine(x) and its Estimation from noisy Measurements')\n",
        "plt.plot(xs, y_real, c='r', label='Sine')\n",
        "plt.plot(xs, y_measured, c='b', label='Estimated')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LeJtz5wrh5CSEiQm1CwhRubIQfn5CXLmS7qa6d6enrlyZwdfFGGM6ktm8p5Wi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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f2042f710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "K33llrEWiLKT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Exercise: plot another two curves\n",
        "\n",
        "Plot the two new curves defined below.\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "OV4jFsAYiLoh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "xs = np.linspace(start=0, stop=1, num=50)\n",
        "y_real = np.cos(xs*2*np.pi)\n",
        "err = np.random.normal(loc=0.0, scale=0.02, size=xs.shape)\n",
        "err = np.cumsum(err)\n",
        "y_measured = np.cos((xs+err)*2*np.pi)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R_iq80JAsxww",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\n",
        "You can try modifying:\n",
        "\n",
        "* colors\n",
        "* data labels,\n",
        "* plot title\n",
        "* adding a legend,\n",
        "* axes labels,\n",
        "* setting axes limits."
      ]
    },
    {
      "metadata": {
        "id": "hvQfpAT1svb-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Your Code\n",
        "\n",
        "## Your code here ##"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VDS4eK7Js3Fj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "cellView": "form",
        "outputId": "6846f6f5-ad01-4992-c869-8425baf6b4b5"
      },
      "cell_type": "code",
      "source": [
        "#@title Solution\n",
        "\n",
        "plt.figure()\n",
        "plt.title('Cos(x) and its Noised Version')\n",
        "plt.plot(xs, y_real, c='r', label='Cos')\n",
        "plt.plot(xs, y_measured, c='b', label='Noised')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OsoJ3wwZJj20wFy8CAwYAxYpJsC9UyIAXyxsvL+nZWLRINrUZP14SAS1YACQk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\nDNT/A2X/hrBhh8tjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f2039b810>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5WAtVbIHMGzL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Displaying images with matplotlib\n",
        "\n",
        "`matplotlib` can display an array as an image. This will come in handy for displaying images our neural networks drew.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "lPaVcH4svpfr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We don't have an image in array form here, so instead we will visualize the pdf of a bivariate normal distribution. The details of how this image is created (the next two cells) are not important.\n",
        "\n",
        "#### Creating an image array"
      ]
    },
    {
      "metadata": {
        "id": "ngV0sbMNulWy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f7c7892f-aa58-4a08-ef39-93b34c0671e6"
      },
      "cell_type": "code",
      "source": [
        "normal = multivariate_normal(mean=[0,0], cov=[[1,0],[0,1]])\n",
        "# We can get the density of a number of points like so:\n",
        "normal.pdf(np.array([[0, 0], [-10, 7], [1, 0.9]]))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.59154943e-01, 7.02880071e-34, 6.43848407e-02])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "CcTWGHNZw2cg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Define the grid of points over which the pdf is evaluated.\n",
        "RANGE = 3\n",
        "GRANULARITY = 30\n",
        "\n",
        "x = np.linspace(-RANGE, RANGE, GRANULARITY)\n",
        "y = np.linspace(-RANGE, RANGE, GRANULARITY)\n",
        "XX, YY = np.meshgrid(x, y)\n",
        "\n",
        "# Now (XX[i,j], YY[i,j]) give the x,y coordinates of points in the grid,\n",
        "# create an array such that XY[i, j] = [x, y].\n",
        "XY = np.stack([XX, YY], axis=-1)\n",
        "\n",
        "probabilities = normal.pdf(XY)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TiEwqRiaxSvb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### Show the image\n",
        "\n",
        "`probabilities` is now a 2D array with values corresponding to densities. We can display it in grayscale.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "g8cpgKzqz05u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "ec19e9f7-8824-4046-ad2a-b91367ea4096"
      },
      "cell_type": "code",
      "source": [
        "plt.gray()\n",
        "plt.imshow(probabilities)\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAFWCAYAAADdbd69AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f20296690>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "dJHNAa4Az5yt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can remove the axes and labels from the display for a cleaner look. Also note the difference in setting grayscale mode in the next cell."
      ]
    },
    {
      "metadata": {
        "id": "y0T50H360FEJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "264f45d7-ba21-41e6-e6b0-4198902595c8"
      },
      "cell_type": "code",
      "source": [
        "plt.axis('off')\n",
        "plt.imshow(probabilities, cmap='gray')\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAFWCAYAAADdbd69AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nk/RbNPkIebUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f2022e550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "OiuNCUy-z2YO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Exercise: make and display a coloured image\n",
        "\n",
        "Display the image in red.\n",
        "\n",
        "Helpful facts:\n",
        "\n",
        "* `imshow()` takes arrays of size `n x m x 3` and interprets the last axis as an rgb channel.\n",
        "* The largest value in `probabilities` was around `0.15`,  so unless you rescale your array the image will be dark.\n",
        "\n",
        "The grayscale image plotting automatically rescaled the entries in the `probabilities` array!"
      ]
    },
    {
      "metadata": {
        "id": "-nAIUl3c09gI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Your Code\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s2XMqD1O080h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "cellView": "form",
        "outputId": "26892eda-a851-4c80-a5ff-3a42c31e6f61"
      },
      "cell_type": "code",
      "source": [
        "#@title Solution\n",
        "\n",
        "# You may not have seen all calls in this solution.\n",
        "# There are many ways to accomplish the same.\n",
        "\n",
        "rescaled_probs = probabilities / np.max(probabilities)\n",
        "red_image = np.zeros(list(probabilities.shape) + [3])\n",
        "red_image[:, :, 0] = rescaled_probs\n",
        "\n",
        "plt.axis('off')\n",
        "plt.imshow(red_image)\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAFWCAYAAADdbd69AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f203b83d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8olyq-0uf2bC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Showing a Grid of Images\n",
        "\n",
        "We define a helper function to turn a batch of images into a grid. You do not need to care about its implementation, but check its documentation as you will use the same function later in this lab and other labs."
      ]
    },
    {
      "metadata": {
        "id": "E-wWhz7-Vwmr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def gallery(array, ncols=10, rescale=True):\n",
        "  \"\"\"Take a batch of images and arrange them in a grid.\n",
        "  \n",
        "  Args:\n",
        "    array: of shape batch_size x height x width x 3. The final x3 in the shape\n",
        "      is optional.\n",
        "    ncols: number of columns to have in the grid\n",
        "    rescale: if true (default), increases the intensity of the images.\n",
        "    \n",
        "  Returns:\n",
        "    A numpy array which contains the batch of images arranged into\n",
        "    a grid.\n",
        "  \"\"\"\n",
        "  if rescale:\n",
        "    array = (array + 1.) / 2\n",
        "  nindex, height, width = array.shape[:3]\n",
        "\n",
        "  nrows = nindex//ncols\n",
        "  assert nindex == nrows*ncols\n",
        "\n",
        "  # want result.shape = (height*nrows, width*ncols, intensity?)\n",
        "  abstract_grid_shape = [nrows, ncols, height, width]\n",
        "  image_grid_shape = [height*nrows, width*ncols]\n",
        "  if len(array.shape) == 4:\n",
        "    intensity = array.shape[3]\n",
        "    abstract_grid_shape.append(intensity)\n",
        "    image_grid_shape.append(intensity)\n",
        " \n",
        "  result = (array.reshape(*abstract_grid_shape)\n",
        "            .swapaxes(1,2)\n",
        "            .reshape(*image_grid_shape))\n",
        "  return result\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yp9Y2dYl7K_-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "In order to showcase the use of this function, we make an array corresponding to a batch of images. For simplicity, our batch will just be 8 copies of the red image above.\n",
        "\n",
        "**Before continuing let `red_image` denote the red image you created in the previous exercise.**"
      ]
    },
    {
      "metadata": {
        "id": "9nSw-q-SINiR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "red_image = red_image  # Replace the second `red_image` by your red image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FWnJD0WjIZyS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "21dec88d-d605-4941-b11e-f169b03239af"
      },
      "cell_type": "code",
      "source": [
        "batch_of_images = np.stack([red_image] * 9, axis=0)\n",
        "batch_of_images.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9, 30, 30, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "94HvpWpBAkwY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Exercise: Use the `gallery` helper function\n",
        "\n",
        "To display the `batch_of_images` we just created in a 3x3 grid."
      ]
    },
    {
      "metadata": {
        "id": "m9QGtoU4BW6j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Your Code\n",
        "\n",
        "## Your code here ##"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YA0rgSbY9OTp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "cellView": "form",
        "outputId": "39c0945d-1423-4e0f-c6f9-2bad296c1632"
      },
      "cell_type": "code",
      "source": [
        "#@title Solution\n",
        "\n",
        "plt.figure()\n",
        "plt.axis('off')\n",
        "plt.imshow(gallery(batch_of_images, ncols=3, rescale=False))\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U2S2oo0fm60U9PpZ8Tg/VxulR9Vxje/0l+Zwems+1S9V/7e5sVt39rfoMwumh2jg9NF9F\nD/1eOj20Pzg9On1D6zG5sxl3rgAAAzC4AgAMcBpbQKlO93Ghp4aUT0L605D+TPLFa/E9Ggbp1KwH\n4vQSDSlfhvQfIf1C8sVr1XOLYhi0e1Pt6nSfLPSMejyRfE6PTyWf08NpsZbX46Xkc3r8IfmO6rEj\n9KxOTcxsMqeHtrnTQzWs6KFTE50e2uZOj44Wa91uU23uXAEABmBwBQAY4K62wO6VJjE00bDl85D+\nIqS/lHzxdXyPlucaLoYgGno+D+mnSVnuF6+6H+aOo4l3rIpzenwu+Zweqo3TI/sSOz2eSz6nR3b3\nUd27d8cx6kdXxamt5fT4QvI5PVTDih4anjs9nkq+o3p09309CneuAAADMLgCAAzA4AoAMMDlpmLp\nNA/nK+mUkuglfR3S30i+eO2rkNYpW+phPRCnl+gUq99COk6FyaYSdTZ4Vo9pcipWrHvmuUY91Ndz\nenwt+ZweTou1vB6/Sb6jemTaxPdMT8VyemgbOT20zZ0eX0m+ih46NdHpodMonR7dzeidHkzFAgC4\nAAyuAAADnMYWqE7F0hDBrQLSMD5OI4mhzveS77uQ/ta8Xz8rEjeZ0CklsU66YibiNhGubqq9Iwyq\nTsWKemSrgOL/rm3p9PhO8jk9nBZreT30++H0yDY8r26qvcOyqU7FcnpoGzk91CZzenwr+Sp66IYs\nTo9O31irvqn2pGUT4c4VAGAABlcAgAEut0Irmy0QwxGdLRDDlvjUU0PPH0I6hkT6dFTLfyBuLKFP\npN1TVA33Y/j00qQ1X/UY8KzNK+/R8rPZAk4PtQWcHj9IPqeH02Itr0c2wyDqoaGs00PzVY8BP6pH\nZgu4vrGW10NnCzg91E6r6KEbshzVQ/uD06N6DHimTQfuXAEABmBwBQAYgMEVAGCA00zFUjqea3VX\nrOgP6ZSS6CVFj0mnqOhUngfiqpPMC8xWcj0L6ThdJTu3qOq5duh6rtVdsZwe6us5PZwWax3X45nk\nc3ro/171XDt0PNfqrlj6bMHpoX54RQ/9njs9spVcrm+s5fWoeq674c4VAGAABlcAgAEuYQvsDoOy\n1UIxLIqhjk7ZqoY+kTgV5fekLHdWVDf03G0L7LZsnB4aojo9qrZARKcFOT2ys6I6oeduW2C3ZZOt\nbHR9Y62aHp2+oeVl59p1bDJsAQCAi8HgCgAwwGlsgerKlep5TtUQNVu58plJr/XvJ95vIzu2O35u\nN7zprDSphkGd8rL6VUPUbJWd06Oihb5Hy3Z6VK2Y7qq4o3p0z56r2mlOD/1u79RD+2THiumsittt\nEXDnCgAwAIMrAMAADK4AAAMwuAIADMDgCgAwAIMrAMAAp5mKpefVvCmk1/rnGTjZMdTxHJ24MYRu\nchxXirww6YzsPbHs+Lm6UUX1/KX4Omuj7JqjU15Wv+zsI6eHrqLaqYeW7fSonr+k/3v1+3tUj6y8\nrH5OD/0uOj20/St6ZO9xfUPrVD0rzvUNfd3pG1W4cwUAGIDBFQBggNPYAko19KyGN/F8nRiO6J6Q\n8VyfbP/PyuYUvyRlx8/VcCnWtROidkNPRzX0rFoxevaR00PPIHN6VDduiXpo2U4PrWsnRN0delZt\nsqoV4/rGWl6Po31jLa+H9kmnR9dOm7QCIty5AgAMwOAKADDAJWyBHaHn85COIYiGMO6IX3263Dla\n+8eQ/jnJF+u6OwzqsDv0fC75nB7ZccuxnTtHa/8o+ZweWtfdlk2H3TaZ6xtr7dUjO1rb9Q3N5/rG\nWsctm91w5woAMACDKwDAAAyuAAAD3NVz7aw0yTzXbHVPnN4RN+N9nNQvejh6ro9u6Pu2OuiUkugl\n/RTSv0q++L5s5crRqSeZ37Rjuo/TQ9vlqB5OC61D/Fz19ZweWlenx45pch09qp5rthLR9Y21vB7q\n4Vb00Do4PX6SfE6PbJXdjmlyR/1Y7lwBAAZgcAUAGOA0U7G6tsCrkI5hga4Gyc7eceXFMqpTtmK4\npHWIU0piqKMrudxqIQ2rYl13r0jp2AKvJJ/TIzuXKqLlOT2yKUJOD51y5PTIVgvF/0/runv1XMcW\ncH1jLa9Hp2+sVdNDrQSnh9pkTg+tg9PjlqvnIty5AgAMwOAKADDAaWwBxd2uV1do6ZNE9yui5cXQ\n4llIP5F8ruFifbJVYk9NWj83/h/VFVq33LilukIr/h/ZL3osT0NZp0f2JXZ66Morp8czyef0yGYL\n3HLjluoKraN6aLtU9NA2cnpof3B6aB+vrtBi4xYAgAvD4AoAMACDKwDAAKdZoZURp048kmuvzTXN\n58rLfKDo9ej0EjdlJfo72U5E2XlE8Vp1V6wd5zlVcXpoWx7VQz1rp0c2fcjpoWU7PdTXq+6KdfQ8\npyqqtdND29/poeU5PdQXreih3qfTIzs7zfUNLc/1DX3NCi0AgIvB4AoAMMDlpmJlYUu1vGxVUQw1\nYqijDeV+lTLLwR0hnR0lHNNaVxf63OsMrY4Wa3k9NOxzemR3CK5+2RHS2bHnTo/MFrjXGVpH+8Za\nXg+1ySp6ZH03O+7e6aHaOD04QwsA4D2CwRUAYIDT2AJ6e/7IXNNb/EgWBlVXFcUQJNvQohL6ZKvJ\nYjoL911aP6t6LlBnoxB9snxUj+qqIg0PnR5VWyDT3emR5es8kVaO6pHZApFO39D3ub6xVk2P6t6z\nWlenR6ZNdfZM5e9duHMFABiAwRUAYAAGVwCAAU7juSqd1VuRzJ/MvLcPTVp/hdwKl+pGxi6trzPv\n6FYrTY5qsVbuTzo91Od2emSrv6qbrle1cXpMr4pzZWd09Mg8V9c31qrpUd10vapN1h8mV8VV4c4V\nAGAABlcAgAFOawtEOmFQ90yuD0y6uvFFdfVMNaTshDeToc67lF+1LZwe+svv9KjaApllUw0pO1bM\npB5di6BzJpfrG2vV9Kj2yar91bVipvvHA9y5AgAMwOAKADDAJWyBSHW1S7aqKF7L9sPMQp2OLdAJ\nW6rh5a1CnXf53OqqIqdH1uZHbYHd2mSfe0uqq4+cHtqWTo+qNlkdJrWp/H0a7lwBAAZgcAUAGIDB\nFQBggMt5rkrV16u8522vHVVfqVKHrnd3Ly/JUfX1svdVvVT3Of9Vp0q+TpufTYu16s8gsr/v1KOj\nRfa+s/cN7lwBAAZgcAUAGODytkCkEwpkYdAk3RDpShzV41Za6OfuyHc2uvW+hx7vixbcuQIADMDg\nCgAwAIMrAMAADK4AAAMwuAIADMDgCgAwwHs1FSubKuKunX2FVrWMM1Jtc3ftjCu0jn7Ovej0Db12\nthVa71LGPeDOFQBgAAZXAIABLm8LdML9zqa/99osO/t7dWOUW9ENL3drE7nVBuXVjVFuSUeP3dpE\nbrlB+Rn6BneuAAADMLgCAAxwOVugG7a4Y5mz45uvdLR25e8TdELF7Fjm7PjmKx2t7eowTSeMz77n\nTo8rHa39X/WYgjtXAIABGFwBAAZgcAUAGOASnmvHO8r8ug9NOruWeUyRzBP6u5DW1/FzMi8qY7fH\n1PG5M21cm2faZD5hxOmRtXmmjdPjDFro686zhWp/0HwVPTL/utofXN/Q8u6lR4Q7VwCAARhcAQAG\nOK0tcDT01H/sI5N+XMyXhUGRLPR8bdKvivleL89kGLQj9HTtqq8fF/Nllk3E6aFt6fTI8p1Bj65N\n5vTQNnd6aL6KHplNlrWr0yPTJvtcx26LgDtXAIABGFwBAAY4jS1Q3WSiGnpquP+xSX8i+T4x+bSh\nKqGPhil/hfSfJq35snaphkGdDS06emShZ9TjY8nn9FBtnB5VWyC211+Sz+mh+Vy7VC2C7uY71Q2K\nqjaZ00O1cXpovooe+r10emh/cHp0+obWY3LzHe5cAQAGYHAFABiAwRUAYIDTeK5KdbqP8/XUr3sS\n0p+G9GeSL16L71GPSadmPRCnl6hf9zKk/wjpF5IvXqueWxQ9pt2balen+2S+XtTjieRzenwq+Zwe\nTou1vB4vJZ/T4w/Jd1SPHb5edWpi9gzC6aFt7vRQDSt66NREp4e2udOjo8Vat9tUmztXAIABGFwB\nAAa4qy2we6VJDE00bPk8pL8I6S8lX3wd36PluYaLIYiGns9D+mlSlvvFq242vONo4h2r4pwen0s+\np4dq4/TIvsROj+eSz+mR3X1UN0bfcYz60VVxams5Pb6QfE4P1bCih4bnTo+nku+oHt1NtY/CnSsA\nwAAMrgAAA1xutoA+iXShjz71jOHO1yH9jeSL174KaZ1VoGHWA/EJqM4C+C2k49Pa7Gl3Zw9SDYMm\nZwvEume2QNRDQ0+nx9eSz+nhtFjL6/Gb5DuqR6ZNfM/0bAGnh7aR00Pb3OnxleSr6KGzZ5weOtPH\n6dHdL9npwWwBAIALwOAKADAAgysAwACn8VyrU7HUf3GrgNQjjdNIoo/0veT7LqS/Ne/Xz4rEHXx0\nSkmsk66YibhNhKubau/wmKpTsaIe2Sqg+L9rWzo9vpN8Tg+nxVpeD/1+OD2yDc+rm2rv8MOrU7Gc\nHtpGTg99BuH0+FbyVfTQ3a6cHp2+sVZ9U+1JPzzCnSsAwAAMrgAAA1xuhVY2FSuGIzoVK4YtcUqJ\nhp4/hHQMiXTqiZb/QNxYQqf7uCkqGu7H8OmlSWu+6jHgWZtX3qPlZ1OxnB5qCzg9fpB8Tg+nxVpe\nj2z6VtRDQ1mnh+arHgN+VI/MFnB9Yy2vh07FcnqonVbRQzdkOaqH9genR/UY8EybDty5AgAMwOAK\nADDAaWYLKB1boLpxSwxh9KlnDHdiGKRPUfVp8wNx1UkWrmYruZ6FdHyimp1bVLUFOnRtgerGLU4P\nDT2dHk6LtY7r8UzyOT2qR00f1ULLqNoC1Y1b1P5yeqhlU9FDv+dOj2wll+sba3k9qrbAbrhzBQAY\ngMEVAGAABlcAgAEu4bnu9piy1ULRc4o+kk7ZqvpKkTgV5fekLHdWVNfX2+257vbDnR7q/zk9qp5r\nRKcFOT2ys6I6vt5uz3W3H56tbHR9Y62aHp2+oeVl59p1nkHguQIAXAwGVwCAAU5jC1RXrlTPc6qG\nqNnKlc9Meq1/Tyd6G9mx3fFzu+FNZ6VJNQzqlJfVrxqiZqvsnB4VLfQ9WrbTo2rFdFfFHdWje/Zc\n1U5zeuh3e6ce2ic7VkxnVdxui4A7VwCAARhcAQAGYHAFABiAwRUAYAAGVwCAAU4zW0CPVHhTSK/1\nz2MasmOo41EPcWMI3YczTmZ+YdIZ2Xti2fFzdaOK6hEh8XXWRtk1R6e8rH7Z8RxOD53ov1MPLdvp\nUT0iRP/36vf3qB5ZeVn9nB76XXR6aPtX9Mje4/qG1ql6nJHrG/q60zeqcOcKADAAgysAwAAMrgAA\nA5zGc1Wqvl7VO4rn60SvRzfcjef6ZJsrVzan+CUpO36uelGxrh3/r+vrOaq+XtXn1rOPnB56BpnT\no7pxS9RDy3Z6aF07/t9uX6/6DKLqc7u+sZbX42jfWMvroX3S6dF9VjHps0a4cwUAGIDBFQBggEvY\nAjtCz+chHUMQDWHcEb86dadztPaPIf1zki/WdXcY1GF36Plc8jk9suOWYzt3jtb+UfI5PbSuuy2b\nDrttMtc31tqrR3a0tusbms/1jbWOWza74c4VAGAABlcAgAHuagt0VppktkC2uic+gYz7RT5O6hfD\nDD16QvecfFsd9KlnDHd+CulfJV98X7Zy5ejT0Swk2vFE2umh7XJUD6eF1iF+roaeTg+tq9Njx0yO\njh5VWyBbiej6xlpeD7UZKnpoHZweP0k+p0e2ym7HTI6jlgF3rgAAAzC4AgAMwOAKADDAaaZidT3X\nVyEdPRddDZKdvePKi2VUp2xFL0rrEKeURB9JV3K51ULqWcW67l6R0vFcX0k+p0d2LlVEy3N6ZFOE\nnB465cjpka0Wiv+f1nX36rmO5+r6xlpej07fWKumh/q0Tg99BuH00Do4PW65ei7CnSsAwAAMrgAA\nA5zGFlDc7Xp1hZZO03C/IlpeDC2ehfQTyecaLtYnWyX21KT1c+P/UV2hdcuNW6ortOL/kf2ix/I0\nlHV6ZF9ip4euvHJ6PJN8To9sKtYtN26prtA6qoe2S0UPbSOnh/YHp4f28eoKLTZuAQC4MAyuAAAD\nnGaFVkZ8uvdIrr021zSfKy8LVWI4ok9A3VPVGIJkm2Vk5xHFa9WNW3ac51TF6aFteVQPtVWcHtkT\nbqeHlu300NCzunHL0fOcqqhUrUuxAAADJUlEQVTWTg9tf6eHluf00NC9ooeG506P7Ow01ze0PNc3\n9DUrtAAALgaDKwDAAAyuAAADXG4qVuYJVcvLVhVFHyf6SNpQ7lcp83OjJ/SnSWu+mNa6Ol/pXmdo\ndbRYy+uhnprTI7tDcPVT/9rpofmcHpnneq8ztI72jbW8HvoMoqJH1ndd39DXrm+s5fXgDC0AgPcI\nBlcAgAFOYwvo7fkjc01v8SNZGFRdVRRDkGxDi0rok60mi+ks3Hdp/azquUCdjUJ02s5RPaqrijQ8\ndHpUbYFMd6dHlq8z3Uc5qkdmC0Q6fUPf5/rGWjU9qht7a12dHpk21amJlb934c4VAGAABlcAgAFO\nYwsondVbkSyEzsLDD01af4XcCpfqXpsura+z8OZWK02OarFWHkI7PdSKcXpkq7+q+wJXtXF6TK+K\nc2VndPTIbAHXN9aq6VHdF7iqTdYfJlfFVeHOFQBgAAZXAIABGFwBAAY4reca6XhM3TO5PjDp6q5C\n1dUzVb+u4x1N+kjvUn7VE3Z66C+/06PquWZ+eNWv6/jck3p0/dfOmVyub6xV06PaJ6vPFro+93T/\neIA7VwCAARhcAQAGuIQtEKmudslWFcVr2WbDWajTsQU6YUs1vLxVqPMun1tdVeT0yNr8qC2wW5vs\nc29JdfWR00Pb0ulR1Sarw6Q2lb9Pw50rAMAADK4AAANczhZQqqFn5T1ve+2ohj6VOnTDy3uFO45q\n6Jm9rxruu8/5rzpV8nXa/GxarFW3ybK/79Sjo0X2vrP3De5cAQAGYHAFABiAwRUAYIDLe66Rjs+S\neUyTdP2nK3FUj1tpoZ+7I9/Z6Nb7Hnq8L1pw5woAMACDKwDAAHe1Bd68OfuNPQBAD+5cAQAGYHAF\nABiAwRUAYAAGVwCAARhcAQAGYHAFABiAwRUAYAAGVwCAARhcAQAGYHAFABiAwRUAYAAGVwCAARhc\nAQAGYHAFABiAwRUAYAAGVwCAARhcAQAGYHAFABiAwRUAYAAGVwCAARhcAQAGYHAFABiAwRUAYAAG\nVwCAARhcAQAGYHAFABiAwRUAYAAGVwCAARhcAQAG+D/Pzq28X3G8DAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f201039d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "EUL0KYtHJqC4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "You may want to change the size of the figure, like so:"
      ]
    },
    {
      "metadata": {
        "id": "6q3Lf6zhJyRE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "1302ee55-4050-4b31-8a3b-b5e7ff264d74"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(3, 3))\n",
        "plt.gray()\n",
        "plt.axis('off')\n",
        "plt.imshow(gallery(batch_of_images, ncols=3, rescale=False))\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3f201774d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "DgtndZYlZ4Wn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Final Notes\n",
        "\n",
        "`matplotlib` has many different types of plots and is really flexible. You can make any plot you can imagine with it. $^{TM}$.\n",
        "\n",
        "#### Seaborn\n",
        "\n",
        "The [seaborn](https://seaborn.pydata.org/) package is very good at making many standard, beautiful and potentially complex plots. We do not cover it here, though. You *may* have noticed that we imported it with `import seaborn as sns`: we use it for its style presets only. This was applied by running `sns.set_style('ticks')`.\n"
      ]
    },
    {
      "metadata": {
        "id": "XtvAE6FiaXkI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}