Copy_of_FMML_Aug22_M1Lab3_Terms&Metrics.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/GVGunasekhar/FMML_COURSE_ASSIGNMENT/blob/main/Copy_of_FMML_Aug22_M1Lab3_Terms%26Metrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Eu9VZbF01eq"
      },
      "source": [
        "# Machine learning terms and metrics\n",
        "\n",
        "FMML Module 1, Lab 3<br>\n",
        "Module Coordinator: Amit Pandey amit.pandey@research.iiit.ac.in <br>\n",
        "<br>\n",
        "\n",
        " In this lab, we will show a part of the ML pipeline by extracting features, training and testing. We will also explore how data augmentation affects accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qBvyEem0vLi"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "# set randomseed\n",
        "rng = np.random.default_rng(seed=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3t59g5s1HfC"
      },
      "source": [
        "In this lab, we will use the California Housing dataset. There are 20640 samples, each with 8 attributes like income of the block, age of the houses per district etc. The task is to predict the cost of the houses per district. \n",
        "\n",
        "Let us download and examine the dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LpqjN991GGJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16fcece5-40b4-457f-8ad3-c132a70c1538"
      },
      "source": [
        " dataset =  datasets.fetch_california_housing()\n",
        " # print(dataset.DESCR)  # uncomment this if you want to know more about this dataset\n",
        " # print(dataset.keys())  # if you want to know what else is there in this dataset\n",
        " dataset.target = dataset.target.astype(np.int) # so that we can classify\n",
        " print(dataset.data.shape)\n",
        " print(dataset.target.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20640, 8)\n",
            "(20640,)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  after removing the cwd from sys.path.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNx4174W5xRg"
      },
      "source": [
        "Here is a function for calculating the 1-nearest neighbours"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(dataset), dataset.DESCR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ut5lj4HsaTfp",
        "outputId": "89dcacb1-5b0c-4da5-8212-117a8246e676"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(sklearn.utils.Bunch,\n",
              " '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 20640\\n\\n    :Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n    :Attribute Information:\\n        - MedInc        median income in block group\\n        - HouseAge      median house age in block group\\n        - AveRooms      average number of rooms per household\\n        - AveBedrms     average number of bedrooms per household\\n        - Population    block group population\\n        - AveOccup      average number of household members\\n        - Latitude      block group latitude\\n        - Longitude     block group longitude\\n\\n    :Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nAn household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surpinsingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07zpydQj1hIQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3692128-1640-45af-f046-e7e56624aeb8"
      },
      "source": [
        "def NN1(traindata, trainlabel, query):\n",
        "  diff  = traindata - query  # find the difference between features. Numpy automatically takes care of the size here \n",
        "  sq = diff*diff # square the differences\n",
        "  dist = sq.sum(1) # add up the squares\n",
        "  label = trainlabel[np.argmin(dist)] # our predicted label is the label of the training data which has the least distance from the query\n",
        "  return label\n",
        "\n",
        "def NN(traindata, trainlabel, testdata):\n",
        "  # we will run nearest neighbour for each sample in the test data \n",
        "  # and collect the predicted classes in an array using list comprehension\n",
        "  predlabel = np.array([NN1(traindata, trainlabel, i) for i in testdata])\n",
        "  return predlabel\n",
        "\n",
        "\n",
        "\n",
        "5,5\n",
        "6,6\n",
        "8,9\n",
        "\n",
        "\n",
        "0,0"
      ],
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 0)"
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03JktkfIGaje"
      },
      "source": [
        "We will also define a 'random classifier', which randomly allots labels to each sample"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fogWAtjyGhAH"
      },
      "source": [
        "def RandomClassifier(traindata, trainlabel, testdata):\n",
        "  # in reality, we don't need these arguments\n",
        "\n",
        "  classes = np.unique(trainlabel)\n",
        "  rints = rng.integers(low=0, high=len(classes), size=len(testdata))\n",
        "  predlabel = classes[rints]\n",
        "  return predlabel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Hjf1KHs7fU5"
      },
      "source": [
        "Let us define a metric 'Accuracy' to see how good our learning algorithm is. Accuracy is the ratio of the number of correctly classified samples to the total number of samples. The higher the accuracy, the better the algorithm. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouuCqWU07bz-"
      },
      "source": [
        "def Accuracy(gtlabel, predlabel):\n",
        "  assert len(gtlabel)==len(predlabel), \"Length of the groundtruth labels and predicted labels should be the same\"\n",
        "  correct = (gtlabel==predlabel).sum() # count the number of times the groundtruth label is equal to the predicted label.\n",
        "  return correct/len(gtlabel)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vJFwBFa9Klw"
      },
      "source": [
        "Let us make a function to split the dataset with the desired probability."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko0VzpSM2Tdi"
      },
      "source": [
        "def split(data, label, percent):\n",
        "  # generate a random number for each sample\n",
        "  rnd = rng.random(len(label))\n",
        "  split1 = rnd<percent\n",
        "  split2 = rnd>=percent\n",
        "  split1data = data[split1,:]\n",
        "  split1label = label[split1]\n",
        "  split2data = data[split2,:]\n",
        "  split2label = label[split2]\n",
        "  return split1data, split1label, split2data, split2label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AcK3LEAJ_LGC"
      },
      "source": [
        "We will reserve 20% of our dataset as the test set. We will not change this portion throughout our experiments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBZkHBLJ1iU-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2906dfa5-08e5-48b1-fce7-62c717006276"
      },
      "source": [
        "testdata, testlabel, alltraindata, alltrainlabel = split(dataset.data, dataset.target, 20/100)\n",
        "print('Number of test samples = ', len(testlabel))\n",
        "print('Number of other samples = ', len(alltrainlabel))\n",
        "print('Percent of test data = ', len(testlabel)*100/len(dataset.target),'%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of test samples =  4144\n",
            "Number of other samples =  16496\n",
            "Percent of test data =  20.07751937984496 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6Ss0Z6IAGNV"
      },
      "source": [
        "## Experiments with splits\n",
        "\n",
        "Let us reserve some of our train data as a validation set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFew2iry_7W7"
      },
      "source": [
        "traindata, trainlabel, valdata, vallabel = split(alltraindata, alltrainlabel, 75/100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60hiu4clFN1i"
      },
      "source": [
        "What is the accuracy of our classifiers on the train dataset?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBlZDTHUFTZx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f098cdbf-31c4-4e9d-f9f9-b1f75eabc0a7"
      },
      "source": [
        "trainpred = NN(traindata, trainlabel, traindata)\n",
        "trainAccuracy = Accuracy(trainlabel, trainpred)\n",
        "print(\"Train accuracy using nearest neighbour is \", trainAccuracy)\n",
        "\n",
        "trainpred = RandomClassifier(traindata, trainlabel, traindata)\n",
        "trainAccuracy = Accuracy(trainlabel, trainpred)\n",
        "print(\"Train accuracy using random classifier is \", trainAccuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train accuracy using nearest neighbour is  1.0\n",
            "Train accuracy using random classifier is  0.164375808538163\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A,B,C=5,6,7\n",
        "5,5 - A\n",
        "6,6 -B\n",
        "7,7 -C\n",
        "\n",
        "\n",
        "\n",
        "8,8 - A | C\n",
        "0,0 -B | A   - 0\n",
        "\n",
        "\n",
        "10,10     -A\n",
        "15,15     -C"
      ],
      "metadata": {
        "id": "jnMblJyaXKZZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41835757-d862-46a9-8ea4-d95ff41405d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interesting: See how train accuracy of nearest neighbour is 1 i.e. 100%. Ponder and Explain."
      ],
      "metadata": {
        "id": "tW_I-YuhbQqA"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7h08-9gJDtSy"
      },
      "source": [
        "For nearest neighbour, the train accuracy is always 1. The accuracy of the random classifier is close to 1/(number of classes) which is 0.1666 in our case.\n",
        "\n",
        "Let us predict the labels for our validation set and get the accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4h7bXoW_2H3v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c40747d3-0257-4605-da91-a63b29178d9b"
      },
      "source": [
        "valpred = NN(traindata, trainlabel, valdata)\n",
        "valAccuracy = Accuracy(vallabel, valpred)\n",
        "print(\"Validation accuracy using nearest neighbour is \", valAccuracy)\n",
        "\n",
        "valpred = RandomClassifier(traindata, trainlabel, valdata)\n",
        "valAccuracy = Accuracy(vallabel, valpred)\n",
        "print(\"Validation accuracy using random classifier is \", valAccuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation accuracy using nearest neighbour is  0.34108527131782945\n",
            "Validation accuracy using random classifier is  0.1688468992248062\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "py9bLguFEjfg"
      },
      "source": [
        "Validation accuracy of nearest neighbour is considerably less than its train accuracy while the validation accuracy of random classifier is the same. However, the validation accuracy of nearest neighbour is twice that of the random classifier. \n",
        "\n",
        "Now let us try another random split and check the validation accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujm3cyYzEntE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7ebe257-5b29-4c99-d064-38dbb592bb70"
      },
      "source": [
        "traindata, trainlabel, valdata, vallabel = split(alltraindata, alltrainlabel, 75/100)\n",
        "valpred = NN(traindata, trainlabel, valdata)\n",
        "valAccuracy = Accuracy(vallabel, valpred)\n",
        "print(\"Validation accuracy of nearest neighbour is \", valAccuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation accuracy of nearest neighbour is  0.34048257372654156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oSOx7U83EKie"
      },
      "source": [
        "You can run the above cell multiple times to try with different random splits. \n",
        "We notice that the accuracy is different for each run, but close together. \n",
        "\n",
        "Now let us compare it with the accuracy we get on the test dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNEZ5ToYBEDW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba181f21-4076-4b99-e471-d305806bce46"
      },
      "source": [
        "testpred = NN(alltraindata, alltrainlabel, testdata)\n",
        "testAccuracy = Accuracy(testlabel, testpred)\n",
        "print('Test accuracy is ', testAccuracy)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy is  0.34917953667953666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3dGD531K3gH"
      },
      "source": [
        "### Try it out for yourself and answer:\n",
        "1. How is the accuracy of the validation set affected if we increase the percentage of validation set? What happens when we reduce it?\n",
        "2. How does the size of the train and validation set affect how well we can predict the accuracy on the test set using the validation set?\n",
        "3. What do you think is a good percentage to reserve for the validation set so that thest two factors are balanced? \n",
        "\n",
        "Answer for both nearest neighbour and random classifier. You can note down the values for your experiments and plot a graph using  <a href=https://matplotlib.org/stable/gallery/lines_bars_and_markers/step_demo.html#sphx-glr-gallery-lines-bars-and-markers-step-demo-py>plt.plot<href>. Check also for extreme values for splits, like 99.9% or 0.1%"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# 1.ANSWER"
      ],
      "metadata": {
        "id": "SWmOmpnp69qI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   There is no effect for the accuracy if we increase the percentage of validation set.If we reduce the percentage of validation set we don't get the good accuracy."
      ],
      "metadata": {
        "id": "F00uJLyX6_tt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# 2.ANSWER"
      ],
      "metadata": {
        "id": "BzxTgTPQ7fF3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*  The training set should not small.If the validation set is  small ,then the accuracy,precision,recall and F score will have large variantion and we don't  a proper tanning of model."
      ],
      "metadata": {
        "id": "_-d4et_f7goD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.ANSWER"
      ],
      "metadata": {
        "id": "v0epcsDH7kcB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   In general putting 80% of the data in training set,10% in the validation set,and 10% in the test set is a good to start. Train set depends on  two factors such as structure of model,dimension of data."
      ],
      "metadata": {
        "id": "3XCnZSQY7oM9"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnYvkAZLQY7h"
      },
      "source": [
        "## Multiple Splits\n",
        "\n",
        "One way to get more accurate estimates for the test accuracy is by using <b>crossvalidation</b>. Here, we will try a simple version, where we do multiple train/val splits and take the average of validation accuracies as the test accuracy estimation. Here is a function for doing this. Note that this function will take a long time to execute. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "100 --- 80 --- 75|5,   12345, 1234|5, 2345|1, 1345|2"
      ],
      "metadata": {
        "id": "k-HGXSovYQ1v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d81c0ce5-181e-4ea6-84b3-74fa7c5aaf38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-51, 12345, 1239, 2345, 1347)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4nGCUQXBTzo"
      },
      "source": [
        "# you can use this function for random classifier also\n",
        "def AverageAccuracy(alldata, alllabel, splitpercent, iterations, classifier=NN):\n",
        "  accuracy = 0\n",
        "  for ii in range(iterations):\n",
        "    traindata, trainlabel, valdata, vallabel = split(alldata, alllabel, splitpercent)\n",
        "    valpred = classifier(traindata, trainlabel, valdata)\n",
        "    accuracy += Accuracy(vallabel, valpred)\n",
        "  return accuracy/iterations # average of all accuracies"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3qtNar7Bbik",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7b93a86-0062-451b-b8a7-6b798dd9f4cf"
      },
      "source": [
        "print('Average validation accuracy is ', AverageAccuracy(alltraindata, alltrainlabel, 75/100, 10, classifier=NN))\n",
        "testpred = NN(alltraindata, alltrainlabel, testdata)\n",
        "print('test accuracy is ',Accuracy(testlabel, testpred) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average validation accuracy is  0.33584635395170215\n",
            "test accuracy is  0.34917953667953666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33GIn4x5VH-d"
      },
      "source": [
        "This is a very simple way of doing cross-validation. There are many well-known algorithms for cross-validation, like k-fold cross-validation, leave-one-out etc. This will be covered in detail in a later module. For more information about cross-validation, check <a href=https://en.wikipedia.org/wiki/Cross-validation_(statistics)>Cross-validatioin (Wikipedia)</a>\n",
        "\n",
        "### Questions\n",
        "1. Does averaging the validation accuracy across multiple splits give more consistent results?\n",
        "2. Does it give more accurate estimate of test accuracy?\n",
        "3. What is the effect of the number of iterations on the estimate? Do we get a better estimate with higher iterations?\n",
        "4. Consider the results you got for the previous questions. Can we deal with a very small train dataset or validation dataset by increasing the iterations?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.ANSWER"
      ],
      "metadata": {
        "id": "murm6xDz7v5S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*  Yes,averaging the validation accuracy across multiple splits to give more consistent results."
      ],
      "metadata": {
        "id": "6UZ01ap_7xoh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# 2.ANSWER"
      ],
      "metadata": {
        "id": "qvdXJkpJ77px"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*  It's useful for building more accurate machine learning models and evaluating how it is work on an independent test dataset. Cross validation is easy to understand and implement making it go to method for comparing the predictive capabilities of different models and choosing best."
      ],
      "metadata": {
        "id": "v1kHNHSQ7_lp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# 3.ANSWER"
      ],
      "metadata": {
        "id": "7j3UwNPB8FOh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "* There is no effect of the number of iterations on estimate. Yes,We get a better estimate with higher iterations."
      ],
      "metadata": {
        "id": "StGSs7pR8IL2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.ANSWER"
      ],
      "metadata": {
        "id": "9tzcGLlz8QbP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Yes, we can deal with a very small train dataset or validation dataset by increasing the iterations."
      ],
      "metadata": {
        "id": "vTHf46n28S2t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now let us see how augmentation affects accuracy. We will pick components from what we studied in the last module"
      ],
      "metadata": {
        "id": "UUft9G1acwlB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "we will experiment with MNIST dataset\n"
      ],
      "metadata": {
        "id": "XtU3AfVDkkYM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.datasets import mnist\n",
        "# set randomseed\n",
        "rng = np.random.default_rng(seed=42)\n",
        "from sklearn.utils.extmath import cartesian\n",
        "from skimage.transform import rotate, AffineTransform, warp\n",
        "import math\n",
        "\n",
        "#loading the dataset\n",
        "(train_X, train_y), (test_X, test_y) = mnist.load_data()\n",
        "\n",
        "print(train_X[0].shape,train_y[0])"
      ],
      "metadata": {
        "id": "47HIwFsMkebm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd514736-dcdc-451a-f4e8-f2ba7c2dd71d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n",
            "(28, 28) 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_X = train_X/255\n",
        "test_X = test_X/255\n",
        "\n",
        "#train_X[0].shape, train_X[0]"
      ],
      "metadata": {
        "id": "WW6ZGBcTkyXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_X = train_X[::1200,:,:].copy() # subsample. Otherwise it will take too long!\n",
        "train_y = train_y[::1200].copy() # do the same to the labels"
      ],
      "metadata": {
        "id": "64ATS9mtk_mA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1WileBUqC404"
      },
      "source": [
        "## Augmentation functions:\n",
        "\n",
        "\n",
        "\n",
        "def augRotate(sample, angleconstraint):\n",
        "  if angleconstraint==0:\n",
        "    return sample\n",
        "\n",
        "  print(\"shape of sample before is:\", sample.shape)\n",
        "  print(\"len(sample.shape)\", len(sample.shape))\n",
        "  if len(sample.shape)==2:\n",
        "    sample = np.expand_dims(sample, 0)  # make sure the sample is 3 dimensional\n",
        "    print(\"shape of sample is:\", sample.shape)\n",
        "  angle = rng.random(len(sample)) # generate random numbers for angles\n",
        "  print(\"angle is:\", angle)\n",
        "  angle = (angle-0.5)*angleconstraint # make the random angle constrained\n",
        "  print(\"angle with constraint is:\", angle)\n",
        "  nsample = sample.copy() # preallocate the augmented array to make it faster\n",
        "  for ii in range(len(sample)):\n",
        "    nsample[ii] = rotate(sample[ii], angle[ii])\n",
        "  return np.squeeze(nsample) # take care if the input had only one sample.\n",
        "\n",
        "\n",
        "def NN1(traindata, trainlabel, query):\n",
        "  diff  = traindata - query  # find the difference between features. Numpy automatically takes care of the size here \n",
        "  sq = diff*diff # square the differences\n",
        "  dist = sq.sum(1) # add up the squares\n",
        "  label = trainlabel[np.argmin(dist)] # our predicted label is the label of the training data which has the least distance from the query\n",
        "  return label\n",
        "\n",
        "def NN(traindata, trainlabel, testdata):\n",
        "  # we will run nearest neighbour for each sample in the test data \n",
        "  # and collect the predicted classes in an array using list comprehension\n",
        "  traindata = traindata.reshape(-1, 28*28)\n",
        "  testdata = testdata.reshape(-1,28*28)\n",
        "  predlabel = np.array([NN1(traindata, trainlabel, i) for i in testdata])\n",
        "  return predlabel\n",
        "\n",
        "def Accuracy(gtlabel, predlabel):\n",
        "  assert len(gtlabel)==len(predlabel), \"Length of the groundtruth labels and predicted labels should be the same\"\n",
        "  correct = (gtlabel==predlabel).sum() # count the number of times the groundtruth label is equal to the predicted label.\n",
        "  return correct/len(gtlabel)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def shear(sample, amount):\n",
        "  tform = AffineTransform(shear = amount) # create the shear transform\n",
        "  img = warp(sample, tform) # apply the shear\n",
        "  # this makes the digit off-center. Since all the images in the test set are centralized, we will do the same here\n",
        "  col = img.sum(0).nonzero()[0]\n",
        "  row = img.sum(1).nonzero()[0]\n",
        "  if len(col)>0 and len(row)>0:\n",
        "    xshift = int(sample.shape[0]/2 - (row[0]+row[-1])/2)\n",
        "    yshift = int(sample.shape[1]/2 - (col[0]+col[-1])/2)\n",
        "    img = np.roll(img, (xshift, yshift),(0,1))\n",
        "  return img\n",
        "\n",
        "def augShear(sample, shearconstraint):\n",
        "  if shearconstraint==0:\n",
        "    return sample\n",
        "  if len(sample.shape)==2:\n",
        "    sample = np.expand_dims(sample, 0)  # make sure the sample is 3 dimensional\n",
        "  amt = rng.random(len(sample)) # generate random numbers for shear\n",
        "  amt = (amt-0.5)*shearconstraint # make the random shear constrained\n",
        "  nsample = sample.copy() # preallocate the augmented array to make it faster\n",
        "  for ii in range(len(sample)):\n",
        "    nsample[ii] = shear(sample[ii], amt[ii])\n",
        "  return np.squeeze(nsample) # take care if the input had only one sample."
      ],
      "metadata": {
        "id": "BIsZ_Q5LqWEs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this lab, we will use the image pixels themselves as features, instead of extracting features. Each image has 28*28 pixels, so we will flatten them to 784 pixels to use as features. Note that this is very compute intensive and will take a long time.\n",
        "\n",
        "Let us check the baseline accuracy on the test set without any augmentations. We hope that adding augmentations will help us to get better results."
      ],
      "metadata": {
        "id": "vAFpOzQFj-2r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "testpred = NN(train_X, train_y, test_X)\n",
        "print('Baseline accuracy without augmentation is ', Accuracy(test_y, testpred))"
      ],
      "metadata": {
        "id": "dPNdNyFVj4pX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "139477b4-47c8-4102-c9d4-089911443257"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline accuracy without augmentation is  0.6472\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us try to improve this accuracy using augmentations. When we create augmentations, we have to make sure that the changes reflect what will naturally occur in the dataset. For example, we should not add colour to our samples as an augmentation because they do not naturally occur. We should not also flip the images in MNIST, because flipped images have different meanings for digits."
      ],
      "metadata": {
        "id": "MckdgpPflSYh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us augment the whole dataset and see if this improves the test accuracy"
      ],
      "metadata": {
        "id": "7aWfHHggmqu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# hyperparameters\n",
        "angleconstraint = 60\n",
        "naugmentations = 5\n",
        "\n",
        "# augment\n",
        "augdata = train_X # we include the original images also in the augmented dataset\n",
        "auglabel = train_y\n",
        "for ii in range(naugmentations):\n",
        "  augdata = np.concatenate((augdata, augRotate(train_X, angleconstraint))) # concatenate the augmented data to the set\n",
        "  auglabel = np.concatenate((auglabel, train_y))  # the labels don't change when we augment\n",
        "\n",
        "# check the test accuracy\n",
        "testpred = NN(augdata, auglabel, test_X)\n",
        "print('Accuracy after rotation augmentation is ', Accuracy(test_y, testpred))"
      ],
      "metadata": {
        "id": "Kdn0TZ_tlMg3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "610ceedc-2e05-4adc-fbc1-481132fa36c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.77395605 0.43887844 0.85859792 0.69736803 0.09417735 0.97562235\n",
            " 0.7611397  0.78606431 0.12811363 0.45038594 0.37079802 0.92676499\n",
            " 0.64386512 0.82276161 0.4434142  0.22723872 0.55458479 0.06381726\n",
            " 0.82763117 0.6316644  0.75808774 0.35452597 0.97069802 0.89312112\n",
            " 0.7783835  0.19463871 0.466721   0.04380377 0.15428949 0.68304895\n",
            " 0.74476216 0.96750973 0.32582536 0.37045971 0.46955581 0.18947136\n",
            " 0.12992151 0.47570493 0.22690935 0.66981399 0.43715192 0.8326782\n",
            " 0.7002651  0.31236664 0.8322598  0.80476436 0.38747838 0.2883281\n",
            " 0.6824955  0.13975248]\n",
            "angle with constraint is: [ 16.43736291  -3.66729361  21.51587519  11.84208174 -24.34935913\n",
            "  28.5373411   15.66838212  17.16385832 -22.31318204  -2.97684373\n",
            "  -7.75211855  25.60589933   8.6319072   19.3656968   -3.39514807\n",
            " -16.36567669   3.27508722 -26.17096463  19.65787032   7.89986395\n",
            "  15.48526441  -8.72844191  28.24188146  23.58726728  16.70300982\n",
            " -18.32167753  -1.99673978 -27.37177405 -20.74263048  10.98293719\n",
            "  14.68572935  28.05058395 -10.45047851  -7.77241764  -1.82665132\n",
            " -18.63171845 -22.20470968  -1.45770443 -16.38543906  10.18883968\n",
            "  -3.77088487  19.96069176  12.01590612 -11.25800152  19.93558808\n",
            "  18.28586145  -6.75129726 -12.70031376  10.94973024 -21.61485098]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.1999082  0.00736227 0.78692438 0.66485086 0.70516538 0.78072903\n",
            " 0.45891578 0.5687412  0.139797   0.11453007 0.66840296 0.47109621\n",
            " 0.56523611 0.76499886 0.63471832 0.5535794  0.55920716 0.3039501\n",
            " 0.03081783 0.43671739 0.21458467 0.40852864 0.85340307 0.23393949\n",
            " 0.05830274 0.28138389 0.29359376 0.66191651 0.55703215 0.78389821\n",
            " 0.66431354 0.40638686 0.81402038 0.16697292 0.02271207 0.09004786\n",
            " 0.72235935 0.46187723 0.16127178 0.50104478 0.1523121  0.69632038\n",
            " 0.44615628 0.38102123 0.30151209 0.63028259 0.36181261 0.08764992\n",
            " 0.1180059  0.96189766]\n",
            "angle with constraint is: [-18.00550785 -29.55826381  17.21546265   9.8910514   12.30992272\n",
            "  16.84374186  -2.46505347   4.12447176 -21.61218011 -23.12819559\n",
            "  10.10417771  -1.73422763   3.91416639  15.89993144   8.0830992\n",
            "   3.21476404   3.55242964 -11.76299412 -28.15092993  -3.79695665\n",
            " -17.12491963  -5.48828138  21.2041844  -15.96363085 -26.5018355\n",
            " -13.11696648 -12.38437453   9.71499088   3.42192914  17.03389255\n",
            "   9.85881242  -5.61678831  18.84122308 -19.98162481 -28.63727561\n",
            " -24.59712835  13.34156104  -2.28736618 -20.32369326   0.06268651\n",
            " -20.86127384  11.7792225   -3.23062347  -7.13872643 -11.90927465\n",
            "   7.81695559  -8.29124337 -24.74100484 -22.91964587  27.71385987]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.90858069 0.69970713 0.26586996 0.96917638 0.7787509  0.71689019\n",
            " 0.4493615  0.27224156 0.09639096 0.9026024  0.45577629 0.20236336\n",
            " 0.30595662 0.57921957 0.17677278 0.85661428 0.75851953 0.71946296\n",
            " 0.43209304 0.62730884 0.58409797 0.6498466  0.08444432 0.4158074\n",
            " 0.04161417 0.49399082 0.32986121 0.14452419 0.10340297 0.58764457\n",
            " 0.17059297 0.92512012 0.58106114 0.3468698  0.59091549 0.02280387\n",
            " 0.95855921 0.48230344 0.78273523 0.08273    0.48665833 0.49070699\n",
            " 0.93782645 0.57172805 0.4734894  0.26697566 0.331569   0.5206724\n",
            " 0.43891146 0.02161208]\n",
            "angle with constraint is: [ 24.51484144  11.98242803 -14.04780231  28.15058264  16.72505424\n",
            "  13.01341135  -3.03830987 -13.66550629 -24.21654227  24.15614379\n",
            "  -2.65342261 -17.85819811 -11.64260255   4.75317414 -19.39363302\n",
            "  21.39685705  15.51117179  13.16777736  -4.07441761   7.63853044\n",
            "   5.04587813   8.99079609 -24.93334073  -5.05155587 -27.50314957\n",
            "  -0.36055085 -10.20832726 -21.32854867 -23.79582194   5.25867433\n",
            " -19.76442189  25.5072071    4.86366838  -9.18781173   5.45492949\n",
            " -28.63176774  27.51355279  -1.06179378  16.96411364 -25.0362\n",
            "  -0.80050015  -0.55758034  26.2695873    4.30368314  -1.59063594\n",
            " -13.98146021 -10.10586016   1.24034415  -3.66531238 -28.70327521]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.82629192 0.89616077 0.14024909 0.55403614 0.10857574 0.67224009\n",
            " 0.28123378 0.65942263 0.72699461 0.76864749 0.10774095 0.91601185\n",
            " 0.23021399 0.03741256 0.55485247 0.37092228 0.82978974 0.80825147\n",
            " 0.31713889 0.9528994  0.29091784 0.51505713 0.25596509 0.93604357\n",
            " 0.16460782 0.04491062 0.43509706 0.99237556 0.89167727 0.74860802\n",
            " 0.89079249 0.89344664 0.51885836 0.31592905 0.77201243 0.66166126\n",
            " 0.37365773 0.09446667 0.74678961 0.26246052 0.93681315 0.24097058\n",
            " 0.12275793 0.83111267 0.15328432 0.17926831 0.59938279 0.87456204\n",
            " 0.19643467 0.31032367]\n",
            "angle with constraint is: [ 19.57751545  23.76964631 -21.58505466   3.24216861 -23.48545553\n",
            "  10.33440558 -13.12597297   9.56535808  13.61967686  16.11884952\n",
            " -23.53554324  24.96071071 -16.18716055 -27.75524663   3.29114816\n",
            "  -7.74466297  19.78738459  18.49508832 -10.97166643  27.1739637\n",
            " -12.54492971   0.90342775 -14.64209457  26.1626142  -20.12353095\n",
            " -27.30536284  -3.8941764   29.54253384  23.50063598  14.91648117\n",
            "  23.44754945  23.60679838   1.13150162 -11.04425689  16.32074593\n",
            "   9.69967579  -7.58053627 -24.33199992  14.80737668 -14.25236904\n",
            "  26.20878903 -15.5417655  -22.63452406  19.86676033 -20.802941\n",
            " -19.24390151   5.96296749  22.47372245 -18.21392006 -11.38057963]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.77740484 0.97182643 0.50074119 0.1438975  0.01393629 0.22965603\n",
            " 0.13182222 0.67765867 0.1218325  0.50632993 0.69426244 0.58111661\n",
            " 0.19977565 0.80412453 0.71540713 0.738984   0.13105775 0.1237538\n",
            " 0.92756255 0.39757819 0.30094869 0.48858405 0.66286421 0.95562326\n",
            " 0.28644623 0.92480843 0.02485949 0.55519804 0.63397511 0.1058974\n",
            " 0.1403396  0.41911432 0.96623191 0.59604255 0.93302322 0.80436092\n",
            " 0.4673816  0.78476345 0.01783678 0.109144   0.82942861 0.79681709\n",
            " 0.23264074 0.53076959 0.60601582 0.86773895 0.60310716 0.41257157\n",
            " 0.37418404 0.42588209]\n",
            "angle with constraint is: [ 16.64429029  28.30958556   0.04447117 -21.36614985 -29.16382274\n",
            " -16.2206382  -22.09066693  10.65952042 -22.69004972   0.3797959\n",
            "  11.65574614   4.86699655 -18.0134609   18.24747157  12.92442778\n",
            "  14.33904023 -22.13653491 -22.57477178  25.65375306  -6.14530837\n",
            " -11.94307849  -0.68495728   9.77185277  27.33739542 -12.81322639\n",
            "  25.48850576 -28.50843052   3.31188254   8.0385067  -23.64615577\n",
            " -21.57962418  -4.85314084  27.97391473   5.76255319  25.9813933\n",
            "  18.26165494  -1.95710391  17.08580696 -28.92979296 -23.45136019\n",
            "  19.76571689  17.8090253  -16.04155548   1.84617544   6.36094924\n",
            "  22.06433723   6.18642944  -5.24570584  -7.5489574   -4.44707482]\n",
            "Accuracy after rotation augmentation is  0.6742\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The angle constraint is a hyperparameter which we have to tune using a validation set. (Here we are not doing that for time constraints). Let us try a grid search to find the best angle constraint."
      ],
      "metadata": {
        "id": "R6vwKYxNnjvS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "angleconstraints = [0,10,20,30,40,50,60,70,80,90] # the values we want to test\n",
        "accuracies = np.zeros(len(angleconstraints), dtype=np.float) # we will save the values here\n",
        "\n",
        "for ii in range(len(angleconstraints)):\n",
        "  # create the augmented dataset\n",
        "  augdata = train_X # we include the original images also in the augmented dataset\n",
        "  auglabel = train_y\n",
        "  for jj in range(naugmentations):\n",
        "    augdata = np.concatenate((augdata, augRotate(train_X, angleconstraints[ii]))) # concatenate the augmented data to the set\n",
        "    auglabel = np.concatenate((auglabel, train_y))  # the labels don't change when we augment\n",
        "\n",
        "  # check the test accuracy\n",
        "  testpred = NN(augdata, auglabel, test_X)\n",
        "  accuracies[ii] = Accuracy(test_y, testpred)\n",
        "  print('Accuracy after rotation augmentation constrained by ',angleconstraints[ii], ' is ', accuracies[ii], flush=True)"
      ],
      "metadata": {
        "id": "2TuE7cTCnZ9g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d49a3f13-4235-4507-95e2-2d96c35742a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy after rotation augmentation constrained by  0  is  0.6472\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.65193103 0.86749063 0.45389688 0.24783956 0.23666236 0.74601428\n",
            " 0.81656876 0.10527808 0.06655886 0.59443366 0.14617324 0.82466419\n",
            " 0.31033467 0.14387193 0.92097047 0.16553172 0.28472008 0.1536134\n",
            " 0.11549006 0.02114802 0.05539541 0.17464147 0.05338193 0.59114382\n",
            " 0.68071453 0.39363046 0.3179911  0.50452624 0.87500494 0.85113163\n",
            " 0.04347506 0.18149841 0.23674487 0.24938758 0.57123265 0.41626243\n",
            " 0.04925412 0.37361414 0.52375295 0.1016719  0.83345855 0.05196187\n",
            " 0.92484187 0.09911314 0.84357495 0.90265314 0.97957068 0.80202588\n",
            " 0.77947754 0.64248328]\n",
            "angle with constraint is: [ 1.51931026  3.67490632 -0.46103118 -2.52160437 -2.63337637  2.4601428\n",
            "  3.16568763 -3.9472192  -4.33441143  0.94433664 -3.53826756  3.2466419\n",
            " -1.89665326 -3.56128067  4.20970472 -3.34468277 -2.15279918 -3.46386605\n",
            " -3.84509936 -4.78851984 -4.44604591 -3.25358529 -4.46618067  0.91143816\n",
            "  1.80714527 -1.06369543 -1.82008903  0.04526237  3.75004942  3.51131627\n",
            " -4.56524938 -3.1850159  -2.63255129 -2.50612424  0.71232652 -0.83737574\n",
            " -4.5074588  -1.26385862  0.23752949 -3.98328097  3.33458554 -4.48038134\n",
            "  4.24841869 -4.00886858  3.43574952  4.02653144  4.79570681  3.0202588\n",
            "  2.79477541  1.42483276]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.77899635 0.13455221 0.53606804 0.51422287 0.85757214 0.46279937\n",
            " 0.3850895  0.63956327 0.26646332 0.13976841 0.47787727 0.41688937\n",
            " 0.23256994 0.36751181 0.36639245 0.32749556 0.37946408 0.68574335\n",
            " 0.29687647 0.94885793 0.91634802 0.48091043 0.32836121 0.53543479\n",
            " 0.84856049 0.65258734 0.80439183 0.53272228 0.63291763 0.28815561\n",
            " 0.73489316 0.20240459 0.69479813 0.86071907 0.13210284 0.61437974\n",
            " 0.09509575 0.72571563 0.08449322 0.93593982 0.13740793 0.95888025\n",
            " 0.80088418 0.593682   0.7826241  0.79511484 0.94602706 0.25338335\n",
            " 0.5900759  0.0950492 ]\n",
            "angle with constraint is: [ 2.78996355 -3.65447792  0.36068036  0.1422287   3.57572144 -0.37200634\n",
            " -1.14910504  1.39563271 -2.33536682 -3.60231589 -0.22122726 -0.83110631\n",
            " -2.67430059 -1.3248819  -1.3360755  -1.72504436 -1.2053592   1.85743345\n",
            " -2.03123525  4.48857927  4.1634802  -0.19089572 -1.71638795  0.3543479\n",
            "  3.48560489  1.52587341  3.04391828  0.32722276  1.32917629 -2.11844386\n",
            "  2.34893162 -2.97595407  1.94798129  3.60719068 -3.67897163  1.14379741\n",
            " -4.04904252  2.25715628 -4.15506781  4.35939823 -3.6259207   4.58880246\n",
            "  3.00884176  0.93682004  2.82624105  2.95114839  4.46027063 -2.46616646\n",
            "  0.90075895 -4.04950802]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.6161657  0.1712913  0.56495061 0.57243051 0.46598515 0.52263178\n",
            " 0.76392339 0.79924472 0.49215322 0.59959344 0.93123624 0.11973359\n",
            " 0.11710357 0.08770901 0.65786329 0.4186083  0.77432142 0.67123141\n",
            " 0.33363776 0.89836655 0.76253215 0.27053494 0.36419202 0.31443998\n",
            " 0.15761165 0.14778337 0.93612746 0.43790404 0.38331982 0.72968571\n",
            " 0.55299307 0.93613999 0.78030149 0.47936956 0.37635947 0.98663154\n",
            " 0.71776024 0.95119466 0.11847858 0.85053368 0.63707388 0.12192168\n",
            " 0.588258   0.68609637 0.01230269 0.45431796 0.82539951 0.29535903\n",
            " 0.45854808 0.44231413]\n",
            "angle with constraint is: [ 1.161657   -3.28708696  0.64950611  0.72430514 -0.34014847  0.22631776\n",
            "  2.6392339   2.99244717 -0.07846784  0.99593442  4.31236236 -3.80266411\n",
            " -3.82896434 -4.12290988  1.57863285 -0.81391699  2.74321416  1.71231413\n",
            " -1.66362242  3.98366547  2.62532147 -2.29465059 -1.35807982 -1.8556002\n",
            " -3.42388351 -3.52216627  4.36127463 -0.62095963 -1.16680177  2.29685709\n",
            "  0.52993065  4.36139987  2.80301494 -0.20630436 -1.23640527  4.86631545\n",
            "  2.17760236  4.5119466  -3.81521423  3.50533679  1.37073884 -3.78078322\n",
            "  0.88258     1.86096365 -4.87697314 -0.45682038  3.25399511 -2.04640975\n",
            " -0.41451918 -0.57685873]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.30192739 0.9184419  0.78129404 0.11058841 0.99703466 0.87920002\n",
            " 0.28390844 0.83689658 0.10641953 0.99910473 0.66568474 0.65012502\n",
            " 0.09044073 0.8970334  0.0289995  0.24082806 0.14302188 0.77676794\n",
            " 0.19820423 0.91063823 0.65626904 0.03616271 0.00542983 0.05165792\n",
            " 0.60592518 0.80148181 0.23855282 0.84940884 0.05723194 0.80096385\n",
            " 0.92779543 0.7721084  0.69812078 0.83798022 0.0401513  0.20178211\n",
            " 0.12492368 0.50453099 0.74518813 0.63001184 0.8511311  0.15521299\n",
            " 0.73462109 0.19304149 0.27075875 0.7099047  0.98020478 0.61154361\n",
            " 0.05450031 0.61630897]\n",
            "angle with constraint is: [-1.98072609  4.18441896  2.81294035 -3.89411589  4.97034658  3.79200024\n",
            " -2.16091562  3.3689658  -3.93580468  4.9910473   1.65684736  1.50125016\n",
            " -4.09559273  3.97033399 -4.71000497 -2.59171942 -3.56978125  2.76767941\n",
            " -3.01795773  4.10638227  1.56269039 -4.6383729  -4.94570166 -4.48342083\n",
            "  1.05925178  3.01481811 -2.61447179  3.49408843 -4.4276806   3.00963854\n",
            "  4.2779543   2.72108399  1.98120784  3.37980219 -4.598487   -2.98217889\n",
            " -3.75076321  0.0453099   2.45188128  1.30011845  3.511311   -3.44787008\n",
            "  2.34621092 -3.06958509 -2.29241249  2.09904697  4.80204785  1.11543606\n",
            " -4.45499685  1.1630897 ]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.04235055 0.88414571 0.70957829 0.17312785 0.09172101 0.18353323\n",
            " 0.98002718 0.45856064 0.78408095 0.63640834 0.57241315 0.14513025\n",
            " 0.94602445 0.30134263 0.57801722 0.69977594 0.64923316 0.94059441\n",
            " 0.14843899 0.50835274 0.40403439 0.47416873 0.11921753 0.13409461\n",
            " 0.27807555 0.3047046  0.42790321 0.61098755 0.63462912 0.4118109\n",
            " 0.40878311 0.21762853 0.58830625 0.31704091 0.03605983 0.41840004\n",
            " 0.47413268 0.22559287 0.57245793 0.5657719  0.70200218 0.64794848\n",
            " 0.65243306 0.31621415 0.78743222 0.54914438 0.4314182  0.62601248\n",
            " 0.36065733 0.51273924]\n",
            "angle with constraint is: [-4.57649448  3.84145711  2.09578285 -3.26872154 -4.08278994 -3.16466771\n",
            "  4.8002718  -0.41439358  2.84080948  1.36408342  0.7241315  -3.54869745\n",
            "  4.46024454 -1.98657367  0.78017216  1.99775945  1.49233155  4.4059441\n",
            " -3.5156101   0.08352738 -0.95965609 -0.25831271 -3.80782474 -3.6590539\n",
            " -2.21924454 -1.95295396 -0.72096786  1.10987547  1.34629117 -0.88189103\n",
            " -0.91216891 -2.82371473  0.88306248 -1.82959089 -4.63940166 -0.81599956\n",
            " -0.25867325 -2.74407132  0.72457933  0.657719    2.02002181  1.47948482\n",
            "  1.52433057 -1.83785848  2.87432222  0.49144384 -0.68581805  1.26012481\n",
            " -1.39342666  0.12739245]\n",
            "Accuracy after rotation augmentation constrained by  10  is  0.672\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.73670569 0.88640289 0.9210572  0.50363293 0.52027511 0.79987041\n",
            " 0.31445069 0.83738236 0.49414165 0.11585672 0.07205915 0.84199321\n",
            " 0.05556792 0.28061144 0.33413004 0.17299445 0.31389337 0.74269257\n",
            " 0.01468284 0.82717342 0.85654802 0.37226157 0.1536129  0.60084041\n",
            " 0.11967256 0.36491936 0.95842918 0.99546447 0.77210489 0.31096151\n",
            " 0.68766505 0.70540637 0.3878417  0.64088863 0.01072764 0.20905766\n",
            " 0.5250883  0.1637513  0.16590687 0.83630429 0.989133   0.55596943\n",
            " 0.83906973 0.99032166 0.14159589 0.44824561 0.39257272 0.08004928\n",
            " 0.75533017 0.43377903]\n",
            "angle with constraint is: [ 4.73411377  7.72805773  8.42114395  0.0726585   0.40550229  5.99740822\n",
            " -3.71098617  6.74764725 -0.11716707 -7.68286551 -8.55881706  6.83986422\n",
            " -8.88864166 -4.38777128 -3.31739919 -6.5401111  -3.7221326   4.85385133\n",
            " -9.70634313  6.54346849  7.13096047 -2.55476854 -6.92774202  2.01680816\n",
            " -7.60654888 -2.70161278  9.16858362  9.90928945  5.44209783 -3.7807698\n",
            "  3.75330098  4.10812731 -2.2431661   2.81777269 -9.7854471  -5.81884683\n",
            "  0.50176606 -6.72497391 -6.68186264  6.72608581  9.78266005  1.11938856\n",
            "  6.78139462  9.80643329 -7.16808223 -1.03508773 -2.14854568 -8.39901433\n",
            "  5.10660346 -1.32441945]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.46932693 0.15067297 0.18092665 0.90710362 0.04464909 0.23285228\n",
            " 0.29205933 0.49019754 0.58644517 0.49328998 0.08411533 0.24366745\n",
            " 0.84358838 0.6375887  0.64914905 0.67020326 0.76290302 0.05810848\n",
            " 0.36660838 0.53952744 0.33845648 0.84447887 0.48257251 0.76862759\n",
            " 0.85201552 0.50479148 0.90955224 0.58712394 0.8502743  0.3405908\n",
            " 0.49881696 0.53141104 0.10497972 0.39855251 0.91733767 0.63083224\n",
            " 0.17750658 0.33885564 0.19160301 0.02482313 0.92746046 0.44820733\n",
            " 0.30753507 0.59847719 0.00731446 0.27802211 0.70303347 0.63376977\n",
            " 0.98180595 0.62035771]\n",
            "angle with constraint is: [-0.61346132 -6.98654052 -6.38146696  8.14207244 -9.10701822 -5.3429543\n",
            " -4.15881339 -0.19604915  1.72890346 -0.13420048 -8.31769331 -5.12665092\n",
            "  6.8717677   2.75177401  2.982981    3.40406511  5.25806038 -8.83783037\n",
            " -2.6678323   0.79054871 -3.23087033  6.88957747 -0.34854983  5.37255179\n",
            "  7.04031034  0.09582966  8.19104488  1.74247881  7.00548598 -3.18818409\n",
            " -0.02366083  0.62822082 -7.90040568 -2.02894987  8.34675345  2.61664481\n",
            " -6.44986835 -3.22288729 -6.16793981 -9.50353736  8.54920917 -1.03585343\n",
            " -3.84929855  1.96954383 -9.85371087 -4.43955787  4.06066931  2.67539546\n",
            "  9.63611895  2.40715419]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.47750587 0.76143256 0.90332787 0.72069595 0.96321122 0.78200517\n",
            " 0.86680144 0.11410407 0.7324135  0.4400887  0.5531038  0.65410241\n",
            " 0.96981512 0.98457808 0.28822825 0.7337535  0.74998354 0.34649286\n",
            " 0.12386977 0.04094696 0.77734313 0.48969974 0.98554017 0.46497346\n",
            " 0.97791698 0.411576   0.79368215 0.08481927 0.55546171 0.80205979\n",
            " 0.92470167 0.82258309 0.03697073 0.37270234 0.04869847 0.10928229\n",
            " 0.67530563 0.7132582  0.77372068 0.86545655 0.73943147 0.80087159\n",
            " 0.04896371 0.23453515 0.62189777 0.8581253  0.00450012 0.51462934\n",
            " 0.6772874  0.02960729]\n",
            "angle with constraint is: [-0.44988253  5.22865126  8.06655744  4.41391893  9.26422447  5.64010341\n",
            "  7.33602877 -7.71791858  4.64827006 -1.19822601  1.06207605  3.08204819\n",
            "  9.39630233  9.69156163 -4.23543509  4.67506995  4.99967076 -3.07014277\n",
            " -7.5226045  -9.18106079  5.54686255 -0.20600516  9.71080339 -0.70053088\n",
            "  9.55833958 -1.76847996  5.87364301 -8.30361455  1.1092342   6.04119574\n",
            "  8.49403334  6.45166181 -9.26058546 -2.54595317 -9.02603055 -7.81435418\n",
            "  3.50611257  4.26516393  5.47441366  7.30913096  4.78862937  6.01743184\n",
            " -9.0207258  -5.30929699  2.43795547  7.16250609 -9.9099975   0.29258684\n",
            "  3.54574799 -9.40785422]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.40135556 0.89563488 0.67161281 0.23765836 0.85278113 0.34803142\n",
            " 0.85334467 0.29894365 0.59032025 0.39694007 0.27482505 0.88655756\n",
            " 0.18759368 0.08481159 0.34192694 0.71763915 0.80743161 0.99874337\n",
            " 0.29636206 0.40794195 0.13682128 0.57487193 0.99758004 0.70088101\n",
            " 0.59521284 0.39236909 0.91529876 0.4969166  0.13436691 0.36537846\n",
            " 0.06716667 0.20197904 0.01766878 0.45327992 0.63454026 0.34329246\n",
            " 0.42038177 0.95920927 0.75196312 0.54085664 0.28454088 0.8969968\n",
            " 0.23509712 0.32534273 0.90906481 0.52954206 0.74231795 0.59074479\n",
            " 0.65343921 0.29938329]\n",
            "angle with constraint is: [-1.97288885  7.91269763  3.43225618 -5.24683273  7.05562259 -3.03937153\n",
            "  7.06689342 -4.02112698  1.80640502 -2.06119865 -4.50349898  7.73115124\n",
            " -6.24812636 -8.30376818 -3.16146123  4.35278295  6.14863212  9.9748674\n",
            " -4.07275886 -1.84116098 -7.26357444  1.49743859  9.95160075  4.0176202\n",
            "  1.90425681 -2.15261814  8.30597521 -0.06166808 -7.31266173 -2.69243074\n",
            " -8.65666662 -5.96041925 -9.64662438 -0.93440167  2.6908053  -3.13415075\n",
            " -1.59236457  9.18418546  5.03926243  0.81713274 -4.3091825   7.93993598\n",
            " -5.29805766 -3.49314539  8.18129629  0.59084111  4.84635899  1.81489588\n",
            "  3.06878418 -4.01233417]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.24137206 0.32249235 0.15544156 0.87431437 0.28324693 0.56148939\n",
            " 0.79197443 0.78382411 0.43838626 0.47625731 0.99470175 0.67459748\n",
            " 0.81463844 0.90255397 0.78758984 0.18517934 0.56217073 0.10189416\n",
            " 0.65292213 0.95534943 0.51273206 0.43297249 0.03584275 0.95977452\n",
            " 0.10300268 0.04107909 0.24606661 0.06553043 0.45511784 0.5160878\n",
            " 0.31256995 0.05096048 0.11160037 0.38450438 0.06052845 0.69818973\n",
            " 0.20702054 0.30207157 0.39412578 0.41660963 0.00166071 0.11207037\n",
            " 0.86276456 0.00123306 0.50816736 0.48950201 0.33309416 0.43132702\n",
            " 0.78058114 0.84120371]\n",
            "angle with constraint is: [-5.17255877 -3.55015306 -6.89116872  7.48628731 -4.33506133  1.22978789\n",
            "  5.8394885   5.67648219 -1.23227483 -0.47485383  9.89403498  3.49194954\n",
            "  6.29276886  8.05107938  5.75179672 -6.2964131   1.24341467 -7.96211685\n",
            "  3.05844253  9.10698856  0.25464129 -1.34055014 -9.28314497  9.19549045\n",
            " -7.93994632 -9.17841813 -5.07866777 -8.68939132 -0.89764315  0.32175603\n",
            " -3.74860102 -8.98079035 -7.76799264 -2.30991244 -8.78943098  3.96379451\n",
            " -5.85958917 -3.95856862 -2.11748446 -1.66780734 -9.96678585 -7.75859258\n",
            "  7.25529127 -9.97533875  0.16334712 -0.20995987 -3.33811678 -1.3734596\n",
            "  5.61162275  6.82407412]\n",
            "Accuracy after rotation augmentation constrained by  20  is  0.6708\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.26034854 0.32249044 0.24248285 0.4798634  0.68325836 0.22825288\n",
            " 0.33073574 0.93038462 0.04856929 0.46076961 0.71155804 0.15045401\n",
            " 0.04737402 0.13820537 0.91882319 0.00925978 0.18832197 0.03128351\n",
            " 0.11062947 0.62014929 0.24163891 0.56921205 0.59019538 0.84943533\n",
            " 0.00474139 0.85336946 0.61911909 0.16274349 0.77293739 0.85549154\n",
            " 0.25426348 0.91879368 0.45445743 0.60342445 0.98464953 0.36153608\n",
            " 0.81315935 0.31839912 0.79921352 0.60073392 0.2163557  0.41402609\n",
            " 0.31763563 0.07810839 0.02983477 0.34647866 0.01903415 0.16548776\n",
            " 0.72518332 0.70809124]\n",
            "angle with constraint is: [ -7.1895437   -5.32528677  -7.72551441  -0.60409798   5.49775073\n",
            "  -8.15241374  -5.07792774  12.91153853 -13.5429213   -1.17691184\n",
            "   6.34674125 -10.48637965 -13.5787794  -10.85383885  12.56469581\n",
            " -14.72220655  -9.35034076 -14.06149461 -11.68111597   3.6044786\n",
            "  -7.75083274   2.07636151   2.70586148  10.48305999 -14.85775821\n",
            "  10.60108378   3.57357259 -10.11769542   8.18812164  10.66474625\n",
            "  -7.37209549  12.56381043  -1.36627709   3.10273363  14.53948601\n",
            "  -4.15391764   9.39478037  -5.44802647   8.97640567   3.02201757\n",
            "  -8.50932914  -2.57921719  -5.47093097 -12.65674821 -14.1049569\n",
            "  -4.60564034 -14.42897557 -10.03536727   6.75549963   6.24273707]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.73858039 0.31715317 0.89001946 0.59383055 0.12602634 0.14371866\n",
            " 0.69309543 0.17294394 0.5072927  0.99177448 0.0040076  0.0165798\n",
            " 0.99308458 0.58463883 0.12690998 0.8973022  0.8803803  0.53620041\n",
            " 0.62179203 0.27318719 0.0505197  0.59456761 0.29475519 0.66362105\n",
            " 0.83624518 0.01816054 0.59571377 0.23128054 0.8738885  0.25366451\n",
            " 0.61077013 0.55354084 0.3961665  0.67762076 0.72576963 0.5669378\n",
            " 0.75842224 0.98341708 0.41927239 0.51458305 0.01246988 0.79602856\n",
            " 0.52023007 0.40796143 0.09407196 0.88965642 0.39500724 0.6825816\n",
            " 0.14937416 0.96154963]\n",
            "angle with constraint is: [  7.15741159  -5.48540487  11.70058371   2.81491644 -11.21920975\n",
            " -10.68844016   5.79286281  -9.81168191   0.21878105  14.75323428\n",
            " -14.87977186 -14.50260594  14.79253746   2.53916502 -11.19270073\n",
            "  11.91906599  11.411409     1.08601239   3.6537609   -6.80438425\n",
            " -13.48440911   2.83702827  -6.1573442    4.9086314   10.08735531\n",
            " -14.45518368   2.87141324  -8.06158381  11.21665494  -7.3900648\n",
            "   3.323104     1.6062252   -3.115005     5.32862291   6.77308904\n",
            "   2.00813386   7.75266712  14.50251228  -2.42182836   0.43749156\n",
            " -14.62590366   8.88085669   0.60690201  -2.76115709 -12.17784111\n",
            "  11.68969265  -3.1497828    5.47744795 -10.51877531  13.84648889]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.17844176 0.19954199 0.85898286 0.91258274 0.21214741 0.46980089\n",
            " 0.7334949  0.87836573 0.3790527  0.51701949 0.74171641 0.73113598\n",
            " 0.78296013 0.56993382 0.1046151  0.90397329 0.86557254 0.79809744\n",
            " 0.09986248 0.20439737 0.74360037 0.02314009 0.97922463 0.37714468\n",
            " 0.71931253 0.88757109 0.39462989 0.31913249 0.60874113 0.58098937\n",
            " 0.40913998 0.60177476 0.93538137 0.46764026 0.19674218 0.37720598\n",
            " 0.39410698 0.13110869 0.1628715  0.68456207 0.33947775 0.95489839\n",
            " 0.24359675 0.09892451 0.75350456 0.88103605 0.27811336 0.2020881\n",
            " 0.18576613 0.5220123 ]\n",
            "angle with constraint is: [ -9.64674715  -9.01374021  10.76948582  12.37748232  -8.63557777\n",
            "  -0.90597335   7.00484714  11.35097201  -3.62841915   0.51058461\n",
            "   7.25149232   6.9340793    8.4888039    2.09801456 -11.8615471\n",
            "  12.11919873  10.96717618   8.94292307 -12.00412569  -8.86807899\n",
            "   7.30801095 -14.30579716  14.37673901  -3.68565959   6.57937601\n",
            "  11.62713265  -3.16110336  -5.42602534   3.26223387   2.42968121\n",
            "  -2.72580067   3.05324268  13.06144109  -0.9707923   -9.09773458\n",
            "  -3.68382051  -3.17679052 -11.06673921 -10.11385498   5.5368622\n",
            "  -4.81566737  13.64695176  -7.69209764 -12.03226485   7.60513689\n",
            "  11.43108156  -6.65659929  -8.93735695  -9.42701595   0.66036904]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.46841664 0.25943984 0.04517312 0.48149134 0.95933235 0.6525192\n",
            " 0.49550656 0.11069207 0.25243629 0.29477395 0.76480355 0.8767108\n",
            " 0.90164185 0.98456861 0.98236668 0.95299913 0.07182214 0.13779354\n",
            " 0.30446653 0.55289825 0.09697789 0.84579696 0.61675618 0.5423644\n",
            " 0.16531132 0.25325547 0.16031202 0.85193316 0.58422786 0.73512943\n",
            " 0.29603286 0.37125264 0.40485496 0.76001497 0.77235299 0.20681353\n",
            " 0.94153886 0.12065534 0.89612208 0.10039565 0.26453981 0.84647854\n",
            " 0.17949207 0.41356793 0.4498593  0.24525897 0.7102452  0.85133465\n",
            " 0.87457514 0.33932215]\n",
            "angle with constraint is: [ -0.94750076  -7.2168048  -13.64480649  -0.55525979  13.77997043\n",
            "   4.57557602  -0.1348033  -11.67923778  -7.42691128  -6.15678151\n",
            "   7.94410658  11.30132392  12.04925561  14.53705825  14.47100049\n",
            "  13.58997398 -12.84533578 -10.86619392  -5.86600421   1.58694762\n",
            " -12.09066334  10.37390873   3.50268537   1.27093207 -10.0406603\n",
            "  -7.40233577 -10.19063925  10.55799473   2.5268358    7.05388283\n",
            "  -6.11901411  -3.86242089  -2.85435107   7.80044915   8.17058963\n",
            "  -8.79559417  13.2461657  -11.38033988  11.88366247 -11.98813063\n",
            "  -7.06380567  10.39435608  -9.61523783  -2.59296206  -1.504221\n",
            "  -7.64223081   6.30735604  10.54003948  11.2372542   -4.82033563]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.53085039 0.2484064  0.2447965  0.16125406 0.94001901 0.88798883\n",
            " 0.77735871 0.51766152 0.49060792 0.52974567 0.53656801 0.43456308\n",
            " 0.13175483 0.12566385 0.95224972 0.48203076 0.95320552 0.16357688\n",
            " 0.55441655 0.20771725 0.25318461 0.03005458 0.11895331 0.91684841\n",
            " 0.32153299 0.60812921 0.46501413 0.40045125 0.53188609 0.18723894\n",
            " 0.9886039  0.81830787 0.74161463 0.4687593  0.15287712 0.92066586\n",
            " 0.34138707 0.05010669 0.34240368 0.79445703 0.62270874 0.75083858\n",
            " 0.79363538 0.21195461 0.92378332 0.4379896  0.63901365 0.00230877\n",
            " 0.9933685  0.28089639]\n",
            "angle with constraint is: [  0.92551183  -7.54780788  -7.65610495 -10.16237811  13.20057032\n",
            "  11.63966486   8.3207614    0.52984564  -0.28176253   0.89236999\n",
            "   1.09704041  -1.96310746 -11.04735501 -11.23008441  13.56749148\n",
            "  -0.53907721  13.59616575 -10.09269367   1.63249656  -8.7684824\n",
            "  -7.40446182 -14.09836264 -11.43140068  12.50545223  -5.35401026\n",
            "   3.24387636  -1.04957604  -2.98646264   0.95658274  -9.38283166\n",
            "  14.65811696   9.54923606   7.24843898  -0.93722109 -10.41368634\n",
            "  12.61997575  -4.75838798 -13.49679931  -4.7278897    8.83371091\n",
            "   3.68126235   7.52515754   8.80906152  -8.64136167  12.71349948\n",
            "  -1.86031191   4.17040948 -14.93073682  14.80105495  -6.57310842]\n",
            "Accuracy after rotation augmentation constrained by  30  is  0.6852\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.06206311 0.45826204 0.12903006 0.15232671 0.63228281 0.39292739\n",
            " 0.92185245 0.31915649 0.72618013 0.46109943 0.6599513  0.59969536\n",
            " 0.47278397 0.95006109 0.34271364 0.1772355  0.67809406 0.84600756\n",
            " 0.04025374 0.44963269 0.89248769 0.74961862 0.99180772 0.53141381\n",
            " 0.65999572 0.30248028 0.9475356  0.36633549 0.73851071 0.4031402\n",
            " 0.56181893 0.71978254 0.50893415 0.90708633 0.41930099 0.6470718\n",
            " 0.34236243 0.40815667 0.44005011 0.12580702 0.09172368 0.66748992\n",
            " 0.65551774 0.66290971 0.01976645 0.32654684 0.19780985 0.77815142\n",
            " 0.8606304  0.24668038]\n",
            "angle with constraint is: [-17.51747574  -1.66951834 -14.83879771 -13.90693159   5.29131252\n",
            "  -4.28290434  16.87409801  -7.23374022   9.04720514  -1.55602279\n",
            "   6.39805205   3.98781427  -1.08864109  18.00244378  -6.29145429\n",
            " -12.91057995   7.12376244  13.84030245 -18.3898504   -2.01469246\n",
            "  15.69950759   9.98474476  19.67230866   1.25655246   6.39982875\n",
            "  -7.90078875  17.90142399  -5.34658046   9.54042851  -3.87439184\n",
            "   2.4727572    8.7913014    0.35736607  16.28345308  -3.22796048\n",
            "   5.88287193  -6.30550267  -3.67373327  -2.39799567 -14.96771927\n",
            " -16.33105284   6.69959693   6.22070953   6.51638857 -19.20934219\n",
            "  -6.93812629 -12.08760611  11.1260569   14.42521606 -10.13278491]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.66784916 0.1293628  0.27515253 0.1386545  0.28352077 0.67473655\n",
            " 0.91032691 0.20252241 0.54207903 0.7013911  0.95292247 0.61006048\n",
            " 0.25510693 0.62547816 0.32049116 0.97612839 0.39967614 0.68542772\n",
            " 0.02322758 0.40006145 0.87510761 0.61022744 0.44394289 0.9359375\n",
            " 0.85398438 0.32221567 0.5445484  0.48982426 0.6322025  0.33411127\n",
            " 0.244205   0.91267184 0.9238745  0.84332289 0.97820443 0.74499153\n",
            " 0.76466723 0.76259009 0.48060763 0.45645219 0.24022628 0.64481919\n",
            " 0.28932014 0.25780841 0.58596137 0.40998239 0.21885661 0.81605602\n",
            " 0.86056992 0.16769952]\n",
            "angle with constraint is: [  6.71396635 -14.82548786  -8.99389898 -14.45382015  -8.65916934\n",
            "   6.98946189  16.41307641 -11.89910368   1.6831614    8.05564405\n",
            "  18.11689874   4.40241932  -9.79572276   5.01912628  -7.18035353\n",
            "  19.04513563  -4.01295459   7.41710883 -19.07089671  -3.99754215\n",
            "  15.00430453   4.40909778  -2.24228454  17.43749998  14.15937505\n",
            "  -7.11137305   1.78193608  -0.40702976   5.2881      -6.63554932\n",
            " -10.23179997  16.50687341  16.95497998  13.73291543  19.12817723\n",
            "   9.79966103  10.58668914  10.50360368  -0.77569476  -1.74191222\n",
            " -10.39094895   5.79276777  -8.42719456  -9.68766361   3.43845496\n",
            "  -3.60070447 -11.24573565  12.64224067  14.42279677 -13.29201921]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.00750703 0.33612997 0.79751084 0.83917387 0.13832907 0.7743359\n",
            " 0.11570417 0.39006259 0.99860952 0.19259082 0.15396358 0.42280033\n",
            " 0.61966853 0.93290078 0.98082115 0.06964064 0.13799752 0.79073797\n",
            " 0.27063215 0.88484906 0.66345326 0.1118448  0.83252928 0.17635638\n",
            " 0.42349539 0.55218493 0.50076615 0.68616292 0.65268673 0.99204201\n",
            " 0.99942191 0.5164594  0.09519909 0.7278433  0.97666408 0.31832639\n",
            " 0.46120889 0.42477692 0.05141915 0.67226443 0.3533377  0.78746387\n",
            " 0.83212008 0.82123079 0.416666   0.32154568 0.74882078 0.80138943\n",
            " 0.49125745 0.89303102]\n",
            "angle with constraint is: [-19.69971869  -6.55480126  11.90043343  13.56695465 -14.4668372\n",
            "  10.97343605 -15.37183331  -4.39749639  19.9443806  -12.29636707\n",
            " -13.8414567   -3.0879867    4.78674104  17.3160313   19.23284582\n",
            " -17.21437454 -14.48009931  11.62951896  -9.17471391  15.39396238\n",
            "   6.53813029 -15.5262079   13.3011714  -12.9457448   -3.06018421\n",
            "   2.08739714   0.03064596   7.44651662   6.10746939  19.68168022\n",
            "  19.97687637   0.65837611 -16.19203652   9.11373196  19.06656313\n",
            "  -7.26694449  -1.55164457  -3.00892301 -17.94323382   6.89057725\n",
            "  -5.8664922   11.49855499  13.28480301  12.84923146  -3.33336008\n",
            "  -7.13817271   9.95283119  12.05557735  -0.34970214  15.72124078]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [1.43908696e-01 8.79068780e-01 9.60952330e-02 1.53525522e-01\n",
            " 5.33840013e-01 6.75184802e-02 5.28071014e-02 5.18744166e-04\n",
            " 4.36316660e-01 7.74623881e-01 3.48383503e-02 6.52257606e-01\n",
            " 8.23127317e-01 1.68000849e-01 1.46863735e-01 8.60800974e-01\n",
            " 8.23124610e-01 5.38941766e-01 8.19199879e-01 9.27560774e-02\n",
            " 3.94481518e-01 7.37797556e-01 2.57451959e-01 7.52328914e-01\n",
            " 5.04178604e-01 7.50265988e-01 4.64679638e-01 3.67904340e-01\n",
            " 2.19189216e-01 2.08379341e-01 7.51490811e-01 1.18910348e-01\n",
            " 8.59823501e-02 1.76291365e-01 1.75346511e-01 8.23107623e-01\n",
            " 3.98506623e-01 9.90388457e-01 1.37211258e-01 6.53539826e-01\n",
            " 4.48729507e-01 3.92918177e-01 8.75084199e-01 9.75595646e-01\n",
            " 8.72858959e-01 1.92300679e-01 2.20797921e-01 6.56073879e-01\n",
            " 2.89083582e-01 7.34725131e-01]\n",
            "angle with constraint is: [-14.24365216  15.16275119 -16.15619068 -13.85897912   1.35360053\n",
            " -17.29926079 -17.88771594 -19.97925023  -2.54733361  10.98495525\n",
            " -18.60646599   6.09030426  12.9250927  -13.27996606 -14.12545061\n",
            "  14.43203894  12.9249844    1.55767065  12.76799517 -16.2897569\n",
            "  -4.22073928   9.51190224  -9.70192163  10.09315658   0.16714418\n",
            "  10.01063952  -1.41281449  -5.28382639 -11.23243134 -11.66482634\n",
            "  10.05963242 -15.24358608 -16.560706   -12.9483454  -12.98613954\n",
            "  12.92430491  -4.05973506  19.61553829 -14.51154969   6.14159303\n",
            "  -2.05081971  -4.28327293  15.00336796  19.02382583  14.91435835\n",
            " -12.30797284 -11.16808315   6.24295516  -8.43665673   9.38900523]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.56642065 0.55090877 0.82854692 0.71053277 0.02657776 0.04945914\n",
            " 0.60155845 0.48619208 0.26018466 0.41865609 0.75791032 0.82661148\n",
            " 0.56122307 0.38536948 0.27069676 0.52191847 0.31474962 0.56409514\n",
            " 0.67665238 0.06607921 0.00106747 0.21227714 0.8945115  0.64629076\n",
            " 0.17196035 0.89026396 0.46995594 0.47609176 0.93554194 0.05948669\n",
            " 0.21517827 0.58577133 0.19548651 0.67952701 0.21409229 0.09915697\n",
            " 0.20698516 0.42273403 0.1761383  0.13469202 0.86028189 0.3203972\n",
            " 0.36075869 0.05527041 0.35736962 0.26480718 0.61048507 0.2053903\n",
            " 0.88836545 0.92966662]\n",
            "angle with constraint is: [  2.65682603   2.03635074  13.14187699   8.42131087 -18.9368894\n",
            " -18.02163451   4.0623381   -0.55231698  -9.59261379  -3.25375651\n",
            "  10.31641275  13.06445939   2.44892272  -4.58522077  -9.17212954\n",
            "   0.87673883  -7.41001526   2.56380548   7.0660953  -17.35683156\n",
            " -19.9573014  -11.50891445  15.78045991   5.85163053 -13.12158592\n",
            "  15.61055847  -1.20176239  -0.95632973  17.42167775 -17.62053231\n",
            " -11.39286901   3.43085301 -12.18053962   7.18108037 -11.43630843\n",
            " -16.03372118 -11.72059361  -3.09063863 -12.95446803 -14.612319\n",
            "  14.41127545  -7.18411186  -5.56965254 -17.78918362  -5.70521506\n",
            "  -9.40771291   4.41940269 -11.78438809  15.53461812  17.18666484]\n",
            "Accuracy after rotation augmentation constrained by  40  is  0.6808\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.09983106 0.10740296 0.11532644 0.61956926 0.12196896 0.85238232\n",
            " 0.75427151 0.81980886 0.52606053 0.99667721 0.0441751  0.40126496\n",
            " 0.32393667 0.9486455  0.57686096 0.80619097 0.17034119 0.97863414\n",
            " 0.49863955 0.49367208 0.97088473 0.38073236 0.39752502 0.5847527\n",
            " 0.12805261 0.32108563 0.19320832 0.10328348 0.86614461 0.58933117\n",
            " 0.35867279 0.40866938 0.431519   0.63314918 0.92676939 0.93277962\n",
            " 0.39149198 0.32926246 0.4510465  0.56793423 0.46181791 0.65875341\n",
            " 0.55439902 0.32071555 0.34573081 0.37859279 0.09463977 0.16606263\n",
            " 0.7231138  0.38909671]\n",
            "angle with constraint is: [-20.00844714 -19.62985219 -19.23367809   5.97846282 -18.90155179\n",
            "  17.6191159   12.71357575  15.99044291   1.30302634  24.83386058\n",
            " -22.79124485  -4.93675179  -8.80316655  22.43227494   3.84304792\n",
            "  15.30954827 -16.48294032  23.9317072   -0.06802266  -0.31639619\n",
            "  23.54423643  -5.96338194  -5.12374877   4.23763514 -18.59736947\n",
            "  -8.94571868 -15.33958388 -19.83582604  18.30723032   4.46655831\n",
            "  -7.06636042  -4.56653113  -3.42404982   6.65745911  21.33846957\n",
            "  21.63898098  -5.42540102  -8.53687702  -2.44767499   3.39671152\n",
            "  -1.90910443   7.93767049   2.71995117  -8.96422267  -7.71345971\n",
            "  -6.07036062 -20.26801159 -16.69686838  11.15569011  -5.54516442]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.21453897 0.56267241 0.75553954 0.25742236 0.82678988 0.92750486\n",
            " 0.59739453 0.66894766 0.05257599 0.94551989 0.39248473 0.9239004\n",
            " 0.57897752 0.0046188  0.038542   0.68041881 0.56266801 0.02653244\n",
            " 0.74345996 0.85214534 0.49641748 0.46575744 0.00547311 0.78664971\n",
            " 0.33071607 0.87876362 0.37306059 0.56507791 0.27034179 0.16058831\n",
            " 0.7749079  0.49659048 0.53648792 0.96541595 0.96357991 0.85564212\n",
            " 0.18760929 0.59427269 0.87814742 0.37446394 0.10110617 0.8109629\n",
            " 0.48176355 0.56439268 0.9821443  0.60721143 0.42497939 0.36998437\n",
            " 0.41761929 0.11839908]\n",
            "angle with constraint is: [-14.27305137   3.13362053  12.77697712 -12.12888188  16.3394941\n",
            "  21.37524298   4.86972645   8.44738295 -22.37120048  22.27599455\n",
            "  -5.37576329  21.19502003   3.94887624 -24.76906006 -23.07290003\n",
            "   9.02094028   3.13340069 -23.67337814  12.17299806  17.60726685\n",
            "  -0.17912588  -1.71212798 -24.72634456  14.33248562  -8.46419666\n",
            "  18.93818099  -6.34697062   3.25389553 -11.48291059 -16.97058437\n",
            "  13.74539517  -0.17047606   1.82439619  23.27079767  23.17899561\n",
            "  17.78210609 -15.61953536   4.71363472  18.90737123  -6.27680318\n",
            " -19.9446917   15.54814511  -0.91182243   3.21963402  24.10721482\n",
            "   5.36057152  -3.75103072  -6.50078161  -4.11903537 -19.08004584]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.79475056 0.83771686 0.28439713 0.30379662 0.87433426 0.02397036\n",
            " 0.52045744 0.46367571 0.69012677 0.1633917  0.66833074 0.37673401\n",
            " 0.61871478 0.04009501 0.4617272  0.52225089 0.2143264  0.13962365\n",
            " 0.1884743  0.34414108 0.09681015 0.9473198  0.40499406 0.8089696\n",
            " 0.97441712 0.08073528 0.65166947 0.87140909 0.63423443 0.42902612\n",
            " 0.43107636 0.03589889 0.53460075 0.993543   0.95870753 0.04039923\n",
            " 0.705797   0.30176487 0.90298915 0.04511475 0.9358741  0.83747574\n",
            " 0.11750438 0.05982434 0.83470766 0.15575767 0.53489815 0.91574486\n",
            " 0.56744811 0.3972333 ]\n",
            "angle with constraint is: [ 14.73752792  16.88584291 -10.78014327  -9.81016908  18.71671282\n",
            " -23.80148222   1.02287219  -1.81621427   9.50633848 -16.83041499\n",
            "   8.41653704  -6.16329961   5.93573891 -22.99524951  -1.91364014\n",
            "   1.1125447  -14.28367986 -18.01881729 -15.57628485  -7.7929461\n",
            " -20.15949253  22.36598979  -4.75029693  15.44848013  23.72085616\n",
            " -20.963236     7.5834734   18.57045434   6.7117217   -3.54869393\n",
            "  -3.44618212 -23.2050557    1.73003756  24.67714984  22.9353763\n",
            " -22.98003856  10.28985018  -9.91175667  20.14945767 -22.74426271\n",
            "  21.7937051   16.87378719 -19.12478089 -22.00878313  16.73538322\n",
            " -17.21211645   1.74490765  20.78724289   3.37240568  -5.13833493]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.44501605 0.06311717 0.96875232 0.31065419 0.18936349 0.2860473\n",
            " 0.96437319 0.09428605 0.86730304 0.70784389 0.83001918 0.97589754\n",
            " 0.84215466 0.94338235 0.12693526 0.79578857 0.54567569 0.5434956\n",
            " 0.89892158 0.97180382 0.70483832 0.48293839 0.45640386 0.38634146\n",
            " 0.36159903 0.39150684 0.08975324 0.07743609 0.77986984 0.44607289\n",
            " 0.93068449 0.61222101 0.03517598 0.15752334 0.20581979 0.97573602\n",
            " 0.21870423 0.51575568 0.93617693 0.97719273 0.23636044 0.04911764\n",
            " 0.15360183 0.06332848 0.19512424 0.56856971 0.9124488  0.53721888\n",
            " 0.62598956 0.20186725]\n",
            "angle with constraint is: [ -2.74919726 -21.84414146  23.43761607  -9.46729075 -15.53182542\n",
            " -10.69763491  23.21865974 -20.28569733  18.36515203  10.39219472\n",
            "  16.50095896  23.79487702  17.10773278  22.16911756 -18.65323716\n",
            "  14.78942826   2.2837847    2.17477975  19.94607882  23.59019093\n",
            "  10.24191615  -0.85308053  -2.1798071   -5.68292714  -6.92004852\n",
            "  -5.42465792 -20.51233824 -21.12819574  13.99349219  -2.69635535\n",
            "  21.53422443   5.61105043 -23.24120089 -17.12383293 -14.70901035\n",
            "  23.78680122 -14.06478869   0.78778381  21.80884658  23.8596365\n",
            " -13.18197823 -22.54411781 -17.31990837 -21.83357606 -15.24378795\n",
            "   3.42848574  20.62243981   1.86094418   6.29947783 -14.90663742]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.80058362 0.61778891 0.86413683 0.94884311 0.34790078 0.09057906\n",
            " 0.22592896 0.43784157 0.94569365 0.37830982 0.77077321 0.54047669\n",
            " 0.18280877 0.32073412 0.65319189 0.67285339 0.43475975 0.2286456\n",
            " 0.72190678 0.16887304 0.93666969 0.18790358 0.10731236 0.4976477\n",
            " 0.5195496  0.50819291 0.43666749 0.99481489 0.48628061 0.47794658\n",
            " 0.42176606 0.06682575 0.59295663 0.22796777 0.63760898 0.05083346\n",
            " 0.9813871  0.46900508 0.89675537 0.47556117 0.06033082 0.82221355\n",
            " 0.64818373 0.78175705 0.42816986 0.63793674 0.856229   0.63106544\n",
            " 0.34767363 0.66252959]\n",
            "angle with constraint is: [ 15.02918103   5.88944543  18.20684146  22.44215535  -7.60496121\n",
            " -20.47104707 -13.7035521   -3.10792172  22.28468259  -6.08450888\n",
            "  13.53866068   2.02383461 -15.85956148  -8.96329405   7.65959425\n",
            "   8.64266973  -3.26201266 -13.56771987  11.09533898 -16.55634792\n",
            "  21.83348459 -15.60482105 -19.63438186  -0.11761488   0.97747991\n",
            "   0.40964574  -3.16662553  24.74074464  -0.68596937  -1.10267096\n",
            "  -3.91169686 -21.65871226   4.64783156 -13.60161139   6.88044901\n",
            " -22.4583269   24.06935506  -1.54974605  19.83776858  -1.22194134\n",
            " -21.98345907  16.1106773    7.40918648  14.08785238  -3.59150682\n",
            "   6.89683721  17.81145019   6.55327214  -7.61631826   8.12647972]\n",
            "Accuracy after rotation augmentation constrained by  50  is  0.6779\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.67185419 0.96058696 0.37091232 0.42508177 0.81212296 0.50576231\n",
            " 0.73657309 0.45970946 0.21549514 0.74520384 0.13115517 0.19858366\n",
            " 0.62682498 0.7472698  0.89468789 0.2725865  0.11072426 0.95604666\n",
            " 0.15442309 0.19766698 0.29132945 0.52939135 0.88282557 0.76054602\n",
            " 0.70815755 0.17518057 0.35768263 0.47840493 0.14742595 0.2734297\n",
            " 0.3083332  0.1272762  0.55023969 0.7050282  0.06993163 0.4811231\n",
            " 0.7822112  0.74799013 0.8150731  0.44824063 0.82055708 0.24075307\n",
            " 0.36081056 0.17635558 0.46748454 0.39206002 0.24092712 0.75178071\n",
            " 0.31384677 0.27280433]\n",
            "angle with constraint is: [ 10.31125132  27.63521775  -7.74526071  -4.49509395  18.72737767\n",
            "   0.34573888  14.19438559  -2.41743213 -17.07029149  14.71223057\n",
            " -22.13068985 -18.08498019   7.60949897  14.83618801  23.68127321\n",
            " -13.64481016 -23.35654448  27.36279957 -20.73461472 -18.13998106\n",
            " -12.52023279   1.76348124  22.96953396  15.63276136  12.48945323\n",
            " -19.48916593  -8.53904211  -1.29570443 -21.15444279 -13.59421787\n",
            " -11.50000776 -22.3634281    3.01438151  12.30169179 -25.80410211\n",
            "  -1.13261389  16.93267172  14.87940774  18.90438577  -3.10556201\n",
            "  19.23342496 -15.55481573  -8.35136631 -19.41866541  -1.95092763\n",
            "  -6.47639866 -15.54437302  15.10684255 -11.16919367 -13.63174019]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.13089033 0.4388217  0.30296463 0.36927284 0.23599726 0.76315549\n",
            " 0.93000335 0.49793185 0.64299842 0.86717228 0.99882964 0.61351648\n",
            " 0.88977947 0.90860825 0.79145149 0.46093139 0.081154   0.01580556\n",
            " 0.47720063 0.49431453 0.40736239 0.56816735 0.5521627  0.83661304\n",
            " 0.1086571  0.22517767 0.79300498 0.84783687 0.99482421 0.87291202\n",
            " 0.88989901 0.31434794 0.40166163 0.2680851  0.6062994  0.93880535\n",
            " 0.34464089 0.55700178 0.29629963 0.16979997 0.19518553 0.05794534\n",
            " 0.94929352 0.40287961 0.83412089 0.68132947 0.19192116 0.17546225\n",
            " 0.8119882  0.26471171]\n",
            "angle with constraint is: [-22.14658015  -3.67069789 -11.82212227  -7.84362952 -15.84016424\n",
            "  15.78932939  25.80020084  -0.12408883   8.5799054   22.0303366\n",
            "  29.92977824   6.81098908  23.38676799  24.51649479  17.48708927\n",
            "  -2.34411654 -25.13075997 -29.05166667  -1.367962    -0.34112799\n",
            "  -5.55825673   4.09004108   3.1297622   20.19678246 -23.4805738\n",
            " -16.48934003  17.58029874  20.87021203  29.68945282  22.37472098\n",
            "  23.39394042 -11.13912344  -5.90030196 -13.91489371   6.37796389\n",
            "  26.32832125  -9.32154681   3.42010691 -12.22202217 -19.81200157\n",
            " -18.28886821 -26.52327933  26.95761138  -5.82722349  20.04725352\n",
            "  10.87976836 -18.48473032 -19.47226521  18.71929209 -14.11729736]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.91328518 0.1400554  0.67770861 0.81447388 0.23525639 0.27620736\n",
            " 0.96451889 0.26459761 0.42995705 0.43251559 0.25303475 0.77579109\n",
            " 0.77184934 0.37831801 0.83512579 0.21307493 0.00430578 0.57116736\n",
            " 0.99407777 0.03864431 0.20799807 0.51891464 0.80994074 0.08904201\n",
            " 0.39523465 0.7470758  0.37154321 0.18883642 0.19549583 0.41004879\n",
            " 0.47941707 0.86181442 0.64225242 0.69040195 0.98298717 0.41184344\n",
            " 0.4033544  0.92334697 0.24587559 0.72981525 0.75457314 0.097788\n",
            " 0.46823141 0.20056247 0.18537527 0.46382811 0.28942971 0.79606045\n",
            " 0.90760489 0.80272061]\n",
            "angle with constraint is: [ 24.79711102 -21.59667572  10.66251687  18.86843256 -15.8846167\n",
            " -13.42755819  27.87113317 -14.12414368  -4.20257701  -4.04906441\n",
            " -14.817915    16.54746519  16.31096059  -7.30091915  20.10754746\n",
            " -17.21550394 -29.74165346   4.27004172  29.64466635 -27.68134141\n",
            " -17.52011558   1.13487855  18.59644419 -24.6574797   -6.28592077\n",
            "  14.82454804  -7.7074075  -18.66981477 -18.27025036  -5.39707279\n",
            "  -1.23497551  21.70886507   8.53514511  11.42411727  28.97923029\n",
            "  -5.28939358  -5.79873581  25.40081806 -15.24746478  13.78891478\n",
            "  15.27438829 -24.13272015  -1.90611566 -17.96625172 -18.87748363\n",
            "  -2.17031337 -12.63421755  17.76362686  24.45629348  18.16323632]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.26603087 0.27413439 0.25472835 0.1350381  0.94379837 0.4020478\n",
            " 0.22468947 0.84667105 0.3991953  0.03511681 0.16194157 0.63925034\n",
            " 0.78290527 0.21093439 0.91739559 0.85099712 0.84838075 0.22766115\n",
            " 0.05053599 0.86742354 0.30580023 0.61891638 0.75996688 0.12829326\n",
            " 0.78083835 0.93851146 0.72837497 0.44122275 0.87667814 0.52745763\n",
            " 0.74477093 0.82050594 0.74935286 0.28785488 0.1178439  0.23833853\n",
            " 0.50287494 0.50527692 0.58264435 0.39510184 0.78689138 0.91315375\n",
            " 0.24672505 0.73652322 0.67797185 0.5230905  0.18853023 0.79523226\n",
            " 0.96062629 0.72183633]\n",
            "angle with constraint is: [-14.03814764 -13.55193638 -14.71629899 -21.89771424  26.62790209\n",
            "  -5.87713211 -16.51863153  20.80026303  -6.04828184 -27.89299133\n",
            " -20.28350579   8.35502042  16.97431645 -17.34393669  25.04373536\n",
            "  21.0598272   20.90284524 -16.34033119 -26.96784068  22.04541237\n",
            " -11.65198628   7.13498287  15.59801302 -22.30240449  16.85030127\n",
            "  26.31068754  13.70249794  -3.52663509  22.60068833   1.6474578\n",
            "  14.68625567  19.23035663  14.9611717  -12.72870744 -22.92936595\n",
            " -15.69968824   0.17249622   0.31661534   4.95866096  -6.29388975\n",
            "  17.21348289  24.78922502 -15.19649679  14.19139306  10.67831115\n",
            "   1.38542996 -18.68818597  17.71393551  27.63757714  13.31017993]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.91786    0.98418526 0.35205067 0.63822355 0.47393274 0.98662875\n",
            " 0.09380782 0.41237491 0.87553904 0.28405131 0.78779285 0.38278316\n",
            " 0.97997854 0.88352984 0.30132036 0.77019311 0.33685654 0.60636284\n",
            " 0.65848077 0.82959541 0.02445084 0.22015789 0.71191781 0.56352324\n",
            " 0.49560508 0.05520996 0.34827588 0.86510023 0.03089284 0.39643624\n",
            " 0.94514829 0.05088073 0.74970575 0.25761262 0.86660883 0.74606798\n",
            " 0.88570517 0.21592479 0.53129275 0.77926994 0.24569974 0.23074631\n",
            " 0.02718213 0.95851529 0.71001688 0.63556035 0.74399472 0.53161292\n",
            " 0.47613382 0.44953224]\n",
            "angle with constraint is: [ 25.0715998   29.0511159   -8.87695955   8.29341318  -1.56403574\n",
            "  29.19772481 -24.37153056  -5.25750514  22.53234267 -12.95692137\n",
            "  17.26757104  -7.03301023  28.79871239  23.01179019 -11.92077854\n",
            "  16.21158681  -9.78860737   6.38177041   9.5088461   19.77572455\n",
            " -28.53294955 -16.79052653  12.71506864   3.81139422  -0.26369531\n",
            " -26.6874026   -9.10344699  21.90601391 -28.14642978  -6.21382588\n",
            "  26.70889726 -26.94715593  14.98234506 -14.54324303  21.99652993\n",
            "  14.76407874  23.14231025 -17.04451257   1.87756529  16.75619661\n",
            " -15.25801542 -16.15522168 -28.36907199  27.51091764  12.6010128\n",
            "   8.13362124  14.63968336   1.89677512  -1.43197073  -3.02806553]\n",
            "Accuracy after rotation augmentation constrained by  60  is  0.6728\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.64043921 0.20153498 0.8880587  0.81175237 0.34956361 0.5786093\n",
            " 0.1410273  0.97234625 0.90289269 0.92189751 0.33216305 0.16913471\n",
            " 0.21177216 0.09000139 0.12011063 0.07130453 0.98982307 0.07405768\n",
            " 0.13254208 0.8272546  0.58024022 0.7744172  0.63091201 0.88217507\n",
            " 0.63846753 0.52212509 0.71274969 0.44739275 0.34606382 0.46765245\n",
            " 0.16971411 0.79657173 0.26070378 0.15353155 0.24635224 0.84207015\n",
            " 0.3302013  0.68604987 0.85640963 0.07774467 0.76502441 0.30766547\n",
            " 0.21662236 0.68890145 0.90979662 0.29501921 0.56883381 0.28983411\n",
            " 0.55428559 0.62186203]\n",
            "angle with constraint is: [  9.83074485 -20.89255172  27.16410865  21.82266599 -10.5305472\n",
            "   5.50265078 -25.128089    33.06423736  28.20248813  29.53282545\n",
            " -11.74858666 -23.16057048 -20.17594856 -28.69990263 -26.59225589\n",
            " -30.00868261  34.28761484 -29.81596216 -25.72205433  22.90782169\n",
            "   5.61681517  19.20920432   9.16384054  26.75225492   9.69272694\n",
            "   1.5487565   14.89247856  -3.68250737 -10.7755324   -2.26432833\n",
            " -23.12001264  20.76002109 -16.75073531 -24.25279176 -17.75534308\n",
            "  23.94491062 -11.88590931  13.02349087  24.94867415 -29.55787332\n",
            "  18.55170836 -13.46341735 -19.83643451  13.22310167  28.68576359\n",
            " -14.34865539   4.81836638 -14.71161203   3.799991     8.53034215]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [6.10335389e-01 6.23546370e-01 1.35079792e-01 6.93540470e-01\n",
            " 6.74750356e-01 6.82588122e-01 7.40705527e-02 4.46838842e-01\n",
            " 4.09035132e-01 8.67114836e-02 2.22367928e-01 4.40962632e-01\n",
            " 7.42906310e-01 2.37062077e-01 8.29749910e-01 5.45798286e-01\n",
            " 7.60421685e-01 4.72555983e-01 4.99194225e-01 6.19719389e-01\n",
            " 9.27970608e-01 4.91812581e-01 5.28657538e-01 6.01996964e-01\n",
            " 7.37515137e-01 7.18503355e-02 5.15687439e-01 4.81591589e-01\n",
            " 9.21684984e-01 4.93506580e-01 4.68320535e-01 9.62199989e-01\n",
            " 4.50576464e-01 1.80884430e-01 2.16916123e-01 9.51875383e-02\n",
            " 5.68129259e-04 4.31945661e-01 7.69242581e-01 7.33466443e-01\n",
            " 3.21490878e-02 5.69982904e-01 1.54283125e-01 9.41076285e-01\n",
            " 1.67365000e-01 6.26639218e-01 3.26238362e-01 6.41561326e-01\n",
            " 4.12258596e-01 7.59643052e-01]\n",
            "angle with constraint is: [  7.72347722   8.64824587 -25.54441454  13.54783287  12.23252495\n",
            "  12.78116856 -29.81506131  -3.72128103  -6.36754076 -28.93019614\n",
            " -19.43424505  -4.13261577  17.00344169 -18.40565461  23.08249371\n",
            "   3.20588     18.22951796  -1.92108117  -0.05640422   8.38035726\n",
            "  29.95794257  -0.5731193    2.00602767   7.13978745  16.62605961\n",
            " -29.97047652   1.0981207   -1.28858876  29.5179489   -0.45453937\n",
            "  -2.21756257  32.3539992   -3.45964755 -22.33808988 -19.8158714\n",
            " -28.33687232 -34.96023095  -4.76380373  18.84698069  16.342651\n",
            " -32.74956385   4.89880325 -24.20018122  30.87533993 -23.28445002\n",
            "   8.86474524 -12.16331465   9.90929282  -6.14189825  18.17501366]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.23067913 0.90579185 0.50020965 0.75609315 0.84973019 0.99882861\n",
            " 0.22394567 0.28689241 0.25043309 0.26287133 0.58244474 0.99352252\n",
            " 0.99027809 0.52677323 0.63902721 0.85997054 0.93293346 0.20386063\n",
            " 0.52854173 0.83283705 0.30320208 0.43980904 0.9856211  0.9850467\n",
            " 0.90610681 0.79844834 0.2691305  0.32085007 0.54728284 0.56077737\n",
            " 0.50550282 0.63667028 0.40832302 0.74917416 0.33354993 0.330677\n",
            " 0.12722596 0.18871221 0.85052088 0.44628234 0.22786779 0.7593411\n",
            " 0.55398113 0.02115601 0.31636146 0.17700141 0.65587286 0.27503104\n",
            " 0.06494678 0.05493499]\n",
            "angle with constraint is: [-1.88524612e+01  2.84054297e+01  1.46754519e-02  1.79265203e+01\n",
            "  2.44811134e+01  3.49180028e+01 -1.93238034e+01 -1.49175311e+01\n",
            " -1.74696839e+01 -1.65990067e+01  5.77113191e+00  3.45465766e+01\n",
            "  3.43194661e+01  1.87412620e+00  9.73190472e+00  2.51979381e+01\n",
            "  3.03053421e+01 -2.07297561e+01  1.99792103e+00  2.32985938e+01\n",
            " -1.37758546e+01 -4.21336703e+00  3.39934771e+01  3.39532688e+01\n",
            "  2.84274768e+01  2.08913841e+01 -1.61608649e+01 -1.25404948e+01\n",
            "  3.30979888e+00  4.25441622e+00  3.85197355e-01  9.56691959e+00\n",
            " -6.41738842e+00  1.74421910e+01 -1.16515051e+01 -1.18526102e+01\n",
            " -2.60941827e+01 -2.17901453e+01  2.45364615e+01 -3.76023630e+00\n",
            " -1.90492549e+01  1.81538770e+01  3.77867881e+00 -3.35190792e+01\n",
            " -1.28546980e+01 -2.26099012e+01  1.09111003e+01 -1.57478272e+01\n",
            " -3.04537255e+01 -3.11545508e+01]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.5817273  0.02172346 0.84879211 0.15539712 0.60656634 0.44468803\n",
            " 0.44285152 0.07179714 0.80275225 0.89305248 0.01101667 0.17860418\n",
            " 0.90390419 0.54949361 0.15003958 0.20279045 0.65998624 0.72481682\n",
            " 0.59942681 0.95314838 0.4940636  0.30027191 0.47723291 0.35966629\n",
            " 0.22441589 0.04596325 0.35221249 0.71140935 0.0184378  0.43917851\n",
            " 0.6440094  0.61040411 0.77664844 0.81051972 0.71170582 0.1027977\n",
            " 0.47502244 0.60340596 0.24556604 0.04255182 0.22359032 0.4548944\n",
            " 0.41393032 0.17666698 0.82441554 0.92568667 0.20134832 0.92957292\n",
            " 0.01786096 0.92496247]\n",
            "angle with constraint is: [  5.72091086 -33.4793581   24.41544792 -24.12220165   7.45964346\n",
            "  -3.87183804  -4.00039351 -29.97420003  21.19265751  27.51367393\n",
            " -34.22883328 -22.4977077   28.27329322   3.4645529  -24.49722952\n",
            " -20.80466832  11.1990371   15.73717756   6.9598766   31.72038659\n",
            "  -0.41554774 -13.98096615  -1.5936963   -9.82335944 -19.29088751\n",
            " -31.78257255 -10.34512545  14.79865434 -33.7093538   -4.25750408\n",
            "  10.0806581    7.72828764  19.36539051  21.7363801   14.81940746\n",
            " -27.80416127  -1.74842892   7.23841702 -17.81037728 -32.02137276\n",
            " -19.34867754  -3.15739188  -6.02487767 -22.63331131  22.70908799\n",
            "  29.79806681 -20.90561767  30.07010409 -33.74973286  29.74737309]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.95192879 0.01205186 0.7630193  0.97020157 0.90590222 0.97071016\n",
            " 0.55032351 0.07815614 0.8497892  0.61382826 0.61473674 0.41725361\n",
            " 0.57184198 0.15357978 0.73977003 0.07501731 0.72978077 0.84897842\n",
            " 0.47246868 0.50983037 0.31221427 0.96945858 0.73312447 0.36898855\n",
            " 0.17480739 0.21957959 0.46392229 0.9425684  0.73414635 0.21524375\n",
            " 0.85956113 0.37820052 0.85172218 0.2416996  0.61866061 0.92667049\n",
            " 0.25789401 0.69292659 0.98731262 0.15059829 0.08840229 0.67419519\n",
            " 0.33973595 0.07124448 0.47530562 0.75377068 0.2851224  0.33504688\n",
            " 0.8456994  0.52007191]\n",
            "angle with constraint is: [ 31.63501531 -34.15636959  18.41135077  32.91411008  28.41315532\n",
            "  32.94971128   3.5226454  -29.52907021  24.48524385   7.96797791\n",
            "   8.03157209  -5.79224731   5.02893836 -24.24941546  16.78390186\n",
            " -29.74878849  16.08465369  24.42848958  -1.92719257   0.68812562\n",
            " -13.1450009   32.86210086  16.31871322  -9.17080127 -22.76348296\n",
            " -19.62942855  -2.52543997  30.97978802  16.3902442  -19.93293763\n",
            "  25.16927934  -8.52596339  24.62055279 -18.08102814   8.30624284\n",
            "  29.86693417 -16.94741936  13.50486134  34.1118834  -24.45811989\n",
            " -28.81183965  12.19366347 -11.21848366 -30.01288658  -1.72860675\n",
            "  17.76394758 -15.04143197 -11.54671857  24.19895805   1.40503386]\n",
            "Accuracy after rotation augmentation constrained by  70  is  0.6676\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.84273197 0.44638949 0.95297516 0.65079459 0.11589887 0.88509299\n",
            " 0.48926519 0.14163897 0.15269013 0.68753394 0.04737251 0.28659376\n",
            " 0.13978759 0.44107743 0.41435377 0.52187643 0.27963633 0.03204359\n",
            " 0.61438611 0.373718   0.651335   0.10866697 0.01377736 0.24829425\n",
            " 0.45654732 0.39407197 0.81030256 0.37783764 0.53044118 0.59351559\n",
            " 0.02183139 0.51513338 0.34483126 0.42128013 0.1002874  0.78282057\n",
            " 0.94240425 0.02226882 0.60137276 0.76384407 0.28480759 0.78755558\n",
            " 0.63235505 0.92916263 0.98100481 0.04234611 0.44493776 0.54698779\n",
            " 0.14674323 0.33628583]\n",
            "angle with constraint is: [ 27.4185575   -4.28884063  36.23801292  12.06356718 -30.72809077\n",
            "  30.80743906  -0.85878473 -28.66888256 -27.78478929  15.00271481\n",
            " -36.21019946 -17.07249881 -28.81699276  -4.71380563  -6.85169824\n",
            "   1.75011478 -17.62909367 -37.43651285   9.15088852 -10.10255977\n",
            "  12.1067999  -31.30664234 -38.89781158 -20.13646017  -3.47621416\n",
            "  -8.47424207  24.82420442  -9.77298846   2.43529437   7.4812474\n",
            " -38.25348867   1.21067015 -12.41349925  -6.29758979 -31.97700812\n",
            "  22.62564525  35.39234006 -38.21849435   8.10982102  21.10752538\n",
            " -17.21539291  23.00444679  10.58840406  34.33301058  38.48038491\n",
            " -36.61231088  -4.40497912   3.75902301 -28.26054134 -13.09713357]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.98764745 0.01623046 0.45250793 0.84265462 0.32390126 0.4797985\n",
            " 0.97804575 0.47487684 0.13092131 0.05172149 0.94541657 0.26580941\n",
            " 0.50406391 0.09070177 0.42365512 0.93010552 0.50241299 0.97113515\n",
            " 0.25883564 0.50454524 0.85953079 0.44825235 0.27948737 0.85547536\n",
            " 0.38988947 0.53072329 0.97652707 0.18648329 0.30140064 0.0310696\n",
            " 0.39183771 0.20018343 0.68003494 0.18132146 0.67481275 0.26347543\n",
            " 0.35521783 0.34886065 0.71675175 0.46650926 0.3612989  0.85262272\n",
            " 0.57195063 0.21383082 0.90009128 0.3426503  0.03897938 0.49460022\n",
            " 0.12393805 0.23846644]\n",
            "angle with constraint is: [ 39.01179582 -38.70156349  -3.79936548  27.41236974 -14.08789898\n",
            "  -1.61611978  38.24365966  -2.00985308 -29.52629503 -35.86228067\n",
            "  35.6333258  -18.73524694   0.32511253 -32.74385871  -6.10759036\n",
            "  34.40844154   0.19303949  37.69081214 -19.29314902   0.36361918\n",
            "  28.76246334  -4.13981164 -17.64101011  28.43802907  -8.80884249\n",
            "   2.45786328  38.12216531 -25.08133642 -15.88794906 -37.51443199\n",
            "  -8.65298321 -23.98532555  14.40279483 -25.4942833   13.98502019\n",
            " -18.92196574 -11.58257327 -12.09114784  17.34014001  -2.67925895\n",
            " -11.09608809  28.20981752   5.75605005 -22.8935343   32.00730225\n",
            " -12.58797632 -36.8816498   -0.43198208 -30.08495564 -20.9226845 ]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.74711087 0.13075181 0.80694422 0.93690729 0.6065321  0.71035919\n",
            " 0.71358661 0.99280796 0.34153826 0.16285655 0.51916723 0.53324981\n",
            " 0.17791249 0.61310942 0.01412473 0.9276684  0.26457428 0.03956021\n",
            " 0.10824944 0.24024895 0.27390617 0.03281    0.27935481 0.19294174\n",
            " 0.37862093 0.38516774 0.33900774 0.32792388 0.08973128 0.02520495\n",
            " 0.2021143  0.75664943 0.99080977 0.92852618 0.50421936 0.53182386\n",
            " 0.31458157 0.77393944 0.76475669 0.84427236 0.89550458 0.43434925\n",
            " 0.76687557 0.80639873 0.30845962 0.92924224 0.0943415  0.99426918\n",
            " 0.77009072 0.09327091]\n",
            "angle with constraint is: [ 19.76886981 -29.53985493  24.55553795  34.95258305   8.52256803\n",
            "  16.82873536  17.08692879  39.42463658 -12.67693902 -26.9714759\n",
            "   1.53337879   2.65998479 -25.76700094   9.04875352 -38.87002132\n",
            "  34.2134722  -18.83405777 -36.83518333 -31.34004468 -20.78008414\n",
            " -18.08750651 -37.37520004 -17.65161502 -24.56466108  -9.71032581\n",
            "  -9.18658101 -12.87938075 -13.76608932 -32.82149778 -37.98360423\n",
            " -23.83085599  20.53195419  39.26478124  34.282094     0.33754844\n",
            "   2.54590846 -14.83347448  21.91515509  21.18053503  27.54178861\n",
            "  31.64036618  -5.25206014  21.35004545  24.5118983  -15.3232302\n",
            "  34.33937952 -32.45267979  39.54153433  21.60725745 -32.53832757]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.65622965 0.03417941 0.77018695 0.79922292 0.60241025 0.85686039\n",
            " 0.09788328 0.27592384 0.41417752 0.04245795 0.44821609 0.26521346\n",
            " 0.39273501 0.42510263 0.87941173 0.27034097 0.89374313 0.42597096\n",
            " 0.32780772 0.50251205 0.41774329 0.25737231 0.50267932 0.15180173\n",
            " 0.35192994 0.56579022 0.41340824 0.82391236 0.25672176 0.96977158\n",
            " 0.78472202 0.96759303 0.83813655 0.14210393 0.52646009 0.16997852\n",
            " 0.81822735 0.58416215 0.26344931 0.92959897 0.11518605 0.5638624\n",
            " 0.72104055 0.05884854 0.59785603 0.81590122 0.06752731 0.51224297\n",
            " 0.25855718 0.66292445]\n",
            "angle with constraint is: [ 12.49837185 -37.26564713  21.61495596  23.93783373   8.19282025\n",
            "  28.54883135 -32.16933758 -17.92609264  -6.86579827 -36.60336377\n",
            "  -4.14271299 -18.78292351  -8.58119903  -5.99178944  30.35293868\n",
            " -18.37272236  31.4994503   -5.92232281 -13.77538234   0.20096395\n",
            "  -6.58053709 -19.41021496   0.21434598 -27.85586135 -11.84560456\n",
            "   5.26321755  -6.92734045  25.91298888 -19.46225908  37.58172631\n",
            "  22.77776167  37.40744208  27.05092439 -28.63168593   2.11680725\n",
            " -26.4017183   25.45818821   6.73297193 -18.92405485  34.36791776\n",
            " -30.78511573   5.10899228  17.68324437 -35.29211701   7.82848266\n",
            "  25.27209724 -34.59781485   0.97943769 -19.31542575  13.03395612]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.11867737 0.93705143 0.89472858 0.18597442 0.32326588 0.58763497\n",
            " 0.71806234 0.44265602 0.15629132 0.10415149 0.99976534 0.45535212\n",
            " 0.87253175 0.482376   0.92235764 0.08775938 0.11401919 0.92963219\n",
            " 0.94749305 0.31471745 0.35516489 0.45245023 0.41616168 0.19004522\n",
            " 0.89837538 0.2533076  0.70527241 0.06715568 0.03875133 0.65373047\n",
            " 0.33901586 0.65545939 0.0940212  0.94985138 0.74851447 0.87626723\n",
            " 0.13647055 0.32016097 0.36834416 0.53717265 0.29686849 0.06063074\n",
            " 0.36887838 0.25454931 0.38592129 0.19695954 0.26189637 0.62695234\n",
            " 0.38815383 0.34538339]\n",
            "angle with constraint is: [-30.50581063  34.96411445  31.57828654 -25.12204641 -14.13872925\n",
            "   7.01079797  17.44498693  -4.58751879 -27.49669424 -31.6678805\n",
            "  39.98122695  -3.57183028  29.80253993  -1.40991963  33.78861108\n",
            " -32.97924938 -30.87846469  34.37057502  35.7994436  -14.8226042\n",
            " -11.58680904  -3.80398121  -6.70706559 -24.79638272  31.87003072\n",
            " -19.73539194  16.42179276 -34.62754578 -36.89989348  12.29843781\n",
            " -12.87873139  12.43675142 -32.47830383  35.98811039  19.88115735\n",
            "  30.10137864 -29.08235568 -14.38712249 -10.53246686   2.97381187\n",
            " -16.25052116 -35.14954062 -10.48972954 -19.63605483  -9.12629668\n",
            " -24.24323659 -19.04829003  10.15618723  -8.94769323 -12.36932855]\n",
            "Accuracy after rotation augmentation constrained by  80  is  0.6504\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.53390563 0.77186054 0.37886796 0.58751949 0.96620263 0.52198011\n",
            " 0.90979758 0.79367262 0.05871868 0.42909887 0.69831841 0.98671191\n",
            " 0.45837629 0.09278545 0.56200917 0.12656379 0.75151956 0.507677\n",
            " 0.72343137 0.86936183 0.62894802 0.61243933 0.7545703  0.842455\n",
            " 0.86905379 0.0364302  0.5561903  0.21626931 0.86034589 0.64839769\n",
            " 0.42022248 0.18810423 0.33902363 0.70377769 0.20435955 0.09358295\n",
            " 0.88802876 0.76421035 0.19979493 0.29231154 0.97207018 0.70818449\n",
            " 0.610522   0.30762849 0.29912514 0.03952362 0.72848187 0.04317523\n",
            " 0.00485734 0.72949509]\n",
            "angle with constraint is: [  3.05150695  24.46744868 -10.90188393   7.87675433  41.95823628\n",
            "   1.97821027  36.88178189  26.43053618 -39.71531865  -6.38110157\n",
            "  17.84865671  43.80407212  -3.7461338  -36.64930913   5.58082572\n",
            " -33.60925871  22.63676043   0.69092977  20.10882315  33.24256442\n",
            "  11.60532154  10.11953963  22.91132659  30.82095013  33.21484076\n",
            " -41.72128239   5.05712671 -25.53576212  32.43113017  13.35579167\n",
            "  -7.17997689 -28.07061947 -14.48787367  18.33999251 -26.60764093\n",
            " -36.57753492  34.92258824  23.77893146 -27.01845624 -18.69196109\n",
            "  42.48631594  18.73660381   9.94698038 -17.31343631 -18.07873703\n",
            " -41.44287432  20.56336802 -41.11422974 -44.5628396   20.65455791]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.94887376 0.75650305 0.47164019 0.48604525 0.43613642 0.32464367\n",
            " 0.52171421 0.43115096 0.59816788 0.89812008 0.99500942 0.07874329\n",
            " 0.38468347 0.1807062  0.35212047 0.28747302 0.17453713 0.76407605\n",
            " 0.49628874 0.91924558 0.03259244 0.06697801 0.40327358 0.08027732\n",
            " 0.2627296  0.98602974 0.20895961 0.19811774 0.57364067 0.49799829\n",
            " 0.5804383  0.37673528 0.82676236 0.13858055 0.42839265 0.68976185\n",
            " 0.86823644 0.36957161 0.5677456  0.06913995 0.17031909 0.06913816\n",
            " 0.72788089 0.28805997 0.72374529 0.06639828 0.8625478  0.8355302\n",
            " 0.08892874 0.02375651]\n",
            "angle with constraint is: [ 40.39863814  23.0852743   -2.55238284  -1.25592754  -5.74772196\n",
            " -15.78206996   1.95427928  -6.19641386   8.8351092   35.83080725\n",
            "  44.55084756 -37.91310391 -10.37848746 -28.73644238 -13.30915735\n",
            " -19.12742833 -29.29165815  23.76684481  -0.33401315  37.7321024\n",
            " -42.06668011 -38.97197882  -8.70537754 -37.77504116 -21.35433632\n",
            "  43.74267688 -26.19363533 -27.16940327   6.62766019  -0.18015383\n",
            "   7.23944675 -11.09382496  29.40861239 -32.52775037  -6.44466187\n",
            "  17.07856636  33.14127963 -11.73855543   6.09710392 -38.77740495\n",
            " -29.67128207 -38.77756538  20.50928028 -19.07460282  20.13707612\n",
            " -39.02415489  32.62930204  30.19771783 -36.99641313 -42.86191442]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.05628881 0.85363017 0.23470349 0.54827332 0.4262442  0.79552553\n",
            " 0.4888983  0.50066084 0.98909194 0.15012956 0.83070394 0.05174975\n",
            " 0.47764877 0.88633482 0.66521397 0.68521462 0.26331728 0.41857479\n",
            " 0.36658106 0.52144321 0.46222793 0.32689835 0.30548061 0.25085289\n",
            " 0.36504725 0.14994148 0.8721043  0.60211939 0.13378643 0.69385933\n",
            " 0.84766647 0.23753567 0.15060059 0.00861726 0.04725034 0.76225135\n",
            " 0.98718633 0.04488032 0.74807096 0.07186217 0.59213282 0.75607528\n",
            " 0.3592044  0.01093472 0.59518935 0.99124463 0.25761742 0.18949128\n",
            " 0.12392589 0.55525562]\n",
            "angle with constraint is: [-39.93400683  31.82671558 -23.87668599   4.34459861  -6.63802171\n",
            "  26.59729736  -0.99915287   0.05947566  44.01827455 -31.48833989\n",
            "  29.76335449 -40.3425229   -2.01161074  34.77013415  14.8692569\n",
            "  16.66931574 -21.3014446   -7.32826878 -12.00770429   1.92988891\n",
            "  -3.39948629 -15.57914833 -17.50674489 -22.42323975 -12.14574778\n",
            " -31.50526699  33.48938718   9.19074527 -32.9592217   17.44734008\n",
            "  31.28998212 -23.62178952 -31.44594651 -44.22444686 -40.7474695\n",
            "  23.60262135  43.84676988 -40.96077098  22.32638632 -38.53240507\n",
            "   8.29195424  23.04677542 -12.67160425 -44.0158751    8.56704169\n",
            "  44.21201653 -21.81443194 -27.94578505 -33.84666965   4.97300579]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.5964451  0.79227084 0.75307327 0.89254149 0.8057727  0.24726708\n",
            " 0.5558774  0.29741196 0.32978538 0.93194437 0.68189591 0.97459041\n",
            " 0.41691254 0.53743757 0.48711542 0.10728706 0.23670199 0.52763636\n",
            " 0.06737431 0.93146193 0.12384368 0.19798075 0.76532122 0.09693454\n",
            " 0.9002781  0.72570097 0.06862086 0.69184706 0.63162854 0.75734943\n",
            " 0.82819785 0.71835056 0.5861763  0.32246332 0.69727366 0.95697158\n",
            " 0.58752178 0.66434487 0.43712804 0.75612081 0.99766739 0.16038932\n",
            " 0.20240528 0.53943776 0.4813754  0.57156978 0.04907405 0.40427646\n",
            " 0.58297826 0.58519135]\n",
            "angle with constraint is: [  8.68005894  26.30437535  22.77659463  35.32873453  27.51954285\n",
            " -22.74596252   5.02896644 -18.23292368 -15.31931584  38.87499323\n",
            "  16.37063146  42.71313719  -7.4778717    3.3693816   -1.15961221\n",
            " -35.34416482 -23.69682049   2.48727245 -38.93631254  38.83157364\n",
            " -33.85406863 -27.18173282  23.87891015 -36.27589124  36.02502897\n",
            "  20.31308737 -38.82412286  17.26623552  11.8465688   23.16144895\n",
            "  29.53780672  19.65155069   7.75586657 -15.97830133  17.75462956\n",
            "  41.12744262   7.87696025  14.79103816  -5.6584762   23.05087288\n",
            "  44.79006531 -30.56496083 -26.78352484   3.54939828  -1.67621425\n",
            "   6.44128061 -40.5833357   -8.61511893   7.46804359   7.66722162]\n",
            "shape of sample before is: (50, 28, 28)\n",
            "len(sample.shape) 3\n",
            "angle is: [0.65733396 0.72021603 0.6415472  0.79024316 0.62476063 0.53750766\n",
            " 0.60619643 0.10119337 0.27315654 0.52739978 0.87298722 0.67905156\n",
            " 0.09040808 0.59227495 0.22173799 0.73401656 0.82744085 0.59345627\n",
            " 0.59136261 0.33859144 0.44625002 0.86410835 0.72332033 0.51975366\n",
            " 0.36823239 0.05037974 0.1430074  0.19302519 0.37338354 0.94414365\n",
            " 0.50515339 0.23484016 0.10575014 0.33830552 0.81573064 0.38334279\n",
            " 0.77250581 0.80270498 0.11082077 0.60865975 0.95909606 0.40213807\n",
            " 0.05094409 0.92330269 0.88366628 0.52214145 0.55225312 0.2117411\n",
            " 0.81945076 0.49860004]\n",
            "angle with constraint is: [ 14.16005656  19.81944261  12.7392483   26.12188399  11.22845712\n",
            "   3.37568963   9.5576786  -35.89259699 -20.41591133   2.46598026\n",
            "  33.56884939  16.11464007 -36.86327294   8.30474554 -25.04358111\n",
            "  21.06149038  29.46967673   8.41106453   8.22263488 -14.52677011\n",
            "  -4.8374981   32.76975184  20.09882979   1.77782937 -11.85908524\n",
            " -40.46582355 -32.12933388 -27.62773294 -11.39548111  39.97292824\n",
            "   0.46380554 -23.86438531 -35.48248763 -14.55250279  28.4157572\n",
            " -10.49914859  24.52552254  27.24344785 -35.02613073   9.77937774\n",
            "  41.3186457   -8.80757326 -40.41503199  38.09724212  34.52996534\n",
            "   1.99273025   4.70278083 -25.94330075  28.750568    -0.12599654]\n",
            "Accuracy after rotation augmentation constrained by  90  is  0.6467\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Accuracy after rotation augmentation constrained by  90  is  0.6467"
      ],
      "metadata": {
        "id": "ZPOH1A3RnqDr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us see the best value for angle constraint: (Ideally this should be done on validation set, not test set)"
      ],
      "metadata": {
        "id": "8Le-7VJBoP8K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
        "# plot the variation of accuracy\n",
        "ax.plot( angleconstraints , accuracies)\n",
        "ax.set_xlabel('angle')\n",
        "ax.set_ylabel('accuracy')\n",
        "# plot the maximum accuracy\n",
        "maxind = np.argmax(accuracies)\n",
        "plt.scatter(angleconstraints[maxind], accuracies[maxind], c='red')"
      ],
      "metadata": {
        "id": "xG__xE-FoQOg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "5910bcc7-36b0-4746-b74e-7f1ddd197dbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff31bacb690>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us do a grid search to find the best shear constraint."
      ],
      "metadata": {
        "id": "Vfy-LEb-qdUd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shearconstraints = [0, 0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0] # the values we want to test\n",
        "accuracies = np.zeros(len(shearconstraints), dtype=np.float) # we will save the values here\n",
        "\n",
        "for ii in range(len(shearconstraints)):\n",
        "  # create the augmented dataset\n",
        "  augdata = train_X # we include the original images also in the augmented dataset\n",
        "  auglabel = train_y\n",
        "  for jj in range(naugmentations):\n",
        "    augdata = np.concatenate((augdata, augShear(train_X, shearconstraints[ii]))) # concatenate the augmented data to the set\n",
        "    auglabel = np.concatenate((auglabel, train_y))  # the labels don't change when we augment\n",
        "\n",
        "  # check the test accuracy\n",
        "  testpred = NN(augdata, auglabel, test_X)\n",
        "  accuracies[ii] = Accuracy(test_y, testpred)\n",
        "  print('Accuracy after shear augmentation constrained by ',shearconstraints[ii], ' is ', accuracies[ii], flush=True)"
      ],
      "metadata": {
        "id": "22m9xPrBoSDi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fbb445a-cdb0-4740-d54c-546faa6a9a09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy after shear augmentation constrained by  0  is  0.6472\n",
            "Accuracy after shear augmentation constrained by  0.2  is  0.6198\n",
            "Accuracy after shear augmentation constrained by  0.4  is  0.6289\n",
            "Accuracy after shear augmentation constrained by  0.6  is  0.6444\n",
            "Accuracy after shear augmentation constrained by  0.8  is  0.6556\n",
            "Accuracy after shear augmentation constrained by  1.0  is  0.6561\n",
            "Accuracy after shear augmentation constrained by  1.2  is  0.6275\n",
            "Accuracy after shear augmentation constrained by  1.4  is  0.6244\n",
            "Accuracy after shear augmentation constrained by  1.6  is  0.596\n",
            "Accuracy after shear augmentation constrained by  1.8  is  0.6067\n",
            "Accuracy after shear augmentation constrained by  2.0  is  0.6345\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
        "# plot the variation of accuracy\n",
        "ax.plot(shearconstraints, accuracies)\n",
        "ax.set_xlabel('angle')\n",
        "ax.set_ylabel('accuracy')\n",
        "# plot the maximum accuracy\n",
        "maxind = np.argmax(accuracies)\n",
        "plt.scatter(shearconstraints[maxind], accuracies[maxind], c='red')"
      ],
      "metadata": {
        "id": "kBBum9xPqglf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "af7305f1-4e61-4243-fa1d-65af23535ee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ff31ba4cf90>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can do multiple augmentations at the same time. Here is a function to do both shear and rotation to the sample. In this case, we will have two hyperparameters."
      ],
      "metadata": {
        "id": "88Bu9veestqd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def augRotateShear(sample, angleconstraint, shearconstraint):\n",
        "  if len(sample.shape)==2:\n",
        "    sample = np.expand_dims(sample, 0)  # make sure the sample is 3 dimensional\n",
        "  amt = rng.random(len(sample)) # generate random numbers for shear\n",
        "  amt = (amt-0.5)*shearconstraint # make the random shear constrained\n",
        "  angle = rng.random(len(sample)) # generate random numbers for angles\n",
        "  angle = (angle-0.5)*angleconstraint # make the random angle constrained\n",
        "  nsample = sample.copy() # preallocate the augmented array to make it faster\n",
        "  for ii in range(len(sample)):\n",
        "    nsample[ii] = rotate(shear(sample[ii], amt[ii]), angle[ii]) # first apply shear, then rotate\n",
        "  return np.squeeze(nsample) # take care if the input had only one sample."
      ],
      "metadata": {
        "id": "T9CflCiur-lr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we have two hyperparameters, we have to do the grid search on a 2 dimensional matrix. We can use our previous experience to inform where to search for the best hyperparameters. "
      ],
      "metadata": {
        "id": "-EdNnol9s4NZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shearconstraints = [0, 0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6] # the values we want to test\n",
        "angleconstraints = [0,10,20,30,40,50,60] # the values we want to test\n",
        "hyp = cartesian((shearconstraints, angleconstraints)) # cartesian product of both\n",
        "\n",
        "accuracies = np.zeros(len(hyp), dtype=np.float) # we will save the values here\n",
        "\n",
        "for ii in range(len(hyp)):\n",
        "  # create the augmented dataset\n",
        "  augdata = train_X # we include the original images also in the augmented dataset\n",
        "  auglabel = train_y\n",
        "  for jj in range(naugmentations):\n",
        "    augdata = np.concatenate((augdata, augRotateShear(train_X, hyp[ii][0], hyp[ii][1]))) # concatenate the augmented data to the set\n",
        "    auglabel = np.concatenate((auglabel, train_y))  # the labels don't change when we augment\n",
        "\n",
        "  # check the test accuracy\n",
        "  testpred = NN(augdata, auglabel, test_X)\n",
        "  accuracies[ii] = Accuracy(test_y, testpred)\n",
        "  print('Accuracy after augmentation shear:',hyp[ii][0], 'angle:',hyp[ii][1], ' is ', accuracies[ii], flush=True)"
      ],
      "metadata": {
        "id": "UfmRisY-swyX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a541a8d6-1352-44da-aaf1-7d836eee01d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  \"\"\"\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy after augmentation shear: 0.0 angle: 0.0  is  0.6332\n",
            "Accuracy after augmentation shear: 0.0 angle: 10.0  is  0.6057\n",
            "Accuracy after augmentation shear: 0.0 angle: 20.0  is  0.596\n",
            "Accuracy after augmentation shear: 0.0 angle: 30.0  is  0.6142\n",
            "Accuracy after augmentation shear: 0.0 angle: 40.0  is  0.6015\n",
            "Accuracy after augmentation shear: 0.0 angle: 50.0  is  0.6103\n",
            "Accuracy after augmentation shear: 0.0 angle: 60.0  is  0.5736\n",
            "Accuracy after augmentation shear: 0.2 angle: 0.0  is  0.6334\n",
            "Accuracy after augmentation shear: 0.2 angle: 10.0  is  0.6096\n",
            "Accuracy after augmentation shear: 0.2 angle: 20.0  is  0.5994\n",
            "Accuracy after augmentation shear: 0.2 angle: 30.0  is  0.5942\n",
            "Accuracy after augmentation shear: 0.2 angle: 40.0  is  0.5971\n",
            "Accuracy after augmentation shear: 0.2 angle: 50.0  is  0.6032\n",
            "Accuracy after augmentation shear: 0.2 angle: 60.0  is  0.6162\n",
            "Accuracy after augmentation shear: 0.4 angle: 0.0  is  0.6335\n",
            "Accuracy after augmentation shear: 0.4 angle: 10.0  is  0.5994\n",
            "Accuracy after augmentation shear: 0.4 angle: 20.0  is  0.606\n",
            "Accuracy after augmentation shear: 0.4 angle: 30.0  is  0.5943\n",
            "Accuracy after augmentation shear: 0.4 angle: 40.0  is  0.5832\n",
            "Accuracy after augmentation shear: 0.4 angle: 50.0  is  0.6034\n",
            "Accuracy after augmentation shear: 0.4 angle: 60.0  is  0.5979\n",
            "Accuracy after augmentation shear: 0.6 angle: 0.0  is  0.6339\n",
            "Accuracy after augmentation shear: 0.6 angle: 10.0  is  0.5842\n",
            "Accuracy after augmentation shear: 0.6 angle: 20.0  is  0.5929\n",
            "Accuracy after augmentation shear: 0.6 angle: 30.0  is  0.5941\n",
            "Accuracy after augmentation shear: 0.6 angle: 40.0  is  0.6015\n",
            "Accuracy after augmentation shear: 0.6 angle: 50.0  is  0.5978\n",
            "Accuracy after augmentation shear: 0.6 angle: 60.0  is  0.6146\n",
            "Accuracy after augmentation shear: 0.8 angle: 0.0  is  0.6342\n",
            "Accuracy after augmentation shear: 0.8 angle: 10.0  is  0.6061\n",
            "Accuracy after augmentation shear: 0.8 angle: 20.0  is  0.5963\n",
            "Accuracy after augmentation shear: 0.8 angle: 30.0  is  0.6129\n",
            "Accuracy after augmentation shear: 0.8 angle: 40.0  is  0.5882\n",
            "Accuracy after augmentation shear: 0.8 angle: 50.0  is  0.5878\n",
            "Accuracy after augmentation shear: 0.8 angle: 60.0  is  0.6076\n",
            "Accuracy after augmentation shear: 1.0 angle: 0.0  is  0.6333\n",
            "Accuracy after augmentation shear: 1.0 angle: 10.0  is  0.5871\n",
            "Accuracy after augmentation shear: 1.0 angle: 20.0  is  0.6012\n",
            "Accuracy after augmentation shear: 1.0 angle: 30.0  is  0.5937\n",
            "Accuracy after augmentation shear: 1.0 angle: 40.0  is  0.5888\n",
            "Accuracy after augmentation shear: 1.0 angle: 50.0  is  0.5852\n",
            "Accuracy after augmentation shear: 1.0 angle: 60.0  is  0.587\n",
            "Accuracy after augmentation shear: 1.2 angle: 0.0  is  0.6349\n",
            "Accuracy after augmentation shear: 1.2 angle: 10.0  is  0.5899\n",
            "Accuracy after augmentation shear: 1.2 angle: 20.0  is  0.5987\n",
            "Accuracy after augmentation shear: 1.2 angle: 30.0  is  0.5723\n",
            "Accuracy after augmentation shear: 1.2 angle: 40.0  is  0.6027\n",
            "Accuracy after augmentation shear: 1.2 angle: 50.0  is  0.5944\n",
            "Accuracy after augmentation shear: 1.2 angle: 60.0  is  0.6017\n",
            "Accuracy after augmentation shear: 1.4 angle: 0.0  is  0.6345\n",
            "Accuracy after augmentation shear: 1.4 angle: 10.0  is  0.6035\n",
            "Accuracy after augmentation shear: 1.4 angle: 20.0  is  0.6104\n",
            "Accuracy after augmentation shear: 1.4 angle: 30.0  is  0.5657\n",
            "Accuracy after augmentation shear: 1.4 angle: 40.0  is  0.5983\n",
            "Accuracy after augmentation shear: 1.4 angle: 50.0  is  0.5876\n",
            "Accuracy after augmentation shear: 1.4 angle: 60.0  is  0.6093\n",
            "Accuracy after augmentation shear: 1.6 angle: 0.0  is  0.6331\n",
            "Accuracy after augmentation shear: 1.6 angle: 10.0  is  0.6341\n",
            "Accuracy after augmentation shear: 1.6 angle: 20.0  is  0.591\n",
            "Accuracy after augmentation shear: 1.6 angle: 30.0  is  0.6079\n",
            "Accuracy after augmentation shear: 1.6 angle: 40.0  is  0.5987\n",
            "Accuracy after augmentation shear: 1.6 angle: 50.0  is  0.6035\n",
            "Accuracy after augmentation shear: 1.6 angle: 60.0  is  0.5965\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us plot it two dimensionally to see which is the best value for the hyperparameters:"
      ],
      "metadata": {
        "id": "twI4Tk5etBTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes\n",
        "im = ax.imshow(accuracies.reshape((len(shearconstraints), len(angleconstraints))), cmap='inferno') \n",
        "ax.set_xlabel('angle')\n",
        "ax.set_ylabel('shear')\n",
        "ax.set_xticks(np.arange(len(angleconstraints)));\n",
        "ax.set_xticklabels(angleconstraints);\n",
        "ax.set_yticks(np.arange(len(shearconstraints)));\n",
        "ax.set_yticklabels(shearconstraints);\n",
        "plt.colorbar(im)"
      ],
      "metadata": {
        "id": "CS8X6jLAs7hW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "outputId": "0ca33f84-2e33-4dc2-ef51-28b0beaf0601"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7ff31b8f1950>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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/os/9+JWNx7j3kdtrOc7WsCLi3ZK+DLwduFTS9pP4bEQ0ySOleSbe+k3VJHICxd2PR9t+ANiFYkA6IlpW3IjYzRURJzMV49s9z+8B7mmqURExGQN6FSwiuqEPz64qSQKK6LguD0InAUUMgOG2GzBFSUARA2DQJ6NGRJ8aWRO6i5KAIgZAVwehG7sxQNJ5ku6VdNMY70vS/yyLny2XdFBTbYkYdDVWxZhRTd6Z9BXGrwn0JoqFrOcBC8jcsogpKUozV9v6TWMJyPbVwHjrBb0V+CcXrgF2krR7U+2JGGTDFbd+0+a92ZWLmKUwYcTYaq6KMaP6b3LIFtheaHu+7fmzd3t2282J6DvDrrb1mzavglUpgBYRFfRhbqmkzR7QIuBd5dWwVwEPlpNcI2ISaq4NP6Ma6wFJOp+ieuJsSWuATwLPALD9DxR1h44FVgOPAf+5qbZEDLp+HGCuorEEZPukCd438N6m4kdsTfpxgLmKTgxCR8TYTL2X4SUdI+nm8ibhj27h/ZMlrZW0rNze0/PeZyTdVG5vnyhWpmJEDIC6bjKUNAs4BziK4taYJZIW2V45atdv2D591GffTFEx50Bge+AqSZfZfmiseOkBRXRcsR5QbZfhDwVW277N9gbgAoqbhqvYD7ja9pDtR4HljD8bIgkoYhBMYlH62SM39ZbbglGHqnqD8PHlHM6LJI3cTnMDcIykZ0maDbyOzW+1eZqcgkV0nhiuXvNrne350wx4CXC+7ScknQZ8FTjS9vclHQL8BFgL/JSnSnptUXpAEV1Xb1meCW8Qtr3e9hPl03OBg3ve+7TtA20fRVHS/ZbxgiUBRXRczVfBlgDzJM2VtB1wIsVNw5uMmjR+HLCqfH2WpF3LxwcABwDfHy9Y507BttG2bL/dro3H2Wm7WxuP8fATOzQeA+CTdy+bkTiXHPz8xmMsv/eFjccAeOmuaxuP8f2Hn5h4p4rqmudle0jS6RR1AGcB59leIeksYKntRcAZko4DhihWvDi5/PgzgB9JAngIeKftofHidS4BRcTT1TkXzPalFDMVel/7RM/jM4Ezt/C5xymuhFWWBBTRcSnLExGt2tjR6fBJQBEdVyxI1nYrpiYJKGIAZDZ8RLSjT1c7rCIJKKLjTHdXREwCihgA6QFFRGu6OgjdZmXUPypn094o6SeSXt5UWyIGWd0Lks2kNiuj3g681vb+wKeAhQ22JWKgpSzPKLavlrTPOO//pOfpNRSzbiNikkxuRJyuU4DLxnqzXDRpAcBeezU/ETWiU6ovtdF3Wk9Akl5HkYBePdY+thdSnqLNn/+ijn6rI5rTj+M7VbSagMo1Q84F3mR7fZttieiqkTWhu6i1BCRpL+DbwB/bHnfVtIgYX0fzT6uVUT8B7Ap8sVzAaKiGtWojtkrpAY1SoTLqe4D3jLdPRFSTQeiIaMXIjYhdlAQUMQCGO9oFSgKK6Dg7NyJGRGuMO3odLAkoouNyH1BEtCqD0BHRGmcQemYYMzx+scVa/P6Lftl4jDlz72w8BsC6638xI3F+8/CJjce4+cFnNh4D4C0H39F4jO1XbajlOHVfhpd0DPB5isqo59r+m1HvnwyczVM1479g+9zyvc8Cb6ZY6ucK4P0eJzt2LgFFxNPV1QOSNAs4BzgKWAMskbTI9spRu37D9umjPvsfgMMpasID/F/gtcBVY8VrckGyiJghNa6IeCiw2vZttjcAFwBvrdgMAzsA2wHbU0y9+u14H0gCiui44iqYK20UczOX9mwLRh1uD+CunudrytdGO75cUvkiSXMAbP8UuBK4p9wW2141XttzChYxADZWvw9oXQ2Tvi8Bzrf9hKTTgK8CR0p6MfBSnlrd9ApJr7H9o7EOlB5QRMcZM1xxq+BuYE7P8z15arC5iGevt/1E+fRc4ODy8R8C19h+xPYjFKucHjZesCSgiK7zpE7BJrIEmCdprqTtgBOBRb07SNq95+lxwMhp1p3AayVtK+kZFAPQOQWLGHR1TcWwPSTpdGAxxWX482yvkHQWsNT2IuAMSccBQ8B9wMnlxy8CjgRupBiautz2JePFSwKK6LjiPqD6bkS0fSlw6ajXPtHz+EzgzC18biNw2mRitVaYsGe/QyQNSXpbU22JGHQ1jgHNqDYLE47c9PQZ4PsNtiNiwLnyn37TWAKyfTXF+eF43gd8C7i3qXZEDLqRU7Au9oDarIqxB8Vlu9cBh0ywbwoTRoxFMKxuzodv8zL83wN/bnvC75zthbbn254/e7fnzEDTIrrDmI0V//SbNq+CzQcuKEvyzAaOlTRk++IW2xTRSf14elVFawnI9tyRx5K+Anw3ySdiarp6CtZmYcKIqEExFSMJaDMTFSYcte/JTbUjYmuQBBQRLSn6QF2UBBTRcSZjQBHRmowBRUSL3If3+FSRBBTRccYM0XylmCYkAUV0ntMDioh2FJNRMwY0I+whNmxY33icA37Q/PJEf/vi6xqPAXD723ackTg33tb8P4KTfnd14zEArrzhwMZjPPzYXRPvVFEuw0dES8xwTsEiog0mPaCIaI0ZdnpAEdGS9IAioiW5DB8RLTFmo3MjYkS0weCOjgGlNHNEx40sSFblTxWSjpF0s6TVkj66hfdPlrRW0rJye0/5+ut6Xlsm6XFJfzBerPSAIgZAhdoOlZS1+s4BjgLWAEskLbK9ctSu37B9+uZt8JXAgeVxdgFWM0HNv1Yro0o6osyUKyT9W1NtiRhsxSB0la2CQ4HVtm+zvQG4AHjrFBr1NuAy24+Nt1NrlVEl7QR8ETjO9suA/9RgWyIGmj1caaNYo31pz7Zg1KH2AHrniKwpXxvteEnLJV0kac4W3j8ROH+idje5JvTVkvYZZ5d3AN+2fWe5f6qjRkzJpJZkXWd7/jQDXgKcb/sJSacBXwWOHHlT0u7A/sDiiQ7U5iD0vsDOkq6SdJ2kd421o6QFIxl73dpHZ7CJEf3PFFfBqmwV3A309mj2LF97Kp693vYT5dNzgYNHHeME4Du2n5woWJsJaFuKhr8ZOBr4uKR9t7Tj5pVRZ2Zmd0R3eDKnYBNZAsyTNFfSdhSnUot6dyh7OCOOA1aNOsZJVDj9gnavgq0B1tt+FHhU0tXAy4FbWmxTRPcYhmu6EdH2kKTTKU6fZgHn2V4h6Sxgqe1FwBmSjgOGgPuAk0c+Xw67zAEqXVRqMwH9K/AFSdsC2wGvBD7XYnsiOqresjy2LwUuHfXaJ3oenwmcOcZnf8WWB623qLXKqLZXSbocWA4MA+faHvOSfURs2cgYUBe1WhnV9tnA2U21IWLrUCzK2kW5EzpiANR1J/RMSwKK6LyUZo6IViUBRURbcgoWEe3IKVhEtCmX4SOiHca47UZMiexuNVzSWuCOSX5sNrCugea0ESdfy+DE2dv2btMNWt7QO7vi7utsj7lMzkzrXAKaCklLa1iCoC/i5GtJnEGSNaEjojVJQBHRmq0lAS0coDj5WhJnYGwVY0AR0Z+2lh5QRPShJKCIaM3AJ6CJqjxO8ZhPq3kmaRdJV0i6tfx75xrizJF0paSVZe2099cdS9IOkn4m6YYyxl+Vr8+VdG35fftGuT7wtEmaJennkr7bVBxJv5J0Y1lzbmn5Wq0/H0k7lSVpfiFplaTDmvgdGHQDnYB6qjy+CdgPOEnSfjUc+is8vebZR4Ef2p4H/LB8Pl1DwIds7we8Cnhv2f46Yz0BHGn75RRVLY+R9CrgM8DnbL8YuB84ZRoxer2fzRcxbyrO62wf2HNfTt0/n88Dl9t+CcVa5qsaiDH4bA/sBhwGLO55fiZwZk3H3ge4qef5zcDu5ePdgZsb+Hr+laJkbiOxgGcB11Osz70O2HZL38dpHH9Pin+YRwLfBdRQnF8Bs0e9Vtv3DHgecDvlRZyZ/B0YtG2ge0BUr/JYhxfYvqd8/BvgBXUevKw28Arg2rpjladFy4B7gSuAXwIP2JtKLdT1fft74L/x1OI1uzYUx8D3y3pzI5U/6/yezQXWAv9Ynk6eK2nHmmNsFQY9AbXCxX+Btd3fIOnZwLeAD9h+qO5YtjfaPpCih3Io8JLpHG9LJL0FuNf2dXUfewtebfsgilPv90r6vd43a/iebQscBHzJ9iuARxl1ulX378CgGvQENGGVxxr9dqRgW/l3LaWmJT2DIvl8zfa3m4xl+wHgSopToZ3KkklQz/ftcOA4Sb8CLqA4Dft8A3GwfXf5973AdyiSap3fszXAGtvXls8vokhIjfxcBtmgJ6AJqzzWaBHw7vLxuynGa6ZFkoD/Dayy/XdNxJK0m6SdysfPpBhjWkWRiN5WRwwoaknZ3tP2PhQ/h/9j+4/qjiNpR0nPGXkMvBG4iRq/Z7Z/A9wl6XfLl14PrKwzxlaj7UGopjfgWIpqq78EPlbTMc8H7gGepPjf8BSK8YwfArcCPwB2qSHOqym68cuBZeV2bJ2xgAOAn5cxbgI+Ub7+IuBnwGrgQmD7Gn8mRwDfbSJOebwbym3FyM+87p8PxRXDpeX37WJg5yZ+BwZ9y1SMiGjNoJ+CRUQfSwKKiNYkAUVEa5KAIqI1SUAR0ZokoKhE0j69s/8j6pAEFBGtSQLaCki6uJyYuWJkcqakRyR9ulwH6BpJLyhf/3fl8xsl/bWkR7ZwvFmSzpa0RNJySafN9NcUgyEJaOvwJ7YPBuYDZ0jaFdgRuMbFOkBXA6eW+34e+Lzt/Snu8t6SU4AHbR8CHAKcKmluo19BDKQkoK3DGZJuAK6hmJw7D9hAsSYPwHUU6xtBMRH1wvLx18c43huBd5VLeFxLMQVhXv3NjkGX2vADTtIRwBuAw2w/JukqYAfgST81D2cjk/tdEPA+24vrbGtsfdIDGnzPA+4vk89LKJZ2Hc81wPHl4xPH2Gcx8F/LpUKQtG858zxiUpKABt/lwLaSVgF/Q5FgxvMB4IOSlgMvBh7cwj7nUiw/cX15af7LpDcdU5DZ8LEZSc8C/p9tSzoROMn2W9tuVwym/K8Vox0MfKFcDO0B4E9abk8MsPSAIqI1GQOKiNYkAUVEa5KAIqI1SUAR0ZokoIhozf8HHdV+GXZyG5EAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It seems that rotation and shear don't mix! The best accuracy is when rotation is zero."
      ],
      "metadata": {
        "id": "JRnymka4vnla"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Questions\n",
        "Try these questions for better understanding. You may not be able to solve all of them. \n",
        "1. What is the best value for angle constraint and shear constraint you got? How much did the accuracy improve as compared to not using augmentations?\n",
        "2. Can you increase the accuracy by increasing the number of augmentations from each sample?\n",
        "3. Try implementing a few augmentations of your own and experimenting with them. A good reference is <a href=https://www.analyticsvidhya.com/blog/2019/12/image-augmentation-deep-learning-pytorch/>here. </a>\n",
        "4. Try combining various augmentations. What is the highest accuracy you can get? What is the smallest training dataset you can take and still get accuracy above 50%?\n",
        "\n",
        "Whenever you do any experiment, a good practice is to vary the hyperparameters gradually and create a graph of your results, like we did for gridsearch. "
      ],
      "metadata": {
        "id": "cU9AxYWJvs_P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1.ANSWER"
      ],
      "metadata": {
        "id": "XMOP5IzA8wWr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "•The best values of angle constraint is 0.0,0.0,0.0,0.0,0.0,0.0,10.0.\n",
        "The beat values of shear constraint is 0.4,0.6,0.8,1.0,1.2,1.4,1.6"
      ],
      "metadata": {
        "id": "psm83MQr8yp0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2.ANSWER"
      ],
      "metadata": {
        "id": "a__gyUHF83fs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "* Yes,we increase the accuracy by increasing the number of augumentaions from each sample.\n",
        "\n"
      ],
      "metadata": {
        "id": "Z6RIajgz86Y4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3.ANSWER"
      ],
      "metadata": {
        "id": "Zb70ILrf8_EZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow"
      ],
      "metadata": {
        "id": "U8vjAkkdvjOk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!curl -o logo.png https://colab.research.google.com/img/colab_favicon_256px.png\n",
        "import cv2\n",
        "img = cv2.imread('images.png', cv2.IMREAD_UNCHANGED)\n",
        "cv2_imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "sKDlQ5JA9Dax",
        "outputId": "0265603e-94ba-45a4-ef09-26762792fad9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  4534  100  4534    0     0  40846      0 --:--:-- --:--:-- --:--:-- 40846\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=640x480 at 0x7FF39E1F5A10>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "flipLR=np.fliplr(img)\n",
        "plt.imshow(flipLR)\n",
        "plt.title('Left to Right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "k5oYotB39aWH",
        "outputId": "32081d77-a7a2-4f50-e920-3b8b7bd53c26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Left to Right')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4.ANSWER"
      ],
      "metadata": {
        "id": "eWxlpyD4BV3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCABFBLJ9bGn",
        "outputId": "b0f9870c-32b2-44ca-b396-ee435e0aa2f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing all the required libraries\n",
        "import skimage.io as io\n",
        "image=io.imread('/content/images.png')\n",
        "print(image.shape)\n",
        "io.imshow(image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "IJzktpDMBYk1",
        "outputId": "d6839cee-df73-4fb9-9e7a-4773868c032f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(480, 640, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7ff37e61e690>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# flip image up to down \n",
        "import numpy as np\n",
        "flipUD=np.flipud(img)\n",
        "plt.imshow(flipUD)\n",
        "plt.title('up to down')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "9-ATCqa5BefJ",
        "outputId": "435acfa7-8058-4383-9d0d-0aaf73d53af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'up to down')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BzA2CC7wBhxT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}