Copy_of_demoHCR.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of demoHCR.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ganesh-deshmukh/Google-colab-hcr/blob/master/Copy_of_demoHCR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "Pi-gHSL5jmiT",
        "colab_type": "code",
        "outputId": "6252f099-333a-4e18-eed7-58e5270c356d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.13.0-rc1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5_2RwHPFj0pd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.examples.tutorials.mnist import input_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "86PqFCCoj_t3",
        "colab_type": "code",
        "outputId": "cd5d6d40-a4e5-4d4d-b1a0-e41a5b7623df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        }
      },
      "cell_type": "code",
      "source": [
        "mnist = input_data.read_data_sets(\"/MNIST_data/\", one_hot=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-6-13c79b205b5a>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:252: _internal_retry.<locals>.wrap.<locals>.wrapped_fn (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use urllib or similar directly.\n",
            "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting /MNIST_data/train-images-idx3-ubyte.gz\n",
            "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting /MNIST_data/train-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.one_hot on tensors.\n",
            "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
            "Extracting /MNIST_data/t10k-images-idx3-ubyte.gz\n",
            "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
            "Extracting /MNIST_data/t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "00ZAqDfG9Wbi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "n_train = mnist.train.num_examples\n",
        "n_validation = mnist.validation.num_examples\n",
        "n_test = mnist.test.num_examples"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ik1K42539vbM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "n_input = 784   # input layer (28x28 pixels)\n",
        "n_hidden1 = 512 # 1st hidden layer\n",
        "n_hidden2 = 256 # 2nd hidden layer\n",
        "n_hidden3 = 128 # 3rd hidden layer\n",
        "n_output = 10   # output layer (0-9 digits)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YoQp_wzzOF9o",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "learning_rate = 0.01\n",
        "n_iterations = 1000\n",
        "batch_size = 128\n",
        "dropout = 0.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hq3vksV1ns8t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = tf.placeholder(\"float\", [None, n_input])\n",
        "Y = tf.placeholder(\"float\", [None, n_output])\n",
        "keep_prob = tf.placeholder(tf.float32) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uMD_6Ug1oKqp",
        "colab_type": "code",
        "outputId": "630627ae-59fb-4228-b38f-f4f657f085a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "weights = {\n",
        "    'w1': tf.Variable(tf.truncated_normal([n_input, n_hidden1], stddev=0.1)),\n",
        "    'w2': tf.Variable(tf.truncated_normal([n_hidden1, n_hidden2], stddev=0.1)),\n",
        "    'w3': tf.Variable(tf.truncated_normal([n_hidden2, n_hidden3], stddev=0.1)),\n",
        "    'out': tf.Variable(tf.truncated_normal([n_hidden3, n_output], stddev=0.1)),\n",
        "}"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "meFHmiH8yYPS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "biases = {\n",
        "    'b1': tf.Variable(tf.constant(0.1, shape=[n_hidden1])),\n",
        "    'b2': tf.Variable(tf.constant(0.1, shape=[n_hidden2])),\n",
        "    'b3': tf.Variable(tf.constant(0.1, shape=[n_hidden3])),\n",
        "    'out': tf.Variable(tf.constant(0.1, shape=[n_output]))\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7xsebVY60VSE",
        "colab_type": "code",
        "outputId": "3c63f74a-234a-4a3d-b153-9d73eb5283b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "layer_1 = tf.add(tf.matmul(X, weights['w1']), biases['b1'])\n",
        "layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])\n",
        "layer_3 = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])\n",
        "layer_drop = tf.nn.dropout(layer_3, keep_prob)\n",
        "output_layer = tf.matmul(layer_3, weights['out']) + biases['out']"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-13-63c38a250018>:5: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "0Jz_Jaw0yk1N",
        "colab_type": "code",
        "outputId": "1c20f582-7037-40d6-80e3-30a901ea4a3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        }
      },
      "cell_type": "code",
      "source": [
        "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=output_layer))\n",
        "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-14-812430f7e244>:1: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_UYaVuxg08hT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "correct_pred = tf.equal(tf.argmax(output_layer, 1), tf.argmax(Y, 1))\n",
        "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "v1FvMvpw1WSV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "init = tf.global_variables_initializer()\n",
        "sess = tf.Session()\n",
        "sess.run(init)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "h4lLYEjd1YZw",
        "colab_type": "code",
        "outputId": "5aaae601-e3e8-4a7d-9bd1-883ee1a5d7b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "cell_type": "code",
      "source": [
        "# train on mini batches\n",
        "for i in range(n_iterations):\n",
        "    batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
        "    sess.run(train_step, feed_dict={X: batch_x, Y: batch_y, keep_prob:dropout})\n",
        "\n",
        "    # print loss and accuracy (per minibatch)\n",
        "    if i%100==0:\n",
        "        minibatch_loss, minibatch_accuracy = sess.run([cross_entropy, accuracy], feed_dict={X: batch_x, Y: batch_y, keep_prob:1.0})\n",
        "        print(\"Iteration\", str(i), \"\\t| Loss =\", str(minibatch_loss), \"\\t| Accuracy =\", str(minibatch_accuracy))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration 0 \t| Loss = 3.7526503 \t| Accuracy = 0.1328125\n",
            "Iteration 100 \t| Loss = 0.5110942 \t| Accuracy = 0.84375\n",
            "Iteration 200 \t| Loss = 0.3695987 \t| Accuracy = 0.890625\n",
            "Iteration 300 \t| Loss = 0.27453738 \t| Accuracy = 0.90625\n",
            "Iteration 400 \t| Loss = 0.44472927 \t| Accuracy = 0.84375\n",
            "Iteration 500 \t| Loss = 0.27206367 \t| Accuracy = 0.890625\n",
            "Iteration 600 \t| Loss = 0.22126667 \t| Accuracy = 0.9375\n",
            "Iteration 700 \t| Loss = 0.337704 \t| Accuracy = 0.921875\n",
            "Iteration 800 \t| Loss = 0.29385513 \t| Accuracy = 0.921875\n",
            "Iteration 900 \t| Loss = 0.6744746 \t| Accuracy = 0.875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qD75S8P11bS7",
        "colab_type": "code",
        "outputId": "a593598c-6bcb-4990-d49d-df42ea602fec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "test_accuracy = sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels, keep_prob:1.0})\n",
        "print(\"\\nAccuracy on test set:\", test_accuracy)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy on test set: 0.9206\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "pt4bZlIK5UhK",
        "colab_type": "code",
        "outputId": "e83cf26c-e2ca-4055-9867-562736326914",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from PIL import Image\n",
        "\n",
        "img = np.invert(Image.open(\"six-resized.png\").convert('L')).ravel()\n",
        "prediction = sess.run(tf.argmax(output_layer,1), feed_dict={X: [img]})\n",
        "print (\"Prediction for test image:\", np.squeeze(prediction))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction for test image: 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OzMkrnwx1eKO",
        "colab_type": "code",
        "outputId": "1a52d962-fbb4-498c-c3be-1016eb422c0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from PIL import Image\n",
        "# print(\"Image.open\",type(Image.open(\"test_img.png\")))\n",
        "img = Image.open(\"six-resized.png\")\n",
        "image = img.resize((28,28),Image.ANTIALIAS)\n",
        "img2 = np.invert(image).ravel()\n",
        "# print(\"img is \",(img.size))\n",
        "# print(img.shape)\n",
        "# print(Image.fromarray(image).shape)\n",
        "prediction = sess.run(tf.argmax(output_layer,1), feed_dict={X: [img2]})\n",
        "print (\"Prediction for test image:\", np.squeeze(prediction))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction for test image: 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "7c4da18e-3ed2-4dc4-99af-1bdea6ec2e00",
        "id": "87QLiQQbVhGq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "import cv2\n",
        "from PIL import Image\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "cv_img = cv2.imread(\"six-resized.png\")\n",
        "img2 = cv2.resize(cv_img, (28,28))\n",
        "gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
        "inverted = cv2.bitwise_not(gray)\n",
        "\n",
        "\n",
        "predict = sess.run(tf.argmax(output_layer,1), feed_dict={X: [gray.ravel()]})\n",
        "print (\"Prediction for gray image:\", np.squeeze(predict))\n",
        "\n",
        "predict = sess.run(tf.argmax(output_layer,1), feed_dict={X: [inverted.ravel()]})\n",
        "print (\"Prediction for inverted image:\", np.squeeze(predict))\n",
        "\n",
        "\n",
        "# img3 = np.invert(img2).convert('L')).ravel()\n",
        "# print(\"ravel is \\n\", np.invert(img2).ravel())                 \n",
        "# prediction = sess.run(tf.argmax(output_layer,1), feed_dict={X: [img2.ravel()]})\n",
        "# print (\"Prediction for test image:\", np.squeeze(prediction))                \n",
        "\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction for gray image: 5\n",
            "Prediction for inverted image: 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RN98l-At6XtS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "435a3ng4608B",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}