LinearRegressionCost.ipynb

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      "authorship_tag": "ABX9TyNd3Le/03NdUFpK6EYOtdwd",
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      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gaeunfalo16/Deep_Learning/blob/master/LinearRegressionCost.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lugDZLIc_7HR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "d98522d1-b959-46ad-9dbf-86180ce2b950"
      },
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "X = [1, 2, 3]\n",
        "Y = [1, 2, 3]\n",
        "\n",
        "W = tf.placeholder(tf.float32)\n",
        "# Our hypothesis\n",
        "hypothesis = W * X\n",
        "\n",
        "#cost/loss function\n",
        "cost = tf.reduce_mean(tf.square(hypothesis - Y))\n",
        "\n",
        "#Launch the graph in a session\n",
        "sess = tf.Session()\n",
        "\n",
        "#Initialize global variables in the graph\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "#Variables for plotting function\n",
        "W_val = []\n",
        "cost_val = []\n",
        "\n",
        "for i in range(-30, 50) :\n",
        "  feed_W = W * i\n",
        "  curr_cost, curr_W = sess.run([cost, W], feed_dict={W: feed_W})\n",
        "  W_val.append(curr_W)\n",
        "  cost_val.appned(curr_cost)\n",
        "\n",
        "#show the cost function\n",
        "plt.plot(W_val, cost_val)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-640632ec24b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m   \u001b[0mfeed_W\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mW\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m   \u001b[0mcurr_cost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr_W\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfeed_W\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m   \u001b[0mW_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurr_W\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m   \u001b[0mcost_val\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappned\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurr_cost\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    954\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    955\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 956\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    957\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1129\u001b[0m                             \u001b[0;34m'For reference, the tensor object was '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1130\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_val\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' which was passed to the '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1131\u001b[0;31m                             'feed with key ' + str(feed) + '.')\n\u001b[0m\u001b[1;32m   1132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1133\u001b[0m           \u001b[0msubfeed_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_numpy_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles. For reference, the tensor object was Tensor(\"mul_6:0\", dtype=float32) which was passed to the feed with key Tensor(\"Placeholder_4:0\", dtype=float32)."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsLoN63TDZpz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Minimize : Gradient Descent using Derivative : W -= Learning rate * derivative\n",
        "learning_rate = 0.1\n",
        "gradient = tf.reduce_mean((W * X - Y) * X)\n",
        "descent = W - learning_rate * gradient\n",
        "update = W.assign(descent)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCmLJbaWEF-q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "6858f578-6d2b-45ac-c755-a67b27b1b9e0"
      },
      "source": [
        "import tensorflow as tf\n",
        "x_data = [1, 2, 3]\n",
        "y_data = [1, 2, 3]\n",
        "\n",
        "W = tf.Variable(tf.random_normal([1]), name = 'weight')\n",
        "X = tf.placeholder(tf.float32)\n",
        "Y = tf.placeholder(tf.float32)\n",
        "\n",
        "#hyothesis\n",
        "hypothesis = X * W\n",
        "\n",
        "#cost(loss) function\n",
        "cost = tf.reduce_sum(tf.square(hypothesis - Y))\n",
        "\n",
        "#minimize : Gradient descent using derivative : W -= W - learning_rate * derivative\n",
        "learning_rate = 0.1\n",
        "gradient = tf.reduce_mean((W * X - Y) * X)\n",
        "descent = W - learning_rate * gradient\n",
        "update = W.assign(descent)\n",
        "\n",
        "#Launch the graph in a session\n",
        "sess = tf.Session()\n",
        "#Initialize global variables in the graph\n",
        "sess.run(tf.global_variables_initializer())\n",
        "for step in range(21) :\n",
        "  sess.run(update, feed_dict = {X : x_data, Y : y_data})\n",
        "  print(step, sess.run(cost, feed_dict = {X : x_data, Y : y_data}), sess.run(W))\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 0.14693737 [0.8975523]\n",
            "1 0.041795455 [0.94536126]\n",
            "2 0.011888475 [0.97085935]\n",
            "3 0.0033816213 [0.9844583]\n",
            "4 0.00096187287 [0.99171114]\n",
            "5 0.00027359437 [0.9955793]\n",
            "6 7.78239e-05 [0.9976423]\n",
            "7 2.2138005e-05 [0.9987425]\n",
            "8 6.297203e-06 [0.9993293]\n",
            "9 1.7912924e-06 [0.9996423]\n",
            "10 5.0931703e-07 [0.99980927]\n",
            "11 1.4489261e-07 [0.99989825]\n",
            "12 4.114925e-08 [0.99994576]\n",
            "13 1.1727209e-08 [0.99997103]\n",
            "14 3.3309568e-09 [0.99998456]\n",
            "15 9.531078e-10 [0.9999918]\n",
            "16 2.666134e-10 [0.99999565]\n",
            "17 7.48237e-11 [0.9999977]\n",
            "18 2.2385649e-11 [0.99999875]\n",
            "19 5.7873706e-12 [0.99999934]\n",
            "20 1.5489832e-12 [0.99999964]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnVE7pXZJf9W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "934dc54f-7342-4707-c938-4405b0c80dbe"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "#tf Graph Input\n",
        "X = [1, 2, 3]\n",
        "Y = [1, 2, 3]\n",
        "\n",
        "#Set wrong model weights\n",
        "W = tf.Variable(5.0)\n",
        "#Linear model\n",
        "hypothesis = W * X\n",
        "#cost(loss) function\n",
        "cost = tf.reduce_mean(tf.square(hypothesis - Y))\n",
        "\n",
        "#Minimize : Gradeint Descent Magic\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.1)\n",
        "train = optimizer.minimize(cost)\n",
        "\n",
        "#Launch the graph in a session\n",
        "sess = tf.Session()\n",
        "#Initialize the global variable in the graph\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "for step in range(100) :\n",
        "  print(step, sess.run(W))\n",
        "  sess.run(train)\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 5.0\n",
            "1 1.2666664\n",
            "2 1.0177778\n",
            "3 1.0011852\n",
            "4 1.000079\n",
            "5 1.0000052\n",
            "6 1.0000004\n",
            "7 1.0\n",
            "8 1.0\n",
            "9 1.0\n",
            "10 1.0\n",
            "11 1.0\n",
            "12 1.0\n",
            "13 1.0\n",
            "14 1.0\n",
            "15 1.0\n",
            "16 1.0\n",
            "17 1.0\n",
            "18 1.0\n",
            "19 1.0\n",
            "20 1.0\n",
            "21 1.0\n",
            "22 1.0\n",
            "23 1.0\n",
            "24 1.0\n",
            "25 1.0\n",
            "26 1.0\n",
            "27 1.0\n",
            "28 1.0\n",
            "29 1.0\n",
            "30 1.0\n",
            "31 1.0\n",
            "32 1.0\n",
            "33 1.0\n",
            "34 1.0\n",
            "35 1.0\n",
            "36 1.0\n",
            "37 1.0\n",
            "38 1.0\n",
            "39 1.0\n",
            "40 1.0\n",
            "41 1.0\n",
            "42 1.0\n",
            "43 1.0\n",
            "44 1.0\n",
            "45 1.0\n",
            "46 1.0\n",
            "47 1.0\n",
            "48 1.0\n",
            "49 1.0\n",
            "50 1.0\n",
            "51 1.0\n",
            "52 1.0\n",
            "53 1.0\n",
            "54 1.0\n",
            "55 1.0\n",
            "56 1.0\n",
            "57 1.0\n",
            "58 1.0\n",
            "59 1.0\n",
            "60 1.0\n",
            "61 1.0\n",
            "62 1.0\n",
            "63 1.0\n",
            "64 1.0\n",
            "65 1.0\n",
            "66 1.0\n",
            "67 1.0\n",
            "68 1.0\n",
            "69 1.0\n",
            "70 1.0\n",
            "71 1.0\n",
            "72 1.0\n",
            "73 1.0\n",
            "74 1.0\n",
            "75 1.0\n",
            "76 1.0\n",
            "77 1.0\n",
            "78 1.0\n",
            "79 1.0\n",
            "80 1.0\n",
            "81 1.0\n",
            "82 1.0\n",
            "83 1.0\n",
            "84 1.0\n",
            "85 1.0\n",
            "86 1.0\n",
            "87 1.0\n",
            "88 1.0\n",
            "89 1.0\n",
            "90 1.0\n",
            "91 1.0\n",
            "92 1.0\n",
            "93 1.0\n",
            "94 1.0\n",
            "95 1.0\n",
            "96 1.0\n",
            "97 1.0\n",
            "98 1.0\n",
            "99 1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4mbQHTYK9od",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "019aec8c-f90a-4ca5-e2ad-43ba8e275081"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "X = [1, 2, 3]\n",
        "Y = [1, 2, 3]\n",
        "\n",
        "#Set wrong weights\n",
        "W = tf.Variable(5.)\n",
        "#Linear model\n",
        "hypothesis = X * W\n",
        "#Manual gradient\n",
        "gradient = tf.reduce_mean((X * W - Y) * X) * 2\n",
        "#Cost(Loss) function\n",
        "cost = tf.reduce_mean(tf.square(hypothesis - Y))\n",
        "optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.01)\n",
        "\n",
        "#Get gradients\n",
        "gvs = optimizer.compute_gradients(cost)\n",
        "#Apply gradients\n",
        "apply_gradients = optimizer.apply_gradients(gvs)\n",
        "\n",
        "#Launch the graph in a session\n",
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "for step in range(100) :\n",
        "  print(step, sess.run([gradient, W, gvs]))\n",
        "  sess.run(apply_gradients)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-74c98fbfeb12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgvs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m   \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapply_gradient\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    954\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    955\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 956\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    957\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    958\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1163\u001b[0m     \u001b[0;31m# Create a fetch handler to take care of the structure of fetches.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m     fetch_handler = _FetchHandler(\n\u001b[0;32m-> 1165\u001b[0;31m         self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)\n\u001b[0m\u001b[1;32m   1166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m     \u001b[0;31m# Run request and get response.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, graph, fetches, feeds, feed_handles)\u001b[0m\n\u001b[1;32m    472\u001b[0m     \"\"\"\n\u001b[1;32m    473\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_mapper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_FetchMapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_targets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mfor_fetch\u001b[0;34m(fetch)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m       \u001b[0;31m# NOTE(touts): This is also the code path for namedtuples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0m_ListFetchMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections_abc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_DictFetchMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fetches)\u001b[0m\n\u001b[1;32m    373\u001b[0m     \"\"\"\n\u001b[1;32m    374\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FetchMapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfetch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unique_fetches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_uniquify_fetches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    373\u001b[0m     \"\"\"\n\u001b[1;32m    374\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FetchMapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfetch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unique_fetches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_uniquify_fetches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mfor_fetch\u001b[0;34m(fetch)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m       \u001b[0;31m# NOTE(touts): This is also the code path for namedtuples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0m_ListFetchMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections_abc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_DictFetchMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fetches)\u001b[0m\n\u001b[1;32m    373\u001b[0m     \"\"\"\n\u001b[1;32m    374\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FetchMapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfetch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unique_fetches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_uniquify_fetches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    373\u001b[0m     \"\"\"\n\u001b[1;32m    374\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FetchMapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfetch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unique_fetches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_uniquify_fetches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mfor_fetch\u001b[0;34m(fetch)\u001b[0m\n\u001b[1;32m    264\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m       \u001b[0;31m# NOTE(touts): This is also the code path for namedtuples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0m_ListFetchMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    267\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections_abc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0m_DictFetchMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fetches)\u001b[0m\n\u001b[1;32m    373\u001b[0m     \"\"\"\n\u001b[1;32m    374\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FetchMapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfetch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unique_fetches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_uniquify_fetches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    373\u001b[0m     \"\"\"\n\u001b[1;32m    374\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetch_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 375\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_FetchMapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_fetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfetch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    376\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_unique_fetches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_uniquify_fetches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mappers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36mfor_fetch\u001b[0;34m(fetch)\u001b[0m\n\u001b[1;32m    261\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfetch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m       raise TypeError('Fetch argument %r has invalid type %r' %\n\u001b[0;32m--> 263\u001b[0;31m                       (fetch, type(fetch)))\n\u001b[0m\u001b[1;32m    264\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m       \u001b[0;31m# NOTE(touts): This is also the code path for namedtuples.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Fetch argument None has invalid type <class 'NoneType'>"
          ]
        }
      ]
    }
  ]
}