Multibidder.ipynb

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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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      "version": "3.7.5"
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    "colab": {
      "name": "Multibidder.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/unconst/GradientBidding/blob/master/Multibidder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJ4YLhWm4XFP",
        "colab_type": "text"
      },
      "source": [
        "# Multi-bidder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puSDcME1Gxm5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "90a1e880-294c-45a2-8efb-e6fd89b7baa4"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/unconst/GradientBidding/master/utils_moe.py"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-01-23 01:54:21--  https://raw.githubusercontent.com/unconst/GradientBidding/master/utils_moe.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 37803 (37K) [text/plain]\n",
            "Saving to: ‘utils_moe.py’\n",
            "\n",
            "\rutils_moe.py          0%[                    ]       0  --.-KB/s               \rutils_moe.py        100%[===================>]  36.92K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-01-23 01:54:21 (2.59 MB/s) - ‘utils_moe.py’ saved [37803/37803]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTWxFn1537xZ",
        "colab_type": "code",
        "outputId": "ba849421-41bb-4c1d-ed45-35dcb9b40040",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import types\n",
        "from utils_moe import noisy_top_k_gating\n",
        "from utils_moe import SparseDispatcher\n",
        "from tensorflow.examples.tutorials.mnist import input_data\n",
        "mnist = input_data.read_data_sets(\"../MNIST_data/\", one_hot=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-2-a86030244733>:6: 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_core/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_core/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_core/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_core/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_core/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_core/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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rZahNAm37xe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# FFNN with biases.\n",
        "def expert(i, xs, hparams):\n",
        "    with tf.compat.v1.variable_scope(\"expert\"):\n",
        "        sizes = [hparams.n_inputs] + [hparams.e_hidden for _ in range(hparams.e_layers)] + [hparams.n_embedding]\n",
        "        for i in range(len(sizes) - 1):    \n",
        "            w = tf.Variable(tf.truncated_normal([sizes[i], sizes[i+1]], stddev=0.1))\n",
        "            b = tf.Variable(tf.constant(0.1, shape=[sizes[i+1]]))\n",
        "            for j in range(len(xs)):\n",
        "                xs[j] = tf.matmul(xs[j], w) + b\n",
        "    return xs\n",
        "\n",
        "# Cross entropy loss + accuracy.\n",
        "def target_loss(embedding, targets, hparams):\n",
        "    with tf.compat.v1.variable_scope(\"target_loss\"):\n",
        "        w = tf.Variable(tf.truncated_normal([hparams.n_embedding, hparams.n_targets], stddev=0.1))\n",
        "        b = tf.Variable(tf.constant(0.1, shape=[hparams.n_targets])),\n",
        "        logits = tf.add(tf.matmul(embedding, w), b)\n",
        "        target_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=targets, logits=logits))\n",
        "        correct = tf.equal(tf.argmax(logits, 1), tf.argmax(targets, 1))\n",
        "        accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n",
        "        return target_loss, accuracy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZkwhhZ337xg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def model_fn(hparams):    \n",
        "    x_inputs = tf.placeholder(\"float\", [None, hparams.n_inputs], 'inputs')\n",
        "    y_targets = tf.placeholder(\"float\", [None, hparams.n_targets], 'targets')    \n",
        "    \n",
        "    \n",
        "    expert_inputs = []\n",
        "    client_revenue = []\n",
        "    client_weights = []\n",
        "    client_dispatchers = []\n",
        "    for i in range(hparams.n_clients):\n",
        "        # Sparsely gated mixture of experts with choice k. Produces an importance score \n",
        "        # for each x_input then chooses the topk. These children recieve the outgoing query.\n",
        "        # expert_inputs is a list of tensors, inputs for each expert.\n",
        "        gates, load = noisy_top_k_gating(x_inputs, hparams.n_experts, train = True, k = hparams.k)\n",
        "        dispatcher = SparseDispatcher(hparams.n_experts, gates)\n",
        "        dispactched_inputs = dispatcher.dispatch(x_inputs)\n",
        "        \n",
        "        \n",
        "     \n",
        "        \n",
        "        # Basic importance scores can be attained from the gating network by summing over the importance \n",
        "        # of each example. We choose a 'self-importance' score here which counts as the in loop in our\n",
        "        # incentive function. The network should try to maximize this value.\n",
        "        importance = tf.linalg.normalize(tf.reduce_sum(gates, 0))[0]\n",
        "        self_weight = tf.Variable(tf.constant([1.0]))\n",
        "        weights = tf.linalg.normalize(tf.concat([self_weight, importance], axis=0))\n",
        "    \n",
        "        revenue = tf.slice(weights, [0], [1])\n",
        "        \n",
        "        # Save references.\n",
        "        expert_inputs.append(dispactched_inputs)\n",
        "        client_dispatchers.append(dispatcher)\n",
        "        client_weights.append(weights)\n",
        "        client_revenue.append(client_revenue)\n",
        "        \n",
        "    \n",
        "    expert_outputs = []\n",
        "    for i in range(hparams.n_experts):\n",
        "        # Each expert takes a list of tensors one for each client and produces a list of tensors one\n",
        "        # for each client.\n",
        "        outputs = expert(i, expert_inputs[:][i], hparams)\n",
        "        expert_outputs.append(outputs)\n",
        "        \n",
        "        \n",
        "        \n",
        "    client_embeddings = []\n",
        "    for j in range(hparams.n_clients):\n",
        "        masked_inputs = []\n",
        "        for i in range(hparams.n_experts):\n",
        "            # Each output is masked based on the bid.\n",
        "            shift = tf.reduce_mean(client_weights[:][i])\n",
        "            bid = tf.slice(client_weights[j][i], [0], [1])\n",
        "            mask = tf.nn.relu(bid - shift)\n",
        "            masked_input = mask * expert_outputs[i][j]\n",
        "            masked_inputs.append(masked_input)\n",
        "            \n",
        "        # Combine the expert_inputs.\n",
        "        embedding = client_dispatchers[j].combine(masked_inputs)\n",
        "        client_embeddings.append(embedding)\n",
        "        \n",
        "        \n",
        "    losses = []\n",
        "    accuracies = []\n",
        "    for j in range(hparams.n_clients):\n",
        "        client_embedding = client_embeddings[j]\n",
        "        client_revenue = client_revenues[j]\n",
        "        \n",
        "        # Loss and accuracy stuff.\n",
        "        loss, accuracy = target_loss(client_embedding, y_targets, hparams)\n",
        "\n",
        "        # Run the step: optimize for loss and revenue. \n",
        "        train_step = tf.train.AdamOptimizer(hparams.learning_rate).minimize(loss - client_revenue)\n",
        "        losses.append(loss)\n",
        "        accuracy.append(accuracy)\n",
        "        \n",
        "    \n",
        "    metrics = {\n",
        "        'losses': losses,\n",
        "        'revenues': revenues,\n",
        "        'accuracies': accuracies,\n",
        "    }\n",
        "    return train_step, metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "soxHY7vL37xj",
        "colab_type": "code",
        "outputId": "d3f767d3-2b9c-481b-88c6-b82c47b3ac67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "hparams = types.SimpleNamespace( \n",
        "    n_inputs = 784,\n",
        "    n_targets = 10,\n",
        "    k = 3,\n",
        "    n_experts = 3,\n",
        "    n_clients = 3,\n",
        "    e_layers = 2,\n",
        "    e_hidden = 256,\n",
        "    n_embedding = 256,\n",
        "    batch_size=256,\n",
        "    learning_rate=1e-3,\n",
        "    n_iterations = 10000,\n",
        "    n_print = 100,\n",
        "    market_shift = 0.2,\n",
        ")\n",
        "\n",
        "graph = tf.Graph()\n",
        "session = tf.Session(graph=graph)\n",
        "with graph.as_default():\n",
        "    train_step, metrics = model_fn(hparams)\n",
        "    session.run(tf.global_variables_initializer())\n",
        "\n",
        "for i in range(hparams.n_iterations):\n",
        "    batch_x, batch_y = mnist.train.next_batch(hparams.batch_size)\n",
        "    feeds = {'inputs:0': batch_x, 'targets:0': batch_y}\n",
        "    session.run(train_step, feeds)\n",
        "\n",
        "    if i % hparams.n_print == 0:\n",
        "        feeds = {'inputs:0': batch_x, 'targets:0': batch_y}\n",
        "        train_metrics = session.run(metrics, feeds)\n",
        "        for key in train_metrics:\n",
        "            print (str(key) + \":  \" + str(train_metrics[key]))\n",
        "        print ('-')\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /content/utils_moe.py:487: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/utils_moe.py:489: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/utils_moe.py:499: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/utils_moe.py:501: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:3413: The name tf.get_variable_scope is deprecated. Please use tf.compat.v1.get_variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/utils_moe.py:504: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/utils_moe.py:386: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/utils_moe.py:249: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Deprecated in favor of operator or tf.math.divide.\n",
            "WARNING:tensorflow:From /content/utils_moe.py:250: The name tf.unsorted_segment_sum is deprecated. Please use tf.math.unsorted_segment_sum instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/utils_moe.py:846: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1606\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[0;32m-> 1607\u001b[0;31m     \u001b[0mc_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_FinishOperation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_desc\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   1608\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: Dimension 0 in both shapes must be equal, but are 4 and 1. Shapes are [4] and [1].\n\tFrom merging shape 0 with other shapes. for 'Slice/input' (op: 'Pack') with input shapes: [4], [1].",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    527\u001b[0m                 \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_arg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_ref\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 528\u001b[0;31m                 preferred_dtype=default_dtype)\n\u001b[0m\u001b[1;32m    529\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\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/framework/ops.py\u001b[0m in \u001b[0;36minternal_convert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m   1296\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\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[0;32m-> 1297\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\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   1298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/array_ops.py\u001b[0m in \u001b[0;36m_autopacking_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m   1266\u001b[0m     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_cast_nested_seqs_to_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_autopacking_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"packed\"\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   1268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/array_ops.py\u001b[0m in \u001b[0;36m_autopacking_helper\u001b[0;34m(list_or_tuple, dtype, name)\u001b[0m\n\u001b[1;32m   1202\u001b[0m               constant_op.constant(elem, dtype=dtype, name=str(i)))\n\u001b[0;32m-> 1203\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melems_as_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\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   1204\u001b[0m     \u001b[0;32melse\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/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mpack\u001b[0;34m(values, axis, name)\u001b[0m\n\u001b[1;32m   6302\u001b[0m   _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[0;32m-> 6303\u001b[0;31m         \"Pack\", values=values, axis=axis, name=name)\n\u001b[0m\u001b[1;32m   6304\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    793\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m                          op_def=op_def)\n\u001b[0m\u001b[1;32m    795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    506\u001b[0m                 instructions)\n\u001b[0;32m--> 507\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   3356\u001b[0m     return self._create_op_internal(op_type, inputs, dtypes, input_types, name,\n\u001b[0;32m-> 3357\u001b[0;31m                                     attrs, op_def, compute_device)\n\u001b[0m\u001b[1;32m   3358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_op_internal\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[1;32m   3425\u001b[0m           \u001b[0moriginal_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_original_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3426\u001b[0;31m           op_def=op_def)\n\u001b[0m\u001b[1;32m   3427\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_op_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute_device\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/framework/ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)\u001b[0m\n\u001b[1;32m   1769\u001b[0m       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,\n\u001b[0;32m-> 1770\u001b[0;31m                                 control_input_ops)\n\u001b[0m\u001b[1;32m   1771\u001b[0m     \u001b[0;31m# pylint: enable=protected-access\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/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1609\u001b[0m     \u001b[0;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1610\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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   1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Dimension 0 in both shapes must be equal, but are 4 and 1. Shapes are [4] and [1].\n\tFrom merging shape 0 with other shapes. for 'Slice/input' (op: 'Pack') with input shapes: [4], [1].",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1606\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[0;32m-> 1607\u001b[0;31m     \u001b[0mc_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_FinishOperation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_desc\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   1608\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: Dimension 0 in both shapes must be equal, but are 4 and 1. Shapes are [4] and [1].\n\tFrom merging shape 0 with other shapes. for 'Slice/packed' (op: 'Pack') with input shapes: [4], [1].",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    541\u001b[0m               observed = ops.internal_convert_to_tensor(\n\u001b[0;32m--> 542\u001b[0;31m                   values, as_ref=input_arg.is_ref).dtype.name\n\u001b[0m\u001b[1;32m    543\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\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/framework/ops.py\u001b[0m in \u001b[0;36minternal_convert_to_tensor\u001b[0;34m(value, dtype, name, as_ref, preferred_dtype, ctx, accepted_result_types)\u001b[0m\n\u001b[1;32m   1296\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mret\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[0;32m-> 1297\u001b[0;31m       \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mas_ref\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   1298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/array_ops.py\u001b[0m in \u001b[0;36m_autopacking_conversion_function\u001b[0;34m(v, dtype, name, as_ref)\u001b[0m\n\u001b[1;32m   1266\u001b[0m     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_cast_nested_seqs_to_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_autopacking_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"packed\"\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   1268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/array_ops.py\u001b[0m in \u001b[0;36m_autopacking_helper\u001b[0;34m(list_or_tuple, dtype, name)\u001b[0m\n\u001b[1;32m   1202\u001b[0m               constant_op.constant(elem, dtype=dtype, name=str(i)))\n\u001b[0;32m-> 1203\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melems_as_tensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscope\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   1204\u001b[0m     \u001b[0;32melse\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/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mpack\u001b[0;34m(values, axis, name)\u001b[0m\n\u001b[1;32m   6302\u001b[0m   _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[0;32m-> 6303\u001b[0;31m         \"Pack\", values=values, axis=axis, name=name)\n\u001b[0m\u001b[1;32m   6304\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    793\u001b[0m                          \u001b[0minput_types\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattr_protos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 794\u001b[0;31m                          op_def=op_def)\n\u001b[0m\u001b[1;32m    795\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    506\u001b[0m                 instructions)\n\u001b[0;32m--> 507\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36mcreate_op\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   3356\u001b[0m     return self._create_op_internal(op_type, inputs, dtypes, input_types, name,\n\u001b[0;32m-> 3357\u001b[0;31m                                     attrs, op_def, compute_device)\n\u001b[0m\u001b[1;32m   3358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py\u001b[0m in \u001b[0;36m_create_op_internal\u001b[0;34m(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)\u001b[0m\n\u001b[1;32m   3425\u001b[0m           \u001b[0moriginal_op\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_original_op\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3426\u001b[0;31m           op_def=op_def)\n\u001b[0m\u001b[1;32m   3427\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_op_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompute_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute_device\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/framework/ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)\u001b[0m\n\u001b[1;32m   1769\u001b[0m       self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,\n\u001b[0;32m-> 1770\u001b[0;31m                                 control_input_ops)\n\u001b[0m\u001b[1;32m   1771\u001b[0m     \u001b[0;31m# pylint: enable=protected-access\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/framework/ops.py\u001b[0m in \u001b[0;36m_create_c_op\u001b[0;34m(graph, node_def, inputs, control_inputs)\u001b[0m\n\u001b[1;32m   1609\u001b[0m     \u001b[0;31m# Convert to ValueError for backwards compatibility.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1610\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\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   1611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Dimension 0 in both shapes must be equal, but are 4 and 1. Shapes are [4] and [1].\n\tFrom merging shape 0 with other shapes. for 'Slice/packed' (op: 'Pack') with input shapes: [4], [1].",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-62d2bca779d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\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---> 20\u001b[0;31m     \u001b[0mtrain_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhparams\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     21\u001b[0m     \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_variables_initializer\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     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-4-f3fe435f58fc>\u001b[0m in \u001b[0;36mmodel_fn\u001b[0;34m(hparams)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimportance\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\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     27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m         \u001b[0mrevenue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;31m# Save references.\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/ops/array_ops.py\u001b[0m in \u001b[0;36mslice\u001b[0;34m(input_, begin, size, name)\u001b[0m\n\u001b[1;32m    853\u001b[0m     \u001b[0mA\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;32mas\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    854\u001b[0m   \"\"\"\n\u001b[0;32m--> 855\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbegin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\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    856\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36m_slice\u001b[0;34m(input, begin, size, name)\u001b[0m\n\u001b[1;32m   9220\u001b[0m   \u001b[0;31m# Add nodes to the TensorFlow graph.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9221\u001b[0m   _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[0;32m-> 9222\u001b[0;31m         \"Slice\", input=input, begin=begin, size=size, name=name)\n\u001b[0m\u001b[1;32m   9223\u001b[0m   \u001b[0m_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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   9224\u001b[0m   \u001b[0m_inputs_flat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_op\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\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/framework/op_def_library.py\u001b[0m in \u001b[0;36m_apply_op_helper\u001b[0;34m(self, op_type_name, name, **keywords)\u001b[0m\n\u001b[1;32m    544\u001b[0m               raise ValueError(\n\u001b[1;32m    545\u001b[0m                   \u001b[0;34m\"Tried to convert '%s' to a tensor and failed. Error: %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m                   (input_name, err))\n\u001b[0m\u001b[1;32m    547\u001b[0m             prefix = (\"Input '%s' of '%s' Op has type %s that does not match\" %\n\u001b[1;32m    548\u001b[0m                       (input_name, op_type_name, observed))\n",
            "\u001b[0;31mValueError\u001b[0m: Tried to convert 'input' to a tensor and failed. Error: Dimension 0 in both shapes must be equal, but are 4 and 1. Shapes are [4] and [1].\n\tFrom merging shape 0 with other shapes. for 'Slice/packed' (op: 'Pack') with input shapes: [4], [1]."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G94Ta7EX37xm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}