U4-M4-tf-prep.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    },
    "colab": {
      "name": "U4-M4-tf-prep.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/DJCordhose/deep-learning-crash-course-notebooks/blob/master/U4-M4-tf-prep.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9FucynAtTGl",
        "colab_type": "text"
      },
      "source": [
        "# Preparing our Keras model for serving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQcc0wmXtTGo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "60d4b7f4-bc6b-41ea-ed81-db196abf8c83"
      },
      "source": [
        "import pandas as pd\n",
        "print(pd.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.25.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pP1I1kR6tTGt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "10dbe5b2-a4de-4086-8e34-7e07829a93f9"
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 1.x\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.15.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5kvapPatTGw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f149e5b4-5894-418f-be70-612845cfdad3"
      },
      "source": [
        "# let's see what compute devices we have available, hopefully a GPU \n",
        "sess = tf.Session()\n",
        "devices = sess.list_devices()\n",
        "for d in devices:\n",
        "    print(d.name)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/job:localhost/replica:0/task:0/device:CPU:0\n",
            "/job:localhost/replica:0/task:0/device:XLA_CPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wH8XJpvdtTGz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "993c5a7c-c643-480c-89bc-1a0261f21834"
      },
      "source": [
        "# a small sane check, does tf seem to work ok?\n",
        "hello = tf.constant('Hello TF!')\n",
        "print(sess.run(hello))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "b'Hello TF!'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9TjGD0HtTG2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7209a1c6-9111-4a42-8d15-6d4c32f93948"
      },
      "source": [
        "from tensorflow import keras\n",
        "print(keras.__version__)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.4-tf\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fm945SNgtTG5",
        "colab_type": "text"
      },
      "source": [
        "## Loading and validating our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Q3iIwiHtTG5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "be1bccb9-b9dc-48a3-af3c-7918c5e83e49"
      },
      "source": [
        "!curl -O https://raw.githubusercontent.com/DJCordhose/deep-learning-crash-course-notebooks/master/model/insurance.h5"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  175k  100  175k    0     0  1218k      0 --:--:-- --:--:-- --:--:-- 1218k\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7RkWxTmtTG9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "45de5c6c-3de5-4d84-a43c-3ce24d28eaea"
      },
      "source": [
        "!ls -l insurance.h5"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 179704 Mar 21 15:48 insurance.h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G23JxooFtTG_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "a459e998-a8b1-4368-d097-a2bded853d16"
      },
      "source": [
        "model = keras.models.load_model('./insurance.h5')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/init_ops.py:97: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
            "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkctZ6gntTHC",
        "colab_type": "text"
      },
      "source": [
        "### Descison boundaries for 2 dimensions\n",
        "\n",
        "![Descison Boundaries for 2 Dimensions](https://djcordhose.github.io/ai/img/manning/nn-reg.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MmQgVlxtTHD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "49f34243-4c3e-44ec-c2be-7fda82bacc43"
      },
      "source": [
        "# a little sanity check, does it work at all?\n",
        "\n",
        "# within this code, we expect Olli to be a green customer with a high prabability\n",
        "# 0: red\n",
        "# 1: green\n",
        "# 2: yellow\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "olli_data = [100, 47, 10]\n",
        "\n",
        "X = np.array([olli_data])\n",
        "model.predict(X)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00259071, 0.8607016 , 0.1367076 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SiolnrmtTHG",
        "colab_type": "text"
      },
      "source": [
        "## Converting our Keras model to the alternative high-level Estimator model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nacdpzVtTHL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "4f3e8d54-d23b-4da0-aea8-81e3c953e4c1"
      },
      "source": [
        "# https://cloud.google.com/blog/products/gcp/new-in-tensorflow-14-converting-a-keras-model-to-a-tensorflow-estimator\n",
        "estimator_model = tf.keras.estimator.model_to_estimator(keras_model=model)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Using default config.\n",
            "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmph6tstskk\n",
            "INFO:tensorflow:Using the Keras model provided.\n",
            "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
            "WARNING:tensorflow:Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
            "INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmph6tstskk', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
            "graph_options {\n",
            "  rewrite_options {\n",
            "    meta_optimizer_iterations: ONE\n",
            "  }\n",
            "}\n",
            ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f360c5d0be0>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxOtTG8VtTHQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "54261c30-79b2-4c4c-be3c-3599cdf89591"
      },
      "source": [
        "# it still works the same, with a different style of API, though\n",
        "x = {\"hidden1_input\": X}\n",
        "list(estimator_model.predict(input_fn=tf.estimator.inputs.numpy_input_fn(x, shuffle=False)))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Could not find trained model in model_dir: /tmp/tmph6tstskk, running initialization to predict.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:62: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:500: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n",
            "INFO:tensorflow:Calling model_fn.\n",
            "INFO:tensorflow:Done calling model_fn.\n",
            "INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmph6tstskk/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})\n",
            "INFO:tensorflow:Warm-starting from: /tmp/tmph6tstskk/keras/keras_model.ckpt\n",
            "INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.\n",
            "INFO:tensorflow:Warm-started 10 variables.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/ops/array_ops.py:1475: 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",
            "INFO:tensorflow:Graph was finalized.\n",
            "INFO:tensorflow:Running local_init_op.\n",
            "INFO:tensorflow:Done running local_init_op.\n",
            "WARNING:tensorflow:From /tensorflow-1.15.0/python3.6/tensorflow_core/python/training/monitored_session.py:882: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "To construct input pipelines, use the `tf.data` module.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'softmax': array([1.3424826e-05, 0.0000000e+00, 9.9998653e-01], dtype=float32)}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6ai3EhctTHT",
        "colab_type": "text"
      },
      "source": [
        "## Preparing our model for serving\n",
        "* https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md\n",
        "* https://www.tensorflow.org/serving/serving_basic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GlxBYynXtTHU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "775db2ae-6391-4054-8f02-a2a66fc4edee"
      },
      "source": [
        "!rm -rf tf\n",
        "\n",
        "import os\n",
        "\n",
        "export_path_base = 'tf'\n",
        "version = 1\n",
        "export_path = os.path.join(\n",
        "      tf.compat.as_bytes(export_path_base),\n",
        "      tf.compat.as_bytes(str(version)))\n",
        "\n",
        "tf.keras.backend.set_learning_phase(0)\n",
        "sess = tf.keras.backend.get_session()\n",
        "\n",
        "classification_inputs = tf.saved_model.utils.build_tensor_info(model.input)\n",
        "classification_outputs_scores = tf.saved_model.utils.build_tensor_info(model.output)\n",
        "\n",
        "signature =  tf.saved_model.signature_def_utils.build_signature_def(\n",
        "    inputs={'inputs': classification_inputs},\n",
        "    outputs={'scores': classification_outputs_scores},\n",
        "    method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)\n",
        "\n",
        "builder = tf.saved_model.builder.SavedModelBuilder(export_path)\n",
        "builder.add_meta_graph_and_variables(\n",
        "      sess, [tf.saved_model.tag_constants.SERVING],\n",
        "      signature_def_map={\n",
        "           tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature\n",
        "      })\n",
        "builder.save()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-12-7168130fd6b1>:14: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
            "INFO:tensorflow:No assets to save.\n",
            "INFO:tensorflow:No assets to write.\n",
            "INFO:tensorflow:SavedModel written to: tf/1/saved_model.pb\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b'tf/1/saved_model.pb'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnUic_HPx-Of",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}