examples/SavedModel-fashion/training-serving.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.2.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import subprocess\n",
    "\n",
    "print('TensorFlow version: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "train_images.shape: (60000, 28, 28, 1), of float64\n",
      "test_images.shape: (10000, 28, 28, 1), of float64\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "\n",
    "# scale the values to 0.0 to 1.0\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0\n",
    "\n",
    "# reshape for feeding into the model\n",
    "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)\n",
    "test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)\n",
    "\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "print('\\ntrain_images.shape: {}, of {}'.format(train_images.shape, train_images.dtype))\n",
    "print('test_images.shape: {}, of {}'.format(test_images.shape, test_images.dtype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Conv1 (Conv2D)               (None, 13, 13, 8)         80        \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1352)              0         \n",
      "_________________________________________________________________\n",
      "Softmax (Dense)              (None, 10)                13530     \n",
      "=================================================================\n",
      "Total params: 13,610\n",
      "Trainable params: 13,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5718 - accuracy: 0.7982\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4505 - accuracy: 0.8441\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4141 - accuracy: 0.8556\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3859 - accuracy: 0.8646\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3630 - accuracy: 0.8718\n",
      "313/313 [==============================] - 0s 1ms/step - loss: 0.3918 - accuracy: 0.8608\n",
      "\n",
      "Test accuracy: 0.86080002784729\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "  keras.layers.Conv2D(input_shape=(28,28,1), filters=8, kernel_size=3, \n",
    "                      strides=2, activation='relu', name='Conv1'),\n",
    "  keras.layers.Flatten(),\n",
    "  keras.layers.Dense(10, activation=tf.nn.softmax, name='Softmax')\n",
    "])\n",
    "model.summary()\n",
    "\n",
    "testing = False\n",
    "epochs = 5\n",
    "\n",
    "model.compile(optimizer='adam', \n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(train_images, train_labels, epochs=epochs)\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print('\\nTest accuracy: {}'.format(test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "export_path = /home/gaocegege/code/gopath/src/github.com/caicloud/ormb/examples/1\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0527 14:00:18.795316 140652355835712 deprecation.py:506] From /home/gaocegege/Applications/anaconda3/envs/dev/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:1817: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saved model:\n",
      "total 84\n",
      "drwxr-xr-x 2 gaocegege gaocegege  4096 5月  27 14:00 assets\n",
      "-rw-r--r-- 1 gaocegege gaocegege 76008 5月  27 14:00 saved_model.pb\n",
      "drwxr-xr-x 2 gaocegege gaocegege  4096 5月  27 14:00 variables\n"
     ]
    }
   ],
   "source": [
    "import tempfile\n",
    "\n",
    "MODEL_DIR = \"/home/gaocegege/code/gopath/src/github.com/caicloud/ormb/examples\"\n",
    "version = 1\n",
    "export_path = os.path.join(MODEL_DIR, str(version))\n",
    "print('export_path = {}\\n'.format(export_path))\n",
    "\n",
    "tf.keras.models.save_model(\n",
    "    model,\n",
    "    export_path,\n",
    "    overwrite=True,\n",
    "    include_optimizer=True,\n",
    "    save_format=None,\n",
    "    signatures=None,\n",
    "    options=None\n",
    ")\n",
    "\n",
    "\n",
    "print('\\nSaved model:')\n",
    "\n",
    "!ls -l {export_path}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show(idx, title):\n",
    "  plt.figure()\n",
    "  plt.imshow(test_images[idx].reshape(28,28))\n",
    "  plt.axis('off')\n",
    "  plt.title('\\n\\n{}'.format(title), fontdict={'size': 16})\n",
    "\n",
    "import random\n",
    "rando = random.randint(0,len(test_images)-1)\n",
    "show(rando, 'An Example Image: {}'.format(class_names[test_labels[rando]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "data = json.dumps({\"signature_name\": \"serving_default\", \"instances\": test_images[0:3].tolist()})\n",
    "import requests\n",
    "headers = {\"content-type\": \"application/json\"}\n",
    "json_response = requests.post('http://localhost:8501/v1/models/fashion_model:predict', data=data, headers=headers)\n",
    "predictions = json.loads(json_response.text)['predictions']\n",
    "\n",
    "show(0, 'The model thought this was a {} (class {}), and it was actually a {} (class {})'.format(\n",
    "  class_names[np.argmax(predictions[0])], np.argmax(predictions[0]), class_names[test_labels[0]], test_labels[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}