fashion-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_2 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 4s 70us/sample - loss: 0.5231 - accuracy: 0.8142\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 4s 64us/sample - loss: 0.3985 - accuracy: 0.8552\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 4s 64us/sample - loss: 0.3629 - accuracy: 0.8673\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 4s 63us/sample - loss: 0.3441 - accuracy: 0.8739\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 4s 63us/sample - loss: 0.3310 - accuracy: 0.8788\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 4s 70us/sample - loss: 0.3177 - accuracy: 0.8827\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 4s 71us/sample - loss: 0.3074 - accuracy: 0.8869\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 4s 67us/sample - loss: 0.2986 - accuracy: 0.8905\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 4s 65us/sample - loss: 0.2918 - accuracy: 0.8917\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 4s 65us/sample - loss: 0.2825 - accuracy: 0.8933\n",
      "10000/1 - 0s - loss: 0.2661 - accuracy: 0.8765\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "class MyFashionMnist(object):\n",
    "  def train(self):\n",
    "\n",
    "    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n",
    "    x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "    model = tf.keras.models.Sequential([\n",
    "      tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "      tf.keras.layers.Dense(128, activation='relu'),\n",
    "      tf.keras.layers.Dropout(0.2),\n",
    "      tf.keras.layers.Dense(10, activation='softmax')\n",
    "    ])\n",
    "    model.summary()\n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "    model.fit(x_train, y_train, epochs=10)\n",
    "\n",
    "    model.evaluate(x_test,  y_test, verbose=2)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    local_train = MyFashionMnist()\n",
    "    local_train.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name:         fashion-mnist-cpu-experiment-v2-1\n",
      "Namespace:    kubeflow\n",
      "Labels:       controller-tools.k8s.io=1.0\n",
      "Annotations:  kubectl.kubernetes.io/last-applied-configuration:\n",
      "                {\"apiVersion\":\"kubeflow.org/v1alpha3\",\"kind\":\"Experiment\",\"metadata\":{\"annotations\":{},\"labels\":{\"controller-tools.k8s.io\":\"1.0\"},\"name\":\"...\n",
      "API Version:  kubeflow.org/v1alpha3\n",
      "Kind:         Experiment\n",
      "Metadata:\n",
      "  Creation Timestamp:  2020-02-16T04:50:19Z\n",
      "  Finalizers:\n",
      "    update-prometheus-metrics\n",
      "  Generation:        2\n",
      "  Resource Version:  6503819\n",
      "  Self Link:         /apis/kubeflow.org/v1alpha3/namespaces/kubeflow/experiments/fashion-mnist-cpu-experiment-v2-1\n",
      "  UID:               4b533dd9-4e69-4bb2-a077-b95b81d8d35f\n",
      "Spec:\n",
      "  Algorithm:\n",
      "    Algorithm Name:        random\n",
      "    Algorithm Settings:    <nil>\n",
      "  Max Failed Trial Count:  3\n",
      "  Max Trial Count:         50\n",
      "  Metrics Collector Spec:\n",
      "    Collector:\n",
      "      Kind:  StdOut\n",
      "  Objective:\n",
      "    Additional Metric Names:\n",
      "      accuracy\n",
      "      loss\n",
      "      Validation-loss\n",
      "    Goal:                   0.99\n",
      "    Objective Metric Name:  Validation-accuracy\n",
      "    Type:                   maximize\n",
      "  Parallel Trial Count:     5\n",
      "  Parameters:\n",
      "    Feasible Space:\n",
      "      Max:           0.0015\n",
      "      Min:           0.0005\n",
      "    Name:            --learning_rate\n",
      "    Parameter Type:  double\n",
      "    Feasible Space:\n",
      "      Max:           0.9\n",
      "      Min:           0.1\n",
      "    Name:            --dropout_rate\n",
      "    Parameter Type:  double\n",
      "    Feasible Space:\n",
      "      Max:           5\n",
      "      Min:           1\n",
      "    Name:            --layer\n",
      "    Parameter Type:  int\n",
      "    Feasible Space:\n",
      "      Max:           15\n",
      "      Min:           5\n",
      "    Name:            --epoch\n",
      "    Parameter Type:  int\n",
      "    Feasible Space:\n",
      "      List:\n",
      "        relu\n",
      "        sigmoid\n",
      "        softmax\n",
      "        tanh\n",
      "    Name:            --act\n",
      "    Parameter Type:  categorical\n",
      "  Trial Template:\n",
      "    Go Template:\n",
      "      Raw Template:  apiVersion: batch/v1\n",
      "kind: Job\n",
      "metadata:\n",
      "  name: {{.Trial}}\n",
      "  namespace: {{.NameSpace}}\n",
      "spec:\n",
      "  template:\n",
      "    spec:\n",
      "      containers:\n",
      "      - name: {{.Trial}}\n",
      "        image: kubeflow-registry.default.svc.cluster.local:30000/fairing-job:E5A850A0\n",
      "        command:\n",
      "        - \"python\"\n",
      "        - \"/app/fashion-mnist-katib.py\" \n",
      "        {{- with .HyperParameters}}\n",
      "        {{- range .}}\n",
      "        - \"{{.Name}}={{.Value}}\"\n",
      "        {{- end}}\n",
      "        {{- end}}\n",
      "      restartPolicy: Never\n",
      "Status:\n",
      "  Completion Time:  2020-02-16T06:19:07Z\n",
      "  Conditions:\n",
      "    Last Transition Time:  2020-02-16T04:50:19Z\n",
      "    Last Update Time:      2020-02-16T04:50:19Z\n",
      "    Message:               Experiment is created\n",
      "    Reason:                ExperimentCreated\n",
      "    Status:                True\n",
      "    Type:                  Created\n",
      "    Last Transition Time:  2020-02-16T06:19:07Z\n",
      "    Last Update Time:      2020-02-16T06:19:07Z\n",
      "    Message:               Experiment is running\n",
      "    Reason:                ExperimentRunning\n",
      "    Status:                False\n",
      "    Type:                  Running\n",
      "    Last Transition Time:  2020-02-16T06:19:07Z\n",
      "    Last Update Time:      2020-02-16T06:19:07Z\n",
      "    Message:               Experiment has succeeded because max trial count has reached\n",
      "    Reason:                ExperimentSucceeded\n",
      "    Status:                True\n",
      "    Type:                  Succeeded\n",
      "  Current Optimal Trial:\n",
      "    Observation:\n",
      "      Metrics:\n",
      "        Name:   Validation-accuracy\n",
      "        Value:  0.8825\n",
      "    Parameter Assignments:\n",
      "      Name:          --learning_rate\n",
      "      Value:         0.0013978861770555214\n",
      "      Name:          --dropout_rate\n",
      "      Value:         0.18135057185715314\n",
      "      Name:          --layer\n",
      "      Value:         2\n",
      "      Name:          --epoch\n",
      "      Value:         11\n",
      "      Name:          --act\n",
      "      Value:         sigmoid\n",
      "  Start Time:        2020-02-16T04:50:19Z\n",
      "  Trials:            50\n",
      "  Trials Succeeded:  50\n",
      "Events:              <none>\n"
     ]
    }
   ],
   "source": [
    "!kubectl describe experiment fashion-mnist-cpu-experiment-v2-1 -n kubeflow "
   ]
  }
 ],
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