Add files via upload

Author: jwlee-ml

lab-11-1-mnist-cnn-keras-sequential-eager.ipynb

@@ -64,7 +64,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Random Seed"
+    "## Hyper Parameters"
    ]
   },
   {
@@ -73,6 +73,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "learning_rate = 0.001\n",
+    "training_epochs = 15\n",
+    "batch_size = 100\n",
+    "\n",
     "tf.set_random_seed(777)"
    ]
   },
@@ -127,14 +131,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model Function"
+    "## Datasets"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()    \n",
+    "    \n",
+    "train_images = train_images.astype(np.float32) / 255.\n",
+    "test_images = test_images.astype(np.float32) / 255.\n",
+    "train_images = np.expand_dims(train_images, axis=-1)\n",
+    "test_images = np.expand_dims(test_images, axis=-1)\n",
+    "    \n",
+    "train_labels = to_categorical(train_labels, 10)\n",
+    "test_labels = to_categorical(test_labels, 10)    \n",
+    "    \n",
+    "train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).shuffle(\n",
+    "                buffer_size=100000).batch(batch_size)\n",
+    "test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def create_model():\n",
     "    model = keras.Sequential()\n",
@@ -154,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -196,52 +228,6 @@
     "model.summary()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Hyper Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "learning_rate = 0.001\n",
-    "training_epochs = 15\n",
-    "batch_size = 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()    \n",
-    "    \n",
-    "train_images = train_images.astype(np.float32) / 255.\n",
-    "test_images = test_images.astype(np.float32) / 255.\n",
-    "train_images = np.expand_dims(train_images, axis=-1)\n",
-    "test_images = np.expand_dims(test_images, axis=-1)\n",
-    "    \n",
-    "train_labels = to_categorical(train_labels, 10)\n",
-    "test_labels = to_categorical(test_labels, 10)    \n",
-    "    \n",
-    "train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).shuffle(\n",
-    "                buffer_size=100000).batch(batch_size)\n",
-    "test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(batch_size)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -251,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -326,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -342,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "scrolled": false
    },
@@ -352,21 +338,21 @@
      "output_type": "stream",
      "text": [
       "Learning started. It takes sometime.\n",
-      "Epoch: 1 loss = 0.18155795 train accuracy =  0.9561 test accuracy =  0.9864\n",
-      "Epoch: 2 loss = 0.04595109 train accuracy =  0.9901 test accuracy =  0.9893\n",
-      "Epoch: 3 loss = 0.03227887 train accuracy =  0.9933 test accuracy =  0.9913\n",
-      "Epoch: 4 loss = 0.02327809 train accuracy =  0.9955 test accuracy =  0.9908\n",
-      "Epoch: 5 loss = 0.01696701 train accuracy =  0.9970 test accuracy =  0.9931\n",
-      "Epoch: 6 loss = 0.01443311 train accuracy =  0.9977 test accuracy =  0.9928\n",
-      "Epoch: 7 loss = 0.01259885 train accuracy =  0.9981 test accuracy =  0.9923\n",
-      "Epoch: 8 loss = 0.01018594 train accuracy =  0.9985 test accuracy =  0.9919\n",
-      "Epoch: 9 loss = 0.00939758 train accuracy =  0.9987 test accuracy =  0.9927\n",
-      "Epoch: 10 loss = 0.00814385 train accuracy =  0.9990 test accuracy =  0.9928\n",
-      "Epoch: 11 loss = 0.00715813 train accuracy =  0.9991 test accuracy =  0.9925\n",
-      "Epoch: 12 loss = 0.00585301 train accuracy =  0.9995 test accuracy =  0.9935\n",
-      "Epoch: 13 loss = 0.00585623 train accuracy =  0.9994 test accuracy =  0.9915\n",
-      "Epoch: 14 loss = 0.00530782 train accuracy =  0.9993 test accuracy =  0.9920\n",
-      "Epoch: 15 loss = 0.00454885 train accuracy =  0.9993 test accuracy =  0.9940\n",
+      "Epoch: 1 loss = 0.17322077 train accuracy =  0.9588 test accuracy =  0.9843\n",
+      "Epoch: 2 loss = 0.04714254 train accuracy =  0.9894 test accuracy =  0.9889\n",
+      "Epoch: 3 loss = 0.03016450 train accuracy =  0.9940 test accuracy =  0.9904\n",
+      "Epoch: 4 loss = 0.02271373 train accuracy =  0.9957 test accuracy =  0.9927\n",
+      "Epoch: 5 loss = 0.01871874 train accuracy =  0.9966 test accuracy =  0.9935\n",
+      "Epoch: 6 loss = 0.01522499 train accuracy =  0.9976 test accuracy =  0.9942\n",
+      "Epoch: 7 loss = 0.01154464 train accuracy =  0.9982 test accuracy =  0.9912\n",
+      "Epoch: 8 loss = 0.01073314 train accuracy =  0.9983 test accuracy =  0.9922\n",
+      "Epoch: 9 loss = 0.00970077 train accuracy =  0.9985 test accuracy =  0.9931\n",
+      "Epoch: 10 loss = 0.00773255 train accuracy =  0.9990 test accuracy =  0.9926\n",
+      "Epoch: 11 loss = 0.00702506 train accuracy =  0.9994 test accuracy =  0.9919\n",
+      "Epoch: 12 loss = 0.00577464 train accuracy =  0.9993 test accuracy =  0.9929\n",
+      "Epoch: 13 loss = 0.00647555 train accuracy =  0.9992 test accuracy =  0.9916\n",
+      "Epoch: 14 loss = 0.00516995 train accuracy =  0.9995 test accuracy =  0.9920\n",
+      "Epoch: 15 loss = 0.00516423 train accuracy =  0.9994 test accuracy =  0.9931\n",
       "Learning Finished!\n"
      ]
     }