variance_scaling_initializer 수정, 텐서보드 그래프 분리, keras mnist 적용

11_deep_learning_exercises.ipynb

@@ -163,7 +163,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "he_init = tf.contrib.layers.variance_scaling_initializer()\n",
+    "he_init = tf.variance_scaling_initializer()\n",
     "\n",
     "def dnn(inputs, n_hidden_layers=5, n_neurons=100, name=None,\n",
     "        activation=tf.nn.elu, initializer=he_init):\n",
@@ -189,7 +189,7 @@
     "reset_graph()\n",
     "\n",
     "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n",
-    "y = tf.placeholder(tf.int64, shape=(None), name=\"y\")\n",
+    "y = tf.placeholder(tf.int32, shape=(None), name=\"y\")\n",
     "\n",
     "dnn_outputs = dnn(X)\n",
     "\n",
@@ -246,6 +246,13 @@
     "MNIST 데이터셋을 로드합니다:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "주의: `tf.examples.tutorials.mnist`은 삭제될 예정이므로 대신 `tf.keras.datasets.mnist`를 사용하겠습니다."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -263,10 +270,13 @@
     }
    ],
    "source": [
-    "from tensorflow.examples.tutorials.mnist import input_data\n",
-    "tf.logging.set_verbosity(tf.logging.ERROR)  # deprecated 경고 메세지를 출력하지 않기 위해 \n",
-    "mnist = input_data.read_data_sets(\"/tmp/data/\")\n",
-    "tf.logging.set_verbosity(tf.logging.INFO)"
+    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
+    "X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\n",
+    "X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\n",
+    "y_train = y_train.astype(np.int32)\n",
+    "y_test = y_test.astype(np.int32)\n",
+    "X_valid, X_train = X_train[:5000], X_train[5000:]\n",
+    "y_valid, y_train = y_train[:5000], y_train[5000:]"
    ]
   },
   {
@@ -282,12 +292,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train1 = mnist.train.images[mnist.train.labels < 5]\n",
-    "y_train1 = mnist.train.labels[mnist.train.labels < 5]\n",
-    "X_valid1 = mnist.validation.images[mnist.validation.labels < 5]\n",
-    "y_valid1 = mnist.validation.labels[mnist.validation.labels < 5]\n",
-    "X_test1 = mnist.test.images[mnist.test.labels < 5]\n",
-    "y_test1 = mnist.test.labels[mnist.test.labels < 5]"
+    "X_train1 = X_train[y_train < 5]\n",
+    "y_train1 = y_train[y_train < 5]\n",
+    "X_valid1 = X_valid[y_valid < 5]\n",
+    "y_valid1 = y_valid[y_valid < 5]\n",
+    "X_test1 = X_test[y_test < 5]\n",
+    "y_test1 = y_test[y_test < 5]"
    ]
   },
   {
@@ -20536,12 +20546,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train2_full = mnist.train.images[mnist.train.labels >= 5]\n",
-    "y_train2_full = mnist.train.labels[mnist.train.labels >= 5] - 5\n",
-    "X_valid2_full = mnist.validation.images[mnist.validation.labels >= 5]\n",
-    "y_valid2_full = mnist.validation.labels[mnist.validation.labels >= 5] - 5\n",
-    "X_test2 = mnist.test.images[mnist.test.labels >= 5]\n",
-    "y_test2 = mnist.test.labels[mnist.test.labels >= 5] - 5"
+    "X_train2_full = X_train[y_train >= 5]\n",
+    "y_train2_full = y_train[y_train >= 5] - 5\n",
+    "X_valid2_full = X_valid[y_valid >= 5]\n",
+    "y_valid2_full = y_valid[y_valid >= 5] - 5\n",
+    "X_test2 = X_test[y_test >= 5]\n",
+    "y_test2 = y_test[y_test >= 5] - 5"
    ]
   },
   {
@@ -21577,14 +21587,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_train1 = mnist.train.images\n",
-    "y_train1 = mnist.train.labels\n",
+    "X_train1 = X_train\n",
+    "y_train1 = y_train\n",
     "\n",
-    "X_train2 = mnist.validation.images\n",
-    "y_train2 = mnist.validation.labels\n",
+    "X_train2 = X_valid\n",
+    "y_train2 = y_valid\n",
     "\n",
-    "X_test = mnist.test.images\n",
-    "y_test = mnist.test.labels"
+    "X_test = X_test\n",
+    "y_test = y_test"
    ]
   },
   {
@@ -21907,7 +21917,7 @@
     "with tf.Session() as sess:\n",
     "    init.run()\n",
     "    for epoch in range(n_epochs):\n",
-    "        for iteration in range(mnist.train.num_examples // batch_size):\n",
+    "        for iteration in range(len(X_train1) // batch_size):\n",
     "            X_batch, y_batch = generate_batch(X_train1, y_train1, batch_size)\n",
     "            loss_val, _ = sess.run([loss, training_op], feed_dict={X: X_batch, y: y_batch})\n",
     "        print(epoch, \"훈련 손실:\", loss_val)\n",
@@ -22142,7 +22152,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.5"
+   "version": "3.6.5"
   },
   "nav_menu": {
    "height": "360px",