#remove dropout

Author: rainmaker712

2.NLP_PREP/2.1.1.tf.keras.layers.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,12 +78,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "input = tf.placeholder(tf.float32, shape = INPUT_SIZE)\n",
-    "dropout = tf.keras.layers.Dropout(rate = 0.5)(input, training = IS_TRAINING)"
+    "dropout = tf.keras.layers.Dropout(rate = 0.5)(input)"
    ]
   },
   {
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
    "outputs": [],
    "source": [
     "input = tf.placeholder(tf.float32, shape = CONV_INPUT_SIZE)\n",
-    "dropout = tf.keras.layers.Dropout(rate=0.2)(input, training = IS_TRAINING)\n",
+    "dropout = tf.keras.layers.Dropout(rate=0.2)(input)\n",
     "conv = tf.keras.layers.Conv1D(\n",
     "         filters=10,\n",
     "         kernel_size=3,\n",
@@ -157,12 +157,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "input = tf.placeholder(tf.float32, shape = CONV_INPUT_SIZE)\n",
-    "dropout = tf.keras.layers.Dropout(rate = 0.2)(input, training = IS_TRAINING)\n",
+    "dropout = tf.keras.layers.Dropout(rate = 0.2)(input)\n",
     "conv = tf.keras.layers.Conv1D(\n",
     "         filters=10,\n",
     "         kernel_size=3,\n",
@@ -173,6 +173,13 @@
     "hidden = tf.keras.layers.Dense(units = 50, activation = tf.nn.relu)(flatten)\n",
     "output = tf.keras.layers.Dense(units = 10, activation = tf.nn.softmax)(hidden)  "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -191,7 +198,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,