Fix the typo error in custom_train_step_in_tensorflow.ipynb (#1914)

* Fix the typo error in custom_train_step_in_tensorflow.ipynb * fix the typo
keras-team · Aug 21, 2024 · 1922870 · 1922870
1 parent 4c89b46
commit 1922870
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Showing 2 changed files with 17 additions and 21 deletions.
diff --git a/guides/ipynb/custom_train_step_in_tensorflow.ipynb b/guides/ipynb/custom_train_step_in_tensorflow.ipynb
@@ -267,7 +267,7 @@
     "outputs = keras.layers.Dense(1)(inputs)\n",
     "model = CustomModel(inputs, outputs)\n",
     "\n",
-    "# We don't passs a loss or metrics here.\n",
+    "# We don't pass a loss or metrics here.\n",
     "model.compile(optimizer=\"adam\")\n",
     "\n",
     "# Just use `fit` as usual -- you can use callbacks, etc.\n",
@@ -642,4 +642,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
diff --git a/guides/ipynb/customizing_what_happens_in_fit.ipynb b/guides/ipynb/customizing_what_happens_in_fit.ipynb
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -138,8 +138,7 @@
     "            else:\n",
     "                metric.update_state(y, y_pred)\n",
     "        # Return a dict mapping metric names to current value\n",
-    "        return {m.name: m.result() for m in self.metrics}\n",
-    ""
+    "        return {m.name: m.result() for m in self.metrics}\n"
    ]
   },
   {
@@ -153,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -200,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -248,14 +247,13 @@
     "outputs = keras.layers.Dense(1)(inputs)\n",
     "model = CustomModel(inputs, outputs)\n",
     "\n",
-    "# We don't passs a loss or metrics here.\n",
+    "# We don't pass a loss or metrics here.\n",
     "model.compile(optimizer=\"adam\")\n",
     "\n",
     "# Just use `fit` as usual -- you can use callbacks, etc.\n",
     "x = np.random.random((1000, 32))\n",
     "y = np.random.random((1000, 1))\n",
-    "model.fit(x, y, epochs=5)\n",
-    ""
+    "model.fit(x, y, epochs=5)\n"
    ]
   },
   {
@@ -278,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -352,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -404,13 +402,12 @@
     "- A discriminator network meant to classify 28x28x1 images into two classes (\"fake\" and\n",
     "\"real\").\n",
     "- One optimizer for each.\n",
-    "- A loss function to train the discriminator.\n",
-    ""
+    "- A loss function to train the discriminator.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -463,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -534,8 +531,7 @@
     "        return {\n",
     "            \"d_loss\": self.d_loss_tracker.result(),\n",
     "            \"g_loss\": self.g_loss_tracker.result(),\n",
-    "        }\n",
-    ""
+    "        }\n"
    ]
   },
   {
@@ -549,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -615,4 +611,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}