Merge pull request tensorflow#5032 from HughKu/fix/#21199

MarkDaoust · web-flow · commit ad3526a98e7d · 2018-08-08T09:31:12.000-07:00
Fix a doc typo fashion-mnist example in #21199
diff --git a/samples/core/tutorials/keras/basic_classification.ipynb b/samples/core/tutorials/keras/basic_classification.ipynb
@@ -613,7 +613,7 @@
       "source": [
         "The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels. Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
         "\n",
-        "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely-connected, or fully-connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer is a 10-node *softmax* layer—this returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 digit classes.\n",
+        "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely-connected, or fully-connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer is a 10-node *softmax* layer—this returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 classes.\n",
         "\n",
         "### Compile the model\n",
         "\n",
@@ -1025,4 +1025,4 @@
       ]
     }
   ]
-}
+}

Original file line number	Diff line number	Diff line change
`@@ -613,7 +613,7 @@`
`613`	`613`	`"source": [`
`614`	`614`	"The first layer in this network, `tf.keras.layers.Flatten`, transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels. Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn; it only reformats the data.\n",
`615`	`615`	`"\n",`
`616`		- "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely-connected, or fully-connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer is a 10-node softmax layer—this returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 digit classes.\n",
	`616`	+ "After the pixels are flattened, the network consists of a sequence of two `tf.keras.layers.Dense` layers. These are densely-connected, or fully-connected, neural layers. The first `Dense` layer has 128 nodes (or neurons). The second (and last) layer is a 10-node softmax layer—this returns an array of 10 probability scores that sum to 1. Each node contains a score that indicates the probability that the current image belongs to one of the 10 classes.\n",
`617`	`617`	`"\n",`
`618`	`618`	`"### Compile the model\n",`
`619`	`619`	`"\n",`
`@@ -1025,4 +1025,4 @@`
`1025`	`1025`	`]`
`1026`	`1026`	`}`
`1027`	`1027`	`]`
`1028`		`-}`
	`1028`	`+}`