Update cnn.ipynb

edmontdants · Dec 17, 2020 · 4efd96f · 4efd96f
1 parent 238fad6
commit 4efd96f
Showing 1 changed file with 2 additions and 2 deletions.
diff --git a/site/en/tutorials/images/cnn.ipynb b/site/en/tutorials/images/cnn.ipynb
@@ -233,7  233,7 @@
       },
       "source": [
         "### Add Dense layers on top\n",
-        "To complete our model, you will feed the last output tensor from the convolutional base (of shape (4, 4, 64)) into one or more Dense layers to perform classification. Dense layers take vectors as input (which are 1D), while the current output is a 3D tensor. First, you will flatten (or unroll) the 3D output to 1D,  then add one or more Dense layers on top. CIFAR has 10 output classes, so you use a final Dense layer with 10 outputs and a softmax activation."
         "To complete our model, you will feed the last output tensor from the convolutional base (of shape (4, 4, 64)) into one or more Dense layers to perform classification. Dense layers take vectors as input (which are 1D), while the current output is a 3D tensor. First, you will flatten (or unroll) the 3D output to 1D,  then add one or more Dense layers on top. CIFAR has 10 output classes, so you use a final Dense layer with 10 outputs."
       ]
     },
     {
@@ -246,7  246,7 @@
       "source": [
         "model.add(layers.Flatten())\n",
         "model.add(layers.Dense(64, activation='relu'))\n",
-        "model.add(layers.Dense(10, activation='softmax'))"
         "model.add(layers.Dense(10))"
       ]
     },
     {