Add .md and .ipynb

Author: pksX01

examples/vision/img/vit_small_ds/vit_small_ds_13_0.png

@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -324,8 +324,7 @@
     "        else:\n",
     "            # Linearly project the flat patches\n",
     "            tokens = self.projection(flat_patches)\n",
-    "        return (tokens, patches)\n",
-    ""
+    "        return (tokens, patches)\n"
    ]
   },
   {
@@ -339,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -402,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -423,8 +422,7 @@
     "    def call(self, encoded_patches):\n",
     "        encoded_positions = self.position_embedding(self.positions)\n",
     "        encoded_patches = encoded_patches + encoded_positions\n",
-    "        return encoded_patches\n",
-    ""
+    "        return encoded_patches\n"
    ]
   },
   {
@@ -472,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -496,8 +494,7 @@
     "        attention_output = tf.einsum(\n",
     "            self._combine_equation, attention_scores_dropout, value\n",
     "        )\n",
-    "        return attention_output, attention_scores\n",
-    ""
+    "        return attention_output, attention_scores\n"
    ]
   },
   {
@@ -511,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -541,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -589,8 +586,7 @@
     "    logits = layers.Dense(NUM_CLASSES)(features)\n",
     "    # Create the Keras model.\n",
     "    model = keras.Model(inputs=inputs, outputs=logits)\n",
-    "    return model\n",
-    ""
+    "    return model\n"
    ]
   },
   {
@@ -604,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab_type": "code"
    },
@@ -754,4 +750,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}