From c5c12e36c23792c564d76216a169ce5d1e1d58dc Mon Sep 17 00:00:00 2001
From: Diwakar Gupta <39624018+Diwakar-Gupta@users.noreply.github.com>
Date: Thu, 14 Jul 2022 23:24:47 +0530
Subject: [PATCH] DataAugmentation

---
 22-07-14-CNN/DataAugmentation.ipynb | 396 +++++++++++++++++++---------
 1 file changed, 266 insertions(+), 130 deletions(-)

diff --git a/22-07-14-CNN/DataAugmentation.ipynb b/22-07-14-CNN/DataAugmentation.ipynb
index 9184de3..169df71 100644
--- a/22-07-14-CNN/DataAugmentation.ipynb
+++ b/22-07-14-CNN/DataAugmentation.ipynb
@@ -5,7 +5,7 @@
     "colab": {
       "name": "April-DataAugmentation.ipynb",
       "provenance": [],
-      "authorship_tag": "ABX9TyN6wDq1SwuB99bCUQ0DJKEh",
+      "authorship_tag": "ABX9TyNs84y+6Kn8T8NjtfwRRzAI",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -31,7 +31,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 26,
       "metadata": {
         "id": "DafKfRHDbhtY"
       },
@@ -48,7 +48,7 @@
       "metadata": {
         "id": "0LbTU6cGcCDQ"
       },
-      "execution_count": 2,
+      "execution_count": 27,
       "outputs": []
     },
     {
@@ -69,9 +69,9 @@
           "height": 369
         },
         "id": "dFqTulz8bkXy",
-        "outputId": "ab117ad9-c463-4de8-fb32-bef8448f2291"
+        "outputId": "fbc021be-1b50-430f-c5c3-a93125fa179f"
       },
-      "execution_count": 3,
+      "execution_count": 28,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -79,10 +79,10 @@
             "text/plain": [
               "<IPython.core.display.Image object>"
             ],
-            "image/png": 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\n"
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\n"
           },
           "metadata": {},
-          "execution_count": 3
+          "execution_count": 28
         }
       ]
     },
@@ -109,29 +109,29 @@
           "height": 848
         },
         "id": "YjrSZ-swcWOZ",
-        "outputId": "daca408f-c3c7-49e5-fb8e-6bb23bbdc25b"
+        "outputId": "572afd58-d36b-4925-a960-06a90b8c07dc"
       },
-      "execution_count": 4,
+      "execution_count": 29,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Model: \"model\"\n",
+            "Model: \"model_1\"\n",
             "__________________________________________________________________________________________________\n",
             " Layer (type)                   Output Shape         Param #     Connected to                     \n",
             "==================================================================================================\n",
-            " input_2 (InputLayer)           [(None, 240, 240, 3  0           []                               \n",
+            " input_8 (InputLayer)           [(None, 240, 240, 3  0           []                               \n",
             "                                )]                                                                \n",
             "                                                                                                  \n",
-            " conv1 (Conv2D)                 (None, 240, 240, 5)  140         ['input_2[0][0]']                \n",
+            " conv1 (Conv2D)                 (None, 240, 240, 5)  140         ['input_8[0][0]']                \n",
             "                                                                                                  \n",
             " conv2 (Conv2D)                 (None, 240, 240, 3)  138         ['conv1[0][0]']                  \n",
             "                                                                                                  \n",
-            " add (Add)                      (None, 240, 240, 3)  0           ['input_2[0][0]',                \n",
+            " add_1 (Add)                    (None, 240, 240, 3)  0           ['input_8[0][0]',                \n",
             "                                                                  'conv2[0][0]']                  \n",
             "                                                                                                  \n",
-            " conv3 (Conv2D)                 (None, 240, 240, 2)  56          ['add[0][0]']                    \n",
+            " conv3 (Conv2D)                 (None, 240, 240, 2)  56          ['add_1[0][0]']                  \n",
             "                                                                                                  \n",
             "==================================================================================================\n",
             "Total params: 334\n",
@@ -147,10 +147,10 @@
             "text/plain": [
               "<IPython.core.display.Image object>"
             ],
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\n"
+            "image/png": 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\n"
           },
           "metadata": {},
-          "execution_count": 4
+          "execution_count": 29
         }
       ]
     },
@@ -172,24 +172,10 @@
         "data_dir = tf.keras.utils.get_file('flower_photos',origin=dataset_url,untar=True)"
       ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "0E5fr6_3gCaK",
-        "outputId": "5877edb6-e4bb-4932-d57c-aeb86ed1ce4c"
+        "id": "0E5fr6_3gCaK"
       },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\n",
-            "228818944/228813984 [==============================] - 2s 0us/step\n",
-            "228827136/228813984 [==============================] - 2s 0us/step\n"
-          ]
-        }
-      ]
+      "execution_count": 30,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -202,9 +188,9 @@
           "height": 35
         },
         "id": "Y0ylfqz_rG9j",
-        "outputId": "83747ce1-3186-442c-8f5a-6dd7820384d5"
+        "outputId": "ef44502e-180b-4be4-c0e8-71d5fd176b42"
       },
-      "execution_count": 6,
+      "execution_count": 31,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -217,7 +203,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 6
+          "execution_count": 31
         }
       ]
     },
@@ -231,15 +217,15 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "8Jg2VU4yrIrz",
-        "outputId": "aa5bc123-737b-480f-d410-542168d144aa"
+        "outputId": "ed776c70-1afa-49d8-a20e-764987d3e323"
       },
-      "execution_count": 7,
+      "execution_count": 32,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "daisy  dandelion  LICENSE.txt  roses  sunflowers  tulips\n"
+            "daisy  dandelion  roses  sunflowers  tulips\n"
           ]
         }
       ]
@@ -250,10 +236,22 @@
         "! rm /root/.keras/datasets/flower_photos/LICENSE.txt"
       ],
       "metadata": {
-        "id": "C40IvsqjrSR_"
+        "id": "C40IvsqjrSR_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "549d490a-b2eb-43d8-fddc-d22b289cb0ab"
       },
-      "execution_count": 8,
-      "outputs": []
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "rm: cannot remove '/root/.keras/datasets/flower_photos/LICENSE.txt': No such file or directory\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -265,9 +263,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Ojl2lnb2rc_9",
-        "outputId": "cd0efbb6-6e1c-4615-bd3b-bf14eb94644f"
+        "outputId": "983c1ccd-60a2-476e-9970-ea1c81cb51e7"
       },
-      "execution_count": 9,
+      "execution_count": 34,
       "outputs": [
         {
           "output_type": "stream",
@@ -295,9 +293,9 @@
           "height": 367
         },
         "id": "c1KKSn5cr2X8",
-        "outputId": "821443a8-9f4c-4792-fc6d-035602ef7333"
+        "outputId": "be51b235-2ad2-40d8-b5a3-16b00d36689e"
       },
-      "execution_count": 10,
+      "execution_count": 35,
       "outputs": [
         {
           "output_type": "stream",
@@ -310,12 +308,12 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x7FED402C2990>"
+              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x7FECBD5FD810>"
             ],
             "image/png": 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\n"
           },
           "metadata": {},
-          "execution_count": 10
+          "execution_count": 35
         }
       ]
     },
@@ -347,9 +345,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "VWJ6ocJArmhK",
-        "outputId": "bb63b1cb-6aec-4ba9-cb09-6d6c0439dc63"
+        "outputId": "005f8ede-bd5e-4246-87d0-c24df634c1d9"
       },
-      "execution_count": 15,
+      "execution_count": 36,
       "outputs": [
         {
           "output_type": "stream",
@@ -372,35 +370,22 @@
         "id": "81TOeJeKwoi9"
       }
     },
-    {
-      "cell_type": "code",
-      "source": [
-        "data_augmentation = tf.keras.Sequential([\n",
-        "  tf.keras.layers.RandomFlip(\"horizontal_and_vertical\"),\n",
-        "  tf.keras.layers.RandomRotation(0.2),\n",
-        "])"
-      ],
-      "metadata": {
-        "id": "lb0k5c7su2ia"
-      },
-      "execution_count": 20,
-      "outputs": []
-    },
     {
       "cell_type": "code",
       "source": [
         "\n",
         "model = tf.keras.Sequential([\n",
-        "            tf.keras.Input(shape=(256, 256, 3)),\n",
-        "            data_augmentation, # only works during training, not functional in testing\n",
-        "            tf.keras.layers.Conv2D(8, (3,3)),\n",
-        "            tf.keras.layers.BatchNormalization(),\n",
-        "            tf.keras.layers.Conv2D(16, (3,3)),\n",
-        "            tf.keras.layers.BatchNormalization(),\n",
-        "            tf.keras.layers.Conv2D(32, (3,3)),\n",
-        "            tf.keras.layers.BatchNormalization(),\n",
-        "            tf.keras.layers.GlobalAveragePooling2D(),\n",
-        "            tf.keras.layers.Dense(5, activation='sigmoid'),\n",
+        "  tf.keras.Input(shape=(256, 256, 3)),\n",
+        "  tf.keras.layers.Rescaling(1./255),\n",
+        "  tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
+        "  tf.keras.layers.MaxPooling2D(),\n",
+        "  tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
+        "  tf.keras.layers.MaxPooling2D(),\n",
+        "  tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
+        "  tf.keras.layers.MaxPooling2D(),\n",
+        "  tf.keras.layers.Flatten(),\n",
+        "  tf.keras.layers.Dense(128, activation='relu'),\n",
+        "  tf.keras.layers.Dense(5, activation='sigmoid')\n",
         "])\n",
         "\n",
         "model.summary()"
@@ -410,44 +395,45 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "0CEwPDB6sprL",
-        "outputId": "8aab2752-d40c-462c-d226-bf717336409c"
+        "outputId": "09507556-02ba-484a-aa23-df8822145f9d"
       },
-      "execution_count": 22,
+      "execution_count": 45,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Model: \"sequential_5\"\n",
+            "Model: \"sequential_10\"\n",
             "_________________________________________________________________\n",
             " Layer (type)                Output Shape              Param #   \n",
             "=================================================================\n",
-            " sequential_3 (Sequential)   (None, 256, 256, 3)       0         \n",
+            " rescaling_1 (Rescaling)     (None, 256, 256, 3)       0         \n",
             "                                                                 \n",
-            " conv2d_11 (Conv2D)          (None, 254, 254, 8)       224       \n",
+            " conv2d_26 (Conv2D)          (None, 256, 256, 16)      448       \n",
             "                                                                 \n",
-            " batch_normalization_6 (Batc  (None, 254, 254, 8)      32        \n",
-            " hNormalization)                                                 \n",
+            " max_pooling2d_9 (MaxPooling  (None, 128, 128, 16)     0         \n",
+            " 2D)                                                             \n",
             "                                                                 \n",
-            " conv2d_12 (Conv2D)          (None, 252, 252, 16)      1168      \n",
+            " conv2d_27 (Conv2D)          (None, 128, 128, 32)      4640      \n",
             "                                                                 \n",
-            " batch_normalization_7 (Batc  (None, 252, 252, 16)     64        \n",
-            " hNormalization)                                                 \n",
+            " max_pooling2d_10 (MaxPoolin  (None, 64, 64, 32)       0         \n",
+            " g2D)                                                            \n",
             "                                                                 \n",
-            " conv2d_13 (Conv2D)          (None, 250, 250, 32)      4640      \n",
+            " conv2d_28 (Conv2D)          (None, 64, 64, 64)        18496     \n",
             "                                                                 \n",
-            " batch_normalization_8 (Batc  (None, 250, 250, 32)     128       \n",
-            " hNormalization)                                                 \n",
+            " max_pooling2d_11 (MaxPoolin  (None, 32, 32, 64)       0         \n",
+            " g2D)                                                            \n",
             "                                                                 \n",
-            " global_average_pooling2d_3   (None, 32)               0         \n",
-            " (GlobalAveragePooling2D)                                        \n",
+            " flatten_3 (Flatten)         (None, 65536)             0         \n",
             "                                                                 \n",
-            " dense_3 (Dense)             (None, 5)                 165       \n",
+            " dense_10 (Dense)            (None, 128)               8388736   \n",
+            "                                                                 \n",
+            " dense_11 (Dense)            (None, 5)                 645       \n",
             "                                                                 \n",
             "=================================================================\n",
-            "Total params: 6,421\n",
-            "Trainable params: 6,309\n",
-            "Non-trainable params: 112\n",
+            "Total params: 8,412,965\n",
+            "Trainable params: 8,412,965\n",
+            "Non-trainable params: 0\n",
             "_________________________________________________________________\n"
           ]
         }
@@ -465,50 +451,84 @@
       "metadata": {
         "id": "viwaYa_0tX1i"
       },
-      "execution_count": 23,
+      "execution_count": 46,
       "outputs": []
     },
     {
       "cell_type": "code",
       "source": [
-        "model.fit(train_ds, epochs=6)"
+        "model.fit(train_ds, epochs=21)"
       ],
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/"
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
         },
         "id": "yN1Pdxwmt8Pk",
-        "outputId": "c3bc0088-ddf0-4f98-9c35-20a4d93e7f09"
+        "outputId": "9c24fe58-e9ef-48e5-e6ad-fedaf9b85129"
       },
-      "execution_count": 24,
+      "execution_count": 47,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Epoch 1/6\n",
-            "81/81 [==============================] - 11s 106ms/step - loss: 1.4376 - accuracy: 0.3472\n",
-            "Epoch 2/6\n",
-            "81/81 [==============================] - 9s 107ms/step - loss: 1.4077 - accuracy: 0.3605\n",
-            "Epoch 3/6\n",
-            "81/81 [==============================] - 9s 108ms/step - loss: 1.4105 - accuracy: 0.3569\n",
-            "Epoch 4/6\n",
-            "81/81 [==============================] - 9s 108ms/step - loss: 1.4135 - accuracy: 0.3531\n",
-            "Epoch 5/6\n",
-            "81/81 [==============================] - 9s 109ms/step - loss: 1.4015 - accuracy: 0.3616\n",
-            "Epoch 6/6\n",
-            "81/81 [==============================] - 9s 108ms/step - loss: 1.4027 - accuracy: 0.3612\n"
+            "Epoch 1/30\n",
+            "81/81 [==============================] - 7s 70ms/step - loss: 1.4259 - accuracy: 0.4465\n",
+            "Epoch 2/30\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.9567 - accuracy: 0.6333\n",
+            "Epoch 3/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.7041 - accuracy: 0.7423\n",
+            "Epoch 4/30\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.4444 - accuracy: 0.8462\n",
+            "Epoch 5/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.1847 - accuracy: 0.9362\n",
+            "Epoch 6/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0720 - accuracy: 0.9805\n",
+            "Epoch 7/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.3852 - accuracy: 0.8980\n",
+            "Epoch 8/30\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.1409 - accuracy: 0.9545\n",
+            "Epoch 9/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0270 - accuracy: 0.9969\n",
+            "Epoch 10/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0160 - accuracy: 0.9988\n",
+            "Epoch 11/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0153 - accuracy: 0.9984\n",
+            "Epoch 12/30\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.0066 - accuracy: 0.9992\n",
+            "Epoch 13/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0052 - accuracy: 0.9992\n",
+            "Epoch 14/30\n",
+            "81/81 [==============================] - 7s 77ms/step - loss: 0.0047 - accuracy: 0.9992\n",
+            "Epoch 15/30\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.0048 - accuracy: 0.9992\n",
+            "Epoch 16/30\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.0030 - accuracy: 0.9992\n",
+            "Epoch 17/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0033 - accuracy: 0.9992\n",
+            "Epoch 18/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0034 - accuracy: 0.9992\n",
+            "Epoch 19/30\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.0028 - accuracy: 0.9992\n",
+            "Epoch 20/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0027 - accuracy: 0.9992\n",
+            "Epoch 21/30\n",
+            "81/81 [==============================] - 6s 69ms/step - loss: 0.0026 - accuracy: 0.9992\n"
           ]
         },
         {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.callbacks.History at 0x7fecbc2bbe10>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 24
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-47-616639438410>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_ds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1371\u001b[0m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m       \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterator\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menumerate_epochs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1373\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_metrics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1374\u001b[0m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1375\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_stop_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
         }
       ]
     },
@@ -522,61 +542,177 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Z2BQBVFXvv2j",
-        "outputId": "8ec537da-5824-4d31-e8fa-56e237546c84"
+        "outputId": "734fdca0-794f-41bb-d752-ae043d381e00"
       },
-      "execution_count": 25,
+      "execution_count": 48,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "35/35 [==============================] - 2s 55ms/step - loss: 1.4126 - accuracy: 0.3542\n"
+            "35/35 [==============================] - 2s 57ms/step - loss: 2.6256 - accuracy: 0.5940\n"
           ]
         },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[1.4126111268997192, 0.35422343015670776]"
+              "[2.6255574226379395, 0.5940054655075073]"
             ]
           },
           "metadata": {},
-          "execution_count": 25
+          "execution_count": 48
         }
       ]
     },
     {
       "cell_type": "code",
       "source": [
-        "model.evaluate(validation_ds)"
+        "data_augmentation = tf.keras.Sequential([\n",
+        "  tf.keras.layers.RandomFlip(\"horizontal_and_vertical\"),\n",
+        "  tf.keras.layers.RandomRotation(0.2),\n",
+        "])\n",
+        "\n",
+        "\n",
+        "model_augmentated = tf.keras.Sequential([\n",
+        "  tf.keras.Input(shape=(256, 256, 3)),\n",
+        "  tf.keras.layers.Rescaling(1./255),\n",
+        "  data_augmentation,\n",
+        "  tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
+        "  tf.keras.layers.MaxPooling2D(),\n",
+        "  tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
+        "  tf.keras.layers.MaxPooling2D(),\n",
+        "  tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
+        "  tf.keras.layers.MaxPooling2D(),\n",
+        "  tf.keras.layers.Flatten(),\n",
+        "  tf.keras.layers.Dense(128, activation='relu'),\n",
+        "  tf.keras.layers.Dense(5, activation='sigmoid')\n",
+        "])\n",
+        "\n",
+        "model_augmentated.summary()\n",
+        "\n",
+        "model_augmentated.compile(\n",
+        "    optimizer='Nadam',\n",
+        "    loss='sparse_categorical_crossentropy',\n",
+        "    metrics=['accuracy'],\n",
+        ")\n",
+        "model_augmentated.fit(train_ds, epochs=21)\n",
+        "model_augmentated.evaluate(validation_ds)"
       ],
       "metadata": {
+        "id": "lb0k5c7su2ia",
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "2gHsavvvuAtG",
-        "outputId": "23272d53-5f51-4c82-f6be-ee18d1bb738a"
+        "outputId": "6521896c-892b-48a2-ad10-377cac9fcb49"
       },
-      "execution_count": 19,
+      "execution_count": 49,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "35/35 [==============================] - 3s 61ms/step - loss: 1.4243 - accuracy: 0.3406\n"
+            "Model: \"sequential_12\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " rescaling_2 (Rescaling)     (None, 256, 256, 3)       0         \n",
+            "                                                                 \n",
+            " sequential_11 (Sequential)  (None, 256, 256, 3)       0         \n",
+            "                                                                 \n",
+            " conv2d_29 (Conv2D)          (None, 256, 256, 16)      448       \n",
+            "                                                                 \n",
+            " max_pooling2d_12 (MaxPoolin  (None, 128, 128, 16)     0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " conv2d_30 (Conv2D)          (None, 128, 128, 32)      4640      \n",
+            "                                                                 \n",
+            " max_pooling2d_13 (MaxPoolin  (None, 64, 64, 32)       0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " conv2d_31 (Conv2D)          (None, 64, 64, 64)        18496     \n",
+            "                                                                 \n",
+            " max_pooling2d_14 (MaxPoolin  (None, 32, 32, 64)       0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " flatten_4 (Flatten)         (None, 65536)             0         \n",
+            "                                                                 \n",
+            " dense_12 (Dense)            (None, 128)               8388736   \n",
+            "                                                                 \n",
+            " dense_13 (Dense)            (None, 5)                 645       \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 8,412,965\n",
+            "Trainable params: 8,412,965\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n",
+            "Epoch 1/21\n",
+            "81/81 [==============================] - 8s 71ms/step - loss: 1.3431 - accuracy: 0.4605\n",
+            "Epoch 2/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 1.0677 - accuracy: 0.5710\n",
+            "Epoch 3/21\n",
+            "81/81 [==============================] - 7s 82ms/step - loss: 0.9575 - accuracy: 0.6162\n",
+            "Epoch 4/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.8930 - accuracy: 0.6477\n",
+            "Epoch 5/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.8372 - accuracy: 0.6617\n",
+            "Epoch 6/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.8195 - accuracy: 0.6789\n",
+            "Epoch 7/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.7741 - accuracy: 0.7022\n",
+            "Epoch 8/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.7358 - accuracy: 0.7112\n",
+            "Epoch 9/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.7239 - accuracy: 0.7197\n",
+            "Epoch 10/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.6919 - accuracy: 0.7256\n",
+            "Epoch 11/21\n",
+            "81/81 [==============================] - 6s 70ms/step - loss: 0.6661 - accuracy: 0.7396\n",
+            "Epoch 12/21\n",
+            "81/81 [==============================] - 6s 72ms/step - loss: 0.6466 - accuracy: 0.7423\n",
+            "Epoch 13/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.6179 - accuracy: 0.7591\n",
+            "Epoch 14/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.6100 - accuracy: 0.7707\n",
+            "Epoch 15/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.6007 - accuracy: 0.7591\n",
+            "Epoch 16/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.5828 - accuracy: 0.7766\n",
+            "Epoch 17/21\n",
+            "81/81 [==============================] - 6s 72ms/step - loss: 0.5664 - accuracy: 0.7906\n",
+            "Epoch 18/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.5505 - accuracy: 0.7894\n",
+            "Epoch 19/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.5250 - accuracy: 0.7999\n",
+            "Epoch 20/21\n",
+            "81/81 [==============================] - 7s 78ms/step - loss: 0.5259 - accuracy: 0.7984\n",
+            "Epoch 21/21\n",
+            "81/81 [==============================] - 6s 71ms/step - loss: 0.5022 - accuracy: 0.8073\n",
+            "35/35 [==============================] - 2s 52ms/step - loss: 0.8481 - accuracy: 0.7030\n"
           ]
         },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[1.424289584159851, 0.3405994474887848]"
+              "[0.848121166229248, 0.7029972672462463]"
             ]
           },
           "metadata": {},
-          "execution_count": 19
+          "execution_count": 49
         }
       ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "AgGWrBsB0G9A"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ]
 }
\ No newline at end of file