{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2d6f096-6123-4dd0-ae4f-c9abf70889c4",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2024-04-04T11:32:37.677559Z",
     "shell.execute_reply": "2024-04-04T11:32:37.674899Z",
     "shell.execute_reply.started": "2024-04-04T11:32:11.084821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "89f99ed2104c400c89a0a4079cc36f94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/1.63k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b925c2c62913473aa1a8047fb2003609",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/1.01k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset mnist/mnist (download: 11.06 MiB, generated: 19.44 MiB, post-processed: Unknown size, total: 30.50 MiB) to /root/.cache/huggingface/datasets/mnist/mnist/1.0.0/fda16c03c4ecfb13f165ba7e29cf38129ce035011519968cdaf74894ce91c9d4...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62bc67afecc2488d84e6dc0d59fb3f1f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a2f6c3e41574694948f051a0fe10c3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/9.91M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e7d94cbfe3147ff80323ebe74b0e960",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/28.9k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67f4f2d45c354e7da9114a7638fd2098",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/1.65M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3ed5a8fe6c64817933a0cd892152949",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/4.54k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "347a6af016c54c5098f4544fe8666d2c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5d6a4133e5f4b2f99fb819bb879db0f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/60000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a4468bfba904b6bad4575a5b73a2be1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset mnist downloaded and prepared to /root/.cache/huggingface/datasets/mnist/mnist/1.0.0/fda16c03c4ecfb13f165ba7e29cf38129ce035011519968cdaf74894ce91c9d4. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9065c53e61d4dc7b258f008eb91d136",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"mnist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddf255fe-a5dc-47b7-acf8-9bb1c636679f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T11:32:37.680763Z",
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     "shell.execute_reply.started": "2024-04-04T11:32:37.680723Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import io\n",
    "import tensorflow as tf\n",
    "\n",
    "dataset_train = dataset['train']\n",
    "dataset_test = dataset['test']\n",
    "\n",
    "X_train = np.array([np.array(PIL_object) for PIL_object in dataset_train['image']], dtype='float32')\n",
    "X_test = np.array([np.array(PIL_object) for PIL_object in dataset_test['image']], dtype='float32')\n",
    "\n",
    "y_train = np.array(dataset_train['label'])\n",
    "y_test = np.array(dataset_test['label'])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "72022fd2-000d-4d5c-88d5-9afc62c283d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T11:32:53.682038Z",
     "iopub.status.busy": "2024-04-04T11:32:53.681712Z",
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     "shell.execute_reply": "2024-04-04T11:32:55.196384Z",
     "shell.execute_reply.started": "2024-04-04T11:32:53.682010Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dd7871ac-cacd-4866-bdda-67651f592262",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T11:32:55.224011Z",
     "iopub.status.busy": "2024-04-04T11:32:55.223548Z",
     "iopub.status.idle": "2024-04-04T11:32:55.254034Z",
     "shell.execute_reply": "2024-04-04T11:32:55.252607Z",
     "shell.execute_reply.started": "2024-04-04T11:32:55.223968Z"
    }
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "280e0d9d-d9e8-41d9-b9ad-666e84fc0bfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T11:32:55.260216Z",
     "iopub.status.busy": "2024-04-04T11:32:55.259580Z",
     "iopub.status.idle": "2024-04-04T11:34:50.315214Z",
     "shell.execute_reply": "2024-04-04T11:34:50.310372Z",
     "shell.execute_reply.started": "2024-04-04T11:32:55.260155Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 14s 6ms/step - loss: 2.5253 - sparse_categorical_accuracy: 0.8622 - val_loss: 0.4438 - val_sparse_categorical_accuracy: 0.8893\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3722 - sparse_categorical_accuracy: 0.9146 - val_loss: 0.3441 - val_sparse_categorical_accuracy: 0.9107\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.2708 - sparse_categorical_accuracy: 0.9325 - val_loss: 0.2953 - val_sparse_categorical_accuracy: 0.9309\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 10s 6ms/step - loss: 0.2511 - sparse_categorical_accuracy: 0.9359 - val_loss: 0.2580 - val_sparse_categorical_accuracy: 0.9378\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2224 - sparse_categorical_accuracy: 0.9435 - val_loss: 0.2646 - val_sparse_categorical_accuracy: 0.9400\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2134 - sparse_categorical_accuracy: 0.9463 - val_loss: 0.2550 - val_sparse_categorical_accuracy: 0.9456\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1993 - sparse_categorical_accuracy: 0.9509 - val_loss: 0.2359 - val_sparse_categorical_accuracy: 0.9508\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1871 - sparse_categorical_accuracy: 0.9545 - val_loss: 0.2501 - val_sparse_categorical_accuracy: 0.9499\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1875 - sparse_categorical_accuracy: 0.9549 - val_loss: 0.2230 - val_sparse_categorical_accuracy: 0.9496\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1766 - sparse_categorical_accuracy: 0.9570 - val_loss: 0.2856 - val_sparse_categorical_accuracy: 0.9465\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd5e51fc5e0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c7317f9a-14f4-4908-9895-8bc085900e28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T11:34:50.323252Z",
     "iopub.status.busy": "2024-04-04T11:34:50.322859Z",
     "iopub.status.idle": "2024-04-04T11:34:51.995390Z",
     "shell.execute_reply": "2024-04-04T11:34:51.994131Z",
     "shell.execute_reply.started": "2024-04-04T11:34:50.323222Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 2s 5ms/step - loss: 0.2856 - sparse_categorical_accuracy: 0.9465\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9465000033378601"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4aaf2641-a8e4-450b-b5c9-230c18211377",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T11:34:52.005924Z",
     "iopub.status.busy": "2024-04-04T11:34:52.004025Z",
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     "shell.execute_reply": "2024-04-04T11:34:52.104405Z",
     "shell.execute_reply.started": "2024-04-04T11:34:52.005782Z"
    }
   },
   "outputs": [],
   "source": [
    "model.save(\"../models/mnist-digit-classification.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a9debbe-0995-403c-8667-947824f0735e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-04T11:40:29.415780Z",
     "iopub.status.busy": "2024-04-04T11:40:29.415049Z",
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     "shell.execute_reply.started": "2024-04-04T11:40:29.415741Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 33ms/step\n",
      "[[ -1.8562375  41.656296   46.951298   45.414635   20.483383   27.385012\n",
      "  -48.246223   58.661873   26.281921   26.166122 ]]\n",
      "1/1 [==============================] - 0s 38ms/step\n",
      "[[ -1.8562375  41.656296   46.951298   45.414635   20.483383   27.385012\n",
      "  -48.246223   58.661873   26.281921   26.166122 ]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAxUlEQVR4nGNgGDaAEUKFpD77sfTFHeyS9xQYGBg+X4UKPuk6w8DAwMDAAuGm6l/TMnSweCzLwPDntSTDozPIOhkYGBgYBA3PmDIw/Lh1XShnGi5nBP+9KIRLTuzl/2AokwlDMlv0/U1cGq1//rPDJcfQ+m83Ky45zrM/rHBqrPu3Daec9+8PlrjkhO/+W4ZLjvn0v9vKuCTV/v3zxSUn/+BfMSMuydZ//0xwydl+QpdEClsbHoa7X1AkWZA5F53f4TIWEwAAaRE8kJuHrgAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=L size=28x28>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = 0\n",
    "IMAGE_HEIGHT = 28\n",
    "IMAGE_WIDTH = 28\n",
    "IMAGE_CHANNEL = 1\n",
    "\n",
    "# image_to_predict = np.reshape(X_test[0], (1, 28, 28, 1))\n",
    "image_to_predict = np.reshape(np.array(dataset_test['image'][index]), (1, IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNEL))\n",
    "print(model.predict(image_to_predict))\n",
    "\n",
    "\n",
    "image_to_predict = np.reshape(X_test[index], (1, 28, 28, 1))\n",
    "print(model.predict(image_to_predict))\n",
    "\n",
    "dataset_test['image'][index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e213156-9fe7-422a-bbf1-05ba92584d0a",
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     "shell.execute_reply.started": "2024-04-04T11:36:15.067008Z"
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   "outputs": [
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