Delete Coding Excercise.ipynb

Coding Excercise.ipynb

@@ -1,266 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37aa8c7a",
-   "metadata": {
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-     "end_time": "2023-03-26T07:31:34.213141Z",
-     "start_time": "2023-03-26T07:31:14.082603Z"
-    },
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-   },
-   "outputs": [],
-   "source": [
-    "!pip install huggingface_hub\n",
-    "!pip install datasets\n",
-    "!pip install keras"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "59bed9bc",
-   "metadata": {
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-     "end_time": "2023-03-26T14:03:42.287776Z",
-     "start_time": "2023-03-26T14:03:39.989670Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from huggingface_hub import notebook_login\n",
-    "from datasets import load_dataset\n",
-    "import pandas as pd\n",
-    "from datasets import load_dataset\n",
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.applications.vgg16 import VGG16\n",
-    "from tensorflow.keras.models import Model\n",
-    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "from PIL import Image\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "57bfa96e",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-03-26T14:03:42.317000Z",
-     "start_time": "2023-03-26T14:03:42.289947Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Login successful\n",
-      "Your token has been saved to /root/.huggingface/token\n",
-      "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
-      "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
-      "\n",
-      "git config --global credential.helper store\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "notebook_login()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "e2407eb7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-03-26T14:03:52.591875Z",
-     "start_time": "2023-03-26T14:03:48.476822Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Found cached dataset parquet (/root/.cache/huggingface/datasets/competitions___parquet/competitions--aiornot-f559c639a48093b2/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load dataset from hugging face\n",
-    "# prepare data for training, validation and testing\n",
-    "train_ds, val_ds = load_dataset('competitions/aiornot', split=\"train\").train_test_split(test_size=0.15).values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e58c6fe1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-03-26T14:04:10.210069Z",
-     "start_time": "2023-03-26T14:03:53.833533Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "data_sz = 1000\n",
-    "X_train = train_ds[:data_sz]['image']\n",
-    "X_val = val_ds[:data_sz]['image']\n",
-    "Y_train = to_categorical(train_ds[:data_sz]['label'])\n",
-    "Y_val = to_categorical(val_ds[:data_sz]['label'])\n",
-    "# Convert the input data to a NumPy array\n",
-    "X_train = np.stack([np.array(image) for image in X_train])\n",
-    "X_val = np.stack([np.array(image) for image in X_val])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad809dae",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-03-26T14:04:33.658Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "22/32 [===================>..........] - ETA: 27s - loss: 2.8218 - accuracy: 0.6630"
-     ]
-    }
-   ],
-   "source": [
-    "with tf.device('/device:GPU:3'):\n",
-    "    # Load the VGG16 model pre-trained on ImageNet\n",
-    "    base_model = VGG16(weights='imagenet', include_top=False)\n",
-    "\n",
-    "    # Add a global spatial average pooling layer\n",
-    "    x = base_model.output\n",
-    "    x = GlobalAveragePooling2D()(x)\n",
-    "\n",
-    "    # Add a fully-connected layer\n",
-    "    x = Dense(1024, activation='relu')(x)\n",
-    "\n",
-    "    # Add a logistic layer with the number of classes of target variable\n",
-    "    num_classes = 2\n",
-    "    predictions = Dense(num_classes, activation='softmax')(x)\n",
-    "\n",
-    "    # Create the final model\n",
-    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
-    "\n",
-    "    # Freeze all layers in the base VGG16 model\n",
-    "    for layer in base_model.layers:\n",
-    "        layer.trainable = False\n",
-    "\n",
-    "    # Compile the model\n",
-    "    model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
-    "\n",
-    "# Train the model on your new dataset\n",
-    "model.fit(X_train, Y_train, epochs=10, validation_data=(X_val, Y_val))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a0a91c9",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-03-26T14:05:03.786Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Generate predictions for the data\n",
-    "y_pred = model.predict(X_val)\n",
-    "# Convert predictions and true labels to class indices\n",
-    "y_pred_classes = y_pred.argmax(axis=1)\n",
-    "y_true_classes = Y_val.argmax(axis=1)\n",
-    "# Find the indices of the misclassified samples\n",
-    "misclassified_indices = np.where(y_pred_classes != y_true_classes)[0]\n",
-    "\n",
-    "# Get the misclassified samples\n",
-    "# x_misclassified = X_val[misclassified_indices]\n",
-    "# y_misclassified_true = Y_val[misclassified_indices]\n",
-    "# y_misclassified_pred = y_pred[misclassified_indices]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "254d9907",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-03-26T14:05:06.090Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# a helper function to view missclassfied data with the image and prediction\n",
-    "def checkMiss(idx):\n",
-    "    print(\"\\ncorrect:\", Y_val[idx])\n",
-    "    print(\"miss:\", y_pred[idx])\n",
-    "    img = Image.fromarray(X_val[idx])\n",
-    "    img.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60bbb1b9",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2023-03-26T14:05:07.650Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# view 5 miss classified data to see what could be improved\n",
-    "for i in range(10):\n",
-    "    checkMiss(misclassified_indices[i])"
-   ]
-  }
- ],
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