lab_10_cnn.ipynb

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    "# 10. Convolutional neural networks\n",
    "\n",
    "In this assignment you'll use a photo-Z dataset acquired from the observations of the SDSS telescope located in New Mexico. The goal is to predict redshifts from multiband images of galaxies. As a warmup you'll work with the SVHN dataset.\n",
    "\n",
    "**<font color='red'>[WARN]:</font> For this assignment you'll need significantly more computational power compared to previous assignments! If you don't have a CUDA-capable GPU with >4Gb VRAM and >8Gb RAM, then you're advised to work on Google Colab!**"
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    "### 1. Load the Street View House Numbers (SVHN) dataset\n",
    "\n",
    "-   Download the SVHN database and load the train and test datasets!\n",
    "    There are multiple ways to do this. The easiest one is probably to install\n",
    "    and use the `extra-keras-datasets` Python package. You need to use the\n",
    "    standard/normal SVHN dataset only and NOT the one titled as `extra`!\n",
    "    (Of course, if you have enough RAM and VRAM, you can work with that one\n",
    "    too, if you want...)\n",
    "-   Preprocess the downloaded data if needed to be able to use it for training\n",
    "    and testing!\n",
    "-   Normalize the pixel values into the interval of [0,1]!\n",
    "-   How many and what classes do we have in the dataset? How many train and test\n",
    "    examples do we have?\n",
    "-   What are the dimensions of the images?\n",
    "-   Show some images randomly from the dataset!\n",
    "-   Make one-hot encoding for the labels!"
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    "### 2. Create a convolutional neural network for the SVHN dataset\n",
    "\n",
    "-   Train the following network on the training set and generate\n",
    "    prediction for the test images:\n",
    "    ```\n",
    "          > conv2D, 16 kernels, kernel size = (3,3), valid padding, relu actvation\n",
    "          > conv2D, 16 kernels, kernel size = (3,3), valid padding, relu actvation\n",
    "          > maxpooling kernel size = (2,2)\n",
    "          > conv2D, 32 kernels, kernel size = (3,3), valid padding, relu actvation\n",
    "          > conv2D, 32 kernels, kernel size = (3,3), valid padding, relu actvation\n",
    "          > maxpooling pool size = (2,2) strides = (2,2)\n",
    "          > flatten\n",
    "          > dense, 10 neurons, softmax activation\n",
    "    ```\n",
    "    -   Use Adam optimizer with default parameters\n",
    "    -   Use categorical crossentropy as loss function\n",
    "    -   Compile the model\n",
    "    -   Print out a summary of the model\n",
    "    -   Train the CNN on the training data for 25 epochs with batch size\n",
    "        of 64\n",
    "    -   Use the test data as validation data\n",
    "\n",
    "-   Calculate the categorical cross-entropy loss and the accuracy!\n",
    "    **<font color='green'>[HINT]:</font>** you should get at least $\\approx$ 80-90%\n",
    "    accuracy.\n",
    "-   Plot the training and the validation loss and accuracy on the same plot!\n",
    "    Do we experience overfitting?\n",
    "-   Show the confusion matrix of the predictions!"
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    "### 3. Load the Sload Digital Sky Survey (SDSS) Dataset\n",
    "\n",
    "You can download the dataset from Kaggle via\n",
    "[this link](https://www.kaggle.com/masterdesky/multiband-photoz-sdss-dr16).\n",
    "\n",
    "-   Download the images from Kaggle (~10'000 images total) \n",
    "-   Preprocess the images similarly to the SVHN dataset if needed! (Normalize\n",
    "    pixel values to [0,1], etc.)\n",
    "-   What are the dimensions of the images?\n",
    "-   Show 15 images randomly from the dataset!\n",
    "-   Create a train-test-validation split using `train_test_split` from `sklearn`\n",
    "    where the test size is $0.33$ and the validation size is $0.2$\n",
    "    -   Set a random seed\n",
    "    -   Print the number of images in each of these sets after you've created\n",
    "        them"
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    "### 4. Create a convolutional neural network for the SDSS dataset\n",
    "\n",
    "-   Train the following network on the training set and generate\n",
    "    prediction for the test images:\n",
    "    ```\n",
    "          > conv2D, 32 kernels, kernel size = (3,3), same padding\n",
    "          > batch normalization\n",
    "          > relu actvation\n",
    "          > conv2D, 32 kernels, kernel size = (3,3), same padding\n",
    "          > batch normalization\n",
    "          > relu actvation\n",
    "          > maxpooling pool size = (2,2), strides = (2,2)\n",
    "          \n",
    "          > conv2D, 64 kernels, kernel size = (3,3), same padding\n",
    "          > batch normalization\n",
    "          > relu actvation\n",
    "          > conv2D, 64 kernels, kernel size = (1,1), same padding\n",
    "          > batch normalization\n",
    "          > relu actvation\n",
    "          > conv2D, 64 kernels, kernel size = (3,3), same padding\n",
    "          > batch normalization\n",
    "          > relu actvation\n",
    "          > maxpooling pool size = (2,2), strides = (2,2)\n",
    "          \n",
    "          > global pooling\n",
    "          > dense, 1 neuron, no activation\n",
    "    ```\n",
    "\n",
    "    -   Use Adam optimizer with default parameters\n",
    "    -   Use mean squared error as loss function\n",
    "    -   Compile the model\n",
    "    -   Print out a summary of the model\n",
    "    -   Use the created validation set as validation during the training\n",
    "    -   Train the CNN on the training data for 25 epochs with batch size\n",
    "        of 64\n",
    "\n",
    "-   Calculate and print out the final accuracy using the R2 score!"
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    "### 5. Evaluate performance\n",
    "\n",
    "-   Plot the training and the validation loss on the same plot!\n",
    "-   Show the predicted values vs the actual labels on a plot!\n",
    "-   Where does the model make mistakes? Try to plot the images corresponding to\n",
    "    some outlier values!"
   ]
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    "### 6. Train an other CNN\n",
    "\n",
    "-   The previous architecture can be further improved.\n",
    "-   Come up with an architecture that can achieve more than 80-85% accuracy on \n",
    "    the test set if the accuracy metric is the R2 score!\n",
    "    -   You can use any tool for this task.\n",
    "    -   Remember that there are different losses and optimizers, early stopping,\n",
    "        regularization, etc. that can be useful for you. You can find more about \n",
    "        these eg. in the\n",
    "        [tensorflow/keras documentation](https://www.tensorflow.org/api_docs/python/tf/all_symbols).\n",
    "    -   Don't forget that you can add more layers to the model and train for\n",
    "        more epochs too... :)\n",
    "-   Print out the summary of this model!\n",
    "-   Plot the loss curves for this model too!"
   ]
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