"
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "x_train shape: (50000, 32, 32, 3)\n",
+ "50000 train samples\n",
+ "x_ttest shape: (10000, 32, 32, 3)\n",
+ "10000 test samples\n",
+ "-------------------\n",
+ "[[0. 0. 0. ... 0. 0. 0.]\n",
+ " [0. 0. 0. ... 0. 0. 1.]\n",
+ " [0. 0. 0. ... 0. 0. 1.]\n",
+ " ...\n",
+ " [0. 0. 0. ... 0. 0. 1.]\n",
+ " [0. 1. 0. ... 0. 0. 0.]\n",
+ " [0. 1. 0. ... 0. 0. 0.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from PIL import Image\n",
+ "from numpy import asarray\n",
+ "from matplotlib import image\n",
+ "from matplotlib import pyplot\n",
+ "import random\n",
+ "from random import randrange\n",
+ "\n",
+ "\n",
+ "## load the image\n",
+ "#image = Image.open('data/Flowers for image classification.png')\n",
+ "\n",
+ "## convert image to numpy array\n",
+ "#data = asarray(image)\n",
+ "#print(type(data))\n",
+ "\n",
+ "# summarize shape\n",
+ "print(x_train[0].shape)\n",
+ "\n",
+ "display(x_train[0][:,:,0])\n",
+ "\n",
+ "# display the array of pixels as an image\n",
+ "for i in range(10):\n",
+ " pyplot.rcParams[\"figure.figsize\"] = [2, 2]\n",
+ " pyplot.rcParams[\"figure.autolayout\"] = True\n",
+ " r = randrange(0, 50000)\n",
+ " pyplot.imshow(x_train[r])\n",
+ " print (y_train[r])\n",
+ " pyplot.show()\n",
+ "\n",
+ "\n",
+ "# Preprocess the data (these are NumPy arrays)\n",
+ "x_train = x_train.astype(\"float32\") / 255\n",
+ "x_test = x_test.astype(\"float32\") / 255\n",
+ "\n",
+ "\n",
+ "print(\"x_train shape:\", x_train.shape)\n",
+ "print(x_train.shape[0], \"train samples\")\n",
+ "\n",
+ "print(\"x_ttest shape:\", x_test.shape)\n",
+ "print(x_test.shape[0], \"test samples\")\n",
+ "\n",
+ "from sklearn.preprocessing import OneHotEncoder\n",
+ "\n",
+ "\n",
+ "encoder = OneHotEncoder(sparse_output=False)\n",
+ "y_train = encoder.fit_transform(y_train)\n",
+ "y_test = encoder.fit_transform(y_test)\n",
+ "print(\"-------------------\")\n",
+ "print(y_train)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2ER5WlMNRydp"
+ },
+ "source": [
+ "## Define the following model (same as the one in tutorial)\n",
+ "\n",
+ "For the convolutional front-end, start with a single convolutional layer with a small filter size (3,3) and a modest number of filters (32) followed by a max pooling layer.\n",
+ "\n",
+ "Use the input as (32,32,3).\n",
+ "\n",
+ "The filter maps can then be flattened to provide features to the classifier.\n",
+ "\n",
+ "Use a dense layer with 100 units before the classification layer (which is also a dense layer with softmax activation)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WfWCHxh8HGhN"
+ },
+ "outputs": [],
+ "source": [
+ "from keras.backend import clear_session\n",
+ "clear_session()"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
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+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7200\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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+ "│ activation (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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+ "source": [
+ "import numpy as np\n",
+ "from tensorflow import keras\n",
+ "from tensorflow.keras import layers\n",
+ "from tensorflow.keras.layers import Dense, Dropout, Activation\n",
+ "\n",
+ "\"\"\"\n",
+ "## Build the model\n",
+ "\"\"\"\n",
+ "# Model / data parameters\n",
+ "num_classes = 10\n",
+ "input_shape = (32, 32, 3)\n",
+ "\n",
+ "model = keras.Sequential(\n",
+ " [\n",
+ " keras.Input(shape=input_shape),\n",
+ " layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " #layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " #layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " #layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " #layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " #layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " #layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Flatten(),\n",
+ " #layers.Dropout(0.5),\n",
+ "\n",
+ " layers.Dense(100, activation='relu'),\n",
+ " layers.Activation(\"relu\"),\n",
+ "\n",
+ "\n",
+ " layers.Dense(num_classes, activation=\"softmax\"),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "model.summary()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PGtivbQJT39U"
+ },
+ "source": [
+ "* Compile the model using categorical_crossentropy loss, SGD optimizer and\n",
+ "\n",
+ "1. List item\n",
+ "2. List item\n",
+ "\n",
+ "use 'accuracy' as the metric.\n",
+ "* Use the above defined model to train CIFAR-10 and train the model for 50 epochs with a batch size of 512."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hn8UzPBZugVp",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "d81db6b8-537a-4ccd-8bc9-dea0eaef9794"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 30ms/step - accuracy: 0.1294 - loss: 2.2885 - val_accuracy: 0.2244 - val_loss: 2.2081\n",
+ "Epoch 2/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.2335 - loss: 2.1818 - val_accuracy: 0.2314 - val_loss: 2.0931\n",
+ "Epoch 3/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.2700 - loss: 2.0642 - val_accuracy: 0.2986 - val_loss: 2.0093\n",
+ "Epoch 4/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.2993 - loss: 1.9857 - val_accuracy: 0.3160 - val_loss: 1.9578\n",
+ "Epoch 5/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3212 - loss: 1.9331 - val_accuracy: 0.3236 - val_loss: 1.9270\n",
+ "Epoch 6/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3371 - loss: 1.9007 - val_accuracy: 0.3366 - val_loss: 1.8852\n",
+ "Epoch 7/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3451 - loss: 1.8732 - val_accuracy: 0.3392 - val_loss: 1.8686\n",
+ "Epoch 8/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3604 - loss: 1.8408 - val_accuracy: 0.3606 - val_loss: 1.8357\n",
+ "Epoch 9/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3696 - loss: 1.8148 - val_accuracy: 0.3618 - val_loss: 1.8204\n",
+ "Epoch 10/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3692 - loss: 1.8018 - val_accuracy: 0.3696 - val_loss: 1.7972\n",
+ "Epoch 11/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3841 - loss: 1.7750 - val_accuracy: 0.3802 - val_loss: 1.7871\n",
+ "Epoch 12/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3901 - loss: 1.7600 - val_accuracy: 0.3850 - val_loss: 1.7578\n",
+ "Epoch 13/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.3987 - loss: 1.7301 - val_accuracy: 0.3870 - val_loss: 1.7372\n",
+ "Epoch 14/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4011 - loss: 1.7205 - val_accuracy: 0.3934 - val_loss: 1.7355\n",
+ "Epoch 15/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.4094 - loss: 1.6971 - val_accuracy: 0.3848 - val_loss: 1.7379\n",
+ "Epoch 16/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4132 - loss: 1.6870 - val_accuracy: 0.4124 - val_loss: 1.6855\n",
+ "Epoch 17/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4195 - loss: 1.6671 - val_accuracy: 0.4080 - val_loss: 1.6786\n",
+ "Epoch 18/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4224 - loss: 1.6561 - val_accuracy: 0.4196 - val_loss: 1.6657\n",
+ "Epoch 19/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.4244 - loss: 1.6426 - val_accuracy: 0.4256 - val_loss: 1.6397\n",
+ "Epoch 20/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4308 - loss: 1.6253 - val_accuracy: 0.4256 - val_loss: 1.6247\n",
+ "Epoch 21/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4383 - loss: 1.6084 - val_accuracy: 0.4318 - val_loss: 1.6478\n",
+ "Epoch 22/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4461 - loss: 1.5867 - val_accuracy: 0.4438 - val_loss: 1.5823\n",
+ "Epoch 23/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4457 - loss: 1.5804 - val_accuracy: 0.4256 - val_loss: 1.6363\n",
+ "Epoch 24/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4556 - loss: 1.5547 - val_accuracy: 0.4436 - val_loss: 1.5668\n",
+ "Epoch 25/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4563 - loss: 1.5436 - val_accuracy: 0.4440 - val_loss: 1.5613\n",
+ "Epoch 26/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4605 - loss: 1.5316 - val_accuracy: 0.4364 - val_loss: 1.6059\n",
+ "Epoch 27/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4587 - loss: 1.5341 - val_accuracy: 0.4466 - val_loss: 1.5485\n",
+ "Epoch 28/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.4691 - loss: 1.5089 - val_accuracy: 0.4564 - val_loss: 1.5245\n",
+ "Epoch 29/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4766 - loss: 1.4916 - val_accuracy: 0.4706 - val_loss: 1.4966\n",
+ "Epoch 30/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4850 - loss: 1.4645 - val_accuracy: 0.4814 - val_loss: 1.4860\n",
+ "Epoch 31/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4840 - loss: 1.4644 - val_accuracy: 0.4664 - val_loss: 1.5255\n",
+ "Epoch 32/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4865 - loss: 1.4603 - val_accuracy: 0.4780 - val_loss: 1.4649\n",
+ "Epoch 33/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4916 - loss: 1.4465 - val_accuracy: 0.4708 - val_loss: 1.4844\n",
+ "Epoch 34/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5004 - loss: 1.4184 - val_accuracy: 0.4956 - val_loss: 1.4382\n",
+ "Epoch 35/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4997 - loss: 1.4257 - val_accuracy: 0.5004 - val_loss: 1.4124\n",
+ "Epoch 36/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5107 - loss: 1.3998 - val_accuracy: 0.5020 - val_loss: 1.4302\n",
+ "Epoch 37/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5110 - loss: 1.3848 - val_accuracy: 0.5126 - val_loss: 1.3886\n",
+ "Epoch 38/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5142 - loss: 1.3826 - val_accuracy: 0.5048 - val_loss: 1.3884\n",
+ "Epoch 39/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - accuracy: 0.5179 - loss: 1.3672 - val_accuracy: 0.5222 - val_loss: 1.3752\n",
+ "Epoch 40/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.5235 - loss: 1.3595 - val_accuracy: 0.5038 - val_loss: 1.4228\n",
+ "Epoch 41/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5193 - loss: 1.3570 - val_accuracy: 0.5290 - val_loss: 1.3490\n",
+ "Epoch 42/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5259 - loss: 1.3469 - val_accuracy: 0.4686 - val_loss: 1.4722\n",
+ "Epoch 43/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5273 - loss: 1.3431 - val_accuracy: 0.5254 - val_loss: 1.3495\n",
+ "Epoch 44/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5312 - loss: 1.3271 - val_accuracy: 0.5110 - val_loss: 1.3824\n",
+ "Epoch 45/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5312 - loss: 1.3277 - val_accuracy: 0.5346 - val_loss: 1.3386\n",
+ "Epoch 46/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5416 - loss: 1.3053 - val_accuracy: 0.5376 - val_loss: 1.3177\n",
+ "Epoch 47/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5397 - loss: 1.3030 - val_accuracy: 0.5300 - val_loss: 1.3253\n",
+ "Epoch 48/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5433 - loss: 1.2972 - val_accuracy: 0.5404 - val_loss: 1.3115\n",
+ "Epoch 49/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.5505 - loss: 1.2810 - val_accuracy: 0.5372 - val_loss: 1.3278\n",
+ "Epoch 50/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.5535 - loss: 1.2761 - val_accuracy: 0.5508 - val_loss: 1.2916\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.compile(loss='categorical_crossentropy',\n",
+ " optimizer='SGD', metrics=['accuracy'])\n",
+ "\n",
+ "history = model.fit(x_train, y_train, batch_size=512, epochs=50, verbose=1, validation_split=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "score = model.evaluate(x_test, y_test, verbose=1)\n",
+ "print(\"Test loss:\", score[0])\n",
+ "print(\"Test accuracy:\", score[1])"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RYcFYoEZ5YJW",
+ "outputId": "724caadd-4a45-4f93-e9d0-381561ef57f7"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.5369 - loss: 1.2940\n",
+ "Test loss: 1.295694351196289\n",
+ "Test accuracy: 0.5393999814987183\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rQ3MSXoUMX7b"
+ },
+ "source": [
+ "* Plot the cross entropy loss curve and the accuracy curve"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 807
+ },
+ "id": "SLx-wsy7MX7b",
+ "outputId": "e764a049-1a45-443d-8a8e-81001eb35e92"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "acc = history.history['accuracy']\n",
+ "val_acc = history.history['val_accuracy']\n",
+ "\n",
+ "loss = history.history['loss']\n",
+ "val_loss = history.history['val_loss']\n",
+ "\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "plt.subplot(2, 1, 1)\n",
+ "plt.plot(acc, label='Training Accuracy')\n",
+ "plt.plot(val_acc, label='Validation Accuracy')\n",
+ "plt.legend(loc='lower right')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.ylim([min(plt.ylim()),1])\n",
+ "plt.title('Training and Validation Accuracy')\n",
+ "\n",
+ "plt.subplot(2, 1, 2)\n",
+ "plt.plot(loss, label='Training Loss')\n",
+ "plt.plot(val_loss, label='Validation Loss')\n",
+ "plt.legend(loc='upper right')\n",
+ "plt.ylabel('Cross Entropy')\n",
+ "plt.ylim([0,1.4])\n",
+ "plt.title('Training and Validation Loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "G2mrWK5hSB_o"
+ },
+ "source": [
+ "## Defining Deeper Architectures: VGG Models\n",
+ "\n",
+ "* Define a deeper model architecture for CIFAR-10 dataset and train the new model for 50 epochs with a batch size of 512. We will use VGG model as the architecture.\n",
+ "\n",
+ "Stack two convolutional layers with 32 filters, each of 3 x 3.\n",
+ "\n",
+ "Use a max pooling layer and next flatten the output of the previous layer and add a dense layer with 128 units before the classification layer.\n",
+ "\n",
+ "For all the layers, use ReLU activation function.\n",
+ "\n",
+ "Use same padding for the layers to ensure that the height and width of each layer output matches the input\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "A80vLxW9FIek"
+ },
+ "outputs": [],
+ "source": [
+ "from keras.backend import clear_session\n",
+ "clear_session()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "cgca5dUNSFNc"
+ },
+ "outputs": [],
+ "source": [
+ "# Your code here :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 513
+ },
+ "outputId": "0d1bc243-ed7a-4274-a610-ba789c7ca369",
+ "id": "hoImY60ZUOJy"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+ ],
+ "text/html": [
+ "Model: \"sequential\"\n",
+ "
\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+ "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+ "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m896\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m3\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m36,928\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m1\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m8,320\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ activation (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
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+ "data": {
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+ "source": [
+ "import numpy as np\n",
+ "from tensorflow import keras\n",
+ "from tensorflow.keras import layers\n",
+ "from tensorflow.keras.layers import Dense, Dropout, Activation\n",
+ "\n",
+ "\"\"\"\n",
+ "## Build the model\n",
+ "\"\"\"\n",
+ "# Model / data parameters\n",
+ "num_classes = 10\n",
+ "input_shape = (32, 32, 3)\n",
+ "\n",
+ "model1 = keras.Sequential(\n",
+ " [\n",
+ " keras.Input(shape=input_shape),\n",
+ " layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
+ " layers.MaxPooling2D(pool_size=(2, 2)),\n",
+ " layers.Flatten(),\n",
+ " #layers.Dropout(0.5),\n",
+ "\n",
+ " layers.Dense(128, activation='relu'),\n",
+ " layers.Activation(\"relu\"),\n",
+ "\n",
+ "\n",
+ " layers.Dense(num_classes, activation=\"softmax\"),\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "model1.summary()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZwaPphEBUtlC"
+ },
+ "source": [
+ "* Compile the model using categorical_crossentropy loss, SGD optimizer and use 'accuracy' as the metric.\n",
+ "* Use the above defined model to train CIFAR-10 and train the model for 50 epochs with a batch size of 512."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Bc2qtU0mUvVA"
+ },
+ "outputs": [],
+ "source": [
+ "# Your code here :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6526d99c-5bab-41ac-cf6e-d8ffd3d4e51c",
+ "collapsed": true,
+ "id": "TcRn7BMzVJas"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 69ms/step - accuracy: 0.0920 - loss: 2.3031 - val_accuracy: 0.1174 - val_loss: 2.3007\n",
+ "Epoch 2/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.1204 - loss: 2.3000 - val_accuracy: 0.1338 - val_loss: 2.2980\n",
+ "Epoch 3/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 25ms/step - accuracy: 0.1363 - loss: 2.2977 - val_accuracy: 0.1350 - val_loss: 2.2951\n",
+ "Epoch 4/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.1408 - loss: 2.2949 - val_accuracy: 0.1324 - val_loss: 2.2913\n",
+ "Epoch 5/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.1281 - loss: 2.2914 - val_accuracy: 0.1286 - val_loss: 2.2858\n",
+ "Epoch 6/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.1273 - loss: 2.2849 - val_accuracy: 0.1202 - val_loss: 2.2780\n",
+ "Epoch 7/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.1291 - loss: 2.2767 - val_accuracy: 0.1426 - val_loss: 2.2669\n",
+ "Epoch 8/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.1432 - loss: 2.2651 - val_accuracy: 0.1632 - val_loss: 2.2492\n",
+ "Epoch 9/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.1661 - loss: 2.2450 - val_accuracy: 0.2042 - val_loss: 2.2188\n",
+ "Epoch 10/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.1985 - loss: 2.2098 - val_accuracy: 0.2298 - val_loss: 2.1601\n",
+ "Epoch 11/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.2248 - loss: 2.1408 - val_accuracy: 0.2312 - val_loss: 2.1048\n",
+ "Epoch 12/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.2298 - loss: 2.0984 - val_accuracy: 0.2354 - val_loss: 2.0921\n",
+ "Epoch 13/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.2450 - loss: 2.0595 - val_accuracy: 0.2602 - val_loss: 2.0144\n",
+ "Epoch 14/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.2615 - loss: 2.0199 - val_accuracy: 0.2742 - val_loss: 1.9866\n",
+ "Epoch 15/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.2695 - loss: 1.9999 - val_accuracy: 0.2756 - val_loss: 2.0143\n",
+ "Epoch 16/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.2818 - loss: 1.9778 - val_accuracy: 0.2960 - val_loss: 1.9372\n",
+ "Epoch 17/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.2917 - loss: 1.9419 - val_accuracy: 0.2954 - val_loss: 1.9181\n",
+ "Epoch 18/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.2950 - loss: 1.9455 - val_accuracy: 0.2952 - val_loss: 1.9198\n",
+ "Epoch 19/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3041 - loss: 1.9189 - val_accuracy: 0.3064 - val_loss: 1.8974\n",
+ "Epoch 20/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.3147 - loss: 1.8953 - val_accuracy: 0.2840 - val_loss: 2.0319\n",
+ "Epoch 21/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.3193 - loss: 1.8833 - val_accuracy: 0.3024 - val_loss: 1.9163\n",
+ "Epoch 22/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.3299 - loss: 1.8630 - val_accuracy: 0.3382 - val_loss: 1.8327\n",
+ "Epoch 23/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3399 - loss: 1.8311 - val_accuracy: 0.3232 - val_loss: 1.8656\n",
+ "Epoch 24/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3396 - loss: 1.8358 - val_accuracy: 0.3406 - val_loss: 1.8085\n",
+ "Epoch 25/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.3475 - loss: 1.8138 - val_accuracy: 0.3312 - val_loss: 1.8423\n",
+ "Epoch 26/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3550 - loss: 1.7932 - val_accuracy: 0.3396 - val_loss: 1.8010\n",
+ "Epoch 27/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3568 - loss: 1.7855 - val_accuracy: 0.3654 - val_loss: 1.7413\n",
+ "Epoch 28/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3703 - loss: 1.7593 - val_accuracy: 0.3600 - val_loss: 1.7511\n",
+ "Epoch 29/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3698 - loss: 1.7578 - val_accuracy: 0.3582 - val_loss: 1.7448\n",
+ "Epoch 30/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 29ms/step - accuracy: 0.3845 - loss: 1.7295 - val_accuracy: 0.3678 - val_loss: 1.7712\n",
+ "Epoch 31/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.3833 - loss: 1.7189 - val_accuracy: 0.3630 - val_loss: 1.7744\n",
+ "Epoch 32/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3966 - loss: 1.6938 - val_accuracy: 0.3966 - val_loss: 1.6747\n",
+ "Epoch 33/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.3962 - loss: 1.6871 - val_accuracy: 0.3886 - val_loss: 1.6949\n",
+ "Epoch 34/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4004 - loss: 1.6840 - val_accuracy: 0.3642 - val_loss: 1.7229\n",
+ "Epoch 35/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.4006 - loss: 1.6644 - val_accuracy: 0.4066 - val_loss: 1.6441\n",
+ "Epoch 36/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.4090 - loss: 1.6566 - val_accuracy: 0.4142 - val_loss: 1.6239\n",
+ "Epoch 37/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4118 - loss: 1.6494 - val_accuracy: 0.3796 - val_loss: 1.7548\n",
+ "Epoch 38/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4165 - loss: 1.6335 - val_accuracy: 0.3614 - val_loss: 1.7887\n",
+ "Epoch 39/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 25ms/step - accuracy: 0.4219 - loss: 1.6154 - val_accuracy: 0.4154 - val_loss: 1.6210\n",
+ "Epoch 40/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.4260 - loss: 1.5999 - val_accuracy: 0.3616 - val_loss: 1.7897\n",
+ "Epoch 41/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.4291 - loss: 1.6062 - val_accuracy: 0.4168 - val_loss: 1.6090\n",
+ "Epoch 42/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4319 - loss: 1.5953 - val_accuracy: 0.4174 - val_loss: 1.5978\n",
+ "Epoch 43/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4367 - loss: 1.5802 - val_accuracy: 0.4282 - val_loss: 1.5803\n",
+ "Epoch 44/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4443 - loss: 1.5602 - val_accuracy: 0.4410 - val_loss: 1.5602\n",
+ "Epoch 45/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4467 - loss: 1.5509 - val_accuracy: 0.4476 - val_loss: 1.5463\n",
+ "Epoch 46/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 27ms/step - accuracy: 0.4450 - loss: 1.5575 - val_accuracy: 0.4522 - val_loss: 1.5251\n",
+ "Epoch 47/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4551 - loss: 1.5367 - val_accuracy: 0.4486 - val_loss: 1.5269\n",
+ "Epoch 48/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4552 - loss: 1.5290 - val_accuracy: 0.4432 - val_loss: 1.5419\n",
+ "Epoch 49/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4581 - loss: 1.5267 - val_accuracy: 0.4488 - val_loss: 1.5368\n",
+ "Epoch 50/50\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 26ms/step - accuracy: 0.4608 - loss: 1.5165 - val_accuracy: 0.4584 - val_loss: 1.5144\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1.compile(loss='categorical_crossentropy',\n",
+ " optimizer='SGD', metrics=['accuracy'])\n",
+ "\n",
+ "history1 = model1.fit(x_train, y_train, batch_size=512, epochs=50, verbose=1, validation_split=0.1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_2cRr2ZFSFds"
+ },
+ "source": [
+ "* Compare the performance of both the models by plotting the loss and accuracy curves of both the training steps. Does the deeper model perform better? Comment on the observation.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "F8OSHAf5SJPr"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "e7e68242-fba1-4fb6-9a80-b3f95eb1b94a",
+ "id": "W2-wDGcMVhgs"
+ },
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "==== 2nd model =====\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": 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\n"
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "acc = history.history['accuracy']\n",
+ "val_acc = history.history['val_accuracy']\n",
+ "\n",
+ "loss = history.history['loss']\n",
+ "val_loss = history.history['val_loss']\n",
+ "\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "plt.subplot(2, 1, 1)\n",
+ "plt.plot(acc, label='Training Accuracy')\n",
+ "plt.plot(val_acc, label='Validation Accuracy')\n",
+ "plt.legend(loc='lower right')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.ylim([min(plt.ylim()),1])\n",
+ "plt.title('Training and Validation Accuracy')\n",
+ "\n",
+ "plt.subplot(2, 1, 2)\n",
+ "plt.plot(loss, label='Training Loss')\n",
+ "plt.plot(val_loss, label='Validation Loss')\n",
+ "plt.legend(loc='upper right')\n",
+ "plt.ylabel('Cross Entropy')\n",
+ "plt.ylim([0,2])\n",
+ "plt.title('Training and Validation Loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()\n",
+ "\n",
+ "\n",
+ "print(\"==== 2nd model =====\")\n",
+ "\n",
+ "acc = history.history['accuracy']\n",
+ "val_acc = history.history['val_accuracy']\n",
+ "\n",
+ "loss = history.history['loss']\n",
+ "val_loss = history.history['val_loss']\n",
+ "\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "plt.subplot(2, 1, 1)\n",
+ "plt.plot(acc, label='Training Accuracy')\n",
+ "plt.plot(val_acc, label='Validation Accuracy')\n",
+ "plt.legend(loc='lower right')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.ylim([min(plt.ylim()),1])\n",
+ "plt.title('Training and Validation Accuracy')\n",
+ "\n",
+ "plt.subplot(2, 1, 2)\n",
+ "plt.plot(loss, label='Training Loss')\n",
+ "plt.plot(val_loss, label='Validation Loss')\n",
+ "plt.legend(loc='upper right')\n",
+ "plt.ylabel('Cross Entropy')\n",
+ "plt.ylim([0,2])\n",
+ "plt.title('Training and Validation Loss')\n",
+ "plt.xlabel('epoch')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ri9kU3wa3Rei"
+ },
+ "source": [
+ "**Comment on the observation**\n",
+ "\n",
+ "*(Double-click or enter to edit)*\n",
+ "\n",
+ "..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MzXmO1WoSKMY"
+ },
+ "source": [
+ "* Use predict function to predict the output for the test split\n",
+ "* Plot the confusion matrix for the new model and comment on the class confusions.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "DObaoxhaSMUg"
+ },
+ "outputs": [],
+ "source": [
+ "# Your code here :"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WUBrvRomU5O_"
+ },
+ "source": [
+ "**Comment here :**\n",
+ "\n",
+ "*(Double-click or enter to edit)*\n",
+ "\n",
+ "..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ffwVz-FLSNG7"
+ },
+ "source": [
+ "* Print the test accuracy for the trained model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "L4WX3_uLSN5I"
+ },
+ "outputs": [],
+ "source": [
+ "# Your code here :"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dySqfA6PVBjQ"
+ },
+ "source": [
+ "## Define the complete VGG architecture.\n",
+ "\n",
+ "Stack two convolutional layers with 64 filters, each of 3 x 3 followed by max pooling layer.\n",
+ "\n",
+ "Stack two more convolutional layers with 128 filters, each of 3 x 3, followed by max pooling, followed by two more convolutional layers with 256 filters, each of 3 x 3, followed by max pooling.\n",
+ "\n",
+ "Flatten the output of the previous layer and add a dense layer with 128 units before the classification layer.\n",
+ "\n",
+ "For all the layers, use ReLU activation function.\n",
+ "\n",
+ "Use same padding for the layers to ensure that the height and width of each layer output matches the input\n",
+ "\n",
+ "* Change the size of input to 64 x 64."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zm35siILFNT0"
+ },
+ "outputs": [],
+ "source": [
+ "from keras.backend import clear_session\n",
+ "clear_session()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oH4lDVBuVA_Q"
+ },
+ "outputs": [],
+ "source": [
+ "# Your code here :"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qu_B8kJGWhcM"
+ },
+ "source": [
+ "* Compile the model using categorical_crossentropy loss, SGD optimizer and use 'accuracy' as the metric.\n",
+ "* Use the above defined model to train CIFAR-10 and train the model for 10 epochs with a batch size of 512.\n",
+ "* Predict the output for the test split and plot the confusion matrix for the new model and comment on the class confusions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4elnDWnjEbmO"
+ },
+ "outputs": [],
+ "source": [
+ "# Your code here :"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2dlzFt0SXGDQ"
+ },
+ "source": [
+ "# Understanding deep networks\n",
+ "\n",
+ "* What is the use of activation functions in network? Why is it needed?\n",
+ "* We have used softmax activation function in the exercise. There are other activation functions available too. What is the difference between sigmoid activation and softmax activation?\n",
+ "* What is the difference between categorical crossentropy and binary crossentropy loss?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sPy_1EWXX6fp"
+ },
+ "source": [
+ "**Write the answers below :**\n",
+ "\n",
+ "1 - Use of activation functions:\n",
+ "\n",
+ "\n",
+ "\n",
+ "_\n",
+ "\n",
+ "2 - Key Differences between sigmoid and softmax:\n",
+ "\n",
+ "\n",
+ "\n",
+ "_\n",
+ "\n",
+ "3 - Key Differences between categorical crossentropy and binary crossentropy loss:\n",
+ "\n",
+ "\n",
+ "_\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "provenance": [],
+ "gpuType": "T4",
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index d584552..1497df8 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,218 +1,366 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# List Comprehensions\n",
- "\n",
- "Complete the following set of exercises to solidify your knowledge of list comprehensions."
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xf2PDtoIKW-5"
+ },
+ "source": [
+ "# List Comprehensions\n",
+ "\n",
+ "Complete the following set of exercises to solidify your knowledge of list comprehensions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "Mc8LgWCqKW-6"
+ },
+ "outputs": [],
+ "source": [
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1GKcGQkqKW-6"
+ },
+ "source": [
+ "#### 1. Use a list comprehension to create and print a list of consecutive integers starting with 1 and ending with 50."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "id": "wzVWQ-QMKW-7",
+ "outputId": "44f4bf39-2534-419d-babe-0cbcb2b51823",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#your code here\n",
+ "list=[num for num in range(1,51)]\n",
+ "print(list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZoNC011mKW-7"
+ },
+ "source": [
+ "#### 2. Use a list comprehension to create and print a list of even numbers starting with 2 and ending with 200."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "aFbFGDKpKW-7",
+ "outputId": "f010ad5c-0efd-42d4-9f8d-3e9f4ed5182d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#your code here\n",
+ "\n",
+ "\n",
+ "even_numbers=[num for num in range(2,201) if num%2==0]\n",
+ "print(even_numbers)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WrvdsTxfKW-8"
+ },
+ "source": [
+ "#### 3. Use a list comprehension to create and print a list containing all elements of the 10 x 4 array below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "Kyv3VH4uKW-8"
+ },
+ "outputs": [],
+ "source": [
+ "a = [[0.84062117, 0.48006452, 0.7876326 , 0.77109654],\n",
+ " [0.44409793, 0.09014516, 0.81835917, 0.87645456],\n",
+ " [0.7066597 , 0.09610873, 0.41247947, 0.57433389],\n",
+ " [0.29960807, 0.42315023, 0.34452557, 0.4751035 ],\n",
+ " [0.17003563, 0.46843998, 0.92796258, 0.69814654],\n",
+ " [0.41290051, 0.19561071, 0.16284783, 0.97016248],\n",
+ " [0.71725408, 0.87702738, 0.31244595, 0.76615487],\n",
+ " [0.20754036, 0.57871812, 0.07214068, 0.40356048],\n",
+ " [0.12149553, 0.53222417, 0.9976855 , 0.12536346],\n",
+ " [0.80930099, 0.50962849, 0.94555126, 0.33364763]];"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "POHs9x-QKW-8",
+ "outputId": "4221a720-2179-4f70-880e-ac9d79d5037d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[0.84062117, 0.48006452, 0.7876326, 0.77109654, 0.44409793, 0.09014516, 0.81835917, 0.87645456, 0.7066597, 0.09610873, 0.41247947, 0.57433389, 0.29960807, 0.42315023, 0.34452557, 0.4751035, 0.17003563, 0.46843998, 0.92796258, 0.69814654, 0.41290051, 0.19561071, 0.16284783, 0.97016248, 0.71725408, 0.87702738, 0.31244595, 0.76615487, 0.20754036, 0.57871812, 0.07214068, 0.40356048, 0.12149553, 0.53222417, 0.9976855, 0.12536346, 0.80930099, 0.50962849, 0.94555126, 0.33364763]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#your code here\n",
+ "\n",
+ "\n",
+ "list=[n for ls in a for n in ls]\n",
+ "print(list)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7vpx5jlnKW-9"
+ },
+ "source": [
+ "#### 4. Add a condition to the list comprehension above so that only values greater than or equal to 0.5 are printed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "TMszT7owKW-9",
+ "outputId": "01e5b602-c81f-480b-d31d-c6ec277c5272",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[0.84062117, 0.7876326, 0.77109654, 0.81835917, 0.87645456, 0.7066597, 0.57433389, 0.92796258, 0.69814654, 0.97016248, 0.71725408, 0.87702738, 0.76615487, 0.57871812, 0.53222417, 0.9976855, 0.80930099, 0.50962849, 0.94555126]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#your code here\n",
+ "list=[n for ls in a for n in ls if n>=0.5 ]\n",
+ "print(list)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xatv58weKW-9"
+ },
+ "source": [
+ "#### 5. Use a list comprehension to create and print a list containing all elements of the 5 x 2 x 3 array below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "36dl1aXpKW-9"
+ },
+ "outputs": [],
+ "source": [
+ "b = [[[0.55867166, 0.06210792, 0.08147297],\n",
+ " [0.82579068, 0.91512478, 0.06833034]],\n",
+ "\n",
+ " [[0.05440634, 0.65857693, 0.30296619],\n",
+ " [0.06769833, 0.96031863, 0.51293743]],\n",
+ "\n",
+ " [[0.09143215, 0.71893382, 0.45850679],\n",
+ " [0.58256464, 0.59005654, 0.56266457]],\n",
+ "\n",
+ " [[0.71600294, 0.87392666, 0.11434044],\n",
+ " [0.8694668 , 0.65669313, 0.10708681]],\n",
+ "\n",
+ " [[0.07529684, 0.46470767, 0.47984544],\n",
+ " [0.65368638, 0.14901286, 0.23760688]]];"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "wnH0jqJ4KW-9",
+ "outputId": "3e2d2957-11af-4044-c755-8ef2d3328ed5",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[0.55867166, 0.06210792, 0.08147297, 0.82579068, 0.91512478, 0.06833034, 0.05440634, 0.65857693, 0.30296619, 0.06769833, 0.96031863, 0.51293743, 0.09143215, 0.71893382, 0.45850679, 0.58256464, 0.59005654, 0.56266457, 0.71600294, 0.87392666, 0.11434044, 0.8694668, 0.65669313, 0.10708681, 0.07529684, 0.46470767, 0.47984544, 0.65368638, 0.14901286, 0.23760688]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#your code here\n",
+ "\n",
+ "\n",
+ "list=[k for i in b for j in i for k in j]\n",
+ "print(list)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6ac3fvtIKW-9"
+ },
+ "source": [
+ "#### 6. Add a condition to the list comprehension above so that the last value in each subarray is printed, but only if it is less than or equal to 0.5."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "w-E8NRV7KW--",
+ "outputId": "c244ea0c-86b2-46ca-b9b3-e3f6c3032205",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "[0.55867166, 0.06210792, 0.08147297, 0.82579068, 0.91512478, 0.06833034, 0.05440634, 0.65857693, 0.30296619, 0.09143215, 0.71893382, 0.45850679, 0.71600294, 0.87392666, 0.11434044, 0.8694668, 0.65669313, 0.10708681, 0.07529684, 0.46470767, 0.47984544, 0.65368638, 0.14901286, 0.23760688]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#your code here\n",
+ "\n",
+ "\n",
+ "list=[k for i in b for j in i for k in j if j[-1]<=0.5]\n",
+ "print(list)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iIR6bXSkKW--"
+ },
+ "source": [
+ "### Bonus"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6sKGb55jKW--"
+ },
+ "source": [
+ "Try to solve these katas using list comprehensions."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GUWpqoGhKW--"
+ },
+ "source": [
+ "**Easy**\n",
+ "- [Insert values](https://www.codewars.com/kata/invert-values)\n",
+ "- [Sum Square(n)](https://www.codewars.com/kata/square-n-sum)\n",
+ "- [Digitize](https://www.codewars.com/kata/digitize)\n",
+ "- [List filtering](https://www.codewars.com/kata/list-filtering)\n",
+ "- [Arithmetic list](https://www.codewars.com/kata/541da001259d9ca85d000688)\n",
+ "\n",
+ "**Medium**\n",
+ "- [Multiples of 3 or 5](https://www.codewars.com/kata/514b92a657cdc65150000006)\n",
+ "- [Count of positives / sum of negatives](https://www.codewars.com/kata/count-of-positives-slash-sum-of-negatives)\n",
+ "- [Categorize new member](https://www.codewars.com/kata/5502c9e7b3216ec63c0001aa)\n",
+ "\n",
+ "**Advanced**\n",
+ "- [Queue time counter](https://www.codewars.com/kata/queue-time-counter)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+ }
+ },
+ "colab": {
+ "provenance": [],
+ "include_colab_link": true
+ }
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 1. Use a list comprehension to create and print a list of consecutive integers starting with 1 and ending with 50."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 2. Use a list comprehension to create and print a list of even numbers starting with 2 and ending with 200."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 3. Use a list comprehension to create and print a list containing all elements of the 10 x 4 array below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "a = [[0.84062117, 0.48006452, 0.7876326 , 0.77109654],\n",
- " [0.44409793, 0.09014516, 0.81835917, 0.87645456],\n",
- " [0.7066597 , 0.09610873, 0.41247947, 0.57433389],\n",
- " [0.29960807, 0.42315023, 0.34452557, 0.4751035 ],\n",
- " [0.17003563, 0.46843998, 0.92796258, 0.69814654],\n",
- " [0.41290051, 0.19561071, 0.16284783, 0.97016248],\n",
- " [0.71725408, 0.87702738, 0.31244595, 0.76615487],\n",
- " [0.20754036, 0.57871812, 0.07214068, 0.40356048],\n",
- " [0.12149553, 0.53222417, 0.9976855 , 0.12536346],\n",
- " [0.80930099, 0.50962849, 0.94555126, 0.33364763]];"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 4. Add a condition to the list comprehension above so that only values greater than or equal to 0.5 are printed."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 5. Use a list comprehension to create and print a list containing all elements of the 5 x 2 x 3 array below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "b = [[[0.55867166, 0.06210792, 0.08147297],\n",
- " [0.82579068, 0.91512478, 0.06833034]],\n",
- "\n",
- " [[0.05440634, 0.65857693, 0.30296619],\n",
- " [0.06769833, 0.96031863, 0.51293743]],\n",
- "\n",
- " [[0.09143215, 0.71893382, 0.45850679],\n",
- " [0.58256464, 0.59005654, 0.56266457]],\n",
- "\n",
- " [[0.71600294, 0.87392666, 0.11434044],\n",
- " [0.8694668 , 0.65669313, 0.10708681]],\n",
- "\n",
- " [[0.07529684, 0.46470767, 0.47984544],\n",
- " [0.65368638, 0.14901286, 0.23760688]]];"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 6. Add a condition to the list comprehension above so that the last value in each subarray is printed, but only if it is less than or equal to 0.5."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#your code here"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Bonus"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Try to solve these katas using list comprehensions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Easy**\n",
- "- [Insert values](https://www.codewars.com/kata/invert-values)\n",
- "- [Sum Square(n)](https://www.codewars.com/kata/square-n-sum)\n",
- "- [Digitize](https://www.codewars.com/kata/digitize)\n",
- "- [List filtering](https://www.codewars.com/kata/list-filtering)\n",
- "- [Arithmetic list](https://www.codewars.com/kata/541da001259d9ca85d000688)\n",
- "\n",
- "**Medium**\n",
- "- [Multiples of 3 or 5](https://www.codewars.com/kata/514b92a657cdc65150000006)\n",
- "- [Count of positives / sum of negatives](https://www.codewars.com/kata/count-of-positives-slash-sum-of-negatives)\n",
- "- [Categorize new member](https://www.codewars.com/kata/5502c9e7b3216ec63c0001aa)\n",
- "\n",
- "**Advanced**\n",
- "- [Queue time counter](https://www.codewars.com/kata/queue-time-counter)"
- ]
- }
- ],
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