From 6e973322e5167ea97d578533f7f9ae5e64b81455 Mon Sep 17 00:00:00 2001 From: Gibran Alvarez Date: Sat, 3 Aug 2024 16:22:13 -0400 Subject: [PATCH] assignment 1 --- 02_activities/assignments/assignment_1.ipynb | 758 +++++++++++++++++-- 1 file changed, 711 insertions(+), 47 deletions(-) diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 70bdb302..f78561d2 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "8820fcdc5ae52ae2", "metadata": { "ExecuteTime": { @@ -50,18 +50,11 @@ "collapsed": false, "is_executing": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-26 12:04:27.706527: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], + "outputs": [], "source": [ "from keras.datasets import cifar100\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", "\n", "# Load the CIFAR-100 dataset\n", "(x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')" @@ -69,14 +62,72 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "a386b4072078138f", "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (50000, 32, 32, 3)\n", + "y_train shape: (50000, 1)\n", + "X_test shape: (10000, 32, 32, 3)\n", + "y_test shape: (10000, 1)\n", + "Image dimensions: (32, 32, 3)\n", + "Number of classes: 100\n" + ] + } + ], + "source": [ + "# Shapes\n", + "print(f\"X_train shape: {x_train.shape}\")\n", + "print(f\"y_train shape: {y_train.shape}\")\n", + "print(f\"X_test shape: {x_test.shape}\")\n", + "print(f\"y_test shape: {y_test.shape}\")\n", + "\n", + "#Dimensions\n", + "image_shape = x_train.shape[1:]\n", + "print(f\"Image dimensions: {image_shape}\")\n", + "\n", + "# Number of classes\n", + "num_classes = len(np.unique(y_train))\n", + "print(f\"Number of classes: {num_classes}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "74929c6d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here" + "# Examples\n", + "from random import randint, seed \n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "seed()\n", + "rand = randint(0,10)\n", + "\n", + "for i in range(8):\n", + " plt.subplot(2, 4, i + 1)\n", + " plt.imshow(x_train[i+rand])\n", + " plt.title(f\"Class: {y_train[i+rand][0]}\")\n", + " plt.axis('off')\n", + "plt.show()" ] }, { @@ -94,14 +145,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "b18c10172fa72d0c", "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train shape: (40000, 32, 32, 3)\n", + "y_train shape: (40000, 100)\n", + "x_val shape: (10000, 32, 32, 3)\n", + "y_val shape: (10000, 100)\n", + "x_test shape: (10000, 32, 32, 3)\n", + "y_test shape: (10000, 100)\n" + ] + } + ], "source": [ - "# Your code here" + "# Your code here\n", + "import numpy as np\n", + "from tensorflow.keras.utils import to_categorical\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "#Normalizing\n", + "x_train = x_train.astype('float32') / 255.0\n", + "x_test = x_test.astype('float32') / 255.0\n", + "\n", + "\n", + "#OneHoT encoding\n", + "y_train = to_categorical(y_train, num_classes)\n", + "y_test = to_categorical(y_test, num_classes)\n", + "\n", + "#Splitting training set and validation 80-20%\n", + "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.2, random_state=42)\n", + "\n", + "# Verify the shapes of the new training and validation sets\n", + "print(f\"x_train shape: {x_train.shape}\")\n", + "print(f\"y_train shape: {y_train.shape}\")\n", + "print(f\"x_val shape: {x_val.shape}\")\n", + "print(f\"y_val shape: {y_val.shape}\")\n", + "\n", + "print(f\"x_test shape: {x_test.shape}\")\n", + "print(f\"y_test shape: {y_test.shape}\")" ] }, { @@ -119,17 +207,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "c9edafdaf887b8d5", "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_6 (Conv2D) (None, 32, 32, 64) 1792 \n", + " \n", + " max_pooling2d_6 (MaxPooling (None, 16, 16, 64) 0 \n", + " 2D) \n", + " \n", + " conv2d_7 (Conv2D) (None, 14, 14, 128) 73856 \n", + " \n", + " max_pooling2d_7 (MaxPooling (None, 7, 7, 128) 0 \n", + " 2D) \n", + " \n", + " flatten_3 (Flatten) (None, 6272) 0 \n", + " \n", + " dense_6 (Dense) (None, 256) 1605888 \n", + " \n", + " dropout (Dropout) (None, 256) 0 \n", + " \n", + " dense_7 (Dense) (None, 100) 25700 \n", + " \n", + "=================================================================\n", + "Total params: 1,707,236\n", + "Trainable params: 1,707,236\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], "source": [ "from keras.models import Sequential\n", - "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n", + "from keras.layers import Dropout, Conv2D, MaxPooling2D, Flatten, Dense\n", + "\n", + "# Your code here\n", + "\n", + "model = Sequential()\n", + "\n", + "#Convolutional and maxpool layer 2\n", + "model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same', input_shape=(32, 32, 3)))\n", + "model.add(MaxPooling2D(pool_size=(2,2), padding='Valid'))\n", "\n", - "# Your code here" + "#Convolutional and maxpool layer 2\n", + "model.add(Conv2D(128, kernel_size=(3,3), activation='relu', padding='Valid'))\n", + "model.add(MaxPooling2D(pool_size=(2,2), padding='Valid'))\n", + "\n", + "#Flatten layer\n", + "model.add(Flatten())\n", + "\n", + "# Dense layer\n", + "model.add(Dense(256, activation='relu'))\n", + "model.add(Dropout(rate=0.5)) \n", + "model.add(Dense(units=num_classes, activation='softmax'))\n", + "\n", + "model.summary()" ] }, { @@ -143,13 +285,13 @@ "\n", "- Select an appropriate loss function and optimizer for your model. These can be ones we have looked at already, or they can be different. \n", "- Briefly explain your choices (one or two sentences each).\n", - "- Loss function: ______\n", - "- Optimizer: ______" + "- Loss function: Categorical cross-entropy, because its used when there's 2 or more output labels\n", + "- Optimizer: SGD, because it performs better on image classification" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "ab39f4ba69d684e9", "metadata": { "collapsed": false @@ -157,8 +299,10 @@ "outputs": [], "source": [ "from keras import optimizers\n", + "from keras.optimizers import SGD\n", "\n", - "# Your code here" + "# Your code here\n", + "model.compile(optimizer=SGD(), loss='categorical_crossentropy', metrics=['accuracy'])" ] }, { @@ -178,14 +322,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "id": "9de74f274ad08546", "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20\n", + "1250/1250 [==============================] - 78s 59ms/step - loss: 4.5736 - accuracy: 0.0183 - val_loss: 4.4628 - val_accuracy: 0.0494\n", + "Epoch 2/20\n", + "1250/1250 [==============================] - 76s 61ms/step - loss: 4.3353 - accuracy: 0.0443 - val_loss: 4.1262 - val_accuracy: 0.0786\n", + "Epoch 3/20\n", + "1250/1250 [==============================] - 74s 60ms/step - loss: 4.1239 - accuracy: 0.0703 - val_loss: 3.9184 - val_accuracy: 0.1100\n", + "Epoch 4/20\n", + "1250/1250 [==============================] - 70s 56ms/step - loss: 3.9720 - accuracy: 0.0966 - val_loss: 3.7654 - val_accuracy: 0.1400\n", + "Epoch 5/20\n", + "1250/1250 [==============================] - 75s 60ms/step - loss: 3.8432 - accuracy: 0.1147 - val_loss: 3.6407 - val_accuracy: 0.1635\n", + "Epoch 6/20\n", + "1250/1250 [==============================] - 81s 65ms/step - loss: 3.7298 - accuracy: 0.1362 - val_loss: 3.5394 - val_accuracy: 0.1812\n", + "Epoch 7/20\n", + "1250/1250 [==============================] - 73s 59ms/step - loss: 3.6096 - accuracy: 0.1556 - val_loss: 3.4328 - val_accuracy: 0.1957\n", + "Epoch 8/20\n", + "1250/1250 [==============================] - 72s 58ms/step - loss: 3.5089 - accuracy: 0.1716 - val_loss: 3.3334 - val_accuracy: 0.2208\n", + "Epoch 9/20\n", + "1250/1250 [==============================] - 73s 59ms/step - loss: 3.4039 - accuracy: 0.1936 - val_loss: 3.1850 - val_accuracy: 0.2473\n", + "Epoch 10/20\n", + "1250/1250 [==============================] - 76s 61ms/step - loss: 3.3059 - accuracy: 0.2111 - val_loss: 3.1269 - val_accuracy: 0.2612\n", + "Epoch 11/20\n", + "1250/1250 [==============================] - 92s 74ms/step - loss: 3.2292 - accuracy: 0.2254 - val_loss: 3.0408 - val_accuracy: 0.2716\n", + "Epoch 12/20\n", + "1250/1250 [==============================] - 83s 66ms/step - loss: 3.1484 - accuracy: 0.2382 - val_loss: 2.9826 - val_accuracy: 0.2859\n", + "Epoch 13/20\n", + "1250/1250 [==============================] - 85s 68ms/step - loss: 3.0700 - accuracy: 0.2513 - val_loss: 2.9422 - val_accuracy: 0.2938\n", + "Epoch 14/20\n", + "1250/1250 [==============================] - 81s 65ms/step - loss: 3.0061 - accuracy: 0.2638 - val_loss: 2.8921 - val_accuracy: 0.2925\n", + "Epoch 15/20\n", + "1250/1250 [==============================] - 84s 67ms/step - loss: 2.9462 - accuracy: 0.2763 - val_loss: 2.8574 - val_accuracy: 0.3122\n", + "Epoch 16/20\n", + "1250/1250 [==============================] - 83s 66ms/step - loss: 2.8869 - accuracy: 0.2855 - val_loss: 2.7633 - val_accuracy: 0.3246\n", + "Epoch 17/20\n", + "1250/1250 [==============================] - 90s 72ms/step - loss: 2.8239 - accuracy: 0.2974 - val_loss: 2.7408 - val_accuracy: 0.3288\n", + "Epoch 18/20\n", + "1250/1250 [==============================] - 91s 73ms/step - loss: 2.7764 - accuracy: 0.3064 - val_loss: 2.6943 - val_accuracy: 0.3354\n", + "Epoch 19/20\n", + "1250/1250 [==============================] - 88s 71ms/step - loss: 2.7320 - accuracy: 0.3162 - val_loss: 2.6792 - val_accuracy: 0.3473\n", + "Epoch 20/20\n", + "1250/1250 [==============================] - 83s 66ms/step - loss: 2.6797 - accuracy: 0.3250 - val_loss: 2.6353 - val_accuracy: 0.3469\n" + ] + } + ], "source": [ - "# Your code here" + "# Your code here\n", + "history = model.fit(x_train, y_train, epochs=20, batch_size=32, validation_data=(x_val, y_val))" ] }, { @@ -200,16 +392,16 @@ "- Report the accuracy of your model on the test set.\n", "- While accuracy is a good metric, there are many other ways to numerically evaluate a model. Report at least one other metric, and explain what it measures and how it is calculated.\n", "\n", - "- Accuracy: ______\n", - "- Other metric: ______\n", - "- Reason for selection: _____\n", - "- Value of metric: ______\n", - "- Interpretation of metric value: ______" + "- Accuracy: 0.3250 \n", + "- Other metric: F1 score\n", + "- Reason for selection: it provides balanced evaluation with imbalanced datasets\n", + "- Value of metric: 0.3352\n", + "- Interpretation of metric value: the model performs better when the difference between false negatives and false positives is crucial. " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 78, "id": "f670665fda92fb0e", "metadata": { "ExecuteTime": { @@ -218,9 +410,29 @@ }, "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 6s 19ms/step\n", + "F1-Score: 0.3352\n" + ] + } + ], "source": [ - "# Your code here" + "# Your code here\n", + "from sklearn.metrics import f1_score\n", + "\n", + "y_pred = model.predict(x_test) \n", + "y_pred_classes = y_pred.argmax(axis=-1) \n", + "\n", + "# one-hot \n", + "y_test_classes = y_test.argmax(axis=-1) \n", + "\n", + "# Compute F1-Score\n", + "f1 = f1_score(y_test_classes, y_pred_classes, average='weighted')\n", + "print(f'F1-Score: {f1:.4f}')" ] }, { @@ -234,12 +446,24 @@ "\n", "- Plot the training accuracy and validation accuracy with respect to epochs.\n", "- Select an image that the model correctly classified in the test set, and an image that the model incorrectly classified in the test set. Plot the images and report the model's classification probabilities for each.\n", - "- Briefly discuss the results. What do the plots show? Do the results make sense? What do the classification probabilities indicate?" + "- Briefly discuss the results. \n", + " \n", + " What do the plots show? \n", + "\n", + " it shows that the model is performing well until the 20 epochs where there's still no sign of overfitting on the validation data.\n", + " \n", + " Do the results make sense? \n", + " \n", + " Yes, the model seems to have a good fitting and it may seem like the dropout its making its job preventing overfitting but more epochs might be needed to verify a possible overfitting.\n", + " \n", + " What do the classification probabilities indicate?\n", + "\n", + " They predict the probability distribution over the classes given an input\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 79, "id": "c5b214475a496ca5", "metadata": { "ExecuteTime": { @@ -248,9 +472,143 @@ }, "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Your code here\n", + "\n", + "# Plot the training and validation accuracy:\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Plot accuracy\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(history.history['accuracy'], 'bo-', label='Train')\n", + "plt.plot(history.history['val_accuracy'], 'ro-', label='Validation')\n", + "plt.title('Accuracy')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "cc77e887", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 6s 20ms/step\n", + "Correctly Classified Image\n", + "[4.0680457e-05 3.2543836e-04 2.1767612e-03 3.1414668e-03 2.1509947e-02\n", + " 3.2745686e-04 1.7382683e-02 2.0218727e-03 1.5979989e-03 5.2514777e-04\n", + " 4.4225147e-03 2.9008288e-03 1.0022224e-03 5.7330960e-04 9.8442915e-04\n", + " 1.4491778e-02 1.2063002e-03 7.6311547e-04 3.4807257e-03 1.8038782e-03\n", + " 7.3480126e-03 4.5827273e-04 2.6517012e-03 6.1096449e-05 7.0045562e-03\n", + " 1.6370305e-03 1.9765846e-02 2.1564616e-03 4.2236908e-03 3.0055824e-01\n", + " 6.3166354e-04 1.4476231e-02 1.7287863e-02 2.6664333e-04 6.5807640e-03\n", + " 2.5281196e-03 3.3469170e-03 2.3062341e-03 2.0441949e-02 5.3340415e-03\n", + " 1.0273974e-02 7.7882178e-05 3.8609236e-02 1.7780503e-03 7.3129041e-03\n", + " 5.9748176e-03 2.0038257e-03 3.1070448e-03 2.8518616e-04 3.2452997e-04\n", + " 1.1468811e-01 1.7526357e-03 2.0769285e-03 3.6688554e-05 1.2506318e-04\n", + " 8.3914660e-03 6.5625166e-03 3.8195879e-04 1.5615918e-04 6.8497476e-03\n", + " 6.5366044e-06 3.0044845e-04 9.6358293e-05 7.2319815e-03 5.5197827e-03\n", + " 2.5386133e-03 1.3557017e-02 3.2481330e-03 2.5789990e-04 1.5638629e-04\n", + " 1.8852889e-04 1.9203026e-05 4.3659937e-03 1.0004944e-04 7.5447261e-02\n", + " 4.5296412e-05 1.2225735e-03 3.9368453e-03 6.5176687e-03 6.3411496e-03\n", + " 7.6841288e-03 2.5822030e-04 5.4439687e-04 2.2113395e-04 4.2732870e-03\n", + " 4.4006036e-04 5.6060385e-03 7.5856608e-04 4.9568797e-03 4.4887481e-04\n", + " 3.6399727e-04 1.6293366e-02 4.7671754e-05 4.9639214e-02 2.3278869e-04\n", + " 9.3189167e-04 1.9128972e-03 5.9808280e-02 2.8510035e-03 1.1169995e-03]\n", + "\n", + "Incorrectly Classified Image:\n", + "[9.62499343e-03 3.09339579e-04 1.81980617e-03 2.96007888e-03\n", + " 1.24800690e-02 2.79916753e-03 1.98202468e-02 2.44228891e-03\n", + " 1.19847929e-04 4.42872429e-03 1.07875904e-02 1.07167300e-03\n", + " 2.65839364e-04 8.06181226e-03 1.04828235e-02 2.22446751e-02\n", + " 1.71099149e-03 6.39217021e-03 1.84332987e-03 1.69519393e-03\n", + " 2.47150077e-03 2.05199374e-03 1.49284052e-02 1.14971590e-04\n", + " 3.72253126e-03 8.54039472e-03 8.64927005e-03 4.46733553e-04\n", + " 3.49457085e-04 7.53456503e-02 1.89147802e-04 3.25731095e-03\n", + " 1.09287031e-01 1.34281247e-04 4.88540216e-04 2.24624365e-03\n", + " 3.76353646e-03 7.08511239e-03 2.87739164e-03 1.10983057e-03\n", + " 5.28180366e-03 1.42987398e-02 1.09515677e-03 1.53411203e-03\n", + " 2.88174691e-04 6.95482129e-03 1.11603539e-03 4.26158682e-03\n", + " 9.16376885e-04 1.04613556e-03 6.19990146e-03 3.05371126e-04\n", + " 7.00007519e-03 8.02923052e-04 5.41928923e-04 1.09996013e-02\n", + " 6.90606935e-03 2.47723795e-02 8.43150727e-03 3.89182270e-02\n", + " 6.43460153e-05 6.10624906e-04 5.83004206e-04 1.52334180e-02\n", + " 1.42399652e-03 1.25837009e-02 3.16222711e-03 5.04118809e-03\n", + " 7.25865630e-06 2.07301739e-04 7.73874694e-04 7.39022471e-06\n", + " 8.10827687e-03 1.52299821e-04 2.55093980e-03 1.56752969e-04\n", + " 2.37273227e-04 1.17710745e-02 1.19383086e-03 1.41493080e-03\n", + " 4.21396270e-03 1.10279238e-02 2.80613685e-03 8.13594181e-03\n", + " 3.98107950e-04 1.94733992e-01 6.56217188e-02 8.65431910e-04\n", + " 1.28704240e-03 6.68234080e-02 1.69886164e-02 1.47219608e-02\n", + " 4.28633823e-04 2.73305736e-02 1.09511908e-04 1.86372665e-04\n", + " 3.71787744e-03 3.22710676e-03 1.83173676e-03 1.69328967e-04]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here" + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Get model predictions\n", + "y_pred = model.predict(x_test)\n", + "y_pred_classes = np.argmax(y_pred, axis=1) # Predicted class labels\n", + "y_test_classes = y_test.argmax(axis=-1) # Convert one-hot to class labels\n", + "\n", + "# Select an image that the model classified correctly\n", + "correct_index = np.where(y_pred_classes == y_test_classes)[0]\n", + "incorrect_index = np.where(y_pred_classes != y_test_classes)[0]\n", + "\n", + "# Randomly pick one correctly classified and one incorrectly classified image\n", + "correct_image = np.random.choice(correct_index)\n", + "incorrect_image = np.random.choice(incorrect_index)\n", + "\n", + "# Plot images\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.imshow(x_test[correct_image])\n", + "plt.title(f'Correctly Classified: {y_pred_classes[correct_image]}')\n", + "print(\"Correctly Classified Image\")\n", + "print(y_pred[correct_image])\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.imshow(x_test[incorrect_image])\n", + "plt.title(f'Incorrectly Classified: {y_pred_classes[incorrect_image]}')\n", + "print(\"\\nIncorrectly Classified Image:\")\n", + "print(y_pred[incorrect_image])\n", + "\n", + "plt.show()\n", + "\n", + "\n" ] }, { @@ -265,19 +623,153 @@ "\n", "- Now it's time to improve your model. Implement at least one technique to improve your model's performance. You can use any of the techniques we have covered in class, or you can use a technique that we haven't covered. If you need inspiration, you can refer to the [Keras documentation](https://keras.io/).\n", "- Explain the technique you used and why you chose it.\n", + "\n", + " I will use Data Augmentation given that it adds some randomness to the training data set which will help with overfitting \n", + "\n", "- If you used a technique that requires tuning, explain how you selected the values for the hyperparameters." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "id": "f3659ac83122567f", "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "1250/1250 [==============================] - 103s 82ms/step - loss: 3.0427 - accuracy: 0.2559 - val_loss: 2.6885 - val_accuracy: 0.3320\n", + "Epoch 2/50\n", + "1250/1250 [==============================] - 105s 84ms/step - loss: 2.9970 - accuracy: 0.2622 - val_loss: 2.6713 - val_accuracy: 0.3363\n", + "Epoch 3/50\n", + "1250/1250 [==============================] - 107s 85ms/step - loss: 2.9577 - accuracy: 0.2684 - val_loss: 2.6234 - val_accuracy: 0.3413\n", + "Epoch 4/50\n", + "1250/1250 [==============================] - 107s 85ms/step - loss: 2.9213 - accuracy: 0.2768 - val_loss: 2.6225 - val_accuracy: 0.3479\n", + "Epoch 5/50\n", + "1250/1250 [==============================] - 107s 86ms/step - loss: 2.8955 - accuracy: 0.2810 - val_loss: 2.5538 - val_accuracy: 0.3603\n", + "Epoch 6/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.8706 - accuracy: 0.2874 - val_loss: 2.5542 - val_accuracy: 0.3603\n", + "Epoch 7/50\n", + "1250/1250 [==============================] - 108s 87ms/step - loss: 2.8485 - accuracy: 0.2895 - val_loss: 2.4865 - val_accuracy: 0.3723\n", + "Epoch 8/50\n", + "1250/1250 [==============================] - 119s 95ms/step - loss: 2.8218 - accuracy: 0.2966 - val_loss: 2.4625 - val_accuracy: 0.3744\n", + "Epoch 9/50\n", + "1250/1250 [==============================] - 109s 87ms/step - loss: 2.8055 - accuracy: 0.2987 - val_loss: 2.4979 - val_accuracy: 0.3668\n", + "Epoch 10/50\n", + "1250/1250 [==============================] - 109s 87ms/step - loss: 2.7753 - accuracy: 0.3057 - val_loss: 2.4277 - val_accuracy: 0.3854\n", + "Epoch 11/50\n", + "1250/1250 [==============================] - 110s 88ms/step - loss: 2.7607 - accuracy: 0.3068 - val_loss: 2.4179 - val_accuracy: 0.3880\n", + "Epoch 12/50\n", + "1250/1250 [==============================] - 109s 88ms/step - loss: 2.7429 - accuracy: 0.3123 - val_loss: 2.4350 - val_accuracy: 0.3820\n", + "Epoch 13/50\n", + "1250/1250 [==============================] - 111s 89ms/step - loss: 2.7174 - accuracy: 0.3166 - val_loss: 2.4279 - val_accuracy: 0.3836\n", + "Epoch 14/50\n", + "1250/1250 [==============================] - 111s 89ms/step - loss: 2.7055 - accuracy: 0.3195 - val_loss: 2.3807 - val_accuracy: 0.3909\n", + "Epoch 15/50\n", + "1250/1250 [==============================] - 110s 88ms/step - loss: 2.6816 - accuracy: 0.3240 - val_loss: 2.4792 - val_accuracy: 0.3687\n", + "Epoch 16/50\n", + "1250/1250 [==============================] - 110s 88ms/step - loss: 2.6643 - accuracy: 0.3260 - val_loss: 2.3920 - val_accuracy: 0.3905\n", + "Epoch 17/50\n", + "1250/1250 [==============================] - 119s 95ms/step - loss: 2.6568 - accuracy: 0.3291 - val_loss: 2.3842 - val_accuracy: 0.3904\n", + "Epoch 18/50\n", + "1250/1250 [==============================] - 111s 89ms/step - loss: 2.6393 - accuracy: 0.3314 - val_loss: 2.3753 - val_accuracy: 0.3873\n", + "Epoch 19/50\n", + "1250/1250 [==============================] - 109s 87ms/step - loss: 2.6315 - accuracy: 0.3323 - val_loss: 2.4087 - val_accuracy: 0.3811\n", + "Epoch 20/50\n", + "1250/1250 [==============================] - 111s 89ms/step - loss: 2.6140 - accuracy: 0.3368 - val_loss: 2.3344 - val_accuracy: 0.3981\n", + "Epoch 21/50\n", + "1250/1250 [==============================] - 109s 87ms/step - loss: 2.5956 - accuracy: 0.3374 - val_loss: 2.3440 - val_accuracy: 0.3987\n", + "Epoch 22/50\n", + "1250/1250 [==============================] - 110s 88ms/step - loss: 2.5813 - accuracy: 0.3433 - val_loss: 2.3011 - val_accuracy: 0.4069\n", + "Epoch 23/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.5661 - accuracy: 0.3483 - val_loss: 2.2911 - val_accuracy: 0.4020\n", + "Epoch 24/50\n", + "1250/1250 [==============================] - 107s 86ms/step - loss: 2.5490 - accuracy: 0.3492 - val_loss: 2.3321 - val_accuracy: 0.3978\n", + "Epoch 25/50\n", + "1250/1250 [==============================] - 110s 88ms/step - loss: 2.5339 - accuracy: 0.3537 - val_loss: 2.2797 - val_accuracy: 0.4059\n", + "Epoch 26/50\n", + "1250/1250 [==============================] - 109s 87ms/step - loss: 2.5335 - accuracy: 0.3524 - val_loss: 2.2973 - val_accuracy: 0.4056\n", + "Epoch 27/50\n", + "1250/1250 [==============================] - 107s 86ms/step - loss: 2.5127 - accuracy: 0.3576 - val_loss: 2.2874 - val_accuracy: 0.4149\n", + "Epoch 28/50\n", + "1250/1250 [==============================] - 108s 87ms/step - loss: 2.5030 - accuracy: 0.3573 - val_loss: 2.2256 - val_accuracy: 0.4244\n", + "Epoch 29/50\n", + "1250/1250 [==============================] - 109s 87ms/step - loss: 2.4901 - accuracy: 0.3629 - val_loss: 2.2043 - val_accuracy: 0.4309\n", + "Epoch 30/50\n", + "1250/1250 [==============================] - 107s 86ms/step - loss: 2.4906 - accuracy: 0.3619 - val_loss: 2.2107 - val_accuracy: 0.4220\n", + "Epoch 31/50\n", + "1250/1250 [==============================] - 107s 86ms/step - loss: 2.4698 - accuracy: 0.3659 - val_loss: 2.2008 - val_accuracy: 0.4256\n", + "Epoch 32/50\n", + "1250/1250 [==============================] - 112s 89ms/step - loss: 2.4615 - accuracy: 0.3657 - val_loss: 2.2523 - val_accuracy: 0.4144\n", + "Epoch 33/50\n", + "1250/1250 [==============================] - 106s 85ms/step - loss: 2.4500 - accuracy: 0.3710 - val_loss: 2.2585 - val_accuracy: 0.4147\n", + "Epoch 34/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.4403 - accuracy: 0.3710 - val_loss: 2.2389 - val_accuracy: 0.4189\n", + "Epoch 35/50\n", + "1250/1250 [==============================] - 107s 85ms/step - loss: 2.4226 - accuracy: 0.3735 - val_loss: 2.1673 - val_accuracy: 0.4337\n", + "Epoch 36/50\n", + "1250/1250 [==============================] - 108s 87ms/step - loss: 2.4235 - accuracy: 0.3743 - val_loss: 2.2265 - val_accuracy: 0.4220\n", + "Epoch 37/50\n", + "1250/1250 [==============================] - 110s 88ms/step - loss: 2.4122 - accuracy: 0.3790 - val_loss: 2.2864 - val_accuracy: 0.4058\n", + "Epoch 38/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.4009 - accuracy: 0.3804 - val_loss: 2.2047 - val_accuracy: 0.4245\n", + "Epoch 39/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3993 - accuracy: 0.3805 - val_loss: 2.1375 - val_accuracy: 0.4379\n", + "Epoch 40/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3781 - accuracy: 0.3862 - val_loss: 2.1738 - val_accuracy: 0.4300\n", + "Epoch 41/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3729 - accuracy: 0.3855 - val_loss: 2.1921 - val_accuracy: 0.4273\n", + "Epoch 42/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3679 - accuracy: 0.3863 - val_loss: 2.2139 - val_accuracy: 0.4242\n", + "Epoch 43/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3526 - accuracy: 0.3913 - val_loss: 2.1389 - val_accuracy: 0.4393\n", + "Epoch 44/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3447 - accuracy: 0.3899 - val_loss: 2.1179 - val_accuracy: 0.4447\n", + "Epoch 45/50\n", + "1250/1250 [==============================] - 104s 83ms/step - loss: 2.3371 - accuracy: 0.3916 - val_loss: 2.1226 - val_accuracy: 0.4435\n", + "Epoch 46/50\n", + "1250/1250 [==============================] - 107s 86ms/step - loss: 2.3344 - accuracy: 0.3931 - val_loss: 2.2556 - val_accuracy: 0.4143\n", + "Epoch 47/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3263 - accuracy: 0.3950 - val_loss: 2.1123 - val_accuracy: 0.4448\n", + "Epoch 48/50\n", + "1250/1250 [==============================] - 108s 86ms/step - loss: 2.3176 - accuracy: 0.3966 - val_loss: 2.1170 - val_accuracy: 0.4442\n", + "Epoch 49/50\n", + "1250/1250 [==============================] - 111s 89ms/step - loss: 2.3094 - accuracy: 0.3973 - val_loss: 2.1450 - val_accuracy: 0.4410\n", + "Epoch 50/50\n", + "1250/1250 [==============================] - 116s 92ms/step - loss: 2.2978 - accuracy: 0.4012 - val_loss: 2.1201 - val_accuracy: 0.4481\n" + ] + } + ], "source": [ - "# Your code here" + "# Your code here\n", + "\n", + "from keras.preprocessing.image import ImageDataGenerator\n", + "from keras.callbacks import EarlyStopping\n", + "\n", + "#Data augmentation generator\n", + "datagen = ImageDataGenerator(\n", + " rotation_range=15,\n", + " width_shift_range=0.1,\n", + " height_shift_range=0.1,\n", + " horizontal_flip=True,\n", + " zoom_range=0.1\n", + ")\n", + "\n", + "#Fitting to training data\n", + "datagen.fit(x_train)\n", + "\n", + "#Early stopping\n", + "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n", + "\n", + "#Training the model\n", + "history = model.fit(datagen.flow(x_train, y_train, batch_size=32),\n", + " epochs=50,\n", + " validation_data=(x_val, y_val),\n", + " callbacks=[early_stopping])\n" ] }, { @@ -296,14 +788,166 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "id": "7c4701b36dc8fc55", "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Your code here" + "# Your code here\n", + "\n", + "# Plot accuracy\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(history.history['accuracy'], 'bo-', label='Train')\n", + "plt.plot(history.history['val_accuracy'], 'ro-', label='Validation')\n", + "plt.title('Accuracy')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "d989f052", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 7s 21ms/step\n", + "Correctly Classified Image\n", + "[9.2914300e-03 6.7683210e-04 1.6360898e-02 2.6765310e-03 2.5210204e-03\n", + " 4.2194095e-03 5.5664685e-03 2.8364495e-03 3.9937667e-04 2.9772944e-03\n", + " 9.7084818e-03 7.8023309e-03 7.9188793e-04 1.2175678e-03 2.7742121e-02\n", + " 2.9421379e-03 9.3115335e-03 1.8282588e-04 3.7244104e-03 1.6498705e-03\n", + " 1.8739590e-04 3.4126436e-04 4.7560506e-02 2.7248227e-05 2.6761915e-03\n", + " 2.1122301e-03 2.4790781e-02 1.2906423e-03 8.2259334e-04 9.1828202e-04\n", + " 7.6025841e-05 1.1715472e-04 3.0532579e-03 4.5886173e-04 1.9092537e-03\n", + " 3.5559908e-02 6.2167649e-03 7.0918677e-03 1.5957723e-03 9.4775390e-03\n", + " 6.7449696e-03 3.0707014e-03 1.4602114e-03 6.8895598e-03 6.4106570e-03\n", + " 1.1232094e-02 4.2638397e-03 9.8109187e-05 1.1114705e-03 8.9059831e-06\n", + " 1.6759258e-02 7.0734233e-02 1.4477691e-05 2.4294581e-03 1.8709511e-02\n", + " 2.0900113e-03 2.6080927e-05 1.0948730e-02 1.0189078e-03 1.2847979e-04\n", + " 6.0324946e-06 5.2884065e-02 2.5441281e-02 3.0328014e-03 4.1391842e-02\n", + " 3.5586448e-03 1.3343118e-02 4.3355377e-04 3.6257725e-06 1.6871020e-04\n", + " 2.9261330e-02 2.5856909e-06 5.9053209e-04 2.8782897e-04 4.5799478e-03\n", + " 8.2048355e-03 3.6904064e-05 2.1252507e-02 1.9438919e-01 5.9440359e-04\n", + " 3.4902636e-03 2.0746901e-03 2.8671927e-04 9.0846336e-03 9.1329338e-03\n", + " 1.9671126e-04 1.3966220e-03 1.3278795e-03 2.9372863e-02 1.4133917e-03\n", + " 4.4411255e-04 4.6059443e-03 3.5098270e-02 7.0461808e-03 2.6409372e-04\n", + " 1.3088962e-05 8.0321086e-05 2.3396013e-03 2.5636278e-02 4.4199411e-02]\n", + "\n", + "Incorrectly Classified Image:\n", + "[8.51888675e-04 8.55119061e-03 1.14668095e-04 3.87917353e-05\n", + " 5.88394869e-05 5.27462165e-04 3.79994483e-04 1.48731866e-04\n", + " 5.42166457e-03 2.73878896e-03 4.57985047e-03 2.59160915e-05\n", + " 1.64499259e-04 4.00518707e-04 2.03734860e-04 2.50867761e-05\n", + " 1.36578502e-02 1.71196749e-04 1.43656824e-02 3.49745969e-05\n", + " 3.29284303e-05 8.94664197e-07 3.87440412e-03 9.28537920e-05\n", + " 6.07703328e-07 4.37023677e-03 1.93870655e-04 9.16683639e-04\n", + " 1.44265825e-03 1.29927625e-03 6.33592645e-05 1.35271193e-05\n", + " 1.62846968e-03 1.20240904e-01 2.01650109e-04 4.61095296e-05\n", + " 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get model predictions\n", + "y_pred = model.predict(x_test)\n", + "y_pred_classes = np.argmax(y_pred, axis=1) # Predicted class labels\n", + "y_test_classes = y_test.argmax(axis=-1) # Convert one-hot to class labels\n", + "\n", + "# Select an image that the model classified correctly\n", + "correct_index = np.where(y_pred_classes == y_test_classes)[0]\n", + "incorrect_index = np.where(y_pred_classes != y_test_classes)[0]\n", + "\n", + "# Randomly pick one correctly classified and one incorrectly classified image\n", + "correct_image = np.random.choice(correct_index)\n", + "incorrect_image = np.random.choice(incorrect_index)\n", + "\n", + "# Plot images\n", + "plt.figure(figsize=(12, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.imshow(x_test[correct_image])\n", + "plt.title(f'Correctly Classified: {y_pred_classes[correct_image]}')\n", + "print(\"Correctly Classified Image\")\n", + "print(y_pred[correct_image])\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.imshow(x_test[incorrect_image])\n", + "plt.title(f'Incorrectly Classified: {y_pred_classes[incorrect_image]}')\n", + "print(\"\\nIncorrectly Classified Image:\")\n", + "print(y_pred[incorrect_image])\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "866c13df", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "313/313 [==============================] - 6s 20ms/step\n", + "F1-Score: 0.4333\n" + ] + } + ], + "source": [ + "y_pred = model.predict(x_test) \n", + "y_pred_classes = y_pred.argmax(axis=-1) \n", + "\n", + "# one-hot \n", + "y_test_classes = y_test.argmax(axis=-1) \n", + "\n", + "# Compute F1-Score\n", + "f1 = f1_score(y_test_classes, y_pred_classes, average='weighted')\n", + "print(f'F1-Score: {f1:.4f}')" ] }, { @@ -317,9 +961,21 @@ "\n", "- Briefly discuss the results. \n", "- Did the model's performance improve? \n", + " Yes, from 0.3250 to 0.4012 on the trainin set and from 0.3469 to 0.4481 on the validation set\n", + "\n", "- Why do you think this is?\n", + "\n", + " To the implementation of the Data Augmentation given that it slightly and randomly transforms some of the images it adds a slighlty more random training set to learn from, which in turn helps the model\n", + " to adapt the validation data.\n", + "\n", "- Do you think there is room for further improvement? Why or why not?\n", + "\n", + " yes, since bot curves seems like they haven't reached their plateau yet.\n", + "\n", "- What other techniques might you try in the future?\n", + "\n", + " Learning rate schedulers to dinamically change the learning rate, and I wouldn't do any changes to prevent overfitting like more dropouts since it seems like the .\n", + "\n", "- Your answer should be no more than 200 words.\n", "\n", "# Your answer here" @@ -374,8 +1030,16 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.9.19" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" } }, "nbformat": 4,