Convolutional Neural Networks in TensorFlow/Week 4 - Multiclass Classifications/Exercise_4_Multi_class_classifier_Question-FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wYtuKeK0dImp"
   },
   "outputs": [],
   "source": [
    "# ATTENTION: Please do not alter any of the provided code in the exercise. Only add your own code where indicated\n",
    "# ATTENTION: Please do not add or remove any cells in the exercise. The grader will check specific cells based on the cell position.\n",
    "# ATTENTION: Please use the provided epoch values when training.\n",
    "\n",
    "import csv\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from os import getcwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4kxw-_rmcnVu"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27455, 28, 28)\n",
      "(27455,)\n",
      "(7172, 28, 28)\n",
      "(7172,)\n"
     ]
    }
   ],
   "source": [
    "def get_data(filename):\n",
    "  # You will need to write code that will read the file passed\n",
    "  # into this function. The first line contains the column headers\n",
    "  # so you should ignore it\n",
    "  # Each successive line contians 785 comma separated values between 0 and 255\n",
    "  # The first value is the label\n",
    "  # The rest are the pixel values for that picture\n",
    "  # The function will return 2 np.array types. One with all the labels\n",
    "  # One with all the images\n",
    "  #\n",
    "  # Tips: \n",
    "  # If you read a full line (as 'row') then row[0] has the label\n",
    "  # and row[1:785] has the 784 pixel values\n",
    "  # Take a look at np.array_split to turn the 784 pixels into 28x28\n",
    "  # You are reading in strings, but need the values to be floats\n",
    "  # Check out np.array().astype for a conversion\n",
    "    with open(filename) as training_file:\n",
    "      # Your code starts here\n",
    "        reader = csv.reader(training_file, delimiter=',')    \n",
    "        imgs = []\n",
    "        labels = []\n",
    "\n",
    "        next(reader, None)\n",
    "        \n",
    "        for row in reader:\n",
    "            label = row[0]\n",
    "            data = row[1:]\n",
    "            img = np.array(data).reshape((28, 28))\n",
    "\n",
    "            imgs.append(img)\n",
    "            labels.append(label)\n",
    "\n",
    "        images = np.array(imgs).astype(float)\n",
    "        labels = np.array(labels).astype(float)\n",
    "      # Your code ends here\n",
    "    return images, labels\n",
    "\n",
    "path_sign_mnist_train = f\"{getcwd()}/../tmp2/sign_mnist_train.csv\"\n",
    "path_sign_mnist_test = f\"{getcwd()}/../tmp2/sign_mnist_test.csv\"\n",
    "training_images, training_labels = get_data(path_sign_mnist_train)\n",
    "testing_images, testing_labels = get_data(path_sign_mnist_test)\n",
    "\n",
    "# Keep these\n",
    "print(training_images.shape)\n",
    "print(training_labels.shape)\n",
    "print(testing_images.shape)\n",
    "print(testing_labels.shape)\n",
    "\n",
    "# Their output should be:\n",
    "# (27455, 28, 28)\n",
    "# (27455,)\n",
    "# (7172, 28, 28)\n",
    "# (7172,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "awoqRpyZdQkD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27455, 28, 28, 1)\n",
      "(7172, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "# In this section you will have to add another dimension to the data\n",
    "# So, for example, if your array is (10000, 28, 28)\n",
    "# You will need to make it (10000, 28, 28, 1)\n",
    "# Hint: np.expand_dims\n",
    "\n",
    "training_images = np.expand_dims(training_images, axis=3)\n",
    "testing_images = np.expand_dims(testing_images, axis=3)\n",
    "\n",
    "# Create an ImageDataGenerator and do Image Augmentation\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    rotation_range=40,\n",
    "    width_shift_range=.2,\n",
    "    height_shift_range=.2,\n",
    "    shear_range=.2,\n",
    "    zoom_range=.2,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest'\n",
    ")\n",
    "\n",
    "validation_datagen = ImageDataGenerator(\n",
    "    rescale=1./255\n",
    ")\n",
    "    \n",
    "train_generator = train_datagen.flow(\n",
    "    training_images,\n",
    "    training_labels,\n",
    "    batch_size=20\n",
    ")\n",
    "\n",
    "validation_generator = validation_datagen.flow(\n",
    "    testing_images,\n",
    "    testing_labels,\n",
    "    batch_size=20\n",
    ")\n",
    "# Keep These\n",
    "print(training_images.shape)\n",
    "print(testing_images.shape)\n",
    "    \n",
    "# Their output should be:\n",
    "# (27455, 28, 28, 1)\n",
    "# (7172, 28, 28, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rmb7S32cgRqS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1373/1373 [==============================] - 70s 51ms/step - loss: 2.6053 - acc: 0.1988 - val_loss: 1.6654 - val_acc: 0.4307\n",
      "Epoch 2/20\n",
      "1373/1373 [==============================] - 69s 50ms/step - loss: 1.8709 - acc: 0.4021 - val_loss: 1.3276 - val_acc: 0.5399\n",
      "Epoch 3/20\n",
      "1373/1373 [==============================] - 68s 49ms/step - loss: 1.5223 - acc: 0.5003 - val_loss: 1.0054 - val_acc: 0.6146\n",
      "Epoch 4/20\n",
      "1373/1373 [==============================] - 69s 50ms/step - loss: 1.2942 - acc: 0.5742 - val_loss: 0.8786 - val_acc: 0.6755\n",
      "Epoch 5/20\n",
      "1373/1373 [==============================] - 67s 49ms/step - loss: 1.1343 - acc: 0.6272 - val_loss: 0.7080 - val_acc: 0.7338\n",
      "Epoch 6/20\n",
      "1373/1373 [==============================] - 66s 48ms/step - loss: 1.0062 - acc: 0.6663 - val_loss: 0.6667 - val_acc: 0.7524\n",
      "Epoch 7/20\n",
      "1373/1373 [==============================] - 67s 49ms/step - loss: 0.9130 - acc: 0.6957 - val_loss: 0.5585 - val_acc: 0.8024\n",
      "Epoch 8/20\n",
      "1373/1373 [==============================] - 68s 50ms/step - loss: 0.8241 - acc: 0.7261 - val_loss: 0.5516 - val_acc: 0.7790\n",
      "Epoch 9/20\n",
      "1373/1373 [==============================] - 70s 51ms/step - loss: 0.7520 - acc: 0.7467 - val_loss: 0.4378 - val_acc: 0.8434\n",
      "Epoch 10/20\n",
      "1373/1373 [==============================] - 70s 51ms/step - loss: 0.7028 - acc: 0.7654 - val_loss: 0.3848 - val_acc: 0.8664\n",
      "Epoch 11/20\n",
      "1373/1373 [==============================] - 68s 49ms/step - loss: 0.6599 - acc: 0.7791 - val_loss: 0.4276 - val_acc: 0.8436\n",
      "Epoch 12/20\n",
      "1373/1373 [==============================] - 70s 51ms/step - loss: 0.6272 - acc: 0.7910 - val_loss: 0.2541 - val_acc: 0.9230\n",
      "Epoch 13/20\n",
      "1373/1373 [==============================] - 94s 68ms/step - loss: 0.5883 - acc: 0.8035 - val_loss: 0.3768 - val_acc: 0.8688\n",
      "Epoch 14/20\n",
      "1373/1373 [==============================] - 96s 70ms/step - loss: 0.5568 - acc: 0.8143 - val_loss: 0.2555 - val_acc: 0.9204\n",
      "Epoch 15/20\n",
      "1373/1373 [==============================] - 95s 69ms/step - loss: 0.5282 - acc: 0.8244 - val_loss: 0.3201 - val_acc: 0.8832\n",
      "Epoch 16/20\n",
      "1373/1373 [==============================] - 99s 72ms/step - loss: 0.5074 - acc: 0.8296 - val_loss: 0.2301 - val_acc: 0.9095\n",
      "Epoch 17/20\n",
      "1373/1373 [==============================] - 100s 73ms/step - loss: 0.4886 - acc: 0.8359 - val_loss: 0.2785 - val_acc: 0.8995\n",
      "Epoch 18/20\n",
      "1373/1373 [==============================] - 102s 74ms/step - loss: 0.4699 - acc: 0.8432 - val_loss: 0.1810 - val_acc: 0.9306\n",
      "Epoch 19/20\n",
      "1373/1373 [==============================] - 164s 119ms/step - loss: 0.4601 - acc: 0.8447 - val_loss: 0.2188 - val_acc: 0.9154\n",
      "Epoch 20/20\n",
      "1373/1373 [==============================] - 171s 125ms/step - loss: 0.4397 - acc: 0.8549 - val_loss: 0.3665 - val_acc: 0.8643\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[316.5089845247795, 0.611266]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the model\n",
    "# Use no more than 2 Conv2D and 2 MaxPooling2D\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(512, activation='relu'),\n",
    "    tf.keras.layers.Dense(26, activation='softmax')\n",
    "])\n",
    "\n",
    "# Compile Model. \n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['acc']\n",
    ")\n",
    "\n",
    "# Train the Model\n",
    "history = model.fit_generator(\n",
    "    train_generator,\n",
    "    epochs=10,\n",
    "    validation_data=validation_generator\n",
    ")\n",
    "\n",
    "model.evaluate(testing_images, testing_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_Q3Zpr46dsij"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the chart for accuracy and loss on both training and validation\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission Instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now click the 'Submit Assignment' button above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# When you're done or would like to take a break, please run the two cells below to save your work and close the Notebook. This will free up resources for your fellow learners. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "<!-- Save the notebook -->\n",
    "IPython.notebook.save_checkpoint();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "IPython.notebook.session.delete();\n",
    "window.onbeforeunload = null\n",
    "setTimeout(function() { window.close(); }, 1000);"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Exercise 8 - Question.ipynb",
   "provenance": []
  },
  "coursera": {
   "course_slug": "convolutional-neural-networks-tensorflow",
   "graded_item_id": "8mIh8",
   "launcher_item_id": "gg95t"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}