Course2Week4ProgrammingAssignment1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 2,
   "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",
    "    \n",
    "        csv_reader = csv.reader(training_file, delimiter=',')\n",
    "        \n",
    "        first_line = True\n",
    "        \n",
    "        temp_images = []\n",
    "        \n",
    "        temp_labels = []\n",
    "        \n",
    "        for row in csv_reader:\n",
    "            \n",
    "            if first_line:\n",
    "        \n",
    "                first_line = False\n",
    "            else:\n",
    "                \n",
    "                temp_labels.append(row[0])\n",
    "                \n",
    "                image_data = row[1:785]\n",
    "                \n",
    "                image_data_as_array = np.array_split(image_data, 28)\n",
    "                \n",
    "                temp_images.append(image_data_as_array)\n",
    "                \n",
    "        images = np.array(temp_images).astype('float')\n",
    "        \n",
    "        labels = np.array(temp_labels).astype('float')\n",
    "        \n",
    "      # Your code ends here\n",
    "    \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": 3,
   "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",
    "\n",
    "training_images = np.expand_dims(training_images, axis=3)\n",
    "\n",
    "testing_images = np.expand_dims(testing_images, axis=3)\n",
    "\n",
    "\n",
    "# Create an ImageDataGenerator and do Image Augmentation\n",
    "train_datagen = ImageDataGenerator(rescale=1. / 255,\n",
    "                                    rotation_range=40,\n",
    "                                    width_shift_range=0.2,\n",
    "                                    height_shift_range=0.2,\n",
    "                                    shear_range=0.2,\n",
    "                                    zoom_range=0.2,\n",
    "                                    horizontal_flip=True,\n",
    "                                    fill_mode='nearest')\n",
    "    # Your Code Here\n",
    "\n",
    "validation_datagen = ImageDataGenerator(rescale=1. / 255)\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": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rmb7S32cgRqS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "858/857 [==============================] - 61s 71ms/step - loss: 2.1387 - accuracy: 0.3209 - val_loss: 1.8570 - val_accuracy: 0.4078\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[213.64132515119562, 0.3099554]"
      ]
     },
     "execution_count": 5,
     "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(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    tf.keras.layers.Dense(26, activation=tf.nn.softmax)])\n",
    "\n",
    "    # Your Code Here\n",
    "\n",
    "# Compile Model. \n",
    "model.compile(optimizer = 'Adam',\n",
    "              loss = 'sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "\n",
    "# Train the Model\n",
    "history = model.fit_generator(train_datagen.flow(training_images, training_labels, batch_size=32),\n",
    "                              steps_per_epoch=len(training_images) / 32,\n",
    "                              epochs=2,\n",
    "                              validation_data=validation_datagen.flow(testing_images, testing_labels, batch_size=32),\n",
    "                              validation_steps=len(testing_images) / 32)\n",
    "\n",
    "model.evaluate(testing_images, testing_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_Q3Zpr46dsij"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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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",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "\n",
    "val_loss = history.history['val_loss']\n",
    "\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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}