Multi_class_classifier FINAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 row 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",
    "        freader = csv.reader(training_file, delimiter=',')\n",
    "        next(freader)\n",
    "        images = []\n",
    "        labels = []\n",
    "        \n",
    "        for rows in freader:\n",
    "            names = rows[0]\n",
    "            labels.append(names)\n",
    "            \n",
    "            pictures = rows[1:785]\n",
    "            imgs = np.array(pictures).reshape((28, 28))\n",
    "            images.append(imgs)\n",
    "            \n",
    "        images = np.array(images).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": 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",
    "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": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rmb7S32cgRqS"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "1373/1373 [==============================] - 86s 63ms/step - loss: 2.5175 - accuracy: 0.2290 - val_loss: 1.3949 - val_accuracy: 0.5668\n",
      "Epoch 2/2\n",
      "1373/1373 [==============================] - 80s 58ms/step - loss: 1.6346 - accuracy: 0.4770 - val_loss: 0.7956 - val_accuracy: 0.7416\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[141.20638702784046, 0.5591188]"
      ]
     },
     "execution_count": 7,
     "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(64, (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(optimizer='rmsprop',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "# Train the Model\n",
    "history = model.fit_generator(train_generator,\n",
    "    epochs=2,\n",
    "    validation_data=validation_generator\n",
    ")\n",
    "\n",
    "model.evaluate(testing_images, testing_labels, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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['accuracy'] \n",
    "val_acc = history.history['val_accuracy']\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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "<!-- Save the notebook -->\n",
       "IPython.notebook.save_checkpoint();\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "<!-- Save the notebook -->\n",
    "IPython.notebook.save_checkpoint();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.session.delete();\n",
       "window.onbeforeunload = null\n",
       "setTimeout(function() { window.close(); }, 1000);\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.notebook.session.delete();\n",
    "window.onbeforeunload = null\n",
    "setTimeout(function() { window.close(); }, 1000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}