Keras - Tutorial - Happy House v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras tutorial - the Happy House\n",
    "\n",
    "Welcome to the first assignment of week 2. In this assignment, you will:\n",
    "1. Learn to use Keras, a high-level neural networks API (programming framework), written in Python and capable of running on top of several lower-level frameworks including TensorFlow and CNTK. \n",
    "2. See how you can in a couple of hours build a deep learning algorithm.\n",
    "\n",
    "Why are we using Keras? Keras was developed to enable deep learning engineers to build and experiment with different models very quickly. Just as TensorFlow is a higher-level framework than Python, Keras is an even higher-level framework and provides additional abstractions. Being able to go from idea to result with the least possible delay is key to finding good models. However, Keras is more restrictive than the lower-level frameworks, so there are some very complex models that you can implement in TensorFlow but not (without more difficulty) in Keras. That being said, Keras will work fine for many common models. \n",
    "\n",
    "In this exercise, you'll work on the \"Happy House\" problem, which we'll explain below. Let's load the required packages and solve the problem of the Happy House!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras import layers\n",
    "from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D\n",
    "from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.models import Model\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "import pydot\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "from kt_utils import *\n",
    "\n",
    "import keras.backend as K\n",
    "K.set_image_data_format('channels_last')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: As you can see, we've imported a lot of functions from Keras. You can use them easily just by calling them directly in the notebook. Ex: `X = Input(...)` or `X = ZeroPadding2D(...)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - The Happy House \n",
    "\n",
    "For your next vacation, you decided to spend a week with five of your friends from school. It is a very convenient house with many things to do nearby. But the most important benefit is that everybody has commited to be happy when they are in the house. So anyone wanting to enter the house must prove their current state of happiness.\n",
    "\n",
    "<img src=\"images/happy-house.jpg\" style=\"width:350px;height:270px;\">\n",
    "<caption><center> <u> <font color='purple'> **Figure 1** </u><font color='purple'>  : **the Happy House**</center></caption>\n",
    "\n",
    "\n",
    "As a deep learning expert, to make sure the \"Happy\" rule is strictly applied, you are going to build an algorithm which that uses pictures from the front door camera to check if the person is happy or not. The door should open only if the person is happy. \n",
    "\n",
    "You have gathered pictures of your friends and yourself, taken by the front-door camera. The dataset is labbeled. \n",
    "\n",
    "<img src=\"images/house-members.png\" style=\"width:550px;height:250px;\">\n",
    "\n",
    "Run the following code to normalize the dataset and learn about its shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training examples = 600\n",
      "number of test examples = 150\n",
      "X_train shape: (600, 64, 64, 3)\n",
      "Y_train shape: (600, 1)\n",
      "X_test shape: (150, 64, 64, 3)\n",
      "Y_test shape: (150, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()\n",
    "\n",
    "# Normalize image vectors\n",
    "X_train = X_train_orig/255.\n",
    "X_test = X_test_orig/255.\n",
    "\n",
    "# Reshape\n",
    "Y_train = Y_train_orig.T\n",
    "Y_test = Y_test_orig.T\n",
    "\n",
    "print (\"number of training examples = \" + str(X_train.shape[0]))\n",
    "print (\"number of test examples = \" + str(X_test.shape[0]))\n",
    "print (\"X_train shape: \" + str(X_train.shape))\n",
    "print (\"Y_train shape: \" + str(Y_train.shape))\n",
    "print (\"X_test shape: \" + str(X_test.shape))\n",
    "print (\"Y_test shape: \" + str(Y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc390218b70>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ef0netDw5teO4ml3z4fnHNI4XtkYQBz36LhKOyG2GiNg1ACqR0lC3hnfXc/e+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8L9nQHJ0HB5FtAFZy8LdvtT7u+Fdan6feqt4/FnAe50dLCPjWpOzwPUfnm5ZPcgJolBS7dj3B8z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\nP8S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V57ViGZw36+8sa2KFNfSaiz0DrfVZdG90RLJpO5VAtTVNtaLwpPJ8MKJcXfCD77tK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc391758860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show training images\n",
    "np.random.seed(49)\n",
    "\n",
    "concat_img = None\n",
    "for i in range(10):\n",
    "    idx = np.random.randint(X_train.shape[0])\n",
    "    if concat_img is None:\n",
    "        concat_img = X_train[idx]\n",
    "    else:\n",
    "        concat_img = np.concatenate([concat_img, X_train[idx]], axis=1)\n",
    "plt.figure(figsize=(15, 5)) \n",
    "imshow(concat_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Details of the \"Happy\" dataset**:\n",
    "- Images are of shape (64,64,3)\n",
    "- Training: 600 pictures\n",
    "- Test: 150 pictures\n",
    "\n",
    "It is now time to solve the \"Happy\" Challenge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Building a model in Keras\n",
    "\n",
    "Keras is very good for rapid prototyping. In just a short time you will be able to build a model that achieves outstanding results.\n",
    "\n",
    "Here is an example of a model in Keras:\n",
    "\n",
    "```python\n",
    "def model(input_shape):\n",
    "    # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!\n",
    "    X_input = Input(input_shape)\n",
    "\n",
    "    # Zero-Padding: pads the border of X_input with zeroes\n",
    "    X = ZeroPadding2D((3, 3))(X_input)\n",
    "\n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)\n",
    "    X = BatchNormalization(axis = 3, name = 'bn0')(X)\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    # MAXPOOL\n",
    "    X = MaxPooling2D((2, 2), name='max_pool')(X)\n",
    "\n",
    "    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED\n",
    "    X = Flatten()(X)\n",
    "    X = Dense(1, activation='sigmoid', name='fc')(X)\n",
    "\n",
    "    # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.\n",
    "    model = Model(inputs = X_input, outputs = X, name='HappyModel')\n",
    "    \n",
    "    return model\n",
    "```\n",
    "\n",
    "Note that Keras uses a different convention with variable names than we've previously used with numpy and TensorFlow. In particular, rather than creating and assigning a new variable on each step of forward propagation such as `X`, `Z1`, `A1`, `Z2`, `A2`, etc. for the computations for the different layers, in Keras code each line above just reassigns `X` to a new value using `X = ...`. In other words, during each step of forward propagation, we are just writing the latest value in the commputation into the same variable `X`. The only exception was `X_input`, which we kept separate and did not overwrite, since we needed it at the end to create the Keras model instance (`model = Model(inputs = X_input, ...)` above). \n",
    "\n",
    "**Exercise**: Implement a `HappyModel()`. This assignment is more open-ended than most. We suggest that you start by implementing a model using the architecture we suggest, and run through the rest of this assignment using that as your initial model. But after that, come back and take initiative to try out other model architectures. For example, you might take inspiration from the model above, but then vary the network architecture and hyperparameters however you wish. You can also use other functions such as `AveragePooling2D()`, `GlobalMaxPooling2D()`, `Dropout()`. \n",
    "\n",
    "**Note**: You have to be careful with your data's shapes. Use what you've learned in the videos to make sure your convolutional, pooling and fully-connected layers are adapted to the volumes you're applying it to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: HappyModel\n",
    "\n",
    "def HappyModel(input_shape):\n",
    "    \"\"\"\n",
    "    Implementation of the HappyModel.\n",
    "    \n",
    "    Arguments:\n",
    "    input_shape -- shape of the images of the dataset\n",
    "\n",
    "    Returns:\n",
    "    model -- a Model() instance in Keras\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    # Feel free to use the suggested outline in the text above to get started, and run through the whole\n",
    "    # exercise (including the later portions of this notebook) once. The come back also try out other\n",
    "    # network architectures as well. \n",
    "        # Define the input placeholder as a tensor with shape input_shape. Think of this as your input image!\n",
    "    X_input = Input(input_shape)\n",
    "\n",
    "    # Zero-Padding: pads the border of X_input with zeroes\n",
    "    X = ZeroPadding2D((3, 3))(X_input)\n",
    "\n",
    "    # CONV -> BN -> RELU Block applied to X\n",
    "    X = Conv2D(32, (7, 7), strides = (1, 1), name = 'conv0')(X)\n",
    "    X = BatchNormalization(axis = 3, name = 'bn0')(X)\n",
    "    X = Activation('relu')(X)\n",
    "    \n",
    "    # MORE CONVS\n",
    "    X = MaxPooling2D((2, 2))(X)\n",
    "    #shortcut = X\n",
    "    X = Conv2D(32, (3, 3), strides = (1, 1), padding=\"same\")(X)\n",
    "    X = BatchNormalization()(X)\n",
    "    X = Activation('relu')(X)\n",
    "    X = Conv2D(32, (3, 3), strides = (1, 1), padding=\"same\")(X)\n",
    "    X = BatchNormalization()(X)\n",
    "    #X = layers.add([X, shortcut])\n",
    "    X = Activation('relu')(X)\n",
    "\n",
    "    # MAXPOOL\n",
    "    X = MaxPooling2D((2, 2), name='max_pool')(X)\n",
    "\n",
    "    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED\n",
    "    X = Flatten()(X)\n",
    "    \n",
    "    # MORE DENSE\n",
    "    X = Dense(128)(X)\n",
    "    X = BatchNormalization()(X)\n",
    "    X = Activation('relu')(X)\n",
    "    X = Dropout(0.5)(X)\n",
    "    \n",
    "    X = Dense(1, activation='sigmoid', name='fc')(X)\n",
    "\n",
    "    # Create model. This creates your Keras model instance, you'll use this instance to train/test the model.\n",
    "    model = Model(inputs = X_input, outputs = X, name='HappyModel')\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have now built a function to describe your model. To train and test this model, there are four steps in Keras:\n",
    "1. Create the model by calling the function above\n",
    "2. Compile the model by calling `model.compile(optimizer = \"...\", loss = \"...\", metrics = [\"accuracy\"])`\n",
    "3. Train the model on train data by calling `model.fit(x = ..., y = ..., epochs = ..., batch_size = ...)`\n",
    "4. Test the model on test data by calling `model.evaluate(x = ..., y = ...)`\n",
    "\n",
    "If you want to know more about `model.compile()`, `model.fit()`, `model.evaluate()` and their arguments, refer to the official [Keras documentation](https://keras.io/models/model/).\n",
    "\n",
    "**Exercise**: Implement step 1, i.e. create the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "happyModel = HappyModel(X_train.shape[1:])\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Implement step 2, i.e. compile the model to configure the learning process. Choose the 3 arguments of `compile()` wisely. Hint: the Happy Challenge is a binary classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "happyModel.compile(optimizer=\"adam\", loss='binary_crossentropy', metrics=[\"accuracy\"])\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Implement step 3, i.e. train the model. Choose the number of epochs and the batch size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 600 samples, validate on 150 samples\n",
      "Epoch 1/5\n",
      "600/600 [==============================] - 27s - loss: 0.3449 - acc: 0.8483 - val_loss: 0.5770 - val_acc: 0.6933\n",
      "Epoch 2/5\n",
      "600/600 [==============================] - 25s - loss: 0.1844 - acc: 0.9433 - val_loss: 0.7616 - val_acc: 0.5600\n",
      "Epoch 3/5\n",
      "600/600 [==============================] - 25s - loss: 0.1600 - acc: 0.9483 - val_loss: 0.5677 - val_acc: 0.6133\n",
      "Epoch 4/5\n",
      "600/600 [==============================] - 26s - loss: 0.1540 - acc: 0.9450 - val_loss: 0.3769 - val_acc: 0.9133\n",
      "Epoch 5/5\n",
      "600/600 [==============================] - 27s - loss: 0.0815 - acc: 0.9767 - val_loss: 0.3753 - val_acc: 0.9000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc407c45588>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "happyModel.fit(X_train, Y_train, epochs=5, batch_size=16, validation_data=(X_test, Y_test))\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that if you run `fit()` again, the `model` will continue to train with the parameters it has already learnt instead of reinitializing them.\n",
    "\n",
    "**Exercise**: Implement step 4, i.e. test/evaluate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150/150 [==============================] - 2s     \n",
      "\n",
      "Loss = 0.375307106574\n",
      "Test Accuracy = 0.900000001589\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "preds = happyModel.evaluate(x=X_test, y=Y_test)\n",
    "### END CODE HERE ###\n",
    "print()\n",
    "print (\"Loss = \" + str(preds[0]))\n",
    "print (\"Test Accuracy = \" + str(preds[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If your `happyModel()` function worked, you should have observed much better than random-guessing (50%) accuracy on the train and test sets. To pass this assignment, you have to get at least 75% accuracy. \n",
    "\n",
    "To give you a point of comparison, our model gets around **95% test accuracy in 40 epochs** (and 99% train accuracy) with a mini batch size of 16 and \"adam\" optimizer. But our model gets decent accuracy after just 2-5 epochs, so if you're comparing different models you can also train a variety of models on just a few epochs and see how they compare. \n",
    "\n",
    "If you have not yet achieved 75% accuracy, here're some things you can play around with to try to achieve it:\n",
    "\n",
    "- Try using blocks of CONV->BATCHNORM->RELU such as:\n",
    "```python\n",
    "X = Conv2D(32, (3, 3), strides = (1, 1), name = 'conv0')(X)\n",
    "X = BatchNormalization(axis = 3, name = 'bn0')(X)\n",
    "X = Activation('relu')(X)\n",
    "```\n",
    "until your height and width dimensions are quite low and your number of channels quite large (≈32 for example). You are encoding useful information in a volume with a lot of channels. You can then flatten the volume and use a fully-connected layer.\n",
    "- You can use MAXPOOL after such blocks. It will help you lower the dimension in height and width.\n",
    "- Change your optimizer. We find Adam works well. \n",
    "- If the model is struggling to run and you get memory issues, lower your batch_size (12 is usually a good compromise)\n",
    "- Run on more epochs, until you see the train accuracy plateauing. \n",
    "\n",
    "Even if you have achieved 75% accuracy, please feel free to keep playing with your model to try to get even better results. \n",
    "\n",
    "**Note**: If you perform hyperparameter tuning on your model, the test set actually becomes a dev set, and your model might end up overfitting to the test (dev) set. But just for the purpose of this assignment, we won't worry about that here.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Conclusion\n",
    "\n",
    "Congratulations, you have solved the Happy House challenge! \n",
    "\n",
    "Now, you just need to link this model to the front-door camera of your house. We unfortunately won't go into the details of how to do that here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What we would like you to remember from this assignment:**\n",
    "- Keras is a tool we recommend for rapid prototyping. It allows you to quickly try out different model architectures. Are there any applications of deep learning to your daily life that you'd like to implement using Keras? \n",
    "- Remember how to code a model in Keras and the four steps leading to the evaluation of your model on the test set. Create->Compile->Fit/Train->Evaluate/Test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Test with your own image (Optional)\n",
    "\n",
    "Congratulations on finishing this assignment. You can now take a picture of your face and see if you could enter the Happy House. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Write your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (0 is unhappy, 1 is happy)!\n",
    "    \n",
    "The training/test sets were quite similar; for example, all the pictures were taken against the same background (since a front door camera is always mounted in the same position). This makes the problem easier, but a model trained on this data may or may not work on your own data. But feel free to give it a try! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### START CODE HERE ###\n",
    "img_path = 'images/my_image.jpg'\n",
    "### END CODE HERE ###\n",
    "img = image.load_img(img_path, target_size=(64, 64))\n",
    "imshow(img)\n",
    "\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "print(happyModel.predict(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Other useful functions in Keras (Optional)\n",
    "\n",
    "Two other basic features of Keras that you'll find useful are:\n",
    "- `model.summary()`: prints the details of your layers in a table with the sizes of its inputs/outputs\n",
    "- `plot_model()`: plots your graph in a nice layout. You can even save it as \".png\" using SVG() if you'd like to share it on social media ;). It is saved in \"File\" then \"Open...\" in the upper bar of the notebook.\n",
    "\n",
    "Run the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_5 (InputLayer)         (None, 64, 64, 3)         0         \n",
      "_________________________________________________________________\n",
      "zero_padding2d_5 (ZeroPaddin (None, 70, 70, 3)         0         \n",
      "_________________________________________________________________\n",
      "conv0 (Conv2D)               (None, 64, 64, 32)        4736      \n",
      "_________________________________________________________________\n",
      "bn0 (BatchNormalization)     (None, 64, 64, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pool (MaxPooling2D)      (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               1048704   \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "fc (Dense)                   (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 1,072,961\n",
      "Trainable params: 1,072,513\n",
      "Non-trainable params: 448\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "happyModel.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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    "SVG(model_to_dot(happyModel).create(prog='dot', format='svg'))"
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