C1_W1_Lab_1_functional-practice (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ungraded Lab: Practice with the Keras Functional API\n",
    "\n",
    "This lab will demonstrate how to build models with the Functional syntax. You'll build one using the Sequential API and see how you can do the same with the Functional API. Both will arrive at the same architecture and you can train and evaluate it as usual."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wtf\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "  # %tensorflow_version only exists in Colab.\n",
    "  %tensorflow_version 2.x\n",
    "except Exception:\n",
    "    print('wtf')\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.utils.vis_utils import plot_model\n",
    "import pydot\n",
    "from tensorflow.keras.models import Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sequential API\n",
    "\n",
    "Here is how we use the `Sequential()` class to build a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "04Y-C9RYUTes"
   },
   "outputs": [],
   "source": [
    "def build_model_with_sequential():    \n",
    "    # instantiate a Sequential class and linearly stack the layers of your model\n",
    "    seq_model = tf.keras.models.Sequential([tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "                                            tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "                                            tf.keras.layers.Dense(10, activation=tf.nn.softmax)])\n",
    "    return seq_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "build_model_with_sequential().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functional API\n",
    "\n",
    "And here is how you build the same model above with the functional syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model_with_functional():\n",
    "    \n",
    "    # instantiate the input Tensor\n",
    "    input_layer = tf.keras.Input(shape=(28, 28))\n",
    "    \n",
    "    # stack the layers using the syntax: new_layer()(previous_layer)\n",
    "    flatten_layer = tf.keras.layers.Flatten()(input_layer)\n",
    "    first_dense = tf.keras.layers.Dense(128, activation=tf.nn.relu)(flatten_layer)\n",
    "    output_layer = tf.keras.layers.Dense(10, activation=tf.nn.softmax)(first_dense)\n",
    "    \n",
    "    # declare inputs and outputs\n",
    "    func_model = Model(inputs=input_layer, outputs=output_layer)\n",
    "    \n",
    "    return func_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 28, 28)]          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "build_model_with_functional().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the model and visualize the model graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can choose how to build your model below. Just uncomment which function you'd like to use. You'll notice that the plot will look the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_model_with_functional()\n",
    "#model = build_model_with_sequential()\n",
    "\n",
    "# Plot model graph\n",
    "plot_model(model, show_shapes=True, show_layer_names=True, to_file='model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model\n",
    "\n",
    "Regardless if you built it with the Sequential or Functional API, you'll follow the same steps when training and evaluating your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n",
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 5s 86us/sample - loss: 0.4998 - accuracy: 0.8239\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 5s 81us/sample - loss: 0.3767 - accuracy: 0.8642\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 5s 79us/sample - loss: 0.3368 - accuracy: 0.8767\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 5s 77us/sample - loss: 0.3129 - accuracy: 0.8848\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 5s 77us/sample - loss: 0.2943 - accuracy: 0.8903\n",
      "10000/10000 [==============================] - 0s 30us/sample - loss: 0.3523 - accuracy: 0.8731\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.3523302776813507, 0.8731]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare fashion mnist dataset\n",
    "mnist = tf.keras.datasets.fashion_mnist\n",
    "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
    "training_images = training_images / 255.0\n",
    "test_images = test_images / 255.0\n",
    "\n",
    "# configure, train, and evaluate the model\n",
    "model.compile(optimizer=tf.optimizers.Adam(),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.fit(training_images, training_labels, epochs=5)\n",
    "model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 5s 82us/sample - loss: 0.2814 - accuracy: 0.8947 - val_loss: 0.3568 - val_accuracy: 0.8699\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 5s 83us/sample - loss: 0.2668 - accuracy: 0.9005 - val_loss: 0.3650 - val_accuracy: 0.8725\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 5s 82us/sample - loss: 0.2564 - accuracy: 0.9033 - val_loss: 0.3341 - val_accuracy: 0.8803\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 5s 81us/sample - loss: 0.2486 - accuracy: 0.9067 - val_loss: 0.3441 - val_accuracy: 0.8771\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 5s 81us/sample - loss: 0.2382 - accuracy: 0.9106 - val_loss: 0.3394 - val_accuracy: 0.8828\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fd0c7197390>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(training_images, training_labels, epochs=5\n",
    "         , validation_data= (test_images, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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