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Fully Connected Neural Network

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard (Fully Connected) Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use in Markup cell type\n",
    "#![alt text](imagename.png \"Title\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing Fully connected Neural Net"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Required packages and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "###1. Load Data and Splot Data\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential \n",
    "from keras.layers.core import Dense, Activation\n",
    "from keras.utils import np_utils\n",
    "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "n = 10  # how many digits we will display\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(X_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previous X_train shape: (60000, 28, 28) \n",
      "Previous Y_train shape:(60000,)\n",
      "New X_train shape: (60000, 784) \n",
      "New Y_train shape:(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"Previous X_train shape: {} \\nPrevious Y_train shape:{}\".format(X_train.shape, Y_train.shape))\n",
    "X_train = X_train.reshape(60000, 784)     \n",
    "X_test = X_test.reshape(10000, 784)\n",
    "X_train = X_train.astype('float32')     \n",
    "X_test = X_test.astype('float32')     \n",
    "X_train /= 255    \n",
    "X_test /= 255\n",
    "classes = 10\n",
    "Y_train = np_utils.to_categorical(Y_train, classes)     \n",
    "Y_test = np_utils.to_categorical(Y_test, classes)\n",
    "print(\"New X_train shape: {} \\nNew Y_train shape:{}\".format(X_train.shape, Y_train.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting up parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 784\n",
    "batch_size = 200   \n",
    "hidden1 = 400\n",
    "hidden2 = 20\n",
    "epochs = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the FCN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 400)               314000    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 20)                8020      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                210       \n",
      "=================================================================\n",
      "Total params: 322,230\n",
      "Trainable params: 322,230\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "###4.Build the model\n",
    "model = Sequential()     \n",
    "model.add(Dense(hidden1, input_dim=input_size, activation='relu'))\n",
    "# output = relu (dot (W, input) + bias)\n",
    "model.add(Dense(hidden2, activation='relu'))\n",
    "model.add(Dense(classes, activation='softmax')) \n",
    "\n",
    "# Compilation\n",
    "model.compile(loss='categorical_crossentropy', \n",
    "    metrics=['accuracy'], optimizer='sgd')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      " - 12s - loss: 1.4482 - acc: 0.6251\n",
      "Epoch 2/10\n",
      " - 3s - loss: 0.6239 - acc: 0.8482\n",
      "Epoch 3/10\n",
      " - 3s - loss: 0.4582 - acc: 0.8798\n",
      "Epoch 4/10\n",
      " - 3s - loss: 0.3941 - acc: 0.8936\n",
      "Epoch 5/10\n",
      " - 3s - loss: 0.3579 - acc: 0.9011\n",
      "Epoch 6/10\n",
      " - 4s - loss: 0.3328 - acc: 0.9070\n",
      "Epoch 7/10\n",
      " - 3s - loss: 0.3138 - acc: 0.9118\n",
      "Epoch 8/10\n",
      " - 3s - loss: 0.2980 - acc: 0.9157\n",
      "Epoch 9/10\n",
      " - 3s - loss: 0.2849 - acc: 0.9191\n",
      "Epoch 10/10\n",
      " - 3s - loss: 0.2733 - acc: 0.9223\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x272375a7240>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting on Data\n",
    "model.fit(X_train, Y_train, batch_size=batch_size, epochs=10, verbose=2)\n",
    "###5.Test "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Testing The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 121us/step\n",
      "\n",
      "Test accuracy: 0.9257\n",
      "[0 6 9 0 1 5 9 7 3 4]\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, verbose=1)\n",
    "print('\\n''Test accuracy:', score[1])\n",
    "mask = range(10,20)\n",
    "X_valid = X_test[mask]\n",
    "y_pred = model.predict_classes(X_valid)\n",
    "print(y_pred)\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(X_valid[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
Acerca de este algoritmo

Standard (Fully Connected) Neural Network

#Use in Markup cell type
#![alt text](imagename.png "Title")  

Implementing Fully connected Neural Net

Loading Required packages and Data

###1. Load Data and Splot Data
from keras.datasets import mnist
from keras.models import Sequential 
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
(X_train, Y_train), (X_test, Y_test) = mnist.load_data()
Using TensorFlow backend.

Preprocessing

import matplotlib.pyplot as plt
n = 10  # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
    # display original
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(X_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()
plt.close()
<Figure size 2000x400 with 10 Axes>
print("Previous X_train shape: {} \nPrevious Y_train shape:{}".format(X_train.shape, Y_train.shape))
X_train = X_train.reshape(60000, 784)     
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')     
X_test = X_test.astype('float32')     
X_train /= 255    
X_test /= 255
classes = 10
Y_train = np_utils.to_categorical(Y_train, classes)     
Y_test = np_utils.to_categorical(Y_test, classes)
print("New X_train shape: {} \nNew Y_train shape:{}".format(X_train.shape, Y_train.shape))
Previous X_train shape: (60000, 28, 28) 
Previous Y_train shape:(60000,)
New X_train shape: (60000, 784) 
New Y_train shape:(60000, 10)

Setting up parameters

input_size = 784
batch_size = 200   
hidden1 = 400
hidden2 = 20
epochs = 2

Building the FCN Model

###4.Build the model
model = Sequential()     
model.add(Dense(hidden1, input_dim=input_size, activation='relu'))
# output = relu (dot (W, input) + bias)
model.add(Dense(hidden2, activation='relu'))
model.add(Dense(classes, activation='softmax')) 

# Compilation
model.compile(loss='categorical_crossentropy', 
    metrics=['accuracy'], optimizer='sgd')
model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 400)               314000    
_________________________________________________________________
dense_2 (Dense)              (None, 20)                8020      
_________________________________________________________________
dense_3 (Dense)              (None, 10)                210       
=================================================================
Total params: 322,230
Trainable params: 322,230
Non-trainable params: 0
_________________________________________________________________

Training The Model

# Fitting on Data
model.fit(X_train, Y_train, batch_size=batch_size, epochs=10, verbose=2)
###5.Test 
Epoch 1/10
 - 12s - loss: 1.4482 - acc: 0.6251
Epoch 2/10
 - 3s - loss: 0.6239 - acc: 0.8482
Epoch 3/10
 - 3s - loss: 0.4582 - acc: 0.8798
Epoch 4/10
 - 3s - loss: 0.3941 - acc: 0.8936
Epoch 5/10
 - 3s - loss: 0.3579 - acc: 0.9011
Epoch 6/10
 - 4s - loss: 0.3328 - acc: 0.9070
Epoch 7/10
 - 3s - loss: 0.3138 - acc: 0.9118
Epoch 8/10
 - 3s - loss: 0.2980 - acc: 0.9157
Epoch 9/10
 - 3s - loss: 0.2849 - acc: 0.9191
Epoch 10/10
 - 3s - loss: 0.2733 - acc: 0.9223
<keras.callbacks.History at 0x272375a7240>

Testing The Model

score = model.evaluate(X_test, Y_test, verbose=1)
print('\n''Test accuracy:', score[1])
mask = range(10,20)
X_valid = X_test[mask]
y_pred = model.predict_classes(X_valid)
print(y_pred)
plt.figure(figsize=(20, 4))
for i in range(n):
    # display original
    ax = plt.subplot(2, n, i + 1)
    plt.imshow(X_valid[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()
plt.close()
10000/10000 [==============================] - 1s 121us/step

Test accuracy: 0.9257
[0 6 9 0 1 5 9 7 3 4]