{
"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"
]
},
{
"data": {
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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
}
