LeNet5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.layers import Flatten, Conv2D, MaxPool2D, Dense\n",
    "from keras.models import Sequential\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils import plot_model, to_categorical\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "#数据变成TensorFow为Backend的形式\n",
    "x_train=x_train.reshape(60000,28,28,1)\n",
    "x_test=x_test.reshape(10000,28,28,1)\n",
    "#把标签变成one-hot编码的形式\n",
    "y_train=to_categorical(y_train,num_classes=10)\n",
    "y_test=to_categorical(y_test,num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 24, 24, 6)         156       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 6)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 8, 16)          2416      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 16)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 120)               30840     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 84)                10164     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                850       \n",
      "=================================================================\n",
      "Total params: 44,426\n",
      "Trainable params: 44,426\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 构建LeNet-5网络\n",
    "model = Sequential()\n",
    "model.add(Conv2D(input_shape = (28,28,1), filters=6, kernel_size=(5,5), padding='valid', activation='tanh'))\n",
    "model.add(MaxPool2D(pool_size=(2,2), strides=2))\n",
    "model.add(Conv2D(input_shape=(14,14,6), filters=16, kernel_size=(5,5), padding='valid', activation='tanh'))\n",
    "model.add(MaxPool2D(pool_size=(2,2), strides=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(120, activation='tanh'))\n",
    "model.add(Dense(84, activation='tanh'))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "60000/60000 [==============================] - 11s 189us/step - loss: 0.9287 - acc: 0.7676\n",
      "Epoch 2/30\n",
      "60000/60000 [==============================] - 12s 194us/step - loss: 0.3284 - acc: 0.9180\n",
      "Epoch 3/30\n",
      "60000/60000 [==============================] - 11s 185us/step - loss: 0.2356 - acc: 0.9375\n",
      "Epoch 4/30\n",
      "60000/60000 [==============================] - 11s 186us/step - loss: 0.1897 - acc: 0.9479\n",
      "Epoch 5/30\n",
      "60000/60000 [==============================] - 11s 191us/step - loss: 0.1618 - acc: 0.9543\n",
      "Epoch 6/30\n",
      "60000/60000 [==============================] - 11s 187us/step - loss: 0.1432 - acc: 0.9591\n",
      "Epoch 7/30\n",
      "60000/60000 [==============================] - 11s 188us/step - loss: 0.1308 - acc: 0.9622\n",
      "Epoch 8/30\n",
      "60000/60000 [==============================] - 13s 209us/step - loss: 0.1185 - acc: 0.9658\n",
      "Epoch 9/30\n",
      "60000/60000 [==============================] - 13s 213us/step - loss: 0.1121 - acc: 0.9675\n",
      "Epoch 10/30\n",
      "60000/60000 [==============================] - 11s 184us/step - loss: 0.1036 - acc: 0.9697\n",
      "Epoch 11/30\n",
      "60000/60000 [==============================] - 11s 184us/step - loss: 0.0983 - acc: 0.9713\n",
      "Epoch 12/30\n",
      "60000/60000 [==============================] - 11s 184us/step - loss: 0.0936 - acc: 0.9722\n",
      "Epoch 13/30\n",
      "60000/60000 [==============================] - 11s 183us/step - loss: 0.0895 - acc: 0.9736\n",
      "Epoch 14/30\n",
      "60000/60000 [==============================] - 11s 188us/step - loss: 0.0867 - acc: 0.9742\n",
      "Epoch 15/30\n",
      "60000/60000 [==============================] - 11s 177us/step - loss: 0.0817 - acc: 0.9757\n",
      "Epoch 16/30\n",
      "60000/60000 [==============================] - 11s 179us/step - loss: 0.0784 - acc: 0.9769\n",
      "Epoch 17/30\n",
      "60000/60000 [==============================] - 11s 182us/step - loss: 0.0765 - acc: 0.9775\n",
      "Epoch 18/30\n",
      "60000/60000 [==============================] - 11s 182us/step - loss: 0.0736 - acc: 0.9781\n",
      "Epoch 19/30\n",
      "60000/60000 [==============================] - 11s 190us/step - loss: 0.0707 - acc: 0.9792\n",
      "Epoch 20/30\n",
      "60000/60000 [==============================] - 11s 183us/step - loss: 0.0691 - acc: 0.9797\n",
      "Epoch 21/30\n",
      "60000/60000 [==============================] - 11s 187us/step - loss: 0.0677 - acc: 0.9803\n",
      "Epoch 22/30\n",
      "60000/60000 [==============================] - 11s 179us/step - loss: 0.0658 - acc: 0.9803\n",
      "Epoch 23/30\n",
      "60000/60000 [==============================] - 11s 182us/step - loss: 0.0641 - acc: 0.9812\n",
      "Epoch 24/30\n",
      "60000/60000 [==============================] - 11s 185us/step - loss: 0.0627 - acc: 0.9818\n",
      "Epoch 25/30\n",
      "60000/60000 [==============================] - 11s 179us/step - loss: 0.0610 - acc: 0.9824\n",
      "Epoch 26/30\n",
      "60000/60000 [==============================] - 11s 180us/step - loss: 0.0589 - acc: 0.9827\n",
      "Epoch 27/30\n",
      "60000/60000 [==============================] - 11s 179us/step - loss: 0.0576 - acc: 0.9829\n",
      "Epoch 28/30\n",
      "60000/60000 [==============================] - 11s 183us/step - loss: 0.0567 - acc: 0.9835\n",
      "Epoch 29/30\n",
      "60000/60000 [==============================] - 11s 181us/step - loss: 0.0556 - acc: 0.9837\n",
      "Epoch 30/30\n",
      "60000/60000 [==============================] - 12s 198us/step - loss: 0.0535 - acc: 0.9845\n",
      "dict_keys(['loss', 'acc'])\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.01), metrics=['accuracy'])\n",
    "history = model.fit(x_train, y_train, batch_size=128, epochs=30)\n",
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['loss','acc'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 133us/step\n",
      "Testing Loss: 0.056708887127041814\n",
      "Testing Accuracy of: 0.9825\n"
     ]
    }
   ],
   "source": [
    "score=model.evaluate(x_test,y_test)\n",
    "print(\"Testing Loss:\", score[0])\n",
    "print(\"Testing Accuracy of:\", score[1])"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}