keras_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense, Dropout\n",
    "from keras.models import Sequential \n",
    "\n",
    "from keras.datasets import fashion_mnist\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e9ff28def0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train.reshape(60000, 784).astype('float32') / 255\n",
    "X_test = X_test.reshape(10000, 784).astype('float32') / 255\n",
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, 3, 0, 2, 7, 2, 5, 5], dtype=uint8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np_utils.to_categorical(y_train, 10)\n",
    "y_test = np_utils.to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\professional\\appdata\\local\\programs\\python\\python36\\envs\\keras-jupyter\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(30, input_shape=(784, ), activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(20, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(20, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10, activation='softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 30)                23550     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 30)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 20)                620       \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 20)                420       \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                210       \n",
      "=================================================================\n",
      "Total params: 24,800\n",
      "Trainable params: 24,800\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 48000 samples, validate on 12000 samples\n",
      "Epoch 1/10\n",
      "48000/48000 [==============================] - 2s 31us/step - loss: 0.4222 - acc: 0.8530 - val_loss: 0.3860 - val_acc: 0.8683\n",
      "Epoch 2/10\n",
      "48000/48000 [==============================] - 2s 33us/step - loss: 0.4155 - acc: 0.8568 - val_loss: 0.4022 - val_acc: 0.8661\n",
      "Epoch 3/10\n",
      "48000/48000 [==============================] - 2s 33us/step - loss: 0.4204 - acc: 0.8543 - val_loss: 0.3915 - val_acc: 0.8672\n",
      "Epoch 4/10\n",
      "48000/48000 [==============================] - 2s 35us/step - loss: 0.4193 - acc: 0.8543 - val_loss: 0.3873 - val_acc: 0.8678\n",
      "Epoch 5/10\n",
      "48000/48000 [==============================] - 2s 34us/step - loss: 0.4184 - acc: 0.8547 - val_loss: 0.3951 - val_acc: 0.8614\n",
      "Epoch 6/10\n",
      "48000/48000 [==============================] - 1s 30us/step - loss: 0.4177 - acc: 0.8550 - val_loss: 0.3954 - val_acc: 0.8670\n",
      "Epoch 7/10\n",
      "48000/48000 [==============================] - 1s 30us/step - loss: 0.4174 - acc: 0.8554 - val_loss: 0.4138 - val_acc: 0.8608\n",
      "Epoch 8/10\n",
      "48000/48000 [==============================] - 2s 32us/step - loss: 0.4191 - acc: 0.8540 - val_loss: 0.3920 - val_acc: 0.8697\n",
      "Epoch 9/10\n",
      "48000/48000 [==============================] - 2s 34us/step - loss: 0.4161 - acc: 0.8552 - val_loss: 0.3990 - val_acc: 0.8622\n",
      "Epoch 10/10\n",
      "48000/48000 [==============================] - 2s 35us/step - loss: 0.4188 - acc: 0.8521 - val_loss: 0.3969 - val_acc: 0.8678\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n",
    "\n",
    "history = model.fit(X_train, y_train, batch_size=128, epochs=10, validation_split=0.2, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 0s 31us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.42706936682462693, 0.8605]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# overfitting\n",
    "\n",
    "# underfitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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