fashion_mnist_keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape,x_test.shape,y_test.shape,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.reshape((60000,784))\n",
    "x_test=x_test.reshape((10000,784))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.astype('float32')\n",
    "x_test=x_test.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train/255\n",
    "x_test=x_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=keras.utils.to_categorical(y_train,10)\n",
    "y_test=keras.utils.to_categorical(y_test,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(60000, 10)\n",
      "(10000, 784)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape,y_train.shape,x_test.shape,y_test.shape,sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py:143: 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",
    "model.add(Dense(512,input_shape=(784,),activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(64,activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(128,activation='tanh'))\n",
    "model.add(Dense(64,activation='relu'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Dense(32,activation='relu'))\n",
    "model.add(Dense(10,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=RMSprop() ,loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 9s 158us/sample - loss: 0.6884 - acc: 0.7494 - val_loss: 0.4527 - val_acc: 0.8346\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 8s 132us/sample - loss: 0.4726 - acc: 0.8302 - val_loss: 0.4384 - val_acc: 0.8394\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 8s 132us/sample - loss: 0.4285 - acc: 0.8450 - val_loss: 0.4181 - val_acc: 0.8506\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 8s 131us/sample - loss: 0.4010 - acc: 0.8565 - val_loss: 0.4293 - val_acc: 0.8470\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 8s 131us/sample - loss: 0.3861 - acc: 0.8611 - val_loss: 0.3806 - val_acc: 0.8635\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 8s 129us/sample - loss: 0.3738 - acc: 0.8655 - val_loss: 0.3884 - val_acc: 0.8592\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 8s 129us/sample - loss: 0.3598 - acc: 0.8709 - val_loss: 0.3588 - val_acc: 0.8738\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 8s 131us/sample - loss: 0.3524 - acc: 0.8737 - val_loss: 0.3778 - val_acc: 0.8680\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 8s 129us/sample - loss: 0.3465 - acc: 0.8760 - val_loss: 0.3574 - val_acc: 0.8768\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 8s 131us/sample - loss: 0.3388 - acc: 0.8799 - val_loss: 0.3704 - val_acc: 0.8758ETA\n"
     ]
    }
   ],
   "source": [
    "h=model.fit(x_train,y_train,batch_size=128,epochs=10,validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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