Image-classifiation-python-and-manipulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.14.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist=keras.datasets.fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\krishnakakade\\image classification in python\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names=['T-shirt/top','Trouser','Pullover','Dress','Coat','Sandal','Shirt','Sneaker','Ankle boot','Bag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### now we have to preprocess this data so now we are try to load an image krishna's writes\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images=train_images/255.0\n",
    "test_images=test_images/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    Let’s display some images.\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)#for unshowing the grid lines in the images \n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()\n",
    "# where are displaying classes name below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0909 20:58:43.210985  8032 deprecation.py:506] From C:\\Users\\krishnakakade\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
     ]
    }
   ],
   "source": [
    "#    Setup the layers\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    Compile the Model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 11s 175us/sample - loss: 0.4969 - acc: 0.8244\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 8s 132us/sample - loss: 0.3742 - acc: 0.8648\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 10s 174us/sample - loss: 0.3352 - acc: 0.8781\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 10s 163us/sample - loss: 0.3140 - acc: 0.8848\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 10s 166us/sample - loss: 0.2950 - acc: 0.8916\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 11s 182us/sample - loss: 0.2807 - acc: 0.8962\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 11s 183us/sample - loss: 0.2676 - acc: 0.8999\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 14s 233us/sample - loss: 0.2580 - acc: 0.9034\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 15s 244us/sample - loss: 0.2484 - acc: 0.9076\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 13s 223us/sample - loss: 0.2400 - acc: 0.9103\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1fc6f9c4248>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#    Model Training\n",
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 73us/sample - loss: 0.3443 - acc: 0.8780\n"
     ]
    }
   ],
   "source": [
    "#    Evaluating Accuracy\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.878\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    Making Predictions\n",
    "predictions = model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.4391081e-08, 6.5752843e-09, 1.1430584e-08, 9.4629025e-09,\n",
       "       2.0107700e-07, 8.8929839e-04, 7.8447071e-09, 4.0898874e-02,\n",
       "       1.7107821e-04, 9.5804060e-01], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A prediction is an array of 10 numbers. \n",
    "#These describe the “confidence” of the model that the image corresponds to each of the 10 different articles of clothing. \n",
    "#We can see which label has the highest confidence value.\n",
    "np.argmax(predictions[0])\n",
    "#Model is most confident that it's an ankle boot. Let's see if it's correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#    Now, it’s time to look at the full set of 10 channels\n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'green'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#    Let’s look at the 0th and 10th image first\n",
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 10\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, let’s plot several images and their predictions. Correct ones are green, while the incorrect ones are red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n",
      "(1, 28, 28)\n",
      "[[2.4391081e-08 6.5752843e-09 1.1430584e-08 9.4628847e-09 2.0107682e-07\n",
      "  8.8929804e-04 7.8447071e-09 4.0898882e-02 1.7107837e-04 9.5804060e-01]]\n"
     ]
    }
   ],
   "source": [
    "#    Finally, we will use the trained model to make a prediction about a single image.\n",
    "# Grab an image from the test dataset\n",
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)\n",
    "\n",
    "predictions_single = model.predict(img) \n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_value_array(0, predictions_single, test_labels)\n",
    "plt.xticks(range(10), class_names, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_result = np.argmax(predictions_single[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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