Fashion_MNIST_CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8adf5981",
   "metadata": {},
   "source": [
    "# Fashion MNIST,loading data and transforming it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "315a4baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "mnist = tf.keras.datasets.fashion_mnist\n",
    "(training_images, training_labels), (val_images, val_labels) = mnist.load_data()\n",
    "\n",
    "training_images=training_images / 255.0\n",
    "val_images=val_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2475f3af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first image is\n",
      "of class 9\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "print(\"The first image is\")\n",
    "plt.imshow(training_images[0])\n",
    "\n",
    "print(\"of class\",training_labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "43256b0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,x_valid,y_train,y_valid=train_test_split(training_images, training_labels,train_size=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "73a4ea5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Conv2D(64, (3,3) , activation='relu', input_shape=(28, 28, 1)),\n",
    "    tf.keras.layers.MaxPooling2D(2, 2),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(32, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])\n",
    "model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "5fba5633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_12 (Conv2D)          (None, 26, 26, 64)        640       \n",
      "                                                                 \n",
      " max_pooling2d_12 (MaxPoolin  (None, 13, 13, 64)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_9 (Flatten)         (None, 10816)             0         \n",
      "                                                                 \n",
      " dense_21 (Dense)            (None, 32)                346144    \n",
      "                                                                 \n",
      " dense_22 (Dense)            (None, 10)                330       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 347,114\n",
      "Trainable params: 347,114\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "d2d4cf56",
   "metadata": {},
   "outputs": [],
   "source": [
    "callback=tf.keras.callbacks.EarlyStopping(patience=5,min_delta=0.01,restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "26ca3075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1688/1688 [==============================] - 38s 22ms/step - loss: 0.4546 - accuracy: 0.8423 - val_loss: 0.3443 - val_accuracy: 0.8811\n",
      "Epoch 2/20\n",
      "1688/1688 [==============================] - 27s 16ms/step - loss: 0.3032 - accuracy: 0.8927 - val_loss: 0.2985 - val_accuracy: 0.8929\n",
      "Epoch 3/20\n",
      "1688/1688 [==============================] - 28s 16ms/step - loss: 0.2647 - accuracy: 0.9055 - val_loss: 0.2985 - val_accuracy: 0.8935\n",
      "Epoch 4/20\n",
      "1688/1688 [==============================] - 35s 21ms/step - loss: 0.2379 - accuracy: 0.9134 - val_loss: 0.3010 - val_accuracy: 0.8924\n",
      "Epoch 5/20\n",
      "1688/1688 [==============================] - 33s 19ms/step - loss: 0.2166 - accuracy: 0.9208 - val_loss: 0.2709 - val_accuracy: 0.9033\n",
      "Epoch 6/20\n",
      "1688/1688 [==============================] - 32s 19ms/step - loss: 0.1951 - accuracy: 0.9285 - val_loss: 0.2696 - val_accuracy: 0.9063\n",
      "Epoch 7/20\n",
      "1688/1688 [==============================] - 34s 20ms/step - loss: 0.1771 - accuracy: 0.9359 - val_loss: 0.2986 - val_accuracy: 0.8976\n",
      "Epoch 8/20\n",
      "1688/1688 [==============================] - 32s 19ms/step - loss: 0.1598 - accuracy: 0.9419 - val_loss: 0.2730 - val_accuracy: 0.9100\n",
      "Epoch 9/20\n",
      "1688/1688 [==============================] - 34s 20ms/step - loss: 0.1450 - accuracy: 0.9473 - val_loss: 0.3013 - val_accuracy: 0.9045\n",
      "Epoch 10/20\n",
      "1688/1688 [==============================] - 33s 19ms/step - loss: 0.1331 - accuracy: 0.9522 - val_loss: 0.3363 - val_accuracy: 0.8929\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(X_train, y_train, validation_data=(val_images, val_labels), epochs=20,callbacks=[callback])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "400c5cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df=pd.DataFrame(history.history)\n",
    "df[['loss','val_loss']].plot()\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "5402866e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Filter values for The CNN are;-\n",
      "[ 0.10581041 -0.0587081   0.24660072 -0.02716835  0.47643977 -0.24976905\n",
      " -0.30031347  0.40911064  0.04775826 -0.14136691 -0.08819406  0.15777668\n",
      " -0.28438386 -0.16801637  0.17844881  0.37731788  0.02596144 -0.15619683\n",
      " -0.14601444  0.01233212  0.020657   -0.16098417  0.02202404 -0.00845317\n",
      " -0.05570699  0.17483887  0.13675645 -0.12485469  0.2830309   0.1759554\n",
      " -0.2002471  -0.21178477 -0.07039261 -0.01863088 -0.1592012   0.15950765\n",
      " -0.1919673   0.21068005  0.00829916 -0.17681497 -0.06657811  0.05512445\n",
      " -0.16486727  0.13757887 -0.01345529  0.22526792  0.13858642 -0.01430371\n",
      "  0.17071202 -0.08790324 -0.37386918  0.02055329 -1.0009309  -0.1513519\n",
      " -0.07291467  0.02663676  0.09722563 -0.26102334  0.04483406 -0.11216535\n",
      " -0.01322767 -0.26368648 -0.28836992  0.04036418]\n"
     ]
    }
   ],
   "source": [
    "print(\"The Filter values for The CNN are;-\")\n",
    "arr=model.get_weights()\n",
    "print(arr[0][0][0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "45402aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining a dictionary of clothing items\n",
    "dict={0:'T-shirt/top',\n",
    "1: 'Trouser.',\n",
    "2: 'Pullover.',\n",
    "3: 'Dress.',\n",
    "4: 'Coat.',\n",
    "5: 'Sandal.',\n",
    "6: 'Shirt.',\n",
    "7: 'Sneaker',\n",
    "8: 'Bag',\n",
    "9: 'Ankle boot'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "6ef964f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 15ms/step\n",
      "prediction- Coat.\n",
      "actual category- Coat.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "a=random.randint(0,len(x_valid))\n",
    "arr=model.predict(x_valid[a].reshape(1,28,28))\n",
    "plt.imshow(x_valid[a])\n",
    "print('prediction-',dict.get(np.argmax(arr)))\n",
    "print('actual category-',dict.get(y_valid[a]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e72d87e",
   "metadata": {},
   "source": [
    "Predicting an Image from the online using url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "9cdc4652",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 28ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Coat.'"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import requests\n",
    "from PIL import Image as im\n",
    "url='https://datasets-server.huggingface.co/assets/fashion_mnist/--/fashion_mnist/train/19/image/image.jpg'\n",
    "response=requests.get(url,stream=True)\n",
    "img0=im.open(response.raw)\n",
    "plt.imshow(img0)\n",
    "img = np.array(img0)/255.0\n",
    "dict.get(np.argmax(model.predict(img.reshape(1,28,28))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a34d42a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}