tensorflow-fashion-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow Code Practice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f98dd244e80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_data[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data\n",
    "train_data_norm = train_data / 255.\n",
    "test_data_norm = test_data / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping labels integer to actual labels\n",
    "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 27.5, 27.5, -0.5)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 59999\n",
    "plt.imshow(train_data_norm[index], cmap=plt.cm.binary)\n",
    "plt.title(class_names[train_labels[0]])\n",
    "plt.axis(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot multiple random images of fashion MNIST\n",
    "import random\n",
    "plt.figure(figsize=(4, 5))\n",
    "for i in range(4):\n",
    "    ax = plt.subplot(2, 2, i+1)\n",
    "    rand_index = random.choice(range(1, len(train_data_norm)))\n",
    "    plt.imshow(train_data_norm[rand_index], cmap=plt.cm.binary)\n",
    "    plt.title(class_names[train_labels[rand_index]])\n",
    "    plt.axis(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 19s 9ms/step - loss: 1.1559 - accuracy: 0.5918 - val_loss: 0.7988 - val_accuracy: 0.7246\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.7245 - accuracy: 0.7439 - val_loss: 0.6978 - val_accuracy: 0.7515\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.6527 - accuracy: 0.7653 - val_loss: 0.6594 - val_accuracy: 0.7609\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.6229 - accuracy: 0.7796 - val_loss: 0.6314 - val_accuracy: 0.7752\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.6026 - accuracy: 0.7857 - val_loss: 0.6418 - val_accuracy: 0.7746\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.5881 - accuracy: 0.7905 - val_loss: 0.6140 - val_accuracy: 0.7806\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.5774 - accuracy: 0.7938 - val_loss: 0.6248 - val_accuracy: 0.7740\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.5685 - accuracy: 0.7981 - val_loss: 0.5995 - val_accuracy: 0.7861\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.5613 - accuracy: 0.8015 - val_loss: 0.5846 - val_accuracy: 0.7897\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 17s 9ms/step - loss: 0.5528 - accuracy: 0.8051 - val_loss: 0.5820 - val_accuracy: 0.7968\n"
     ]
    }
   ],
   "source": [
    "# Model\n",
    "tf.random.set_seed(42)\n",
    "model_1 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(4, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(4, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "model_1.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(), \n",
    "                optimizer=tf.keras.optimizers.Adam(),\n",
    "                metrics=[\"accuracy\"])\n",
    "\n",
    "history_1 = model_1.fit(train_data_norm, train_labels, epochs=10, verbose=1, validation_data=(test_data_norm, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_12\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten_9 (Flatten)         (None, 784)               0         \n",
      "                                                                 \n",
      " dense_36 (Dense)            (None, 4)                 3140      \n",
      "                                                                 \n",
      " dense_37 (Dense)            (None, 4)                 20        \n",
      "                                                                 \n",
      " dense_38 (Dense)            (None, 10)                50        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 3210 (12.54 KB)\n",
      "Trainable params: 3210 (12.54 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Loss Accuracy Graph'}>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(history_1.history).plot(title=\"Loss Accuracy Graph\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 19s 9ms/step - loss: 0.8027 - accuracy: 0.7143 - val_loss: 0.5501 - val_accuracy: 0.8161\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.5031 - accuracy: 0.8279 - val_loss: 0.5538 - val_accuracy: 0.8106\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 16s 9ms/step - loss: 0.4705 - accuracy: 0.8375 - val_loss: 0.5024 - val_accuracy: 0.8219\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.4510 - accuracy: 0.8459 - val_loss: 0.4881 - val_accuracy: 0.8269\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.4416 - accuracy: 0.8468 - val_loss: 0.4787 - val_accuracy: 0.8325\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 15s 8ms/step - loss: 0.4300 - accuracy: 0.8516 - val_loss: 0.4932 - val_accuracy: 0.8269\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.4233 - accuracy: 0.8541 - val_loss: 0.4695 - val_accuracy: 0.8337\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.4173 - accuracy: 0.8552 - val_loss: 0.4503 - val_accuracy: 0.8444\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.4125 - accuracy: 0.8572 - val_loss: 0.4506 - val_accuracy: 0.8446\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 16s 8ms/step - loss: 0.4106 - accuracy: 0.8582 - val_loss: 0.4466 - val_accuracy: 0.8445\n"
     ]
    }
   ],
   "source": [
    "### Changing Learning Rate\n",
    "\n",
    "model_2 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(8, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(8, activation=\"relu\"),\n",
    "    tf.keras.layers.Dense(10, activation=\"softmax\")\n",
    "])\n",
    "\n",
    "model_2.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
    "                metrics=[\"accuracy\"])\n",
    "\n",
    "history_2 = model_2.fit(train_data_norm, train_labels, epochs=10, verbose=1, validation_data=(test_data_norm, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten_19 (Flatten)        (None, 784)               0         \n",
      "                                                                 \n",
      " dense_66 (Dense)            (None, 8)                 6280      \n",
      "                                                                 \n",
      " dense_67 (Dense)            (None, 8)                 72        \n",
      "                                                                 \n",
      " dense_68 (Dense)            (None, 10)                90        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6442 (25.16 KB)\n",
      "Trainable params: 6442 (25.16 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history_2.history).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "plot_model(model_2, show_shapes=True)"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}