neuroniniu-tinklu-metodai/Lab2/examples/Lab31-mnist.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST digits dataset\n",
    "\n",
    "- First load and view the MNIST digits dataset\n",
    "- There are 60000 images in this dataset, but we will only view the first 25 of them:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From f:\\KTU\\Neuroninių tinklų metodai\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "test_images shape (10000, 28, 28) train_images shape (60000, 28, 28)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load and visualise the MNIST digits\n",
    "import tensorflow as tf\n",
    "tf.config.experimental.set_visible_devices([], \"GPU\")\n",
    "\n",
    "mnist = tf.keras.datasets.mnist\n",
    "(train_images0, train_labels0),(test_images0, test_labels0) = mnist.load_data()\n",
    "\n",
    "print(\"test_images shape\",test_images0.shape,\"train_images shape\",train_images0.shape)\n",
    "class_names=[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"]\n",
    "import matplotlib.pyplot as plt\n",
    "# plot first few images\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    # define subplot\n",
    "    plt.subplot(5,5,i+1)\n",
    "    # plot raw pixel data\n",
    "    plt.imshow(train_images0[i], cmap=plt.get_cmap('gray'))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    # Add a label underneath...\n",
    "    plt.xlabel(class_names[train_labels0[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next build a neural-network classifier for these digits.\n",
    "\n",
    "- We will build a keras model, with the higher-level API concepts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "# Each MNIST images are 28*28.  Therefore if there are N images, then the\n",
    "# shape of the numpy array holding the images is N*28*28\n",
    "# We will reshape that here to be N*784, using a numpy reshape.\n",
    "# Note that this flattens each image into a single vector length 784.\n",
    "#test_images=test_images0.reshape(10000,784) # 10000 test patterns\n",
    "#train_images=train_images0.reshape(60000,784) # 60000 train patterns\n",
    "\n",
    "test_images=test_images0\n",
    "train_images=train_images0\n",
    "\n",
    "# Also rescale greyscale from 8 bit to floating point (by dividing by 255)\n",
    "test_images=test_images/255.0\n",
    "train_images=train_images/255.0\n",
    "\n",
    "# Create the model\n",
    "\n",
    "#model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
    "#model.add(layers.MaxPooling2D((2, 2)))\n",
    "#model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "#model.add(layers.MaxPooling2D((2, 2)))\n",
    "#model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "\n",
    "\n",
    "keras_model = tf.keras.models.Sequential([\n",
    "tf.keras.layers.Conv2D(28, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "tf.keras.layers.Dropout(.2, input_shape=(2,)),\n",
    "tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "tf.keras.layers.Dropout(.2, input_shape=(2,)),\n",
    "tf.keras.layers.MaxPooling2D((2, 2)),\n",
    "tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "tf.keras.layers.Dropout(.2, input_shape=(2,)),\n",
    "tf.keras.layers.Flatten(),\n",
    "tf.keras.layers.Dense(64, activation='relu'),\n",
    "tf.keras.layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "keras_model.build(input_shape=[None,784])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View the keras model summary information\n",
    "\n",
    "- This shows you how many layers your neural network has, and how many weights, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_6 (Conv2D)           (None, 26, 26, 28)        280       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 26, 26, 28)        0         \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 13, 13, 28)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 11, 11, 64)        16192     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 11, 11, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 3, 3, 64)          36928     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 3, 3, 64)          0         \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 576)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 64)                36928     \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 90,978\n",
      "Trainable params: 90,978\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# View the model summary information...\n",
    "keras_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Keras model\n",
    "\n",
    "- We will use SGD optimiser (ordinary gradient descent)\n",
    "- We will use Cross Entropy loss (\"SparseCategoricalCrossentropy\")\n",
    "- We will run 200 training iterations (epochs)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "WARNING:tensorflow:From f:\\KTU\\Neuroninių tinklų metodai\\venv\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\KTU\\Neuroninių tinklų metodai\\venv\\Lib\\site-packages\\keras\\src\\backend.py:5727: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
      "  output, from_logits = _get_logits(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "600/600 [==============================] - 14s 21ms/step - loss: 0.2375 - sparse_categorical_accuracy: 0.9275 - val_loss: 0.0672 - val_sparse_categorical_accuracy: 0.9833\n",
      "Epoch 2/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0696 - sparse_categorical_accuracy: 0.9789 - val_loss: 0.0431 - val_sparse_categorical_accuracy: 0.9901\n",
      "Epoch 3/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0521 - sparse_categorical_accuracy: 0.9839 - val_loss: 0.0403 - val_sparse_categorical_accuracy: 0.9903\n",
      "Epoch 4/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0408 - sparse_categorical_accuracy: 0.9874 - val_loss: 0.0274 - val_sparse_categorical_accuracy: 0.9927\n",
      "Epoch 5/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0356 - sparse_categorical_accuracy: 0.9887 - val_loss: 0.0294 - val_sparse_categorical_accuracy: 0.9921\n",
      "Epoch 6/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0309 - sparse_categorical_accuracy: 0.9901 - val_loss: 0.0280 - val_sparse_categorical_accuracy: 0.9911\n",
      "Epoch 7/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0288 - sparse_categorical_accuracy: 0.9909 - val_loss: 0.0222 - val_sparse_categorical_accuracy: 0.9942\n",
      "Epoch 8/20\n",
      "600/600 [==============================] - 14s 23ms/step - loss: 0.0261 - sparse_categorical_accuracy: 0.9913 - val_loss: 0.0225 - val_sparse_categorical_accuracy: 0.9937\n",
      "Epoch 9/20\n",
      "600/600 [==============================] - 15s 24ms/step - loss: 0.0220 - sparse_categorical_accuracy: 0.9929 - val_loss: 0.0260 - val_sparse_categorical_accuracy: 0.9927\n",
      "Epoch 10/20\n",
      "600/600 [==============================] - 15s 24ms/step - loss: 0.0207 - sparse_categorical_accuracy: 0.9934 - val_loss: 0.0217 - val_sparse_categorical_accuracy: 0.9940\n",
      "Epoch 11/20\n",
      "600/600 [==============================] - 14s 24ms/step - loss: 0.0198 - sparse_categorical_accuracy: 0.9934 - val_loss: 0.0225 - val_sparse_categorical_accuracy: 0.9926\n",
      "Epoch 12/20\n",
      "600/600 [==============================] - 15s 25ms/step - loss: 0.0169 - sparse_categorical_accuracy: 0.9945 - val_loss: 0.0214 - val_sparse_categorical_accuracy: 0.9930\n",
      "Epoch 13/20\n",
      "600/600 [==============================] - 14s 23ms/step - loss: 0.0169 - sparse_categorical_accuracy: 0.9943 - val_loss: 0.0235 - val_sparse_categorical_accuracy: 0.9929\n",
      "Epoch 14/20\n",
      "600/600 [==============================] - 14s 24ms/step - loss: 0.0149 - sparse_categorical_accuracy: 0.9949 - val_loss: 0.0235 - val_sparse_categorical_accuracy: 0.9933\n",
      "Epoch 15/20\n",
      "600/600 [==============================] - 15s 25ms/step - loss: 0.0145 - sparse_categorical_accuracy: 0.9954 - val_loss: 0.0205 - val_sparse_categorical_accuracy: 0.9939\n",
      "Epoch 16/20\n",
      "600/600 [==============================] - 15s 24ms/step - loss: 0.0143 - sparse_categorical_accuracy: 0.9954 - val_loss: 0.0217 - val_sparse_categorical_accuracy: 0.9934\n",
      "Epoch 17/20\n",
      "600/600 [==============================] - 14s 23ms/step - loss: 0.0128 - sparse_categorical_accuracy: 0.9956 - val_loss: 0.0222 - val_sparse_categorical_accuracy: 0.9938\n",
      "Epoch 18/20\n",
      "600/600 [==============================] - 13s 21ms/step - loss: 0.0132 - sparse_categorical_accuracy: 0.9955 - val_loss: 0.0225 - val_sparse_categorical_accuracy: 0.9939\n",
      "Epoch 19/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0106 - sparse_categorical_accuracy: 0.9966 - val_loss: 0.0224 - val_sparse_categorical_accuracy: 0.9933\n",
      "Epoch 20/20\n",
      "600/600 [==============================] - 13s 22ms/step - loss: 0.0118 - sparse_categorical_accuracy: 0.9961 - val_loss: 0.0214 - val_sparse_categorical_accuracy: 0.9939\n"
     ]
    }
   ],
   "source": [
    "keras_model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
    ")\n",
    "\n",
    "# Train loop\n",
    "history = keras_model.fit(\n",
    "    train_images,\n",
    "    train_labels0,\n",
    "    batch_size=100,\n",
    "    epochs=20,\n",
    "    validation_data=(test_images, test_labels0),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View the training performance\n",
    "\n",
    "- When the Keras fit loop runs, it returns a \"history\" object, which includes a dictionary of the trianing history.\n",
    "\n",
    "- Hence we can plot graphs of the training performance (Accuracy, Loss), for both the \"Training\" and \"Validation\" sets...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key: loss\n",
      "Key: sparse_categorical_accuracy\n",
      "Key: val_loss\n",
      "Key: val_sparse_categorical_accuracy\n"
     ]
    }
   ],
   "source": [
    "# first show keys for data series recorded by fit loop:\n",
    "for item in history.history:\n",
    "    print(\"Key:\",item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(history.history['loss'],label=\"train\")\n",
    "plt.plot(history.history['val_loss'],label=\"validation\")\n",
    "plt.title('Model Loss')\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Cross Entropy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#print(\"history\",history.history)\n",
    "plt.plot(history.history['sparse_categorical_accuracy'],label=\"train\")\n",
    "plt.plot(history.history['val_sparse_categorical_accuracy'],label=\"validation\")\n",
    "plt.title('Model Accuracy')\n",
    "#plt.yscale('log')\n",
    "plt.ylabel('Acc')\n",
    "plt.xlabel('Iteration')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect how well the system is working on a sample of 25 new images (from the test set)...\n",
    "- The test set has a lot of images in it, but we can only view 25 at a time.\n",
    "- Hence rerun this code block several times, to get a different random set of samples from the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "plt.figure(figsize=(10,10))\n",
    "# plot 25 random images from the test set.\n",
    "first_index=np.random.randint(len(test_images)-25)\n",
    "for i in range(first_index,first_index+25):\n",
    "    # define subplot\n",
    "    plt.subplot(5,5,i+1-first_index)\n",
    "    # plot raw pixel data\n",
    "    plt.imshow(test_images0[i], cmap=plt.get_cmap('gray'))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    if class_names!=None:\n",
    "        prediction=keras_model(test_images[i:i+1])[0,:] # This will be a vector of length 10\n",
    "        prediction_class=np.argmax(prediction)  # Pick the index of the largest element of the length-10 vector\n",
    "        # Add a label underneath...\n",
    "        true_label=test_labels0[i]\n",
    "        class_name=class_names[prediction_class]\n",
    "        plt.xlabel(class_name+\" \"+(\"CORRECT\" if prediction_class==true_label else \"WRONG\"))\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}