NEW_TENSORFLOW_MNSIT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0caaa56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3a541e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.3.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39b33f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.Variable(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b390244c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = tf.Variable(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "275f6e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "f=x+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0813260c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\r\n"
     ]
    }
   ],
   "source": [
    "tf.print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "055af06d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.constant([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bd59263",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = tf.constant([[7,8,9],[5,9,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d221ce57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
       "array([[ 8, 10, 12],\n",
       "       [ 9, 14,  7]])>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "655ca2d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
       "array([[-6, -6, -6],\n",
       "       [-1, -4,  5]])>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x-y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37c604dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
       "array([[ 8, 10, 12],\n",
       "       [ 9, 14,  7]])>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.add(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6c89ba14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(2, 3), dtype=int32, numpy=\n",
       "array([[ 7, 16, 27],\n",
       "       [20, 45,  6]])>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x*y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d53d1c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "35db670f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "11493376/11490434 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c93d4507",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abff3b61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19be9fbba90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.imshow(x_train[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cfc9a6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test = x_train/255.0, x_test/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30f93b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "                                    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
    "                                    tf.keras.layers.Dense(128, activation='relu'),\n",
    "                                    tf.keras.layers.Dense(64, activation='relu'),\n",
    "                                    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2a118cd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 109,386\n",
      "Trainable params: 109,386\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "463ba46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e2c4e5c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7463bb36",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=loss_fn, optimizer=optimizer, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ffcff89f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 1s 724us/step - loss: 1.5697 - accuracy: 0.9021 - val_loss: 1.5168 - val_accuracy: 0.9465\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 1s 656us/step - loss: 1.5114 - accuracy: 0.9520 - val_loss: 1.5000 - val_accuracy: 0.9633\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 1s 652us/step - loss: 1.4994 - accuracy: 0.9633 - val_loss: 1.4946 - val_accuracy: 0.9678\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 1s 659us/step - loss: 1.4931 - accuracy: 0.9693 - val_loss: 1.4950 - val_accuracy: 0.9664\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 1s 657us/step - loss: 1.4894 - accuracy: 0.9724 - val_loss: 1.4911 - val_accuracy: 0.9699\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x19be54ad580>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "72afcf5f",
   "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": [
    "plt.plot(model.history.history['accuracy'], label='Train Accuracy')\n",
    "plt.plot(model.history.history['val_accuracy'], label='Test Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f266d114",
   "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": [
    "plt.plot(model.history.history['loss'], label='Train Loss')\n",
    "plt.plot(model.history.history['val_loss'], label='Test Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cf18522a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b9273cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(model.predict(x_test[0].reshape(1,28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6ae774f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19be6a9dc10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.imshow(x_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0f17cc17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(model.predict(x_test[10].reshape(1,28,28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c40448d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19be6aedee0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.imshow(x_test[10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6d30a259",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n",
    "from tensorflow.keras import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5e5d3b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import fashion_mnist\n",
    "#from tensorflow.keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f612b123",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 3s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 0s/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "67b1f12b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "99474462",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0af4d197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x19be6a18f10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.imshow(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e3293f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1a1af6a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8d1bbb61",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train[..., tf.newaxis].astype('float32')\n",
    "x_test = x_test[..., tf.newaxis].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d34d8176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28, 1)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cba0013b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f1013680",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset shapes: ((None, 28, 28, 1), (None,)), types: (tf.float32, tf.uint8)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1e2641b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bb250467",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyModel(Model):\n",
    "  def __init__(self):\n",
    "    super(MyModel, self).__init__()\n",
    "    self.conv1 = Conv2D(32, (3,3), activation='relu')\n",
    "    self.flatten = Flatten()\n",
    "    self.d1 = Dense(128, activation='relu')\n",
    "    self.out = Dense(10, activation='softmax')\n",
    "\n",
    "  def call(self, x):\n",
    "    x = self.conv1(x)\n",
    "    x = self.flatten(x)\n",
    "    x = self.d1(x)\n",
    "    return self.out(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e45d568",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MyModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a7185314",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "optimizer = tf.keras.optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "97bfe864",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loss = tf.keras.metrics.Mean(name='train_loss')\n",
    "train_acc = tf.keras.metrics.SparseCategoricalAccuracy(name='train_Accuracy')\n",
    "\n",
    "test_loss = tf.keras.metrics.Mean(name='test_loss')\n",
    "test_acc = tf.keras.metrics.SparseCategoricalAccuracy(name='test_Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f83c8261",
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(image, labels):\n",
    "  with tf.GradientTape() as tape:\n",
    "    predictions = model(images, training=True)\n",
    "    loss = loss_object(labels, predictions)\n",
    "  gradients = tape.gradient(loss, model.trainable_variables)\n",
    "  optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "\n",
    "  train_loss(loss)\n",
    "  train_acc(labels, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d173bd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def test_step(images, labels):\n",
    "  predictions = model(images, training=False)\n",
    "  loss = loss_object(labels, predictions)\n",
    "\n",
    "  test_loss(loss)\n",
    "  test_acc(labels, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "438e4d3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch : 0, Loss : 2.30260968208313, Accuracy : 0.09708333015441895 \n",
      "                 Test Loss : 2.3025851249694824, Test Accuracy : 0.10000000149011612 \n",
      "**************************************************\n",
      "Epoch : 1, Loss : 2.3026113510131836, Accuracy : 0.0964166671037674 \n",
      "                 Test Loss : 2.3025848865509033, Test Accuracy : 0.10000000149011612 \n",
      "**************************************************\n",
      "Epoch : 2, Loss : 2.302612781524658, Accuracy : 0.09536666423082352 \n",
      "                 Test Loss : 2.3025848865509033, Test Accuracy : 0.10000000149011612 \n",
      "**************************************************\n",
      "Epoch : 3, Loss : 2.30260968208313, Accuracy : 0.09726666659116745 \n",
      "                 Test Loss : 2.3025858402252197, Test Accuracy : 0.10000000149011612 \n",
      "**************************************************\n",
      "Epoch : 4, Loss : 2.3026132583618164, Accuracy : 0.09849999845027924 \n",
      "                 Test Loss : 2.3025856018066406, Test Accuracy : 0.10000000149011612 \n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "epochs = 5\n",
    "for epoch in range(epochs):\n",
    "    train_loss.reset_states()\n",
    "    train_acc.reset_states()\n",
    "    test_loss.reset_states()\n",
    "    test_acc.reset_states()\n",
    "\n",
    "    for images, labels in train_df:\n",
    "        train_step(images, labels)\n",
    "  \n",
    "    for t_images, t_labels in test_df:\n",
    "        test_step(t_images, t_labels)\n",
    "\n",
    "    print(f\"Epoch : {epoch}, Loss : {train_loss.result()}, Accuracy : {train_acc.result()} \")\n",
    "    print(f\"                 Test Loss : {test_loss.result()}, Test Accuracy : {test_acc.result()} \")\n",
    "    print(\"*\" * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cfb44db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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