Untitled4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "51d4e778",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "84bff7ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1cd6fde4",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train) , (x_test,y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8cf42d63",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test = x_train / 255.0 , x_test / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b77ddbc4",
   "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.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ee786fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.26946533, -0.06795286,  0.00222604, -0.6123814 , -0.43279117,\n",
       "         0.49846262, -0.9407326 ,  0.13320987, -0.9383193 , -0.36327052]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model(x_train[:1]).numpy()\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ed25e389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.15045115, 0.10736349, 0.11516882, 0.06228942, 0.07454343,\n",
       "        0.18916804, 0.04485524, 0.13128664, 0.04496362, 0.07991012]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.nn.softmax(predictions).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d54204ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "loss_fn(y_train[:1],predictions).numpy()\n",
    "model.compile(optimizer='adam',\n",
    "             loss=loss_fn,\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "65683bba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2955 - accuracy: 0.9130\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1436 - accuracy: 0.9572\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1092 - accuracy: 0.9668\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0902 - accuracy: 0.9722\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0760 - accuracy: 0.9761\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x17b6fa57790>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2ba7ee91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.0761 - accuracy: 0.9770 - 813ms/epoch - 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07613248378038406, 0.9769999980926514]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb8290e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fc78ef45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "210c9370",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.datasets as tfds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9b37243c",
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train,y_train) , (x_test,y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "08868be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6065f675",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Image data of dtype object cannot be converted to float",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-87c8cde1e1ac>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtfds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmnist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   2722\u001b[0m         \u001b[0mfilternorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresample\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2723\u001b[0m         data=None, **kwargs):\n\u001b[1;32m-> 2724\u001b[1;33m     __ret = gca().imshow(\n\u001b[0m\u001b[0;32m   2725\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2726\u001b[0m         \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1445\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1446\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1447\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1448\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1449\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, filternorm, filterrad, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5521\u001b[0m                               resample=resample, **kwargs)\n\u001b[0;32m   5522\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5523\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5524\u001b[0m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5525\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mset_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    700\u001b[0m         if (self._A.dtype != np.uint8 and\n\u001b[0;32m    701\u001b[0m                 not np.can_cast(self._A.dtype, float, \"same_kind\")):\n\u001b[1;32m--> 702\u001b[1;33m             raise TypeError(\"Image data of dtype {} cannot be converted to \"\n\u001b[0m\u001b[0;32m    703\u001b[0m                             \"float\".format(self._A.dtype))\n\u001b[0;32m    704\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Image data of dtype object cannot be converted to float"
     ]
    },
    {
     "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(tfds.mnist.load_data())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "506f3bff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92d72d86",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}