first.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
    "# rest of the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/c/Users/Apratim\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.6.0'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'tensorflow.python.keras.datasets.mnist' has no attribute 'head'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-b60080bc062b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmnist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow.python.keras.datasets.mnist' has no attribute 'head'"
     ]
    }
   ],
   "source": [
    "mnist.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz\n",
      "11493376/11490434 [==============================]11493376/11490434 [==============================] - 4s 0us/step\n",
      "\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "print(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-b34cf7a9572f>\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[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\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;36mshow\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    252\u001b[0m     \"\"\"\n\u001b[0;32m    253\u001b[0m     \u001b[1;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 254\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_show\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[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    255\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipykernel\\pylab\\backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     39\u001b[0m         \u001b[1;31m# only call close('all') if any to close\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[1;31m# close triggers gc.collect, which can be slow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 41\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mclose\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mGcf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_all_fig_managers\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[0;32m     42\u001b[0m             \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'all'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "plt.show(x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b712b32898>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SK5J+JelaSX+S9G13b/kHbxV669HIS9e/zNx8/j12i3v7R0n/KWm/pM+zxWs08v66sGOX6Gu5CjhufMMPCIpv+AFBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIQfCOr/AcanH/Dq1TtRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b712ab0978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[1], cmap= plt.cm.binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = tf.keras.utils.normalize(x_train,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = tf.keras.utils.normalize(x_test,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b71029acc0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mm/Lszcw2S5qvoU99HZK0WtJLkn4j6VJJ+yX90N0b/sZbid7ma+ip61crN59+jd3g3v5B0n9L+kDSYHHzwxp6fZ3bY5foa5lyeNw4ww8IijP8gKAIPxAU4QeCIvxAUIQfCIrwA0ERfiAowg8E9X+fJxXtR9q5DQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b71025a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[1], cmap= plt.cm.binary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "60000/60000 [==============================]60000/60000 [==============================] - 6s 102us/step - loss: 0.2623 - acc: 0.9239\n",
      "\n",
      "Epoch 2/3\n",
      "60000/60000 [==============================]60000/60000 [==============================] - 7s 109us/step - loss: 0.1063 - acc: 0.9673\n",
      "\n",
      "Epoch 3/3\n",
      "60000/60000 [==============================]60000/60000 [==============================] - 9s 149us/step - loss: 0.0736 - acc: 0.9765\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras._impl.keras.callbacks.History at 0x1b7103387f0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Flatten(input_shape=(28,28)))\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n",
    "\n",
    "model.compile(optimizer = 'adam',\n",
    "             loss = 'sparse_categorical_crossentropy',\n",
    "              metrics = ['accuracy']\n",
    "             )\n",
    "model.fit(x_train, y_train, epochs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================]10000/10000 [==============================] - 0s 34us/step\n",
      "\n",
      "0.10334497709386051 0.9687\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_acc = model.evaluate(x_test, y_test)\n",
    "print(val_loss, val_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('first_tensorflow.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = tf.keras.models.load_model('first_tensorflow.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = new_model.predict([x_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[3.72552300e-09, 6.82341295e-08, 1.01954265e-06, ...,\n",
       "         9.99993801e-01, 1.12003846e-07, 2.63628363e-06],\n",
       "        [6.45591669e-08, 3.06297557e-06, 9.99993682e-01, ...,\n",
       "         6.13054663e-10, 8.54138250e-07, 8.83463649e-11],\n",
       "        [7.74936382e-07, 9.99335706e-01, 3.30086914e-05, ...,\n",
       "         2.48333934e-04, 1.76700225e-04, 1.78330833e-06],\n",
       "        ...,\n",
       "        [8.13191381e-09, 4.13880298e-06, 7.92225308e-08, ...,\n",
       "         4.79244736e-05, 1.59758514e-07, 3.64774169e-05],\n",
       "        [1.62644710e-07, 6.60344540e-07, 5.90656519e-08, ...,\n",
       "         1.53233586e-07, 5.26857388e-04, 1.77207600e-08],\n",
       "        [1.58839532e-06, 5.91644997e-11, 3.98543154e-09, ...,\n",
       "         6.02752483e-12, 6.34207797e-10, 1.89462210e-10]], dtype=float32),\n",
       " (10000, 10),\n",
       " (10000, 28, 28))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions, predictions.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b70ece9278>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b70ed329e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.4"
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 },
 "nbformat": 4,
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}