TensorFlow: Variables y Sesiones.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "constante=tf.constant([2.0,3,4],dtype=tf.float32, name='Constante1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Constante1:0\", shape=(3,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(constante)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "apartado=tf.placeholder(tf.float32, name='Apartado1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Apartado1:0\", dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "print(apartado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "variable=tf.Variable(3,dtype=tf.float32,name='variable1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'variable1:0' shape=() dtype=float32_ref>\n"
     ]
    }
   ],
   "source": [
    "print(variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "matriz=tf.zeros([3, 4], tf.int32, name='matriz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"matriz:0\", shape=(3, 4), dtype=int32)\n"
     ]
    }
   ],
   "source": [
    "print(matriz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inicializar=tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess=tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.run(inicializar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(variable))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiplicacion=apartado*constante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 30.  30.  20.]]\n"
     ]
    }
   ],
   "source": [
    "print(sess.run(multiplicacion,feed_dict={apartado:[[15,10,5]]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 18.  25.  12.]\n",
      " [ 30.  46.  16.]]\n"
     ]
    }
   ],
   "source": [
    "#Multiplicacion de matrices necesita lleva la forma (M,N)x(N,M)=M,M\n",
    "a=tf.placeholder(tf.float32, shape=(2,2))\n",
    "b=tf.placeholder(tf.float32, shape=(2,3))\n",
    "#(2,2)x(2,3)\n",
    "mult=tf.matmul(a,b)\n",
    "init=tf.global_variables_initializer()\n",
    "sess=tf.Session()\n",
    "sess.run(init)\n",
    "print(sess.run(mult,feed_dict={a:[[1,2],[2,2]],b:[[12,21,4],[3,2,4]] }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n"
     ]
    }
   ],
   "source": [
    "c=tf.placeholder(tf.float32, shape=(3))\n",
    "d=tf.placeholder(tf.float32, shape=(3))\n",
    "punto=tf.tensordot(c,d,1)\n",
    "init=tf.global_variables_initializer()\n",
    "sess=tf.Session()\n",
    "sess.run(init)\n",
    "print(sess.run(punto,feed_dict={c:[1,2,3],d:[3,2,1]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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