ex_3.6.3_standard_cross_entroy.ipynb

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    {
     "name": "stdout",
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     "text": [
      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n",
      "Точность: 0.973\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "mnist = input_data.read_data_sets(\"data/MNIST\", one_hot=True)\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "\n",
    "# слой ReLU\n",
    "W_relu = tf.Variable(tf.truncated_normal([784, 100], stddev=0.1))\n",
    "b_relu = tf.Variable(tf.truncated_normal([100], stddev=0.1))\n",
    "h = tf.nn.relu(tf.matmul(x, W_relu) + b_relu)\n",
    "\n",
    "# слой dropout\n",
    "keep_probability = tf.placeholder(tf.float32)\n",
    "h_drop = tf.nn.dropout(h, keep_probability)\n",
    "\n",
    "# слой logit\n",
    "W = tf.Variable(tf.zeros([100, 10]))\n",
    "b = tf.Variable(tf.zeros([10]))\n",
    "logit = tf.matmul(h_drop, W) + b\n",
    "y = tf.nn.softmax(logit)\n",
    "\n",
    "y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "# эта функция включает в себя подсчет softmax и кросс энтропию\n",
    "# с учетом всех сложностей деления на около-нулевые числа\n",
    "# поэтому лучше всегда использовать её\n",
    "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logit, labels=y_))\n",
    "\n",
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for i in range(10000):\n",
    "    batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "    sess.run(train_step, feed_dict={\n",
    "        x: batch_xs, \n",
    "        y_: batch_ys,\n",
    "        keep_probability: 0.5\n",
    "    })\n",
    "\n",
    "# точность\n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "print(\"Точность: %s\" % sess.run(accuracy, feed_dict={\n",
    "    x: mnist.test.images, \n",
    "    y_: mnist.test.labels,\n",
    "    keep_probability: 1.\n",
    "}))"
   ]
  }
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