ex_4.3_tf_normalization.ipynb

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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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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: 0.9645\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",
    "def full_connected_layer(tensor, input_size, out_size):\n",
    "    W = tf.Variable(tf.truncated_normal([input_size, out_size], stddev=0.1))\n",
    "    b = tf.Variable(tf.truncated_normal([out_size], stddev=0.1))\n",
    "    return tf.nn.tanh(tf.matmul(tensor, W) + b)\n",
    "\n",
    "def batch_norm_layer(tensor, size):\n",
    "    batch_mean, batch_var = tf.nn.moments(tensor, [0])\n",
    "    beta = tf.Variable(tf.zeros([size]))\n",
    "    scale = tf.Variable(tf.ones([size]))\n",
    "    return tf.nn.batch_normalization(tensor, batch_mean, batch_var, beta, scale, 0.001)\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y = tf.placeholder(tf.float32, [None, 10])\n",
    "\n",
    "h1      = full_connected_layer(x, 784, 100)\n",
    "h1_bn   = batch_norm_layer(h1, 100)\n",
    "h2      = full_connected_layer(h1_bn, 100, 100)\n",
    "y_logit = full_connected_layer(h2, 100, 10)\n",
    "\n",
    "loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_logit, labels=y)\n",
    "train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for i in range(1000):\n",
    "    batch_xs, batch_ys = mnist.train.next_batch(100)\n",
    "    sess.run(train_op, feed_dict={\n",
    "        x: batch_xs, \n",
    "        y: batch_ys,\n",
    "    })\n",
    "\n",
    "# точность\n",
    "correct_prediction = tf.equal(tf.argmax(y_logit, 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",
    "}))"
   ]
  }
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