code-08-FileWriter.ipynb

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    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "LOGDIR = './tensorflow_logs/mnist_deep'\n",
    "\n",
    "def weight_variable(shape):\n",
    "  \"\"\"Generates a weight variable of a given shape.\"\"\"\n",
    "  initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "  return tf.Variable(initial)\n",
    "\n",
    "\n",
    "def bias_variable(shape):\n",
    "  \"\"\"Generates a bias variable of a given shape.\"\"\"\n",
    "  initial = tf.constant(0.1, shape=shape)\n",
    "  return tf.Variable(initial)\n",
    "\n",
    "\n",
    "old_v = tf.logging.get_verbosity()\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "def main():\n",
    "    mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "    train_data = mnist.train.images  # Return np.array\n",
    "    train_labels = np.asarray(mnist.train.labels, dtype=np.int32)\n",
    "    eval_data = mnist.test.images  # Return np.array\n",
    "    eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)\n",
    "    \n",
    "    # Placeholder that will be fed image data.\n",
    "    x = tf.placeholder(tf.float32, [None, 784])\n",
    "    # Placeholder that will be fed the correct labels.\n",
    "    y_ = tf.placeholder(tf.float32, [None, 10])\n",
    "    # Define weight and bias.\n",
    "    W = weight_variable([784, 10])\n",
    "    b = bias_variable([10])\n",
    "\n",
    "    # Here we define our model which utilizes the softmax regression.\n",
    "    y = tf.nn.softmax(tf.matmul(x, W) + b)\n",
    "\n",
    "    # Define our loss.\n",
    "    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n",
    "\n",
    "    # Define our optimizer.\n",
    "    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n",
    "\n",
    "    # Define accuracy.\n",
    "    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "    correct_prediction = tf.cast(correct_prediction, tf.float32)\n",
    "    accuracy = tf.reduce_mean(correct_prediction)\n",
    "\n",
    "    # Launch session.\n",
    "    sess = tf.InteractiveSession()\n",
    "\n",
    "    # Initialize variables.\n",
    "    tf.global_variables_initializer().run()\n",
    "    \n",
    "    # Create summary writer\n",
    "    writer = tf.summary.FileWriter(LOGDIR, sess.graph)\n",
    "\n",
    "    # Do the training.\n",
    "    for i in range(1100):\n",
    "        batch = mnist.train.next_batch(100)\n",
    "        if i % 100 == 0:\n",
    "            train_accuracy = sess.run(accuracy, feed_dict={x:batch[0], y_: batch[1]})\n",
    "            print(\"Step %d, Training Accuracy %g\" % (i, float(train_accuracy)))\n",
    "        sess.run(train_step, feed_dict={x: batch[0], y_: batch[1]})\n",
    "\n",
    "    # See how model did.\n",
    "    print(\"Test Accuracy %g\" % sess.run(accuracy, feed_dict={x: mnist.test.images,\n",
    "                                                             y_: mnist.test.labels}))\n",
    "\n",
    "    # Close summary writer\n",
    "    writer.close()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()\n",
    "tf.logging.set_verbosity(old_v)"
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