fully_connected.py

import math
import tensorflow as tf

# The MNIST dataset has 10 classes, representing the digits 0 through 9.
NUM_CLASSES = 3

IMAGE_PIXELS = 160 * 120 / 2


def inference(images, hidden1_units, hidden2_units):
  """Build the MNIST model up to where it may be used for inference.
  Args:
    images: Images placeholder, from inputs().
    hidden1_units: Size of the first hidden layer.
    hidden2_units: Size of the second hidden layer.
  Returns:
    softmax_linear: Output tensor with the computed logits.
  """
  # Hidden 1
  with tf.name_scope('hidden1'):
    weights = tf.Variable(
        tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                            stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')
    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
  # Hidden 2
  with tf.name_scope('hidden2'):
    weights = tf.Variable(
        tf.truncated_normal([hidden1_units, hidden2_units],
                            stddev=1.0 / math.sqrt(float(hidden1_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden2_units]),
                         name='biases')
    hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
  # Linear
  with tf.name_scope('softmax_linear'):
    weights = tf.Variable(
        tf.truncated_normal([hidden2_units, NUM_CLASSES],
                            stddev=1.0 / math.sqrt(float(hidden2_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                         name='biases')
    logits = tf.matmul(hidden2, weights) + biases

  return logits


def loss(logits, labels):
  """Calculates the loss from the logits and the labels.
  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size].
  Returns:
    loss: Loss tensor of type float.
  """
  # Convert from sparse integer labels in the range [0, NUM_CLASSES)
  # to 1-hot dense float vectors (that is we will have batch_size vectors,
  # each with NUM_CLASSES values, all of which are 0.0 except there will
  # be a 1.0 in the entry corresponding to the label).
  batch_size = tf.size(labels)
  labels = tf.expand_dims(labels, 1)
  indices = tf.expand_dims(tf.range(0, batch_size), 1)
  concated = tf.concat(1, [indices, labels])
  onehot_labels = tf.sparse_to_dense(
      concated, tf.pack([batch_size, NUM_CLASSES]), 1.0, 0.0)
  cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits,
                                                          onehot_labels,
                                                          name='xentropy')
  loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
  return loss


def training(loss, learning_rate):
  """Sets up the training Ops.
  Creates a summarizer to track the loss over time in TensorBoard.
  Creates an optimizer and applies the gradients to all trainable variables.
  The Op returned by this function is what must be passed to the
  `sess.run()` call to cause the model to train.
  Args:
    loss: Loss tensor, from loss().
    learning_rate: The learning rate to use for gradient descent.
  Returns:
    train_op: The Op for training.
  """
  # Add a scalar summary for the snapshot loss.
  tf.scalar_summary(loss.op.name, loss)
  # Create the gradient descent optimizer with the given learning rate.
  optimizer = tf.train.GradientDescentOptimizer(learning_rate)
  # Create a variable to track the global step.
  global_step = tf.Variable(0, name='global_step', trainable=False)
  # Use the optimizer to apply the gradients that minimize the loss
  # (and also increment the global step counter) as a single training step.
  train_op = optimizer.minimize(loss, global_step=global_step)
  return train_op


def evaluation(logits, labels):
  """Evaluate the quality of the logits at predicting the label.
  Args:
    logits: Logits tensor, float - [batch_size, NUM_CLASSES].
    labels: Labels tensor, int32 - [batch_size], with values in the
      range [0, NUM_CLASSES).
  Returns:
    A scalar int32 tensor with the number of examples (out of batch_size)
    that were predicted correctly.
  """
  # For a classifier model, we can use the in_top_k Op.
  # It returns a bool tensor with shape [batch_size] that is true for
  # the examples where the label's is was in the top k (here k=1)
  # of all logits for that example.
  correct = tf.nn.in_top_k(logits, labels, 1)
  # Return the number of true entries.
  return tf.reduce_sum(tf.cast(correct, tf.int32))