nn.py

import math
import tensorflow as tf


NUM_CLASSES = 1
WINDOW_SIZE = 10 # Tamaño de ventana


def inference_one_hidden_layer(inputs, hidden1_units):

  # Hidden 1
  with tf.name_scope('hidden1'):
    weights = tf.Variable(
        tf.truncated_normal([WINDOW_SIZE, hidden1_units],
                            stddev=1.0 / math.sqrt(float(WINDOW_SIZE))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')
    hidden1 = tf.nn.softmax(tf.matmul(inputs, weights) + biases)
  # Linear
  with tf.name_scope('identity'):
    weights = tf.Variable(
        tf.truncated_normal([hidden1_units, NUM_CLASSES],
                            stddev=1.0 / math.sqrt(float(hidden1_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([NUM_CLASSES]),
                         name='biases')
    logits = tf.matmul(hidden1, weights) + biases

  return logits

# def inference_two_hidden_layers(inputs,hidden1_units,hidden2_units):
#
#     with tf.name_scope('hidden1'):
#         weights = tf.Variable(tf.truncated_normal([WINDOW_SIZE,hidden1_units]),stddev=1.0 / math.sqrt(float(WINDOW_SIZE)))
#         biases = tf.Variable(tf.zeros([hidden1_units]),name='biases')
#         hidden1 = tf.nn.softmax(tf.matmul(inputs, weights) + biases)
#
#     with tf.name_scope('hidden2'):
#         weights= tf.Variable(tf.truncated_normal([hidden1_units, hidden2_units],stddev=1.0 / math.sqrt(float(hidden1_units))))
#         biases = tf.Variable(tf.zeros([hidden2_units]),name='biases')
#         hidden2 = tf.nn.softmax(tf.matmul(hidden1, weights) + biases)
#
#     with tf.truncated_normal('identity'):
#         weights = tf.Variable(tf.truncated_normal([hidden2_units, 1], stddev=1.0 / math.sqrt(float(hidden1_units))))
#         biases = tf.Variable(tf.zeros([NUM_CLASSES]),name='biases')
#         logits = tf.matmul(hidden2,weights) + biases
#
#     return logits

def inference_two_hidden_layers_simplifcado(images, hidden1_units, hidden2_units):

  with tf.name_scope('hidden1'):
    weights = tf.Variable(tf.truncated_normal([WINDOW_SIZE, hidden1_units],stddev=1.0 / math.sqrt(float(WINDOW_SIZE))),name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),name='biases')
    hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)

  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)

  with tf.name_scope('identity'):
    weights = tf.Variable(
        tf.truncated_normal([hidden2_units, 1],stddev=1.0 / math.sqrt(float(hidden2_units))),name='weights')
    biases = tf.Variable(tf.zeros([1]),name='biases')

    logits = tf.matmul(hidden2, weights) + biases

  return logits


def inference_two_hidden_layers(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([WINDOW_SIZE, hidden1_units],
                            stddev=1.0 / math.sqrt(float(WINDOW_SIZE))),
        name='weights')
    biases = tf.Variable(tf.zeros([hidden1_units]),
                         name='biases')
    hidden1 = tf.nn.sigmoid(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.sigmoid(tf.matmul(hidden1, weights) + biases)
  # Linear
  with tf.name_scope('identity'):
    weights = tf.Variable(
        tf.truncated_normal([hidden2_units, 1],
                            stddev=1.0 / math.sqrt(float(hidden2_units))),
        name='weights')
    biases = tf.Variable(tf.zeros([1]),
                         name='biases')
    logits = tf.matmul(hidden2, weights) + biases
  return logits

def mse_cost(logits, outputs):
  mse = tf.reduce_mean(tf.pow(tf.sub(logits, outputs), 2.0))
  return mse

def crossentropy_cost(logits,outputs):
    #crossentropy = -tf.reduce_sum(logits*tf.log(outputs))
    crossentropy = tf.reduce_mean(outputs*tf.log(logits) + (1-outputs)*tf.log(1-logits))
    return crossentropy

def training_simplificado(loss, learning_rate):
    #tf.scalar_summary(loss.op.name, loss)
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    train_op = optimizer.minimize(loss)
    return train_op

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, outputs):
    mse = tf.reduce_mean(tf.pow(tf.sub(logits, outputs), 2.0))
    return mse