old_contents: """
  Author: @gabvaztor
  StartDate: 04/03/2017
  
  This file contains samples and overrides deep learning algorithms.
  """
  
  """
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  # IMPORTS
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  """
  
  '''LOCAL IMPORTS
  '''
  
  import UsefulTools.UtilsFunctions as uf
  from TFBoost.TFEncoder import Dictionary as dict
  
  ''' TensorFlow: https://www.tensorflow.org/
  To upgrade TensorFlow to last version:
  *CPU: pip3 install --upgrade tensorflow
  *GPU: pip3 install --upgrade tensorflow-gpu
  '''
  import tensorflow as tf
  print("TensorFlow: " + tf.__version__)
  
  
  ''' Numpy is an extension to the Python programming language, adding support for large,
  multi-dimensional arrays and matrices, along with a large library of high-level
  mathematical functions to operate on these arrays.
  It is mandatory to install 'Numpy+MKL' before scipy.
  Install 'Numpy+MKL' from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
  http://www.numpy.org/
  https://en.wikipedia.org/wiki/NumPy '''
  import numpy as np
  
  '''
  # You need to install the 64bit version of Scipy, at least on Windows.
  # It is mandatory to install 'Numpy+MKL' before scipy.
  # http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
  # We can find scipi in the url: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy'''
  import scipy.io as sio
  
  ''' Matlab URL: http://matplotlib.org/users/installing.html'''
  import matplotlib.pyplot as plt
  
  
  
  """
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  # ---- GLOBAL VARIABLES ----
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  """
  
  class Models():
      # TODO
      """
      This class
      """
  
      def lineal_model(self, input, test, input_labels, test_labels, number_of_classes,
                       dtype = None ,validation = None, validation_labels = None ):
          """
  
          :param input:
          :param validation:
          :param test:
          :param dtype:
          :return:
          """
          # TODO
  
          x = tf.placeholder(shape=[None,number_of_classes])
  
          W = tf.Variable(tf.zeros([784, 10]))
          b = tf.Variable(tf.zeros([10]))
  
  
new_contents: """
  Author: @gabvaztor
  StartDate: 04/03/2017
  
  This file contains samples and overrides deep learning algorithms.
  """
  
  """
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  # IMPORTS
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  """
  
  '''LOCAL IMPORTS
  '''
  
  import UsefulTools.UtilsFunctions as uf
  from TFBoost.TFEncoder import Dictionary as dict
  
  ''' TensorFlow: https://www.tensorflow.org/
  To upgrade TensorFlow to last version:
  *CPU: pip3 install --upgrade tensorflow
  *GPU: pip3 install --upgrade tensorflow-gpu
  '''
  import tensorflow as tf
  print("TensorFlow: " + tf.__version__)
  
  
  ''' Numpy is an extension to the Python programming language, adding support for large,
  multi-dimensional arrays and matrices, along with a large library of high-level
  mathematical functions to operate on these arrays.
  It is mandatory to install 'Numpy+MKL' before scipy.
  Install 'Numpy+MKL' from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
  http://www.numpy.org/
  https://en.wikipedia.org/wiki/NumPy '''
  import numpy as np
  
  '''
  # You need to install the 64bit version of Scipy, at least on Windows.
  # It is mandatory to install 'Numpy+MKL' before scipy.
  # http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
  # We can find scipi in the url: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy'''
  import scipy.io as sio
  
  ''' Matlab URL: http://matplotlib.org/users/installing.html'''
  import matplotlib.pyplot as plt
  
  
  
  """
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  # ---- GLOBAL VARIABLES ----
  # --------------------------------------------------------------------------
  # --------------------------------------------------------------------------
  """
  
  class Models():
      # TODO
      """
      This class
      """
  
      def lineal_model_basic_with_gradient_descent(self, input, test, input_labels, test_labels,number_of_inputs,number_of_classes,
                                        learning_rate = 0.001, type = None ,validation = None,
                                        validation_labels = None, deviation = None):
          """
          This method doesn't do softmax.
          :param input: Input data
          :param validation: Validation data
          :param test: Test data
          :param type: Type of data (float32, float16, ...)
          :param number_of_inputs: Represents the number of records in input data
          :param number_of_classes: Represents the number of labels in data
          :param deviation: Number of the deviation for the weights and bias
          :return:
          """
  
          x = tf.placeholder(shape=[None,number_of_classes])
          y_ = tf.placeholder([None, number_of_classes])
  
          W = tf.Variable(tf.zeros([number_of_inputs, number_of_classes]))
          b = tf.Variable(tf.zeros([number_of_classes]))
          y = tf.matmul(x, W) + b
  
          cross_entropy_lineal = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y,y_))
          train_step_Lineal = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy_lineal)
  
          # TODO Error
subject: Create Model with comment and without calculating error