parameters: (self, hidden1_units, hidden2_units)
docstring: Create the model parameters.
docstring_summary: Create the model parameters.
docstring_tokens: Create the model parameters .
function: def __init__(self, hidden1_units, hidden2_units):
      """Create the model parameters."""
      self.params = []
      # Hidden 1
      with tf.name_scope('hidden1'):
        self.weights1 = tf.Variable(
            np.random.normal(scale=1.0 / np.sqrt(float(IMAGE_PIXELS)),
                             size=[IMAGE_PIXELS, hidden1_units]),
            dtype=tf.float32,
            name='weights')
        self.biases1 = tf.Variable(
            np.zeros([hidden1_units]),
            dtype=tf.float32,
            name='biases')
      # Hidden 2
      with tf.name_scope('hidden2'):
        self.weights2 = tf.Variable(
            np.random.normal(scale=1.0 / np.sqrt(float(hidden1_units)),
                             size=[hidden1_units, hidden2_units]),
            dtype=tf.float32,
            name='weights')
      self.biases2 = tf.Variable(
          np.zeros([hidden2_units]),
          dtype=tf.float32,
          name='biases')
      # Linear
      with tf.name_scope('softmax_linear'):
        self.sm_w = tf.Variable(
            np.random.normal(scale=1.0 / np.sqrt(float(hidden2_units)),
                             size=[hidden2_units, NUM_CLASSES]),
            dtype=tf.float32,
            name='weights')
        self.sm_b = tf.Variable(
            np.zeros([NUM_CLASSES]),
            dtype=tf.float32,
            name='biases')
      self.params = [self.weights1, self.biases1,
                     self.weights2, self.biases2,
                     self.sm_w, self.sm_b]
function_tokens: def __init__ ( self , hidden1_units , hidden2_units ) : self . params = [ ] # Hidden 1 with tf . name_scope ( 'hidden1' ) : self . weights1 = tf . Variable ( np . random . normal ( scale = 1.0 / np . sqrt ( float ( IMAGE_PIXELS ) ) , size = [ IMAGE_PIXELS , hidden1_units ] ) , dtype = tf . float32 , name = 'weights' ) self . biases1 = tf . Variable ( np . zeros ( [ hidden1_units ] ) , dtype = tf . float32 , name = 'biases' ) # Hidden 2 with tf . name_scope ( 'hidden2' ) : self . weights2 = tf . Variable ( np . random . normal ( scale = 1.0 / np . sqrt ( float ( hidden1_units ) ) , size = [ hidden1_units , hidden2_units ] ) , dtype = tf . float32 , name = 'weights' ) self . biases2 = tf . Variable ( np . zeros ( [ hidden2_units ] ) , dtype = tf . float32 , name = 'biases' ) # Linear with tf . name_scope ( 'softmax_linear' ) : self . sm_w = tf . Variable ( np . random . normal ( scale = 1.0 / np . sqrt ( float ( hidden2_units ) ) , size = [ hidden2_units , NUM_CLASSES ] ) , dtype = tf . float32 , name = 'weights' ) self . sm_b = tf . Variable ( np . zeros ( [ NUM_CLASSES ] ) , dtype = tf . float32 , name = 'biases' ) self . params = [ self . weights1 , self . biases1 , self . weights2 , self . biases2 , self . sm_w , self . sm_b ]