q1_classifier.py 文件源码

def add_model(self, input_data):
    """Adds a linear-layer plus a softmax transformation

    The core transformation for this model which transforms a batch of input
    data into a batch of predictions. In this case, the mathematical
    transformation effected is

    y = softmax(xW + b)

    Hint: Make sure to create tf.Variables as needed. Also, make sure to use
          tf.name_scope to ensure that your name spaces are clean.
    Hint: For this simple use-case, it's sufficient to initialize both weights W
          and biases b with zeros.

    Args:
      input_data: A tensor of shape (batch_size, n_features).
    Returns:
      out: A tensor of shape (batch_size, n_classes)
    """
    ### YOUR CODE HERE
    n_features, n_classes = self.config.n_features, self.config.n_classes
    with tf.name_scope('softmax_linear'):
      weights = tf.Variable(
          tf.zeros([n_features, n_classes]),
          name='weights')
      biases = tf.Variable(tf.zeros([n_classes]),
                           name='biases')
      logits = tf.matmul(input_data, weights) + biases
      out = softmax(logits)
    ### END YOUR CODE
    return out