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
    #raise NotImplementedError

    self.W = tf.Variable(tf.zeros([self.config.n_features, self.config.n_classes]), tf.float32, name="weight")
    self.b = tf.Variable(tf.zeros([self.config.batch_size, self.config.n_classes]), tf.float32, name="bias")
    out = softmax(tf.matmul(input_data, self.W) + self.b)

    ### END YOUR CODE
    return out