q1_classifier.py 文件源码

def add_prediction_op(self):
        """Adds the core transformation for this model which transforms a batch of input
        data into a batch of predictions. In this case, the transformation is a linear layer plus a
        softmax transformation:

        y = softmax(Wx + b)

        Hint: Make sure to create tf.Variables as needed.
        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:
            pred: A tensor of shape (batch_size, n_classes)
        """
        ### YOUR CODE HERE
        W = tf.Variable(tf.zeros((self.config.n_features, self.config.n_classes)))
        b = tf.Variable(tf.zeros((self.config.n_classes)))
        pred = softmax(tf.matmul(self.input_placeholder, W) + b)

        ### END YOUR CODE
        return pred