问简单的tensorflow神经网络不提高精度还是减少损失？

EN

graph = tf.Graph()
with graph.as_default():

    tf_train_dataset = tf.constant(X_train)
    tf_train_labels = tf.constant(y_train)
    tf_valid_dataset = tf.constant(X_test)

    weights = tf.Variable(tf.truncated_normal([X_train.shape[1], 1]))

    biases = tf.Variable(tf.zeros([num_labels]))
    logits = tf.nn.softmax(tf.matmul(tf_train_dataset, weights) + biases)

    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))
    optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
    train_prediction = tf.nn.softmax(logits)
    valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)

num_steps = 10

with tf.Session(graph=graph) as session: 
    tf.initialize_all_variables().run()
    print('Initialized')
    for step in range(num_steps):
        _, l, predictions = session.run([optimizer, loss, train_prediction])
        print("Loss: ",l)
        print('Training accuracy: %.1f' % sklearn.metrics.accuracy_score(predictions.flatten(), y_train.flatten()))

Initialized
Loss:  0.0
Training accuracy: 0.5
Loss:  0.0
Training accuracy: 0.5