在使用 TFlearn 构建深度神经网络后，我想计算网络的准确率。

以下是代码：

def create_model(self):    x = tf.placeholder(dtype= tf.float32, shape=[None, 6], name='x')    # Build neural network    input_layer = tflearn.input_data(shape=[None, 6])    net = input_layer    net = tflearn.fully_connected(net, 128, activation='relu')    net = tflearn.fully_connected(net, 64, activation='relu')    net = tflearn.fully_connected(net, 16, activation='relu')    net = tflearn.fully_connected(net, 2, activation='sigmoid')    net = tflearn.regression(net, optimizer='adam', loss='mean_square', metric='R2')    w = tf.Variable(tf.truncated_normal([2, 2], stddev=0.1))    b = tf.Variable(tf.constant(1.0, shape=[2]))    y = tf.nn.softmax(tf.matmul(net, w) + b, name='y')    model = tflearn.DNN(net, tensorboard_verbose=3)    return model

以下是训练部分：

train_data, train_goal, test_data, test_goal = self.normalize_data()        model = self.create_model()        # 使用训练集训练模型，并在测试集上评估        model.fit(train_data, train_goal, validation_set=0.2, n_epoch=10, show_metric=True, snapshot_epoch=True)        result = model.evaluate(test_data, test_goal)

我如何计算准确率？另外，要将其改为分类问题，我应该做哪些更改？谢谢

回答：

你可以这样做：

def create_model(self):    x = tf.placeholder(dtype= tf.float32, shape=[None, 6], name='x')    # Build neural network    input_layer = tflearn.input_data(shape=[None, 6])    net = input_layer    net = tflearn.fully_connected(net, 128, activation='relu')    net = tflearn.fully_connected(net, 64, activation='relu')    net = tflearn.fully_connected(net, 16, activation='relu')    net = tflearn.fully_connected(net, 2, activation='sigmoid')    net = tflearn.regression(net, optimizer='adam', loss='mean_square', metric='R2')    w = tf.Variable(tf.truncated_normal([2, 2], stddev=0.1))    b = tf.Variable(tf.constant(1.0, shape=[2]))    y = tf.nn.softmax(tf.matmul(net, w) + b, name='y')    return ynetwork = create_model()net = tflearn.regression(network, optimizer='RMSprop', metric='accuracy', loss='categorical_crossentropy')model = tflearn.DNN(net, show_metric=True, tensorboard_verbose=3)