Question

我在控制器内部有一些动作。它包含一个Promise对象，在解析时，应该通过controllerAs绑定更新视图。问题是已解析的值是正确的（在控制台中记录），但将其分配给控制器并不会更新视图。它仅在包含该承诺的操作再次执行时更新。因此，如果我按下按钮继续执行它，则更新会延迟一次执行。

我正在使用AngularJS 1.6.5。我没有使用$ q。也没有$ scope，所以请不要申请$。

我的简化控制器有2个成员来比较有和没有承诺的行为：

var vm = this;

vm.textSync = 0;
vm.textAsync = 0;

vm.onclick = function()
{
    var action = new Promise(function(resolve, reject)
    {
        resolve(vm.textAsync + 1);
    });

    action.then(function(result)
    {
        console.log(result);
        vm.textAsync = result;
    });

    vm.textSync++;
};

记录的值等于递增的textSync（1,2,3，...），但是在eack点击后，视图中的textAsync等于（textSync-1）（所以它是0,1,1 ，2，...）。为什么这样以及如何解决它？

我的简化观点：

<input type="button" value="Click me" ng-click="$ctrl.onclick()" />
<input ng-model="$ctrl.textSync" />
<input ng-model="$ctrl.textAsync" />

Answer 1

由于你不想触发摘要周期，你可以使用$ timeout和0秒来等待摘要周期，所以请像这样包装你的承诺，只需在函数后省略0 - 不必要：

# set variables
num_epochs = 15
tweet_size = 20
hidden_size = 200
vec_size = 300
batch_size = 512
number_of_layers= 1
number_of_classes= 3
learning_rate = 0.001

TRAIN_DIR="/checkpoints"

tf.reset_default_graph()

# Create a session 
session = tf.Session()

# Inputs placeholders
tweets = tf.placeholder(tf.float32, [None, tweet_size, vec_size], "tweets")
labels = tf.placeholder(tf.float32, [None, number_of_classes], "labels")

# Placeholder for dropout
keep_prob = tf.placeholder(tf.float32) 

# make the lstm cells, and wrap them in MultiRNNCell for multiple layers
def lstm_cell():
    cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
    return tf.contrib.rnn.DropoutWrapper(cell=cell, output_keep_prob=keep_prob)

multi_lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(number_of_layers)], state_is_tuple=True)

# Creates a recurrent neural network
outputs, final_state = tf.nn.dynamic_rnn(multi_lstm_cells, tweets, dtype=tf.float32)

with tf.name_scope("final_layer"):
    # weight and bias to shape the final layer
    W = tf.get_variable("weight_matrix", [hidden_size, number_of_classes], tf.float32, tf.random_normal_initializer(stddev=1.0 / math.sqrt(hidden_size)))
    b = tf.get_variable("bias", [number_of_classes], initializer=tf.constant_initializer(1.0))

    sentiments = tf.matmul(final_state[-1][-1], W) + b

prob = tf.nn.softmax(sentiments)
tf.summary.histogram('softmax', prob)

with tf.name_scope("loss"):
    # define cross entropy loss function
    losses = tf.nn.softmax_cross_entropy_with_logits(logits=sentiments, labels=labels)
    loss = tf.reduce_mean(losses)
    tf.summary.scalar("loss", loss)

with tf.name_scope("accuracy"):
    # round our actual probabilities to compute error
    accuracy = tf.to_float(tf.equal(tf.argmax(prob,1), tf.argmax(labels,1)))
    accuracy = tf.reduce_mean(tf.cast(accuracy, dtype=tf.float32))
    tf.summary.scalar("accuracy", accuracy)

# define our optimizer to minimize the loss
with tf.name_scope("train"):
    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

#tensorboard summaries
merged_summary = tf.summary.merge_all()
logdir = "tensorboard/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + "/"
writer = tf.summary.FileWriter(logdir, session.graph)

# initialize any variables
tf.global_variables_initializer().run(session=session)

# Create a saver for writing training checkpoints.
saver = tf.train.Saver()

# load our data and separate it into tweets and labels
train_tweets = np.load('data_es/train_vec_tweets.npy')
train_labels = np.load('data_es/train_vec_labels.npy')

test_tweets = np.load('data_es/test_vec_tweets.npy')
test_labels = np.load('data_es/test_vec_labels.npy')

**HERE I HAVE THE LOOP FOR TRAINING AND TESTING, I KNOW ITS FINE**

解析promise时，AngularJS延迟了视图更新

1 个答案: