Machine learning 使用经过训练的字符级LSTM模型生成文本

我训练了一个模型，目的是生成以下句子： 作为训练示例，我输入了2个序列：x是字符序列，y是相同的移位。该模型基于LSTM，并使用tensorflow创建
我的问题是：既然模型接受一定大小的输入序列（在我的例子中是50个），那么我如何才能预测，只给他一个字符作为种子？我在一些例子中看到，经过训练后，他们只需输入一个字符就可以造句
这是我的密码：

    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [batch_size, truncated_backprop], name='x')
        y = tf.placeholder(tf.int32, [batch_size, truncated_backprop], name='y')

    with tf.name_scope('weights'):
        W = tf.Variable(np.random.rand(n_hidden, num_classes), dtype=tf.float32)
        b = tf.Variable(np.random.rand(1, num_classes), dtype=tf.float32)

    inputs_series = tf.split(x, truncated_backprop, 1)
    labels_series = tf.unstack(y, axis=1)

    with tf.name_scope('LSTM'):
        cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, state_is_tuple=True)
        cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=dropout)
        cell = tf.contrib.rnn.MultiRNNCell([cell] * n_layers)

    states_series, current_state = tf.contrib.rnn.static_rnn(cell, inputs_series, \
        dtype=tf.float32)

    logits_series = [tf.matmul(state, W) + b for state in states_series]
    prediction_series = [tf.nn.softmax(logits) for logits in logits_series]

    losses = [tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels) \
        for logits, labels, in zip(logits_series, labels_series)]
    total_loss = tf.reduce_mean(losses)

    train_step = tf.train.AdamOptimizer(learning_rate).minimize(total_loss)

我建议您使用而不是

static\u rnn

，它在执行期间创建图形，并允许您拥有任意长度的输入。您的输入占位符将是

x = tf.placeholder(tf.float32, [batch_size, None, features], name='x')

接下来，您需要一种将自己的初始状态输入网络的方法。您可以通过将

初始状态

参数传递到

动态

来实现这一点，如：

initialstate = cell.zero_state(batch_sie, tf.float32)
outputs, current_state = tf.nn.dynamic_rnn(cell,
                                           inputs,
                                           initial_state=initialstate)

这样，为了从单个字符生成文本，您可以一次向图形输入1个字符，每次传入前一个字符和状态，如：

prompt = 's' # beginning character, whatever
inp = one_hot(prompt) # preprocessing, as you probably want to feed one-hot vectors
state = None
while True:
    if state is None:
        feed = {x: [[inp]]}
    else:
        feed = {x: [[inp]], initialstate: state}

    out, state = sess.run([outputs, current_state], feed_dict=feed)

    inp = process(out) # extract the predicted character from out and one-hot it

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多谢各位。动态RNN的技巧非常巧妙。现在清楚多了。