我训练了一个模型,目的是生成如下句子:我提供了 2 个序列作为训练示例:x 是字符序列,y 是相同的移位。该模型基于 LSTM,使用 tensorflow 创建。
我的问题是:由于模型采用一定大小的输入序列(在我的情况下为 50),我如何才能做出预测,只给他一个字符作为种子?我在一些示例中看到,在训练后,它们通过简单地输入单个字符来生成句子。
这是我的代码:
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)