Question

我正在为正在使用的网络使用LSTMBlockFusedCell。当输入单个大小为[6，3169]的输入和一个输出为[-1，3169]的输入（转换为输入大小）时，它将正常运行并进行预测。当我尝试批量输入相同的输入时，问题就来了。批量为100时，输入将重塑良好，但输出将广播到[600，3169]中。我尝试将占位符规范完全设置为指定的输入长度，但是发生了相同的错误。我非常有信心自己的数据格式正确。我运行批处理生成器，然后打印输出大小。

这是我的网络：

def rnn(x, weight, bias, n_input, vocab):
    x = tf.reshape(x, [-1, n_input, vocab])
    rnn_cell = tf.contrib.rnn.LSTMBlockFusedCell(n_hidden)
    outputs, states = rnn_cell(x, dtype=tf.float32)
    return tf.matmul(outputs[-1], weight['output']) + bias['output']

我的批处理生成器：

def new_data(dat, dic, n_steps, batch_size=100):
    x = np.zeros(shape=(batch_size, n_steps, len(dic)))
    y = np.zeros(shape=(batch_size, n_steps, len(dic)))
    j = 0
    x_dat = np.zeros(shape=(n_steps, len(dic)))
    for sen in dat:
        if len(sen) - 1 > n_steps:
            for i, word in enumerate(sen[0:n_steps]):
                x_dat[i] = one_hot(word, dic)
            y_dat = one_hot(sen[n_steps], dic)
            x[j % batch_size] = x_dat
            y[j % batch_size] = y_dat
            if j % batch_size == 0:
                yield x,y
                x = np.zeros(shape=(batch_size, n_steps, len(dic)))
                y = np.zeros(shape=(batch_size, n_steps, len(dic)))
            j += 1

和我的设置：

X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
weights = {
    "output" : tf.Variable(tf.random_normal([n_hidden, vocab_size]),name="weight_output")
}

bias = {
    "output" : tf.Variable(tf.random_normal([vocab_size]), name="bias_output")
}
pred = rnn(X, weights, bias, n_input, vocab)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=Y))

with tf.Session() as sess:
    sess.run(init)
    step = 0
    for epoch in range(n_epochs):
        for x,y in new_data(dat, dic, n_steps):

            _ , c = sess.run([optimizer, cost], feed_dict={X: x ,Y: y})

Tensorflow：批处理无法正确成型标签

0 个答案: