Question

我目前正在尝试训练这个RNN网络，但似乎遇到了奇怪的错误，我无法解码。

我的网络输入是数字采样音频文件。由于音频文件可以具有不同的长度，因此采样音频的矢量也具有不同的长度。

神经网络的输出或目标是重新创建包含音频文件的某些信息的14维向量。我已经通过手动计算它已经知道了目标，但需要让它与神经网络一起工作。

我目前正在使用tensorflow作为框架。

我的网络设置如下：

def last_relevant(output):
     max_length = int(output.get_shape()[1])
     relevant = tf.reduce_sum(tf.mul(output, tf.expand_dims(tf.one_hot(length, max_length), -1)), 1)
     return relevant

def length(sequence): ##Zero padding to fit the max lenght... Question whether that is a good idea.
    used = tf.sign(tf.reduce_max(tf.abs(sequence), reduction_indices=2))
    length = tf.reduce_sum(used, reduction_indices=1)
    length = tf.cast(length, tf.int32)
    return length

def cost(output, target):
    # Compute cross entropy for each frame.
    cross_entropy = target * tf.log(output)
    cross_entropy = -tf.reduce_sum(cross_entropy, reduction_indices=2)
    mask = tf.sign(tf.reduce_max(tf.abs(target), reduction_indices=2))
    cross_entropy *= mask
    # Average over actual sequence lengths.
    cross_entropy = tf.reduce_sum(cross_entropy, reduction_indices=1)
    cross_entropy /= tf.reduce_sum(mask, reduction_indices=1)
    return tf.reduce_mean(cross_entropy)
#----------------------------------------------------------------------#
#----------------------------Main--------------------------------------#
### Tensorflow neural network setup

batch_size = None
sequence_length_max = max_length
input_dimension=1

data = tf.placeholder(tf.float32,[batch_size,sequence_length_max,input_dimension])
target = tf.placeholder(tf.float32,[None,14])

num_hidden = 24 ## Hidden layer
cell = tf.nn.rnn_cell.LSTMCell(num_hidden,state_is_tuple=True)  ## Long short term memory

output, state = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32,sequence_length = length(data))  ## Creates the Rnn skeleton

last = last_relevant(output)#tf.gather(val, int(val.get_shape()[0]) - 1) ## Appedning as last

weight = tf.Variable(tf.truncated_normal([num_hidden, int(target.get_shape()[1])]))
bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]]))

prediction = tf.nn.softmax(tf.matmul(last, weight) + bias)

cross_entropy = cost(output,target)# How far am I from correct value?

optimizer = tf.train.AdamOptimizer() ## TensorflowOptimizer
minimize = optimizer.minimize(cross_entropy)

mistakes = tf.not_equal(tf.argmax(target, 1), tf.argmax(prediction, 1))
error = tf.reduce_mean(tf.cast(mistakes, tf.float32))

## Training ##

init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init_op)

    batch_size = 1000
    no_of_batches = int(len(train_data)/batch_size)
    epoch = 5000
    for i in range(epoch):
        ptr = 0
        for j in range(no_of_batches):
            inp, out = train_data[ptr:ptr+batch_size], train_output[ptr:ptr+batch_size]
            ptr+=batch_size
            sess.run(minimize,{data: inp, target: out})
        print "Epoch - ",str(i)
    incorrect = sess.run(error,{data: test_data, target: test_output})
    print('Epoch {:2d} error {:3.1f}%'.format(i + 1, 100 * incorrect))
    sess.close()

错误似乎是函数last_relevant的用法，它应该接受输出并将其反馈。

这是错误消息：

TypeError: Expected binary or unicode string, got <function length at 0x7f846594dde8>

无论如何要说出这里可能出现的问题？

Answer 1

我试图在我的本地构建你的代码。代码中存在一个根本性的错误，即您调用tf.one_hot，但是您传递的内容并不符合预期：

阅读文档： https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.one_hot.md

tf.one_hot(indices, depth, on_value=None, off_value=None, axis=None, dtype=None, name=None)

但是，您传递的是函数指针（＆＃34;长度＆＃34;是代码中的函数，我建议通过避免使用常用关键字以有意义的方式命名函数）而不是第一个参数。

对于狂野指南，您可以将索引作为第一个参数（而不是我的占位符空列表）并将其修复

relevant = tf.reduce_sum(
      tf.mul(output, tf.expand_dims(tf.one_hot([], max_length), -1)), 1)

Tensorflow RNN培训不会执行？

1 个答案: