python - tensorflow 中 nd 数组输入的占位符定义

Question

我正在尝试根据本指南构建 LSTM RNN: http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/ 我的输入是ndarray，大小为89102*39(89102行，39个特征)。数据有 3 个标签 - 0、1、2 我似乎对占位符定义有疑问，但我不确定它是什么。

我的代码是:

    data = tf.placeholder(tf.float32, [None, 1000, 39])
    target = tf.placeholder(tf.float32, [None, 3])
    cell = tf.nn.rnn_cell.LSTMCell(self.num_hidden)

    val, state = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
    val = tf.transpose(val, [1, 0, 2])
    last = tf.gather(val, int(val.get_shape()[0]) - 1)


    weight = tf.Variable(tf.truncated_normal([self.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 = -tf.reduce_sum(target * tf.log(tf.clip_by_value(prediction, 1e-10, 1.0)))

    optimizer = tf.train.AdamOptimizer()
    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))


    init_op = tf.initialize_all_variables()
    sess = tf.Session()
    sess.run(init_op)
    batch_size = 1000
    no_of_batches = int(len(train_input) / batch_size)
    epoch = 5000
    for i in range(epoch):
        ptr = 0
        for j in range(no_of_batches):
            inp, out = train_input[ptr:ptr + batch_size], train_output[ptr:ptr + batch_size]
            ptr += batch_size
            sess.run(minimize, {data: inp, target: out})
        print( "Epoch - ", str(i))

我遇到了以下错误:

File , line 133, in execute_graph
sess.run(minimize, {data: inp, target: out})

  File "/usr/local/lib/python3.5/dist-
packages/tensorflow/python/client/session.py", line 789, in run
    run_metadata_ptr)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 975, in _run
    % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))

ValueError: Cannot feed value of shape (1000, 39) for Tensor 'Placeholder:0', which has shape '(1000, 89102, 39)'

知道是什么原因导致了这个问题吗？

Answer 1

如图所示here , dynamic_rnn 函数采用形状的批量输入

[batch_size、truncated_backprop_length、input_size]

在您提供的链接中，占位符的形状为

data = tf.placeholder(tf.float32, [None, 20,1]) 

这意味着他们选择了 truncated_backprop_length=20 和 input_size=1。
他们的数据是以下 3D 数组:

[
 array([[0],[0],[1],[0],[0],[1],[0],[1],[1],[0],[0],[0],[1],[1],[1],[1],[1],[1],[0],[0]]), 
 array([[1],[1],[0],[0],[0],[0],[1],[1],[1],[1],[1],[0],[0],[1],[0],[0],[0],[1],[0],[1]]), 
 .....
]

根据您的代码，train_input 似乎是一个 2D 数组，而不是 3D 数组。因此，您需要将其转换为 3D 数组。为此，您需要决定将哪些参数用于 truncated_backprop_length 和 input_size。之后，您需要定义 适本地数据。

例如，如果您希望 truncated_backprop_length 和 input_size 分别为 39 和 1，您可以这样做

import numpy as np
train_input=np.reshape(train_input,(len(train_input),39,1))
data = tf.placeholder(tf.float32, [None, 39,1]) 

我根据上述讨论更改了您的代码，并在我生成的一些随机数据上运行它。它运行时不会抛出错误。请参阅下面的代码:

import tensorflow as tf
import numpy as np
num_hidden=5
train_input=np.random.rand(89102,39)
train_input=np.reshape(train_input,(len(train_input),39,1))
train_output=np.random.rand(89102,3)

data = tf.placeholder(tf.float32, [None, 39, 1])
target = tf.placeholder(tf.float32, [None, 3])
cell = tf.nn.rnn_cell.LSTMCell(num_hidden)

val, state = tf.nn.dynamic_rnn(cell, data, dtype=tf.float32)
val = tf.transpose(val, [1, 0, 2])
last = tf.gather(val, int(val.get_shape()[0]) - 1)


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 = -tf.reduce_sum(target * tf.log(tf.clip_by_value(prediction, 1e-10, 1.0)))

optimizer = tf.train.AdamOptimizer()
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))


init_op = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init_op)
batch_size = 1000
no_of_batches = int(len(train_input) / batch_size)
epoch = 5000
for i in range(epoch):
    ptr = 0
    for j in range(no_of_batches):
        inp, out = train_input[ptr:ptr + batch_size], train_output[ptr:ptr + batch_size]
        ptr += batch_size
        sess.run(minimize, {data: inp, target: out})
    print( "Epoch - ", str(i))