Question

我正在尝试为单词识别构建CNN+LSTM+CTC模型。
我最初有一个图像，我正在转换使用CNN在单词图像上提取的功能，并构建一系列功能，我将这些功能作为顺序数据传递给RNN。

Follwing是我将功能转换为顺序数据的方式：
[[a1,b1,c1],[a2,b2,c2],[a3,b3,c3]] -> [[a1,a2,a3],[b1,b2,b3],[c1,c2,c3]]
其中a,b,c是使用CNN提取的3个要素。
目前，我可以将常量batch_size传递给模型common.BATCH_SIZE，但我想要的是能够将变量batch_size传递给模型。
怎么办呢？

inputs = tf.placeholder(tf.float32, [common.BATCH_SIZE, common.OUTPUT_SHAPE[1], common.OUTPUT_SHAPE[0], 1])
# Here we use sparse_placeholder that will generate a
# SparseTensor required by ctc_loss op.
targets = tf.sparse_placeholder(tf.int32)

# 1d array of size [batch_size]
seq_len = tf.placeholder(tf.int32, [common.BATCH_SIZE])

model = tf.layers.conv2d(inputs, 64, (3,3),strides=(1, 1), padding='same', name='c1')
model = tf.layers.max_pooling2d(model, (3,3), strides=(2,2), padding='same', name='m1')
model = tf.layers.conv2d(model, 128,(3,3), strides=(1, 1), padding='same', name='c2')
model = tf.layers.max_pooling2d(model, (3,3),strides=(2,2), padding='same', name='m2')
model = tf.transpose(model, [3,0,1,2])
shape = model.get_shape().as_list()
model = tf.reshape(model, [shape[0],-1,shape[2]*shape[3]])

cell = tf.nn.rnn_cell.LSTMCell(common.num_hidden, state_is_tuple=True)
cell = tf.nn.rnn_cell.DropoutWrapper(cell, input_keep_prob=0.5, output_keep_prob=0.5)
stack = tf.nn.rnn_cell.MultiRNNCell([cell]*common.num_layers, state_is_tuple=True)

outputs, _ = tf.nn.dynamic_rnn(cell, model, seq_len, dtype=tf.float32,time_major=True)

UPDATE：

batch_size = tf.placeholder(tf.int32, None, name='batch_size')

inputs = tf.placeholder(tf.float32, [batch_size, common.OUTPUT_SHAPE[1], common.OUTPUT_SHAPE[0], 1])
# Here we use sparse_placeholder that will generate a
# SparseTensor required by ctc_loss op.
targets = tf.sparse_placeholder(tf.int32)

# 1d array of size [batch_size]
seq_len = tf.placeholder(tf.int32, [batch_size])

model = tf.layers.conv2d(inputs, 64, (3,3),strides=(1, 1), padding='same', name='c1')
model = tf.layers.max_pooling2d(model, (3,3), strides=(2,2), padding='same', name='m1')
model = tf.layers.conv2d(model, 128,(3,3), strides=(1, 1), padding='same', name='c2')
model = tf.layers.max_pooling2d(model, (3,3),strides=(2,2), padding='same', name='m2')
model = tf.transpose(model, [3,0,1,2])
shape = model.get_shape().as_list()
model = tf.reshape(model, [shape[0],-1,shape[2]*shape[3]])

cell = tf.nn.rnn_cell.LSTMCell(common.num_hidden, state_is_tuple=True)
cell = tf.nn.rnn_cell.DropoutWrapper(cell, input_keep_prob=0.5, output_keep_prob=0.5)
stack = tf.nn.rnn_cell.MultiRNNCell([cell]*common.num_layers, state_is_tuple=True)

outputs, _ = tf.nn.dynamic_rnn(cell, model, seq_len, dtype=tf.float32,time_major=True)

我收到如下错误：

    Traceback (most recent call last):
  File "lstm_and_ctc_ocr_train.py", line 203, in <module>
    train()
  File "lstm_and_ctc_ocr_train.py", line 77, in train
    logits, inputs, targets, seq_len, batch_size = model.get_train_model()
  File "/home/himanshu/learning-tf/tf/code/tensorflow_lstm_ctc_ocr/model.py", line 20, in get_train_model
inputs = tf.placeholder(tf.float32, [batch_size, common.OUTPUT_SHAPE[1], common.OUTPUT_SHAPE[0], 1])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 1530, in placeholder
    return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1954, in _placeholder
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 705, in apply_op
    attr_value.shape.CopyFrom(_MakeShape(value, key))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 198, in _MakeShape
    return tensor_shape.as_shape(v).as_proto()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 798, in as_shape
    return TensorShape(shape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 434, in __init__
    self._dims = [as_dimension(d) for d in dims_iter]
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 376, in as_dimension
    return Dimension(value)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_shape.py", line 32, in __init__
    self._value = int(value)
TypeError: int() argument must be a string or a number, not 'Tensor'

Answer 1

您应该能够将batch_size作为占位符传递给动态RNN。根据我的经验，您可能遇到的唯一问题是如果您事先没有指定其形状，那么您应该通过[]来使事情有效，如下所示：

batchsize = tf.placeholder（tf.int32，[]，name =＆＃39; batchsize＆＃39;）

然后以常规方式在sess.run（）期间提供其值。这对我来说非常适用于大批量训练，但随后生成批量为1。

但严格来说，您甚至不需要具体指定dynamic_rnn的批量大小，是吗？如果您使用MultiRNNCell获取零状态，则需要它，但我不会在您的代码中看到您这样做...

***更新：

正如评论中所讨论的，您的问题似乎与dynamic_rnn无关，而更多与您使用占位符inputs指定另一个占位符seq_len的形状有关。{ 1}}。这是重现相同错误的代码：

import tensorflow as tf

a = tf.placeholder(tf.int32, None, name='a')
b = tf.placeholder(tf.int32, [a, 5], name='b')
c = b * 5

with tf.Session() as sess:
    C = sess.run(c, feed_dict={a:1, b:[[1,2,3,4,5]]})

这就是错误：

TypeError: int() argument must be a string, a bytes-like object or a number, not 'Tensor'

在遇到dynamic_rnn的问题之前，我建议您通过更改代码或询问有关如何使用占位符来捏造它的单独问题来找到解决方法。

如何将`None`批量大小传递给tensorflow动态rnn？

1 个答案: