1

我想设计这个损失函数：

sum((y[argmax(y_)] - y_[argmax(y_)])²)

我找不到办法y[argmax(y_)]。我试过了y[k]，y[:,k]但y[None,k]这些都不起作用。这是我的代码： 

    Na = 3
    x = tf.placeholder(tf.float32, [None, 2])
    W = tf.Variable(tf.zeros([2, Na]))
    b = tf.Variable(tf.zeros([Na]))
    y = tf.nn.relu(tf.matmul(x, W) + b)
    y_ = tf.placeholder(tf.float32, [None, 3])
    k = tf.argmax(y_, 1)
    diff = y[k] - y_[k]
    loss = tf.reduce_sum(tf.square(diff))

和错误：

  File "/home/ncarrara/phd/code/cython/robotnavigation/ftq/cftq19.py", line 156, in <module>
    diff = y[k] - y_[k]
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 499, in _SliceHelper
    name=name)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/ops/array_ops.py", line 663, in strided_slice
    shrink_axis_mask=shrink_axis_mask)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 3515, in strided_slice
    shrink_axis_mask=shrink_axis_mask, name=name)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2508, in create_op
    set_shapes_for_outputs(ret)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1873, in set_shapes_for_outputs
    shapes = shape_func(op)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1823, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn
    debug_python_shape_fn, require_shape_fn)
  File "/home/ncarrara/miniconda3/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 676, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Shape must be rank 1 but is rank 2 for 'strided_slice' (op: 'StridedSlice') with input shapes: [?,3], [1,?], [1,?], [1].
4

1 回答 1

4

这可以使用tf.gather_nd：

import tensorflow as tf

Na = 3
x = tf.placeholder(tf.float32, [None, 2])
W = tf.Variable(tf.zeros([2, Na]))
b = tf.Variable(tf.zeros([Na]))
y = tf.nn.relu(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 3])
k = tf.argmax(y_, 1)
# Make index tensor with row and column indices
num_examples = tf.cast(tf.shape(x)[0], dtype=k.dtype)
idx = tf.stack([tf.range(num_examples), k], axis=-1)
diff = tf.gather_nd(y, idx) - tf.gather_nd(y_, idx)
loss = tf.reduce_sum(tf.square(diff))

解释：

在这种情况下， 的想法tf.gather_nd是制作一个矩阵（二维张量），其中每一行包含输出中的行和列的索引。例如，如果我有一个a包含以下内容的矩阵：

| 1 2 3 |
| 4 5 6 |
| 7 8 9 |

一个矩阵i包含：

| 1 2 |
| 0 1 |
| 2 2 |
| 1 0 |

然后的结果tf.gather_nd(a, i)将是包含以下内容的向量（一维张量）：

| 6 |
| 2 |
| 9 |
| 4 |

在这种情况下，列索引由tf.argmaxin给出k；它告诉你，对于每一行，哪一列是具有最高值的。现在您只需要将行索引与其中的每一个一起放置。第一个元素k是第 0 行中最大值列的索引，下一个元素是第 1 行的索引，依此类推。num_examples只是其中的行数，x然后tf.range(num_examples)为您提供一个从 0 到x负 1 的行数的向量（即所有行索引序列）。现在你只需要把它和 放在一起k，这就是tf.stack做的，结果idx就是 的参数tf.gather_nd。

于 2017-08-23T10:51:49.987 回答