Tensorflow Spatial Attention Mec

2018-11-26 本文已影响0人李2狗子

什么是spatial attention

对于image caption这个任务来说，对于image的特征提取后的Tensor , 记为 V，其维度为 H, W, C 分别表示了height, width, channel, 我们将其变换为height * width = m个向量，然后通过下列的公式来获得各个部分的权重。

$a = tanh((W_sV + b_s) \oplus W_{hs}h_{t-1}),$

$\alpha = softmax(W_i a + b_i)$

所以要实现attention, 先要获得 $\alpha$ , 对于上面的变量

$W_s$ 维度为 k , channel
$V$ 维度为 channel , m(height x width)
$b_s$ 维度为 k
$W_{hs}$ 维度为 k, d
$h_{t-1}$ 维度为 d
$a$ 维度为 k, m
$W_i$ 维度为k
$b_i$ 维度为1

    import tensorflow as tf
    import numpy as np
    
    
    """
    实现 spatial attention, 给定了inputs, 然后返回这个
    """
    
    
    def spatial_attention(inputs, hidden_states, k):
        """spatial attention is to achieve spatial attention mechanism
    
        git a tensor named inputs, which's shape is [batch_size, height, width, channel]
        then return a tensor back with attention.
    
        Arguments:
            inputs: a tensor denote the cnn feature [batch_size, height, width, channel]
            hidden_states: the hidden_states after a decoder step [batch_size, embed_size]
            k: a scalar
        Return:
            outputs:
        """
        inputs_shape = map(lambda x: x.value, inputs.shape)
        original_inputs = tf.identity(inputs, name="identity")
        batch_size, height, width, channel = inputs_shape
        d = hidden_states.shape[-1].value   # hidden_states 的 embedding_size 大小
    
        with tf.variable_scope("spatial_attention_variables"):
            Ws = tf.get_variable("Ws", shape=(k, channel), initializer=tf.random_uniform_initializer(-1, 1))
            bs = tf.get_variable("bs", shape=(k,), initializer=tf.random_uniform_initializer(-0.1, 0.1))
            Whs = tf.get_variable("Whs", shape=(k, d), initializer=tf.random_uniform_initializer(-1, 1))
            Wi = tf.get_variable("Wi", shape=(k, ), initializer=tf.random_uniform_initializer(-1, 1))
            bi = tf.get_variable("bi", shape=(), initializer=tf.random_uniform_initializer(-0.1, 0.1))
    
            # reshape
            inputs = tf.reshape(inputs, shape=(batch_size, -1))
            inputs = tf.reshape(inputs, shape=(batch_size, height * width, channel))
    
            # visual
            inputs = tf.reshape(inputs, shape=(-1, channel))
            visual_com = tf.matmul(inputs, Ws, transpose_b=True)    # bs, m, k
            visual_com = tf.reshape(visual_com, shape=(batch_size, -1, k))
            visual_com = visual_com + bs
    
            # textual
            textual_com = tf.matmul(hidden_states, Whs, transpose_b=True)
            textual_com = tf.expand_dims(textual_com, 1)
            a = tf.nn.tanh(visual_com + textual_com)    # bs, m, k
    
            a_shape = map(lambda x: x.value, a.shape)
            a = tf.reshape(a, shape=(-1, a_shape[-1]))
            spatial_attention = tf.matmul(a, tf.expand_dims(Wi, -1)) + bi  # batch_size, m, 1
            spatial_attention = tf.reshape(spatial_attention, shape=a_shape[:-1])
            spatial_attention = tf.nn.softmax(spatial_attention)          # softmax 默认维度为 -1
    
            spatial_attention = tf.reshape(spatial_attention, (batch_size, height, width))
            output = tf.multiply(original_inputs, tf.expand_dims(spatial_attention, -1))
    
            return spatial_attention, output
    
    
    if __name__ == '__main__':
        """手工构造数据看是否正确"""
        inputs = tf.constant(np.asarray(range(1, 2 * 4 * 4 * 3 + 1), dtype=np.float32).reshape((2, 4, 4, 3)))
        hidden_states = tf.constant(np.asarray(range(97, 97 + 2 * 5), dtype=np.float32).reshape((2, 5)))
        k = 6
        attention, output = spatial_attention(inputs, hidden_states, k)
    
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        session = tf.Session(config=config)
    
        for variable in tf.trainable_variables():
            print variable.name, variable.dtype
    
        initializer = tf.global_variables_initializer()
        session.run(initializer)
    
        for o in session.run([attention, output]):
                print o

上面的代码还不知道能不能work, 下面是输出

[[[0.0625     0.0625     0.0625     0.0625    ]
  [0.0625     0.0625     0.0625     0.0625    ]
  [0.0625     0.0625     0.0625     0.0625    ]
  [0.0625     0.0625     0.0625     0.0625    ]]

 [[0.01733495 0.01733495 0.01733495 0.01733495]
  [0.01733495 0.017335   0.01733827 0.01753132]
  [0.02850685 0.11002292 0.12027094 0.12046136]
  [0.12046459 0.12046465 0.12046465 0.12046465]]]
[[[[ 0.0625      0.125       0.1875    ]
   [ 0.25        0.3125      0.375     ]
   [ 0.4375      0.5         0.5625    ]
   [ 0.625       0.6875      0.75      ]]

  [[ 0.8125      0.875       0.9375    ]
   [ 1.          1.0625      1.125     ]
   [ 1.1875      1.25        1.3125    ]
   [ 1.375       1.4375      1.5       ]]

  [[ 1.5625      1.625       1.6875    ]
   [ 1.75        1.8125      1.875     ]
   [ 1.9375      2.          2.0625    ]
   [ 2.125       2.1875      2.25      ]]

  [[ 2.3125      2.375       2.4375    ]
   [ 2.5         2.5625      2.625     ]
   [ 2.6875      2.75        2.8125    ]
   [ 2.875       2.9375      3.        ]]]


 [[[ 0.8494123   0.86674726  0.8840822 ]
   [ 0.90141714  0.91875213  0.9360871 ]
   [ 0.953422    0.97075695  0.9880919 ]
   [ 1.0054269   1.0227618   1.0400968 ]]

  [[ 1.0574317   1.0747666   1.0921016 ]
   [ 1.1094398   1.1267748   1.1441098 ]
   [ 1.1616641   1.1790024   1.1963407 ]
   [ 1.2271923   1.2447237   1.262255  ]]

  [[ 2.081       2.1095068   2.1380136 ]
   [ 8.361742    8.4717655   8.581788  ]
   [ 9.501404    9.6216755   9.741946  ]
   [ 9.877831    9.998293   10.118754  ]]

  [[10.2394905  10.359955   10.480419  ]
   [10.600889   10.7213545  10.841819  ]
   [10.962283   11.082748   11.203213  ]
   [11.323677   11.444142   11.564607  ]]]]

对于使用到的函数功能的探究

tf.identity

>>> inputs = tf.constant(np.asarray([[1, 2], [3, 4]], dtype=np.float32))
>>> inputs
<tf.Tensor 'Const_8:0' shape=(2, 2) dtype=float32>
>>> input_backup = tf.identity(inputs, name="identity")
>>> session.run([inputs, input_backup])
[array([[1., 2.],
       [3., 4.]], dtype=float32), array([[1., 2.],
       [3., 4.]], dtype=float32)]

tf.matmul

>>> session.run([ta, tb])
[array([[[1., 2.],
        [3., 4.]],

       [[5., 6.],
        [7., 8.]]], dtype=float32), array([[1., 2.],
       [3., 4.]], dtype=float32)]
>>> wrong_matmul = tf.matmul(ta, tb)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jack/venv/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 2018, in matmul
    a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
  File "/home/jack/venv/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 4456, in mat_mul
    name=name)
  File "/home/jack/venv/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/jack/venv/local/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 454, in new_func
    return func(*args, **kwargs)
  File "/home/jack/venv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3155, in create_op
    op_def=op_def)
  File "/home/jack/venv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1731, in __init__
    control_input_ops)
  File "/home/jack/venv/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1579, in _create_c_op
    raise ValueError(str(e))
ValueError: Shape must be rank 2 but is rank 3 for 'MatMul_2' (op: 'MatMul') with input shapes: [2,2,2], [2,2].

正确的做法为

>>> ta_shape = map(lambda x: x.value, ta.shape)
>>> ta_shape
[2, 2, 2]
>>> ta = tf.reshape(ta, shape=(-1, ta_shape[-1]))
>>> ta
<tf.Tensor 'Reshape_3:0' shape=(4, 2) dtype=float32>
>>> correct_matmul = tf.matmul(ta, tb)
>>> session.run([ta, tb, correct_matmul])
[array([[1., 2.],
       [3., 4.],
       [5., 6.],
       [7., 8.]], dtype=float32), 
array([[1., 2.],
       [3., 4.]], dtype=float32), 
array([[ 7., 10.],
       [15., 22.],
       [23., 34.],
       [31., 46.]], dtype=float32)]

>>> correct_matmul = tf.reshape(correct_matmul, shape=ta_shape)
>>> session.run(correct_matmul)
array([[[ 7., 10.],
        [15., 22.]],

       [[23., 34.],
        [31., 46.]]], dtype=float32)

tf.matmul 貌似只能在对于两个同rank的tensor进行matmul.

tf.nn.tanh
Computes hyperbolic tangent of x element-wise.

>>> for i in range(-10, 11):
...     session.run(tf.nn.tanh(float(i)))
... 
-1.0
-1.0
-0.99999976
-0.99999833
-0.99998784
-0.99990916
-0.9993292
-0.9950547
-0.9640276
-0.7615942
0.0
0.7615942
0.9640276
0.9950547
0.9993292
0.99990916
0.99998784
0.99999833
0.99999976
1.0
1.0
>>> session.run(tf.nn.tanh(np.asarray(range(-10, 10), dtype=np.float32).reshape(2, 2, 5)))
array([[[-1.        , -1.        , -0.99999976, -0.99999833,
         -0.99998784],
        [-0.99990916, -0.9993292 , -0.9950547 , -0.9640276 ,
         -0.7615942 ]],

       [[ 0.        ,  0.7615942 ,  0.9640276 ,  0.9950547 ,
          0.9993292 ],
        [ 0.99990916,  0.99998784,  0.99999833,  0.99999976,
          1.        ]]], dtype=float32)

+ 同秩但不同维度的tensor之间的加法运算

>>> a = tf.constant([[1, 2], [3, 4]])
>>> b = tf.constant([5, 6])
>>> a + b
<tf.Tensor 'add_2:0' shape=(2, 2) dtype=int32>
>>> c = a + b
>>> session.run([a, b, c])
[array([[1, 2],
       [3, 4]], dtype=int32), array([5, 6], dtype=int32), array([[ 6,  8],
       [ 8, 10]], dtype=int32)]

>>> c = a + b
>>> session.run([c, a, b])
[array([[[ 1,  2,  3,  4],
        [ 5,  6,  7,  8],
        [ 9, 10, 11, 12]],

       [[11, 12, 13, 14],
        [15, 16, 17, 18],
        [19, 20, 21, 22]]], dtype=int32), 
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],

       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]], dtype=int32), 
array([[[ 1,  1,  1,  1]],

       [[-1, -1, -1, -1]]], dtype=int32)]

a的维度为 $2 \times 3 \times 4$ , b维度为 $2 \times 1 \times 4$ ， $a + b$ 操作是在最后一个维度上展开的，这里的第二个维度是重复的，重复了n次。

tf.nn.softmax 默认的维度为-1

>>> def loge(x):
...     return math.log(x, math.e)
... 
>>> a = tf.constant([[loge(10), loge(5), loge(5)], [loge(20), loge(3), loge(7)]])
>>> a
<tf.Tensor 'Const_17:0' shape=(2, 3) dtype=float32>
>>> b = tf.softmax(a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: 'module' object has no attribute 'softmax'
>>> b = tf.nn.softmax(a)
>>> c = tf.nn.softmax(a, axis=0)
>>> session.run([a, b, c])
[array([[2.3025851, 1.609438 , 1.609438 ],
       [2.9957323, 1.0986123, 1.9459101]], dtype=float32), 
array([[0.5       , 0.25      , 0.25      ],
       [0.6666667 , 0.09999999, 0.23333332]], dtype=float32), 
array([[0.33333334, 0.62500006, 0.4166667 ],
       [0.6666667 , 0.375     , 0.5833333 ]], dtype=float32)]

tf.multiply

>>> a = tf.constant([[1, 2, 3], [4, 5, 6]])
>>> a
<tf.Tensor 'Const_18:0' shape=(2, 3) dtype=int32>
>>> b = tf.constant([-1, 1, -1])
>>> b
<tf.Tensor 'Const_19:0' shape=(3,) dtype=int32>
>>> c = tf.multiply(a, b)
>>> session.run(c)
array([[-1,  2, -3],
       [-4,  5, -6]], dtype=int32)

上面的b 在 a 的最后一个维度上滚动的来乘积，好像乘积和加法一样的规则

>>> b = tf.constant([[-1], [1]])
>>> b
<tf.Tensor 'Const_20:0' shape=(2, 1) dtype=int32>
>>> d = tf.multiply(a, b)
>>> session.run(d)
array([[-1, -2, -3],
       [ 4,  5,  6]], dtype=int32)

可以考虑为在b rank 不到 a rank的情况下，调用 n 次 b = np.expand_dims(b, 0) 来增加b的维度。

Tensorflow Spatial Attention Mec

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