tensorflow 笔记8：RNN、Lstm源码，训练代码输入输出，维度分析

tensorflow 官网信息：https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/BasicLSTMCell
tensorflow 版本：1.10
如有错误还望指正，一起探讨；
当前层各个参数含义：
Tensorflow 中RNN单个时刻计算流程：
Tensorflow 中 lstm 单个时刻计算流程：
注：上面计算[H，X] * W后和B维度不同，如何相加，解释如下；
tensorflow代码中，用的这个 nn_ops.bias_add（gate_inputs, self._bias）,这个函数的计算方法是，让每个 batch 的输出值，都加上这个 B；
所以维度不同可以相加：【batch_size，Hidden_size】，【Hidden_size】，见函数演示：nn_ops.bias_add
tensorflow 代码分析：见如下
tensorflow version：1.9
注：以下是一个batch，一个时刻的计算，若计算所有时刻，则循环执行以下代码，num_step(句长)次; tensorflow 已经封装好了，不需要我们写；
  1 RNN 关键代码:
  2 @tf_export("nn.rnn_cell.BasicRNNCell")
  3 class BasicRNNCell(LayerRNNCell):
  4   """The most basic RNN cell.
  5   Args:
  6     num_units: int, The number of units in the RNN cell.
  7     activation: Nonlinearity to use.  Default: `tanh`.
  8     reuse: (optional) Python boolean describing whether to reuse variables
  9      in an existing scope.  If not `True`, and the existing scope already has
 10      the given variables, an error is raised.
 11     name: String, the name of the layer. Layers with the same name will
 12       share weights, but to avoid mistakes we require reuse=True in such
 13       cases.
 14     dtype: Default dtype of the layer (default of `None` means use the type
 15       of the first input). Required when `build` is called before `call`.
 16   """
 17 
 18   def __init__(self,
 19                num_units,
 20                activation=None,
 21                reuse=None,
 22                name=None,
 23                dtype=None):
 24     super(BasicRNNCell, self).__init__(_reuse=reuse, name=name, dtype=dtype)
 25 
 26     # Inputs must be 2-dimensional.
 27     self.input_spec = base_layer.InputSpec(ndim=2)
 28 
 29     self._num_units = num_units
 30     self._activation = activation or math_ops.tanh
 31 
 32   @property
 33   def state_size(self):
 34     return self._num_units
 35 
 36   @property
 37   def output_size(self):
 38     return self._num_units
 39 
 40   def build(self, inputs_shape):
 41     if inputs_shape[1].value is None:
 42       raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s"
 43                        % inputs_shape)
 44 
 45     input_depth = inputs_shape[1].value
 46     
 47     # 初始化生成 W 和 B，shape 大小为 
 48     # W: [input_size + Hidden_size, Hidden_size)
 49     # B: [Hidden_size]
 50     self._kernel = self.add_variable(
 51         _WEIGHTS_VARIABLE_NAME,
 52         shape=[input_depth + self._num_units, self._num_units])
 53     self._bias = self.add_variable(
 54         _BIAS_VARIABLE_NAME,
 55         shape=[self._num_units],
 56         initializer=init_ops.zeros_initializer(dtype=self.dtype))
 57 
 58     self.built = True
 59   # 循环该函数 num_step(句子长度) 次，则该层计算完；
 60   def call(self, inputs, state):
 61     """Most basic RNN: output = new_state = act(W * input + U * state + B)."""
 62     # output = Ht = tanh([x,Ht-1]*W + B)
 63     # 如果是第 0 时刻，那么当前的 state(即上一时刻的输出H0)的值全部为0；
 64     # input 的 shape为: [batch_size,emb_size]
 65     # state 的 shape为：[batch_zize,Hidden_size]
 66     # matmul : 矩阵相乘
 67     # array_ops.concat: 两个矩阵连接,连接后的 shape 为 [batch_size,input_size + Hidden_size],实际就是[Xt,Ht-1]
 68     
 69     # 此时计算: [input,state] * [W,U] == [Xt,Ht-1] * W，得到的shape为:[batch_size,Hidden_size]
 70     gate_inputs = math_ops.matmul(
 71         array_ops.concat([inputs, state], 1), self._kernel)
 72     # B 的shape 为:【Hidden_size】，[Xt,Ht-1] * W 计算后的shape为：[batch_size,Hidden_size]
 73     # nn_ops.bias_add,这个函数的计算方法是，让每个 batch 得到的值，都加上这个 B；
 74     # 这一步，加上B后：Ht = tanh([Xt,Ht-1] * W + B),得到的 shape 还是: [batch_size,Hidden_size]
 75     # 那么这个 Ht 将作为下一时刻的输入和下一层的输入；
 76     gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)
 77     output = self._activation(gate_inputs)
 78     #此时return的维度为:[batch_size,Hidden_size]
 79     # 一个output作为下一时刻的输入Ht，另一个作为下一层的输入 Ht
 80     return output, output
 81 
 82 LSTM 关键代码：
 83 
 84 @tf_export("nn.rnn_cell.BasicLSTMCell")
 85 class BasicLSTMCell(LayerRNNCell):
 86   """Basic LSTM recurrent network cell.
 87   The implementation is based on: http://arxiv.org/abs/1409.2329.
 88   We add forget_bias (default: 1) to the biases of the forget gate in order to
 89   reduce the scale of forgetting in the beginning of the training.
 90   It does not allow cell clipping, a projection layer, and does not
 91   use peep-hole connections: it is the basic baseline.
 92   For advanced models, please use the full @{tf.nn.rnn_cell.LSTMCell}
 93   that follows.
 94   """
 95 
 96   def __init__(self,
 97                num_units,
 98                forget_bias=1.0,
 99                state_is_tuple=True,
100                activation=None,
101                reuse=None,
102                name=None,
103                dtype=None):
104     """Initialize the basic LSTM cell.
105     Args:
106       num_units: int, The number of units in the LSTM cell.
107       forget_bias: float, The bias added to forget gates (see above).
108         Must set to `0.0` manually when restoring from CudnnLSTM-trained
109         checkpoints.
110       state_is_tuple: If True, accepted and returned states are 2-tuples of
111         the `c_state` and `m_state`.  If False, they are concatenated
112         along the column axis.  The latter behavior will soon be deprecated.
113       activation: Activation function of the inner states.  Default: `tanh`.
114       reuse: (optional) Python boolean describing whether to reuse variables
115         in an existing scope.  If not `True`, and the existing scope already has
116         the given variables, an error is raised.
117       name: String, the name of the layer. Layers with the same name will
118         share weights, but to avoid mistakes we require reuse=True in such
119         cases.
120       dtype: Default dtype of the layer (default of `None` means use the type
121         of the first input). Required when `build` is called before `call`.
122       When restoring from CudnnLSTM-trained checkpoints, must use
123       `CudnnCompatibleLSTMCell` instead.
124     """
125     super(BasicLSTMCell, self).__init__(_reuse=reuse, name=name, dtype=dtype)
126     if not state_is_tuple:
127       logging.warn("%s: Using a concatenated state is slower and will soon be "
128                    "deprecated.  Use state_is_tuple=True.", self)
129 
130     # Inputs must be 2-dimensional.
131     self.input_spec = base_layer.InputSpec(ndim=2)
132 
133     self._num_units = num_units
134     self._forget_bias = forget_bias
135     self._state_is_tuple = state_is_tuple
136     self._activation = activation or math_ops.tanh
137 
138   @property
139   def state_size(self):
140     # 隐藏层的 size：
141     return (LSTMStateTuple(self._num_units, self._num_units)
142             if self._state_is_tuple else 2 * self._num_units)
143 
144   @property
145   def output_size(self):
146     # 输出层的size：Hidden_size
147     return self._num_units
148 
149   def build(self, inputs_shape):
150     if inputs_shape[1].value is None:
151       raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s"
152                        % inputs_shape)
153    
154     #inputs的维度为:[batch_size,input_size]
155     #如果是第一层每个时刻词语的输入，则这个input_size 就是 embedding_size,就等于词向量的维度；
156     # 所以 此时 input_depth，就是input_size
157     input_depth = inputs_shape[1].value
158     # h_depth 就是 Hidden_size,隐藏层的维度
159     h_depth = self._num_units
160 
161     # self._kernel == W；则此时 W的维度 为【input_size + Hidden_size，4* Hidden_size】
162     # 此处定义四个 W 和 B，是为了，一次就把 i，j，f，o 计算出来；相当于图中的 ft，it，ct‘，ot
163     self._kernel = self.add_variable(
164         _WEIGHTS_VARIABLE_NAME,
165         shape=[input_depth + h_depth, 4 * self._num_units])
166     # 此时的B的维度为【4 * Hidden_size】
167     self._bias = self.add_variable(
168         _BIAS_VARIABLE_NAME,
169         shape=[4 * self._num_units],
170         initializer=init_ops.zeros_initializer(dtype=self.dtype))
171 
172     self.built = True
173 
174   def call(self, inputs, state):
175     """Long short-term memory cell (LSTM).
176     Args:
177       inputs: `2-D` tensor with shape `[batch_size, input_size]`.
178       state: An `LSTMStateTuple` of state tensors, each shaped
179         `[batch_size, num_units]`, if `state_is_tuple` has been set to
180         `True`.  Otherwise, a `Tensor` shaped
181         `[batch_size, 2 * num_units]`.
182     Returns:
183       A pair containing the new hidden state, and the new state (either a
184         `LSTMStateTuple` or a concatenated state, depending on
185         `state_is_tuple`).
186     """
187     sigmoid = math_ops.sigmoid
188     one = constant_op.constant(1, dtype=dtypes.int32)
189     # Parameters of gates are concatenated into one multiply for efficiency.
190     # 每一层的第0时刻的 c 和 h，元素全部初始化为0；
191     if self._state_is_tuple:
192       c, h = state
193     else:
194       c, h = array_ops.split(value=state, num_or_size_splits=2, axis=one)
195 
196     # 此时刻的 input：Xt 和 上一时刻的输出：Ht-1，进行结合；
197     # inputs shape : [batch_size,input_size],第一层的时候，input_size，就相当于 embedding_size
198     # 结合后的维度为【batch_size，input_size + Hidden_size】，W的维度为【input_size + Hidden_size，4*hidden_size】
199     # 两者进行矩阵相乘后的维度为：【batch_size,4*hidden_size】
200     gate_inputs = math_ops.matmul(
201         array_ops.concat([inputs, h], 1), self._kernel)
202     # B 的shape 为:【4 * Hidden_size】，[Xt,Ht-1] * W 计算后的shape为：[batch_size, 4 * Hidden_size]
203     # nn_ops.bias_add,这个函数的计算方法是，让每个 batch 得到的值，都加上这个 B；
204     # 这一步，加上B后，得到的是，i，j，f，o 的结合， [Xt,Ht-1] * W + B,得到的 shape 还是: [batch_size, 4 * Hidden_size]
205     #  加上偏置B后的维度为：【batch_size，4 * Hidden_size】
206     gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)
207 
208     # i = input_gate, j = new_input, f = forget_gate, o = output_gate
209     # 从以上的矩阵相乘后，分割出来四部分，就是 i，j，f，o的值；
210     # 每个的维度为【batch_size，Hidden_size】
211     i, j, f, o = array_ops.split(
212         value=gate_inputs, num_or_size_splits=4, axis=one)
213 
214     forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype)
215 
216     # Note that using `add` and `multiply` instead of `+` and `*` gives a
217     # performance improvement. So using those at the cost of readability.
218     add = math_ops.add
219     # 此处加上遗忘的 bias，选择遗忘元素；
220     # 以下计算是：对应元素相乘：因为四个参数的维度都是【batch_size，hidden_size】，计算后维度不变；
221     # new_c = c*sigmoid（f+bias) + sigmoid(i)*tanh(o)
222 
223     # 计算后的维度为【batch_size,hidden_size】
224     multiply = math_ops.multiply
225     new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))),
226                 multiply(sigmoid(i), self._activation(j)))
227     # 以下计算是：对应元素相乘：因为2个参数的维度都是【batch_size，hidden_size】，计算后维度不变；
228     #new_h = sigmoid(o) * tanh(new_c)
229 
230     new_h = multiply(self._activation(new_c), sigmoid(o))
231 
232     # 计算后的维度是(值不相等)：new_c == new_h == 【batch_size,hidden_size】
233 
234 
235     if self._state_is_tuple:
236       new_state = LSTMStateTuple(new_c, new_h)
237     else:
238       new_state = array_ops.concat([new_c, new_h], 1)
239     # new_h:最后一个时刻的H，new_state：最后一个时刻的 H和C；循环执行该函数，执行 num_step次(即 最大的步长),则该层计算完全;
240     # 此时的 new_c 和 new_h,作为下一时刻的输入，new_h 和下一时刻的，Xt+1 进行连接，连接后的维度为，【batch_size，input_size + Hidden_size】
241     # 如果还有下一层的话，那么此刻的 new_h,变身为下一时刻的 Xt
242     return new_h, new_state
posted on 2018-07-26 16:06 细雨微光阅读(15916) 评论(1) 编辑收藏举报