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m_stateのc_stateにあるtf.contrib.rnn.BasicLSTMCellの理解



Understanding Tf Contrib



tf.contrib.rnn.BasicLSTMCellソース

class BasicLSTMCell(LayerRNNCell): '''DEPRECATED: Please use `tf.nn.rnn_cell.LSTMCell` instead. Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training. It does not allow cell clipping, a projection layer, and does not use peep-hole connections: it is the basic baseline. For advanced models, please use the full `tf.nn.rnn_cell.LSTMCell` that follows. Note that this cell is not optimized for performance. Please use `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for better performance on CPU. ''' @deprecated(None, 'This class is equivalent as tf.keras.layers.LSTMCell,' ' and will be replaced by that in Tensorflow 2.0.') def __init__(self, num_units, forget_bias=1.0, state_is_tuple=True, activation=None, reuse=None, name=None, dtype=None, **kwargs): '''Initialize the basic LSTM cell. Args: num_units: int, The number of units in the LSTM cell. forget_bias: float, The bias added to forget gates (see above). Must set to `0.0` manually when restoring from CudnnLSTM-trained checkpoints. state_is_tuple: If True, accepted and returned states are 2-tuples of the `c_state` and `m_state`. If False, they are concatenated along the column axis. The latter behavior will soon be deprecated. activation: Activation function of the inner states. Default: `tanh`. It could also be string that is within Keras activation function names. reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised. name: String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. dtype: Default dtype of the layer (default of `None` means use the type of the first input). Required when `build` is called before `call`. **kwargs: Dict, keyword named properties for common layer attributes, like `trainable` etc when constructing the cell from configs of get_config(). When restoring from CudnnLSTM-trained checkpoints, must use `CudnnCompatibleLSTMCell` instead. ''' super(BasicLSTMCell, self).__init__( _reuse=reuse, name=name, dtype=dtype, **kwargs) if not state_is_tuple: logging.warn('%s: Using a concatenated state is slower and will soon be ' 'deprecated. Use state_is_tuple=True.', self) if context.executing_eagerly() and context.num_gpus() > 0: logging.warn('%s: Note that this cell is not optimized for performance. ' 'Please use tf.contrib.cudnn_rnn.CudnnLSTM for better ' 'performance on GPU.', self) # Inputs must be 2-dimensional. self.input_spec = input_spec.InputSpec(ndim=2) self._num_units = num_units self._forget_bias = forget_bias self._state_is_tuple = state_is_tuple if activation: self._activation = activations.get(activation) else: self._activation = math_ops.tanh @property def state_size(self): return (LSTMStateTuple(self._num_units, self._num_units) if self._state_is_tuple else 2 * self._num_units) @property def output_size(self): return self._num_units @tf_utils.shape_type_conversion def build(self, inputs_shape): if inputs_shape[-1] is None: raise ValueError('Expected inputs.shape[-1] to be known, saw shape: %s' % str(inputs_shape)) input_depth = inputs_shape[-1] h_depth = self._num_units self._kernel = self.add_variable( _WEIGHTS_VARIABLE_NAME, shape=[input_depth + h_depth, 4 * self._num_units]) self._bias = self.add_variable( _BIAS_VARIABLE_NAME, shape=[4 * self._num_units], initializer=init_ops.zeros_initializer(dtype=self.dtype)) self.built = True def call(self, inputs, state): '''Long short-term memory cell (LSTM). Args: inputs: `2-D` tensor with shape `[batch_size, input_size]`. state: An `LSTMStateTuple` of state tensors, each shaped `[batch_size, num_units]`, if `state_is_tuple` has been set to `True`. Otherwise, a `Tensor` shaped `[batch_size, 2 * num_units]`. Returns: A pair containing the new hidden state, and the new state (either a `LSTMStateTuple` or a concatenated state, depending on `state_is_tuple`). ''' sigmoid = math_ops.sigmoid one = constant_op.constant(1, dtype=dtypes.int32) # Parameters of gates are concatenated into one multiply for efficiency. if self._state_is_tuple: c, h = state else: c, h = array_ops.split(value=state, num_or_size_splits=2, axis=one) gate_inputs = math_ops.matmul( array_ops.concat([inputs, h], 1), self._kernel) gate_inputs = nn_ops.bias_add(gate_inputs, self._bias) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = array_ops.split( value=gate_inputs, num_or_size_splits=4, axis=one) forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype) # Note that using `add` and `multiply` instead of `+` and `*` gives a # performance improvement. So using those at the cost of readability. add = math_ops.add multiply = math_ops.multiply new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))), multiply(sigmoid(i), self._activation(j))) new_h = multiply(self._activation(new_c), sigmoid(o)) if self._state_is_tuple: new_state = LSTMStateTuple(new_c, new_h) else: new_state = array_ops.concat([new_c, new_h], 1) return new_h, new_state def get_config(self): config = { 'num_units': self._num_units, 'forget_bias': self._forget_bias, 'state_is_tuple': self._state_is_tuple, 'activation': activations.serialize(self._activation), 'reuse': self._reuse, } base_config = super(BasicLSTMCell, self).get_config() return dict(list(base_config.items()) + list(config.items()))

コード呼び出し（self、inputs、state）を詳しく見ると、その戻り値は、それぞれ以下に対応するコメントc_state、m_stateに記載されているパラメーターを知ることができます。
下位層RNNニューロンの現在の入力ニューロンであるhidden_​​stateのm_state対応する現在のLSTMCellここでのc_stateは、現在のLSTMCellに対応する現在のLSTMCellインタクトセル状態、つまりセル状態と非表示状態[cell_state、hidden_​​state]に対応します。したがって、呼び出しの戻り値は（hidden_​​state、[cell_state、hidden_​​state]）です。

Pythonは、クラス__init __、__ new __、__ call__メソッドについて簡単に説明しました

__new__は、新しいクラスのemergeningのメソッドであり、コンストラクターインスタンスを構築する前の役割を理解できます。Pythonでは、コンストラクターは__init内のクラスに存在し、__（）はクラスのインスタンス化を担当し、__ init_では_、__を開始します。 new__ __init__は、このメソッドを使用するかどうかを決定します。これは、__ new__が他のクラスコンストラクターを呼び出すか、このクラスの例として他のオブジェクトに直接戻ることができるためです。
__init__メソッドは、メソッドを実行する前のシステムであるオブジェクトの初期化を担当します。オブジェクトは実際にはすでに存在します。
__call__メソッドそのようなオブジェクトは呼び出し可能なオブジェクトインスタンスになります。
シンプルなディスプレイ ：



class A(object): def __init__(self): print('__init__') def __new__(cls): print('__new__') self = super(A, cls).__new__(cls) print(self) return self def __call__(self,a,d): print('a:',a) print('d:',d) return a+d # Run # Input #a=A() # Output #__new__ # #__init__ # Input #a(1,2) # Output #a: 1 #d: 2 #3