slim.arg_scope原理分析

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关于我之前问你的代码,有几个问题,@property是什么用法 def call(self, inputs, state, scope=‘convLSTM’): “”“Long short-term memory cell (LSTM).”“” with tf.variable_scope(scope or type(self).name): # “BasicLSTMCell” # Parameters of gates are concatenated into one multiply for efficiency. if self._state_is_tuple: c, h = state else: c, h = tf.split(state, 2, 3) concat = _conv_linear([inputs, h], self.filter_size, self.num_features * 4, True) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = tf.split(concat, 4, 3) new_c = (c * tf.nn.sigmoid(f + self._forget_bias) + tf.nn.sigmoid(i) * self._activation(j)) new_h = self._activation(new_c) * tf.nn.sigmoid(o) if self._state_is_tuple: new_state = LSTMStateTuple(new_c, new_h) else: new_state = tf.concat([new_c, new_h], 3) return new_h, new_state 这一段我需要更详细解释 def _conv_linear(args, filter_size, num_features, bias, bias_start=0.0, scope=None): dtype = [a.dtype for a in args][0] with slim.arg_scope([slim.conv2d], stride=1, padding='SAME', activation_fn=None, scope=scope, weights_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=1.0e-3), biases_initializer=bias and tf.constant_initializer(bias_start, dtype=dtype)): if len(args) == 1: res = slim.conv2d(args[0], num_features, [filter_size[0], filter_size[1]], scope='LSTM_conv') else: res = slim.conv2d(tf.concat(args, 3), num_features, [filter_size[0], filter_size[1]], scope='LSTM_conv') return res 这一段也详细解释
03-21
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