--- a
+++ b/build_rnn.py
@@ -0,0 +1,56 @@
+import tensorflow as tf
+
+class AFD_RNN(object):
+
+    def __init__(self, net_config, test_batch_size=None, time_step=None):
+
+        self.class_num = int(net_config['class_num'])
+        self.num_units = int(net_config['num_units'])
+        self.senor_data_num = int(net_config['senor_data_num'])
+
+        if time_step is None:
+            self.time_step = int(net_config['time_step'])
+        else:
+            self.time_step = time_step
+
+        if test_batch_size is None:
+            self.batch_size = int(net_config['batch_size'])
+        else:
+            self.batch_size = test_batch_size
+
+    def build_net_graph(self):
+        self.input_tensor = tf.placeholder(tf.float32, [None, self.time_step, self.senor_data_num])
+
+        # 创建输出层
+        input_x = tf.reshape(self.input_tensor, [-1, self.senor_data_num])
+        weights_x = self._get_variable_weights([self.senor_data_num, self.num_units], 'input_weights')
+        biases_x = self._get_variable_biases([self.num_units], 'input_biases')
+
+        x_output = tf.reshape(tf.add(tf.matmul(input_x, weights_x), biases_x),
+                                   [-1, self.time_step, self.num_units])
+
+        # 创建rnn
+        x_output = tf.unstack(x_output, axis=1)
+        lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(self.num_units)
+        self.cell_state = lstm_cell.zero_state(self.batch_size, dtype=tf.float32)
+
+        # outputs shape =[batch_size, max_time, cell_state_size]
+        # LSTM final_state shape = [2, batch_size, cell_state_size]
+        cell_outputs, final_state = tf.nn.static_rnn(lstm_cell,
+                                                     x_output,
+                                                     initial_state=self.cell_state)
+        # 创建网路输出层
+        outputs = tf.reshape(cell_outputs, [-1, self.num_units])
+        weights_outputs = self._get_variable_weights([self.num_units, self.class_num], 'outputs_weights')
+        biases_outputs = self._get_variable_biases([self.class_num], 'outputs_biases')
+
+        predict = tf.reshape(tf.add(tf.matmul(outputs, weights_outputs), biases_outputs),
+                             [self.time_step, self.batch_size, self.class_num])
+
+        return predict
+
+    def _get_variable_weights(self, shape, name):
+        return tf.Variable(tf.truncated_normal(shape, stddev=0.1), dtype=tf.float32, name=name)
+
+    def _get_variable_biases(self, shape, name):
+        return tf.Variable(tf.constant(0.1, shape=shape), dtype=tf.float32, name=name)
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