(self,)
| 57 | self.train_op = tf.train.AdamOptimizer(LR).minimize(self.cost) |
| 58 | |
| 59 | def add_input_layer(self,): |
| 60 | l_in_x = tf.reshape(self.xs, [-1, self.input_size], name='2_2D') # (batch*n_step, in_size) |
| 61 | # Ws (in_size, cell_size) |
| 62 | Ws_in = self._weight_variable([self.input_size, self.cell_size]) |
| 63 | # bs (cell_size, ) |
| 64 | bs_in = self._bias_variable([self.cell_size,]) |
| 65 | # l_in_y = (batch * n_steps, cell_size) |
| 66 | with tf.name_scope('Wx_plus_b'): |
| 67 | l_in_y = tf.matmul(l_in_x, Ws_in) + bs_in |
| 68 | # reshape l_in_y ==> (batch, n_steps, cell_size) |
| 69 | self.l_in_y = tf.reshape(l_in_y, [-1, self.n_steps, self.cell_size], name='2_3D') |
| 70 | |
| 71 | def add_cell(self): |
| 72 | lstm_cell = tf.contrib.rnn.BasicLSTMCell(self.cell_size, forget_bias=1.0, state_is_tuple=True) |
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