(batch_size, state_size, steps, inputs_size=None)
| 71 | |
| 72 | |
| 73 | def lstm_model_fn(batch_size, state_size, steps, inputs_size=None): |
| 74 | inputs_size = inputs_size or state_size |
| 75 | inputs = [ |
| 76 | random_ops.random_normal([batch_size, inputs_size]) for _ in range(steps) |
| 77 | ] |
| 78 | cell = rnn_cell.BasicLSTMCell(state_size) |
| 79 | init_state = cell.zero_state(batch_size, dtypes.float32) |
| 80 | state = init_state |
| 81 | for inp in inputs: |
| 82 | _, state = cell(inp, state) |
| 83 | return init_state.c, state.c |
| 84 | |
| 85 | |
| 86 | def dynamic_lstm_model_fn(batch_size, state_size, max_steps): |
no test coverage detected