# tf.compat.v1.keras.layers.LSTM
|---| | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/keras/layers/recurrent.py#L2396-L2666) | Long Short-Term Memory layer - Hochreiter 1997. Inherits From: [`RNN`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/layers/RNN) tf.compat.v1.keras.layers.LSTM( units, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, dropout=0.0, recurrent_dropout=0.0, implementation=1, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False, **kwargs ) Note that this cell is not optimized for performance on GPU. Please use [`tf.compat.v1.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/compat/v1/keras/layers/CuDNNLSTM) for better performance on GPU.


| ## Arguments || |---|---| | `units` | Positive integer, dimensionality of the output space. | | `activation` | Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). | | `recurrent_activation` | Activation function to use for the recurrent step. Default: hard sigmoid (`hard_sigmoid`). If you pass `None`, no activation is applied (ie. "linear" activation: `a(x) = x`). | | `use_bias` | Boolean, whether the layer uses a bias vector. | | `kernel_initializer` | Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.. | | `recurrent_initializer` | Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. | | `bias_initializer` | Initializer for the bias vector. | | `unit_forget_bias` | Boolean. If True, add 1 to the bias of the forget gate at initialization. Setting it to true will also force `bias_initializer="zeros"`. This is recommended in [Jozefowicz et al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf). | | `kernel_regularizer` | Regularizer function applied to the `kernel` weights matrix. | | `recurrent_regularizer` | Regularizer function applied to the `recurrent_kernel` weights matrix. | | `bias_regularizer` | Regularizer function applied to the bias vector. | | `activity_regularizer` | Regularizer function applied to the output of the layer (its "activation").. | | `kernel_constraint` | Constraint function applied to the `kernel` weights matrix. | | `recurrent_constraint` | Constraint function applied to the `recurrent_kernel` weights matrix. | | `bias_constraint` | Constraint function applied to the bias vector. | | `dropout` | Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. | | `recurrent_dropout` | Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. | | `implementation` | Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications. | | `return_sequences` | Boolean. Whether to return the last output. in the output sequence, or the full sequence. | | `return_state` | Boolean. Whether to return the last state in addition to the output. | | `go_backwards` | Boolean (default False). If True, process the input sequence backwards and return the reversed sequence. | | `stateful` | Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch. | | `unroll` | Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences. | | `time_major` | The shape format of the `inputs` and `outputs` tensors. If True, the inputs and outputs will be in shape `(timesteps, batch, ...)`, whereas in the False case, it will be `(batch, timesteps, ...)`. Using `time_major = True` is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form. |
#### Call arguments: - **`inputs`**: A 3D tensor. - **`mask`** : Binary tensor of shape `(samples, timesteps)` indicating whether a given timestep should be masked. - **`training`** : Python boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is only relevant if `dropout` or `recurrent_dropout` is used. - **`initial_state`**: List of initial state tensors to be passed to the first call of the cell.


| ## Attributes || |---|---| | `activation` |

| | `bias_constraint` |

| | `bias_initializer` |

| | `bias_regularizer` |

| | `dropout` |

| | `implementation` |

| | `kernel_constraint` |

| | `kernel_initializer` |

| | `kernel_regularizer` |

| | `recurrent_activation` |

| | `recurrent_constraint` |

| | `recurrent_dropout` |

| | `recurrent_initializer` |

| | `recurrent_regularizer` |

| | `states` |

| | `unit_forget_bias` |

| | `units` |

| | `use_bias` |

|
## Methods ### `get_initial_state` [View source](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/keras/layers/recurrent.py#L593-L614) get_initial_state( inputs ) ### `reset_states` [View source](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/keras/layers/recurrent.py#L806-L858) reset_states( states=None )