LSTM

https://keras.io/api/layers/recurrent_layers/lstm/

https://buomsoo-kim.github.io/keras/2019/07/12/Easy-deep-learning-with-Keras-19.md/

Long Short-Term Memory layer - Hochreiter 1997.

See the Keras RNN API guide for details about the usage of RNN API.

Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. If a GPU is available and all the arguments to the layer meet the requirement of the CuDNN kernel (see below for details), the layer will use a fast cuDNN implementation.

The requirements to use the cuDNN implementation are:

1. activation == tanh
2. recurrent_activation == sigmoid
3. recurrent_dropout == 0
4. unroll is False
5. use_bias is True
6. Inputs, if use masking, are strictly right-padded.
7. Eager execution is enabled in the outermost context.

For example:

>>> inputs = tf.random.normal([32, 10, 8])
>>> lstm = tf.keras.layers.LSTM(4)
>>> output = lstm(inputs)
>>> print(output.shape)
(32, 4)
>>> lstm = tf.keras.layers.LSTM(4, return_sequences=True, return_state=True)
>>> whole_seq_output, final_memory_state, final_carry_state = lstm(inputs)
>>> print(whole_seq_output.shape)
(32, 10, 4)
>>> print(final_memory_state.shape)
(32, 4)
>>> print(final_carry_state.shape)
(32, 4)

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: sigmoid (sigmoid). If you pass None, no activation is applied (ie. "linear" activation: a(x) = x).
• use_bias: Boolean (default True), whether the layer uses a bias vector.
• kernel_initializer: Initializer for the kernel weights matrix, used for the linear transformation of the inputs. Default: glorot_uniform.
• recurrent_initializer: Initializer for the recurrent_kernel weights matrix, used for the linear transformation of the recurrent state. Default: orthogonal.
• bias_initializer: Initializer for the bias vector. Default: zeros.
• unit_forget_bias: Boolean (default True). 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..
• kernel_regularizer: Regularizer function applied to the kernel weights matrix. Default: None.
• recurrent_regularizer: Regularizer function applied to the recurrent_kernel weights matrix. Default: None.
• bias_regularizer: Regularizer function applied to the bias vector. Default: None.
• activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). Default: None.
• kernel_constraint: Constraint function applied to the kernel weights matrix. Default: None.
• recurrent_constraint: Constraint function applied to the recurrent_kernel weights matrix. Default: None.
• bias_constraint: Constraint function applied to the bias vector. Default: None.
• dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
• recurrent_dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
• return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. Default: False.
• return_state: Boolean. Whether to return the last state in addition to the output. Default: False.
• 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.
• time_major: The shape format of the inputs and outputs tensors. If True, the inputs and outputs will be in shape [timesteps, batch, feature], whereas in the False case, it will be [batch, timesteps, feature]. 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.
• 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.

Call arguments

• inputs: A 3D tensor with shape [batch, timesteps, feature].
• mask: Binary tensor of shape [batch, timesteps] indicating whether a given timestep should be masked (optional, defaults to None).
• 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 (optional, defaults to None).
• initial_state: List of initial state tensors to be passed to the first call of the cell (optional, defaults to None which causes creation of zero-filled initial state tensors).

LSTM 모델

# 단층 LSTM을 구현하기 위한 함수
from keras.layers import LSTM

def lstm():
    model = Sequential()
    model.add(LSTM(50, input_shape = (49,1), return_sequences = False))
    model.add(Dense(46))
    model.add(Activation('softmax'))
    
    adam = optimizers.Adam(lr = 0.001)
    model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])
    
    return model

model = KerasClassifier(build_fn = lstm, epochs = 200, batch_size = 50, verbose = 1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_test_ = np.argmax(y_test, axis = 1)
print(accuracy_score(y_pred, y_test_))

0.844741235392

기본 RNN 모델에 비해 LSTM 모델을 구현했을 때에는 정확도가 10% 가량 높아진 것을 볼 수 있다. 그렇다면 다중 LSTM모델의 결과는 어떠할지 한번 살펴보자.

# 다층 LSTM을 구현하기 위한 함수
def stacked_lstm():
    model = Sequential()
    model.add(LSTM(50, input_shape = (49,1), return_sequences = True))
    model.add(LSTM(50, return_sequences = False))
    model.add(Dense(46))
    model.add(Activation('softmax'))
    
    adam = optimizers.Adam(lr = 0.001)
    model.compile(loss = 'categorical_crossentropy', optimizer = adam, metrics = ['accuracy'])
    
    return model

model = KerasClassifier(build_fn = stacked_lstm, epochs = 200, batch_size = 50, verbose = 1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_test_ = np.argmax(y_test, axis = 1)
print(accuracy_score(y_pred, y_test_))

0.858096828047

다중 LSTM은 단층 LSTM에 비해 정확도가 다소 올라가는 것을 볼 수 있다. 또한 전반적으로 LSTM 모델이 기본 RNN 모델에 비해서 좋은 성능을 보이는 것을 확인해보았다.