mindspore.ops.BasicLSTMCell
- class mindspore.ops.BasicLSTMCell(*args, **kwargs)[source]
It’s similar to operator DynamicRNN. BasicLSTMCell will be deprecated in the future. Please use DynamicRNN instead.
Applies the long short-term memory (LSTM) to the input.
\[\begin{split}\begin{array}{ll} \\ i_t = \sigma(W_{ix} x_t + b_{ix} + W_{ih} h_{(t-1)} + b_{ih}) \\ f_t = \sigma(W_{fx} x_t + b_{fx} + W_{fh} h_{(t-1)} + b_{fh}) \\ \tilde{c}_t = \tanh(W_{cx} x_t + b_{cx} + W_{ch} h_{(t-1)} + b_{ch}) \\ o_t = \sigma(W_{ox} x_t + b_{ox} + W_{oh} h_{(t-1)} + b_{oh}) \\ c_t = f_t * c_{(t-1)} + i_t * \tilde{c}_t \\ h_t = o_t * \tanh(c_t) \\ \end{array}\end{split}\]Here \(\sigma\) is the sigmoid function, and \(*\) is the Hadamard product. \(W, b\) are learnable weights between the output and the input in the formula. For instance, \(W_{ix}, b_{ix}\) are the weight and bias used to transform from input \(x\) to \(i\). Details can be found in paper LONG SHORT-TERM MEMORY and Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling.
- Parameters
keep_prob (float) – If not 1.0, append Dropout layer on the outputs of each LSTM layer except the last layer. Default 1.0. The range of dropout is [0.0, 1.0].
forget_bias (float) – Add forget bias to forget gate biases in order to decrease former scale. Default: 1.0.
state_is_tuple (bool) – If true, the state is a tuple of 2 tensors, containing h and c; If false, the state is a tensor and it needs to be split first. Default: True.
activation (str) – Activation. Default: “tanh”. Only “tanh” is currently supported.
- Inputs:
x (Tensor) - Current words. Tensor of shape (batch_size, input_size). The data type must be float16 or float32.
h (Tensor) - Hidden state last moment. Tensor of shape (batch_size, hidden_size). The data type must be float16 or float32.
c (Tensor) - Cell state last moment. Tensor of shape (batch_size, hidden_size). The data type must be float16 or float32.
w (Tensor) - Weight. Tensor of shape (input_size + hidden_size, 4 x hidden_size). The data type must be float16 or float32.
b (Tensor) - Bias. Tensor of shape (4 x hidden_size). The data type must be the same as c.
- Outputs:
ct (Tensor) - Forward \(c_t\) cache at moment t. Tensor of shape (batch_size, hidden_size). Has the same type with input c.
ht (Tensor) - Cell output. Tensor of shape (batch_size, hidden_size). With data type of float16.
it (Tensor) - Forward \(i_t\) cache at moment t. Tensor of shape (batch_size, hidden_size). Has the same type with input c.
jt (Tensor) - Forward \(j_t\) cache at moment t. Tensor of shape (batch_size, hidden_size). Has the same type with input c.
ft (Tensor) - Forward \(f_t\) cache at moment t. Tensor of shape (batch_size, hidden_size). Has the same type with input c.
ot (Tensor) - Forward \(o_t\) cache at moment t. Tensor of shape (batch_size, hidden_size). Has the same type with input c.
tanhct (Tensor) - Forward \(tanh c_t\) cache at moment t. Tensor of shape (batch_size, hidden_size), has the same type with input c.
- Raises
- Supported Platforms:
Deprecated
Examples
>>> np.random.seed(0) >>> x = Tensor(np.random.rand(1, 32).astype(np.float16)) >>> h = Tensor(np.random.rand(1, 2).astype(np.float16)) >>> c = Tensor(np.random.rand(1, 2).astype(np.float16)) >>> w = Tensor(np.random.rand(34, 8).astype(np.float16)) >>> b = Tensor(np.random.rand(8, ).astype(np.float16)) >>> lstm = ops.BasicLSTMCell(keep_prob=1.0, forget_bias=1.0, state_is_tuple=True, activation='tanh') >>> output = lstm(x, h, c, w, b) >>> print(output) (Tensor(shape=[1, 2], dtype=Float16, value= ([[7.6953e-01, 9.2432e-01]]), Tensor(shape=[1, 2], dtype=Float16, value= [[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value= [[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value= [[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value= [[1.0000e+00, 1.0000e+00]]), Tensor(shape=[1, 2], dtype=Float16, value= [[7.6953e-01, 9.2432e-01]]), Tensor(shape=[1, 2], dtype=Float16, value= [[0.0000e+00, 0.0000e+00]]))