nn.LSTMCell（）说明

nn.LSTMCell() 是 PyTorch 中的一个类，用于创建一个长短时记忆（LSTM）单元。该单元可以在时间序列数据中捕捉长期依赖关系，对于自然语言处理等任务非常有用。

LSTM 单元包括一个输入门、遗忘门、输出门和细胞状态。nn.LSTMCell() 的输入参数是输入数据和上一时刻的细胞状态和隐藏状态，输出是当前时刻的细胞状态和隐藏状态。它可以在循环神经网络（RNN）中被使用，来对时间序列数据进行建模和预测。

相关问题

nn.LSTMCell

nn.LSTMCell is a PyTorch class that implements a single LSTM cell. It takes in an input tensor and the previous hidden and cell states as inputs, and computes the current hidden and cell states based on the LSTM equations.

The LSTM equations are as follows:

i_t = sigmoid(W_i * [h_{t-1}, x_t] + b_i)
f_t = sigmoid(W_f * [h_{t-1}, x_t] + b_f)
g_t = tanh(W_g * [h_{t-1}, x_t] + b_g)
o_t = sigmoid(W_o * [h_{t-1}, x_t] + b_o)
c_t = f_t * c_{t-1} + i_t * g_t
h_t = o_t * tanh(c_t)

where x_t is the input tensor at time t, h_{t-1} and c_{t-1} are the previous hidden and cell states, i_t, f_t, g_t, and o_t are the input gate, forget gate, cell gate, and output gate activations, and W_i, W_f, W_g, W_o, b_i, b_f, b_g, and b_o are the weights and biases of the LSTM cell.

nn.LSTMCell can be used to build a multi-layer LSTM network by stacking multiple LSTM cells together. It is often used in combination with other PyTorch modules such as nn.Linear and nn.Dropout to create more complex models.

LSTMCell = tf.nn.rnn_cell.LSTMCell AttributeError: module 'tensorflow._api.v2.nn' has no attribute 'rnn_cell'

在 TensorFlow 2.x 中，`rnn_cell` 模块已经被移除了。如果你想使用 LSTM 单元，可以使用 `tf.keras.layers.LSTM` 或 `tf.keras.layers.LSTMCell`。

例如，你可以这样定义一个 LSTM 单元：

lstm = tf.keras.layers.LSTM(units=hidden_size, return_state=True, return_sequences=True)

或者这样定义一个 LSTM 单元的 cell：

lstm_cell = tf.keras.layers.LSTMCell(units=hidden_size)

这两种方式都使用了 Keras API，它是 TensorFlow 2.x 中的默认 API。如果你想使用 TensorFlow 1.x 中的 API，可以使用 `tf.compat.v1.nn.rnn_cell.LSTMCell`。