Lstm Python

LSTM (Long Short-Term Memory) is a type of recurrent neural network that is widely used for analyzing sequential data, such as time series or text data. In Python, you can implement LSTM using various libraries such as Keras, PyTorch, or TensorFlow. Here is an example of how to implement LSTM using Keras:

from keras.models import Sequential
from keras.layers import LSTM, Dense

model = Sequential()
model.add(LSTM(64, input_shape=(timesteps, input_dim)))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32)

In this example, we create a sequential model that consists of an LSTM layer with 64 units and a dense layer with softmax activation. We compile the model with categorical cross-entropy loss and the Adam optimizer, and then fit the model using the training data.

Note that `timesteps` and `input_dim` in the input shape depend on the shape of your data. For example, if you have a time series of length 100 with 3 features at each time step, then `timesteps` would be 100 and `input_dim` would be 3.