import tensorflow.keras as keras model = keras.Sequential() model.add(keras.layers.MaxPooling1D(pool_size=2, strides=None, padding='valid'))
import keras input_shape = (10, 32) model = keras.models.Sequential() model.add(keras.layers.MaxPooling1D(pool_size=2, strides=None, padding='valid', input_shape=input_shape))
import tensorflow.keras as keras model = keras.Sequential([ keras.layers.Conv1D(64, 3, activation='relu', input_shape=input_shape), keras.layers.MaxPooling1D(pool_size=2), keras.layers.Flatten(), keras.layers.Dense(10, activation='softmax') ])This creates a model with a combination of Conv1D, MaxPooling1D, Flatten, and Dense layers. The MaxPooling1D layer reduces the output dimension after the convolutional layer.