一、代码示例
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist
import numpy as np(train_images, train_labels), _ = mnist.load_data()
train_images = train_images.reshape((60000, 28 * 28))
train_images = train_images.astype("float32") / 255train_images_with_zeros_channels = np.concatenate([train_images, np.zeros((len(train_images), 784))], axis=1)def get_model():model = keras.Sequential([layers.Dense(512, activation="relu"),layers.Dense(10, activation="softmax")])model.compile(optimizer="rmsprop",loss="sparse_categorical_crossentropy",metrics=["accuracy"])return modelmodel = get_model()
history_zeros = model.fit(train_images_with_zeros_channels, train_labels,epochs=
