问老式Keras命令"Convolution2D“的现代语法？

EN

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import (Dense, Dropout, Activation, 
                                    Flatten, Conv2D, MaxPooling2D)
from tensorflow.keras.optimizers import SGD, Adadelta, Adagrad
import tensorflow as tf 

# parameters
batch_size = 32
nb_classes = 10
nb_epoch = 5

# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()

# convert class vectors to binary class matrices
Y_train = tf.keras.utils.to_categorical(y_train, nb_classes) 
Y_test = tf.keras.utils.to_categorical(y_test, nb_classes)

# train model
X_train = X_train.astype("float32") / 255
X_test = X_test.astype("float32") / 255

X_train.shape, y_train.shape, X_test.shape, y_test.shape
((50000, 32, 32, 3), (50000, 1), (10000, 32, 32, 3), (10000, 1))

model = Sequential()

model.add(Conv2D(filters=32, kernel_size=(3, 3), 
                 strides=(1, 1), activation='relu', padding="same"))
model.add(Activation('relu'))
model.add(Conv2D(filters=32, kernel_size=(3, 3), 
                strides=(1, 1), activation='relu', padding="same"))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(filters=32, kernel_size=(3, 3), 
                 strides=(1, 1), activation='relu', padding="same"))
model.add(Activation('relu'))
model.add(Conv2D(filters=32, kernel_size=(3, 3), 
                 strides=(1, 1), activation='relu', padding="same"))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(nb_classes))
model.add(Activation('softmax'))

# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch)

# test score & top 1 performance
score = model.evaluate(X_test, Y_test, batch_size=batch_size)
y_hat = model.predict(X_test)
yhat = np.argmax(y_hat, 1)
top1 = np.mean(yhat == np.squeeze(y_test))

print('Test score/Top1', score, top1)