Python 在tensorboard中可视化特征映射

我想在tensorboard中可视化图层之间的贴图特征和权重。 这是我的密码：

# Load the TensorBoard notebook extension
%load_ext tensorboard.notebook

callbacks = [
# Write TensorBoard logs to './logs' directory 
tf.keras.callbacks.TensorBoard(log_dir='./logs/tensorflow_exercise')
]

# network and training
EPOCHS = 20
BATCH_SIZE = 128
VERBOSE = 1
OPTIMIZER = tf.keras.optimizers.Adam()
VALIDATION_SPLIT=0.95
IMG_ROWS, IMG_COLS = 28, 28 
# input image dimensions 
INPUT_SHAPE = (IMG_ROWS, IMG_COLS, 1)
NB_CLASSES = 10 
# number of outputs = number of digits

def build(input_shape, classes):
    model = models.Sequential()
    #First stage
    model.add(tf.keras.layers.Convolution2D(20, (5, 5), activation='relu', input_shape=input_shape))
    model.add(tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)))
    #Second stage
    model.add(tf.keras.layers.Convolution2D(50, (5, 5), activation='relu')) 
    model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    # Flatten => RELU layers
    model.add(tf.keras.layers.Flatten()) 
    model.add(tf.keras.layers.Dense(500, activation='relu'))
    # a softmax classifier
    model.add(tf.keras.layers.Dense(classes, activation="softmax")) 
    model.summary()
    return model


mnist = tf.keras.datasets.mnist
# data: shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = datasets.mnist.load_data()
# reshape
X_train = X_train.reshape((60000, 28, 28, 1)) 
X_test = X_test.reshape((10000, 28, 28, 1))
# normalize


X_train, X_test = X_train / 255.0, X_test / 255.0


# cast
X_train = X_train.astype('float32') 
X_test = X_test.astype('float32')
# 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)
# initialize the optimizer and model
model = build(input_shape=INPUT_SHAPE, classes=NB_CLASSES) 
model.compile(loss="categorical_crossentropy", optimizer=OPTIMIZER,metrics=["accuracy"]) 
model.summary()
# use TensorBoard, princess Aurora! 
callbacks = [
      # Write TensorBoard logs to './logs' directory
        tf.keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=1) ]






log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)


# fit
history = model.fit(X_train, y_train,batch_size=BATCH_SIZE, epochs=EPOCHS, verbose=VERBOSE, validation_split=VALIDATION_SPLIT, callbacks=callbacks)
score = model.evaluate(X_test, y_test, verbose=VERBOSE) 
print("\nTest score:", score[0])
print('Test accuracy:', score[1])

%tensorboard --logdir logs

我想我必须使用tf.keras.callbacks.ModelCheckpoint，但这不起作用。 我没有在谷歌找到任何回应，我看到了keract，但我想用tensorboard实现它。 我该怎么做

多谢各位