如何从keras / tensorflow中的顺序模型获取logit？

Question

我有一个像这样的简单模型：

model = Sequential()

model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))

model.add(Dense(1))
model.add(Activation('sigmoid'))

tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'],
              )

model.fit(X, y,
          batch_size=32,
          epochs=3,
          validation_split=0.3,
          callbacks=[tensorboard])

我想从最后一个密集函数中获取logit，所以我可以为两个类都增加权重

weights = tf.placeholder(name="loss_weights", shape=[None], dtype=tf.float32)
loss_per_example = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels)
loss = tf.reduce_mean(weights * loss_per_example)

如何从该模型中获取日志？