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假设您有一个布尔值tf.placeholder，并且您想在调用时输入它Model.fit。你会怎么做？下面是一些说明问题的可运行虚拟代码。

import tensorflow as tf
from tensorflow.keras.layers import Dense, Input, Flatten
from tensorflow.keras.models import Model
# A boolean value that should have some effect of something
do_stuff = tf.placeholder(tf.bool)
# If do_stuff is true, return tf.ones else tf.zeros, and a 1 or 0 label
if_dostuff = lambda: [tf.ones((5, 5)), tf.constant(1)]
if_not_dostuff = lambda: [tf.zeros((5, 5)), tf.constant(0)]
X, Y_true = tf.cond(do_stuff, if_dostuff, if_not_dostuff)
# Make some dummy labels
# Do some random model operation
X_input = Input(shape=(5, 5))
layer_mod = Flatten()(X_input)
layer_mod = Dense(1)(layer_mod)
out_model = Model(inputs=[X_input], outputs=[layer_mod])
# Compile model
out_model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss=tf.keras.metrics.binary_crossentropy
)
### Other ops with other models and summaries etc. ###
out_model.fit(...) # What do I do at this point?

请记住，布尔值只是为了让事情变得简单。实际上，我有作为迭代器句柄的字符串，需要输入（基于我想要训练的数据集）。

我怎样才能model.fit用这种布局做 keras 的惊人界面？

另一种选择是我在这个问题中提出的问题

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