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当init方法有dropout时，函数fit时报以下错误，加上experimental_run_tf_function=False，错误消失，但是不明白这句话的作用：

        self.rnn_cell0 = layers.SimpleRNNCell(units, dropout=0.2)

TypeError: An op outside of the function building code is being passed

a “Graph” tensor. It is possible to have Graph tensors

leak out of the function building context by including a

tf.init_scope in your function building code.

For example, the following function will fail:

@tf.function

def has_init_scope():

my_constant = tf.constant(1.)

with tf.init_scope():

added = my_constant * 2

The graph tensor has name: my_rnn/simple_rnn_cell/cond/Identity:0

tensorflow.python.eager.core._SymbolicException: Inputs to eager execution function cannot be Keras symbolic tensors, but found [<tf.Tensor ‘my_rnn/simple_rnn_cell/cond/Identity:0’ shape=(None, 100) dtype=float32>]

以下为代码：

from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics, preprocessing
import tensorflow as tf
from tensorflow import keras
import numpy as np
import os
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

config = ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
assert tf.__version__.startswith("2.")

# the most frequent word
batchsz = 128
total_words = 10000
max_review_len = 80
embedding_len = 100
(x_train, y_train), (x_test, y_test) = datasets.imdb.load_data(num_words=total_words)
# x_train: (b, 80)
x_train = preprocessing.sequence.pad_sequences(x_train, maxlen=max_review_len)
x_test = preprocessing.sequence.pad_sequences(x_test, maxlen=max_review_len)

db_train = tf.data.Dataset.from_tensor_slices((x_train, y_train))
db_train = db_train.shuffle(1000).batch(batchsz, drop_remainder=True)
db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
db_test = db_test.shuffle(1000).batch(batchsz, drop_remainder=True)
print(x_train.dtype, x_test.shape, tf.reduce_max(x_train), tf.reduce_max(y_train))



class MyRNN(keras.Model):
    def __init__(self, units):
        super(MyRNN, self).__init__()
        # transform text to enbedding representation
        # [B, 80]->[B,80,100]
        self.state0 = [tf.zeros([batchsz, units])]
        self.embedding = layers.Embedding(total_words, embedding_len, input_length=max_review_len)
        # [b,80,100], h_dim:64
        # RNN: cell, cell2, cell3
        # simpleRNN
        self.rnn_cell0 = layers.SimpleRNNCell(units,)
        # self.rnn_cell1 = layers.SimpleRNN()
        # fc,[b,80,100]=>[b, 64]=>[b,1]
        self.rnn_fc = layers.Dense(1)

    def call(self, inputs, training=None):
        x = inputs
        # embedding [b,80]=>[b,80,q00]
        x = self.embedding(x)
        state0 = self.state0
        for word in tf.unstack(x, axis=1):  # word:[b,100]
            # h = tf.zeros(unit,)
            out, state1 = self.rnn_cell0(word, state0, training)
            state0 = state1
        x = self.rnn_fc(out)
        prob = tf.sigmoid(x)

        return prob


def main():
    units = 64
    epochs = 4

    model = MyRNN(units)
    # model.build(input_shape=(4,80))
    # model.summary()
    model.compile(optimizer = keras.optimizers.Adam(0.001),
                  loss = tf.losses.BinaryCrossentropy(),
                  metrics=['accuracy'])
    model.fit(db_train, epochs=epochs, validation_data=db_test)

    model.evaluate(db_test)

if __name__ == '__main__':
    main()