Как я могу исправить ошибку после первой эпохи на tenorflow?

Question

Python 3.7.3 Tensorflow 2.0.0-alpha0 Я пытаюсь классификатор imdb в tenorflow, я придерживаюсь кода в https://www.coursera.org/learn/natural-language-processing-tensorflow/lecture/Q1Ln5/notebook-for-lesson-1.

Но я получаю следующую ошибку после первой эпохи.

Train on 25000 samples, validate on 25000 samples
Epoch 1/10
24256/25000 [============================>.] - ETA: 0s - loss: 0.4815 - accuracy: 0.7535Traceback (most recent call last):
  File "tf2.py", line 78, in <module>
    model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 873, in fit
    steps_name='steps_per_epoch')
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 398, in model_iteration
    steps_name='validation_steps')
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 352, in model_iteration
    batch_outs = f(ins_batch)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 3211, in __call__
    value = ops.convert_to_tensor(value, dtype=tensor.dtype)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1050, in convert_to_tensor
    return convert_to_tensor_v2(value, dtype, preferred_dtype, name)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1108, in convert_to_tensor_v2
    as_ref=False)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1186, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 304, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 245, in constant
    allow_broadcast=True)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 253, in _constant_impl
    t = convert_to_eager_tensor(value, ctx, dtype)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 114, in convert_to_eager_tensor
    return ops.EagerTensor(value, handle, device, dtype)
TypeError: float() argument must be a string or a number, not 'method'

вот мой код:

import tensorflow as tf
print(tf.__version__)

tf.compat.v1.enable_eager_execution()

import tensorflow_datasets as tfds
imdb, info = tfds.load("imdb_reviews", with_info = True, as_supervised = True)

import numpy as np
train_data, test_data = imdb['train'], imdb['test']
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = [] 

for s, l in train_data:
    training_sentences.append(str(s.numpy()))
    training_labels.append(l.numpy())

for s, l in test_data:
    testing_sentences.append(str(s.numpy()))
    testing_labels.append(l.numpy)

training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)

vocab_size = 10000
embedding_dim =16
max_length = 120
trunc_type = 'post'
oov_tok = "<OOV>"

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

tokenizer = Tokenizer(num_words = vocab_size, oov_token = oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index #key-value dictionary on training_sentences

sequences = tokenizer.texts_to_sequences(training_sentences)

padded = pad_sequences(sequences, maxlen = max_length, truncating = trunc_type)

testing_sequences = tokenizer.texts_to_sequences(testing_sentences)

testing_padded = pad_sequences(testing_sequences, maxlen = max_length)

reverse_word_index = dict([(value, key) for (key,value) in word_index.items()])
def decode_review(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])

print(decode_review(padded[0]))
print(training_sentences[0])

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(6, activation='relu'),
    tf.keras.layers.Dense(1,activation = 'sigmoid'),
])

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

num_epochs = 10
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))

Как исправить ошибку?