当我们将模型部署到云ml时，我们得到Bad model Op类型未注册HashTableV2
码：
def model_fn(features, labels, mode):
if mode == tf.estimator.ModeKeys.TRAIN:
tf.keras.backend.set_learning_phase(True)
else:
tf.keras.backend.set_learning_phase(False)
input_feature = features['x']
table = lookup.index_table_from_file(vocabulary_file='vocab.txt', num_oov_buckets=1, default_value=-1)
text = tf.squeeze(input_feature, [1])
words = tf.string_split(text)
dense_words = tf.sparse_tensor_to_dense(words, default_value=PADWORD)
numbers = table.lookup(dense_words)
padding = tf.constant([[0, 0], [0, MAX_LEN]])
padded = tf.pad(numbers, padding)
sliced = tf.slice(padded, [0, 0], [-1, MAX_LEN])
print('words_sliced={}'.format(words))
embeds = tf.keras.layers.Embedding(MAX_FEATURES+1, 128, input_length=MAX_LEN)(sliced)
print('words_embed={}'.format(embeds))
f1 = tf.keras.layers.Dropout(0.2)(embeds)
f1 = tf.keras.layers.Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1)(f1)
f1 = tf.keras.layers.GlobalAveragePooling1D()(f1)
f1 = tf.keras.layers.Dense(hidden_dims)(f1)
f1 = tf.keras.layers.Dropout(0.5)(f1)
f1 = tf.keras.layers.Activation('relu')(f1)
logits = tf.keras.layers.Dense(11)(f1)
predictions_dict = {
'class': tf.argmax(logits, 1),
'prob': tf.nn.softmax(logits)
}
'''prediction_output = tf.estimator.export.PredictOutput({"classes": tf.argmax(input=logits, axis=1),
"probabilities": tf.nn.softmax(logits,
name="softmax_tensor")})'''
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions_dict, export_outputs={
'prediction': tf.estimator.export.PredictOutput(predictions_dict)
})
loss = tf.losses.sparse_softmax_cross_entropy(labels, logits=logits)
if mode == tf.contrib.learn.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(loss, tf.contrib.framework.get_global_step(), optimizer='Adam',
learning_rate=0.001)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
eval_metrics_ops = {
'accuracy': tf.metrics.accuracy(labels=labels, predictions=predictions_dict['class']),
'precision': tf.metrics.precision(labels=labels, predictions=predictions_dict['class']),
'recall': tf.metrics.recall(labels=labels, predictions=predictions_dict['class'])
}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metrics_ops)
def get_train_record(record):
vector = tf.decode_csv(record, DEFAULTS, use_quote_delim=True)
return vector[1:], vector[0]
def preprocess(text):
text = text.lower()
result = ' '.join([word for word in text.split() if word not in (stop_words)])
return result
def build_vocab(file_name, vocab_file_name):
df = pd.read_csv(file_name, header=None, sep=',', skiprows=[1], names=['product', 'consumer_complaint_narrative'])
df['consumer_complaint_narrative'] = df['consumer_complaint_narrative'].apply(preprocess)
print(df['consumer_complaint_narrative'][0])
vocab_processor = tflearn.preprocessing.VocabularyProcessor(max_document_length=MAX_FEATURES, min_frequency=10,
tokenizer_fn=tflearn.preprocessing.tokenizer)
vocab_processor.fit(df['consumer_complaint_narrative'])
with gfile.Open(vocab_file_name, 'wb') as f:
f.write("{}\n".format(PADWORD))
for word, index in vocab_processor.vocabulary_._mapping.items():
f.write("{}\n".format(word))
nwords = len(vocab_processor.vocabulary_)
print('{} words into {}'.format(nwords, vocab_file_name))
def input_fn(file_name, batch_size, repeat_count, shuffle=False):
def _input_fn():
data_set = tf.data.TextLineDataset(filenames=file_name)
data_set = data_set.map(get_train_record)
if shuffle:
data_set = data_set.shuffle(shuffle)
data_set = data_set.repeat(repeat_count)
batch = data_set.batch(batch_size)
iterator = batch.make_one_shot_iterator()
features, labels = iterator.get_next()
return {'x': features}, labels
return _input_fn()
def get_train_spec(file_name, batch_size, repeat_count):
return tf.estimator.TrainSpec(input_fn=lambda: input_fn(file_name, batch_size, repeat_count, shuffle=True), max_steps=1000)
def get_test_spec(file_name, batch_size, repeat_count=1):
return tf.estimator.EvalSpec(input_fn=lambda: input_fn(file_name, batch_size, repeat_count, shuffle=True))
def serving_input_fn():
feature_tensor = tf.placeholder(tf.string, [None])
# features = tf.py_func(preprocess, [feature_tensor], tf.string)
features = tf.expand_dims(feature_tensor, -1)
return tf.estimator.export.ServingInputReceiver({'x': features}, {'x': features})
finance_classifier = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir)
print('\n Training .....')
finance_classifier.train(input_fn=lambda: input_fn('dataset/train.csv', batch_size, repeat_count=5, shuffle=True))
print('\n Evaluating.....')
eval_results = finance_classifier.evaluate(input_fn=lambda: input_fn('dataset/valid.csv', batch_size, repeat_count=1,
shuffle=False))
for key in eval_results:
print(" {} was {}".format(key, eval_results[key]))
print('\n Exporting')
exported_model_dir = finance_classifier.export_savedmodel(model_dir, serving_input_receiver_fn=serving_input_fn)
decoded_model_dir = exported_model_dir.decode("utf-8")
这里要提到的一件重要事情是，当我尝试使用Tensorflow 1.2并对model_fn中的代码进行一些更改时。基本上不使用tf.keras但使用tf.contrib.keras它正在工作。
在Tensorflow 1.2中导出的模型工作正常。这是Tensorflow 1.4中的错误吗？我们如何解决这个错误？
已经在Tensorflow qazxsw poi中创建了一个github问题