Я искал API интерфейса Tenorflow и хотел использовать Классификатор RNN , доступный в Tensorflow 1.13. В отличие от оценщиков непоследовательности, для этого нужны только столбцы признаков последовательности. Однако я не смог заставить его работать с набором игрушечных данных. Я получаю сообщение об ошибке при использовании sequence_numeric_column .
Вот структура моего набора игрушечных данных:
idSeq,kind,label,size
0,0,dwarf,117.6
0,0,dwarf,134.4
0,0,dwarf,119.0
0,1,human,168.0
0,1,human,145.25
0,2,elve,153.9
0,2,elve,218.49999999999997
0,2,elve,210.9
1,0,dwarf,166.6
1,0,dwarf,168.0
1,0,dwarf,131.6
1,1,human,150.5
1,1,human,208.25
1,1,human,210.0
1,2,elve,199.5
1,2,elve,161.5
1,2,elve,197.6
где idSeq позволяет нам увидеть, какие строки принадлежат какой последовательности.
Я хочу предсказать столбец "kind" благодаря столбцу "size".
Ниже приведен мой код для прохождения обучения RNN в моем наборе данных.
import numpy as np
import pandas as pd
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.logging.set_verbosity(tf.logging.INFO)
dataframe = pd.read_csv("data_rnn.csv")
dataframe_test = pd.read_csv("data_rnn_test.csv")
train_x = dataframe
train_y = dataframe.loc[:,(["kind"])]
size_feature_col = tf.contrib.feature_column.sequence_numeric_column('size ')
estimator = tf.contrib.estimator.RNNClassifier(
sequence_feature_columns=[size_feature_col ],
num_units=[32, 16],
cell_type='lstm',
model_dir=None,
n_classes=3,
optimizer='Adagrad'
)
def make_dataset(
batch_size,
x,
y=None,
shuffle=False,
shuffle_buffer_size=1000,
shuffle_seed=1):
"""
An input function for training, evaluation or prediction.
Parameters
----------------------
batch_size: integer
the size of the batch to use for the training of the neural network
x: pandas dataframe
dataframe that contains the features of the samples to study
y: pandas dataframe or array (Default: None)
dataframe or array that contains the values to predict of the samples
to study. If none, we want a dataset for evaluation or prediction.
shuffle: boolean (Default: False)
if True, we shuffle the elements of the dataset
shuffle_buffer_size: integer (Default: 1000)
if we shuffle the elements of the dataset, it is the size of the buffer
used for it.
shuffle_seed : integer
the random seed for the shuffling
Returns
---------------------
dataset.make_one_shot_iterator().get_next(): Tensor
a nested structure of tf.Tensors containing the next element of the
dataset to study
"""
def input_fn():
if y is not None:
dataset = tf.data.Dataset.from_tensor_slices((dict(x), y))
else:
dataset = tf.data.Dataset.from_tensor_slices(dict(x))
if shuffle:
dataset = dataset.shuffle(
buffer_size=shuffle_buffer_size,
seed=shuffle_seed).batch(batch_size).repeat()
else:
dataset = dataset.batch(batch_size)
return dataset.make_one_shot_iterator().get_next()
return input_fn
batch_size = 50
random_seed = 1
input_fn_train = make_dataset(
batch_size=batch_size,
x=train_x,
y=train_y,
shuffle=True,
shuffle_buffer_size=len(train_x),
shuffle_seed=random_seed)
estimator.train(input_fn=input_fn_train, steps=5000)
Но я получил только следующую ошибку:
INFO:tensorflow:Calling model_fn.
Traceback (most recent call last):
File "main.py", line 125, in <module>
estimator.train(input_fn=input_fn_train, steps=5000)
File "/usr/local/lib/python3.5/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 358, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.5/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1124, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.5/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1154, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/usr/local/lib/python3.5/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1112, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow_estimator/contrib/estimator/python/estimator/rnn.py", line 512, in _model_fn
config=config)
File "/usr/local/lib/python3.5/dist-packages/tensorflow_estimator/contrib/estimator/python/estimator/rnn.py", line 332, in _rnn_model_fn
logits, sequence_length_mask = logit_fn(features=features, mode=mode)
File "/usr/local/lib/python3.5/dist-packages/tensorflow_estimator/contrib/estimator/python/estimator/rnn.py", line 226, in rnn_logit_fn
features=features, feature_columns=sequence_feature_columns)
File "/root/.local/lib/python3.5/site-packages/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py", line 120, in sequence_input_layer
trainable=trainable)
File "/root/.local/lib/python3.5/site-packages/tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py", line 496, in _get_sequence_dense_tensor
sp_tensor, default_value=self.default_value)
File "/root/.local/lib/python3.5/site-packages/tensorflow/python/ops/sparse_ops.py", line 1432, in sparse_tensor_to_dense
sp_input = _convert_to_sparse_tensor(sp_input)
File "/root/.local/lib/python3.5/site-packages/tensorflow/python/ops/sparse_ops.py", line 68, in _convert_to_sparse_tensor
raise TypeError("Input must be a SparseTensor.")
TypeError: Input must be a SparseTensor.
Так что я не понимаю, что я сделал неправильно, потому что в документации написано, что мы должны передать столбец последовательности в RNNEstimator. Они ничего не говорят о предоставлении разреженного тензора.
Заранее спасибо за помощь и советы.