Question

我正在使用以下简单代码使用tensorflow加载csv并使用keras执行建模...

无法发现此错误！

import tensorflow as tf

train_dataset_fp = tf.keras.utils.get_file(fname=file_path, origin=URL)
columns = ["X","Y"]

features = columns[:-1]
labels = columns[-1]

batch_size = 32

train_dataset = tf.data.experimental.make_csv_dataset(
    train_dataset_fp,
    batch_size,
    column_names = columns,
    label_name= labels,
    num_epochs=1
)

data_iterator = train_dataset.make_one_shot_iterator()

X_train, Y_train = data_iterator.get_next()

from tensorflow import keras

model = keras.Sequential([
    keras.layers.Dense(10, input_shape=[len(X_train)]),
    keras.layers.Dense(1)
])

model.compile(loss='mse',
                optimizer='adam',
                metrics=['mae', 'mse'])

model.summary()

model.fit(X_train, Y_train, epochs=1000, steps_per_epoch=batch_size)

虽然其余代码工作正常，但我无法弄清为什么我得到密集的输入错误。

如果使用pandas的话，相同的代码也可以正常工作，我试图使用tensorflow组件删除对其他库的依赖，但似乎失败了。

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 10)                30        
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 11        
=================================================================
Total params: 41
Trainable params: 41
Non-trainable params: 0
_________________________________________________________________
Traceback (most recent call last):
  File "/Users/abhinavasrivastava/anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py", line 267, in standardize_input_data
    for x in names
  File "/Users/abhinavasrivastava/anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py", line 267, in <listcomp>
    for x in names
KeyError: 'dense_input'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "simple_linear_keras.py", line 47, in <module>
    model.fit(X_train, Y_train, epochs=1000, callbacks=[tb], steps_per_epoch=batch_size)
  File "/Users/abhinavasrivastava/anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1536, in fit
    validation_split=validation_split)
  File "/Users/abhinavasrivastava/anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 992, in _standardize_user_data
    class_weight, batch_size)
  File "/Users/abhinavasrivastava/anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1117, in _standardize_weights
    exception_prefix='input')
  File "/Users/abhinavasrivastava/anaconda3/envs/ml/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_utils.py", line 271, in standardize_input_data
    'for each key in: ' + str(names))
ValueError: No data provided for "dense_input". Need data for each key in: ['dense_input']

Answer 1

错误No data provided for "dense_input"表示Keras根本没有获得输入数据或没有获得预期格式的输入数据，即，在Python中表示为numpy数组的数组形式。

假设其他一切正常，应该只需添加一行即可转换X_train和Y_train：

import numpy as np
X_train = np.array(X_train)
Y_train = np.array(Y_train)

Answer 2

好像您正在使用* * * * * * * ********* * * * * * * *来适合Keras模型。 Keras模型无法正确获取数据，因为您没有在数据集中指定要素cols的要素名称。要解决此问题，您需要在tf.Dataset函数中明确提供功能名称name=features：

model.fit

ValueError：未为“ dense_input”提供数据

2 个答案: