DNNClassifier：'DataFrame' 對象沒有屬性 'dtype'

Question

我正在嘗試使用從 csv 讀取的一些數據運行 tensorflow DNNClassifier 模型。 即使我將每列的數據類型轉換為 float32，我仍然得到 'DataFrame' 對象沒有屬性 'dtype' 錯誤。 如果您能幫助我，我將不勝感激。

數據格式：27 列，23 個輸入，4 個類

謝謝

import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline 

factors = pd.read_csv('xxx.csv')

#Formatting data to float32
factors['1'] = factors['1'].astype('float32')
factors['2'] = factors['2'].astype('float32')
...
factors['27'] = factors['27'].astype('float32')

#Definition of in- and output
feat_data = factors[['1', '2', ... '23']]
labels = factors[['24', '25','26', '27']]

#Train-Test Split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(feat_data,labels, test_size=0.3, random_state=101)

from sklearn.preprocessing import MinMaxScalerscaler = MinMaxScaler()
scaled_x_train = scaler.fit_transform(X_train) scaled_x_test = scaler.transform(X_test)

#Model
from tensorflow import estimator 

feat_cols = [tf.feature_column.numeric_column('x', shape [23],dtype=tf.float32)]

deep_model = estimator.DNNClassifier(hidden_units=[23,23,23],
                        feature_columns=feat_cols,
                        n_classes=4, optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.01) )

input_fn = estimator.inputs.numpy_input_fn(x {'x':scaled_x_train},y=y_train,shuffle=True,batch_size=10,num_epochs=5)

deep_model.train(input_fn=input_fn,steps=50)


---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-169-9b2e050e4e40> in <module>()
----> 1 deep_model.train(input_fn=input_fn,steps=50)

~\Anaconda\envs\tfdeeplearning\lib\site- packages\tensorflow\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps)
    239       hooks.append(training.StopAtStepHook(steps, max_steps))
    240 
 --> 241     loss = self._train_model(input_fn=input_fn, hooks=hooks)
    242     logging.info('Loss for final step: %s.', loss)
    243     return self

~\Anaconda\envs\tfdeeplearning\lib\site-packages\tensorflow\python\estimator\estimator.py in _train_model(self, input_fn, hooks)
    626       global_step_tensor = self._create_and_assert_global_step(g)
    627       features, labels = self._get_features_and_labels_from_input_fn(
--> 628           input_fn, model_fn_lib.ModeKeys.TRAIN)
    629       estimator_spec = self._call_model_fn(features, labels,
    630                                            model_fn_lib.ModeKeys.TRAIN)

~\Anaconda\envs\tfdeeplearning\lib\site-packages\tensorflow\python\estimator\estimator.py in _get_features_and_labels_from_input_fn(self, input_fn, mode)
    497 
    498   def _get_features_and_labels_from_input_fn(self, input_fn, mode):
--> 499     result = self._call_input_fn(input_fn, mode)
    500     if isinstance(result, (list, tuple)):
    501       if len(result) != 2:

~\Anaconda\envs\tfdeeplearning\lib\site-packages\tensorflow\python\estimator\estimator.py in _call_input_fn(***failed resolving arguments***)
    583       kwargs['config'] = self.config
    584     with ops.device('/cpu:0'):
--> 585       return input_fn(**kwargs)
    586 
    587   def _call_model_fn(self, features, labels, mode):

~\Anaconda\envs\tfdeeplearning\lib\site-packages\tensorflow\python\estimator\inputs\numpy_io.py in input_fn()
    122         num_threads=num_threads,
    123         enqueue_size=batch_size,
--> 124         num_epochs=num_epochs)
    125 
    126     features = (queue.dequeue_many(batch_size) if num_epochs is None

~\Anaconda\envs\tfdeeplearning\lib\site-packages\tensorflow\python\estimator\inputs\queues\feeding_functions.py in _enqueue_data(data, capacity, shuffle, min_after_dequeue, num_threads, seed, name, enqueue_size, num_epochs)
    315     elif isinstance(data, collections.OrderedDict):
    316       types = [dtypes.int64] + [
--> 317           dtypes.as_dtype(col.dtype) for col in data.values()
    318       ]
    319       queue_shapes = [()] + [col.shape[1:] for col in data.values()]

~\Anaconda\envs\tfdeeplearning\lib\site-packages\tensorflow\python\estimator\inputs\queues\feeding_functions.py in <listcomp>(.0)
    315     elif isinstance(data, collections.OrderedDict):
    316       types = [dtypes.int64] + [
--> 317           dtypes.as_dtype(col.dtype) for col in data.values()
    318       ]
    319       queue_shapes = [()] + [col.shape[1:] for col in data.values()]

 ~\Anaconda\envs\tfdeeplearning\lib\site-packages\pandas\core\generic.py in __getattr__(self, name)
   3079             if name in self._info_axis:
   3080                 return self[name]
-> 3081             return object.__getattribute__(self, name)
   3082 
   3083     def __setattr__(self, name, value):

 AttributeError: 'DataFrame' object has no attribute 'dtype'`$`

Answer 1

Tensorflow假設你通過numpy的陣列不是大熊貓DataFrames（具有dtype屬性）。 因此，您應該將df.values而不是df傳遞給 tensorflow 函數。