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在Python 3.5.2中使用TensorFlow 1.3.0版。我試圖在TensorFlow網站的Iris數據教程中模仿DNNClassifier的功能，並且遇到了困難。我正在導入一個包含大約155行數據和15列的CSV文件，將數據分解爲訓練和測試數據（我嘗試對正面或負面運動進行分類），並在開始訓練分類器時收到錯誤。這裏的數據是如何建立ValueError：標籤形狀必須是[batch_size，labels_dimension]，（128,2）
#import values from csv
mexicof1 = pd.read_csv('Source/mexicoR.csv')
#construct pandas dataframe
mexico_df = pd.DataFrame(mexicof1)
#start counting from mexico.mat.2.nrow.mexico.mat...1.
mexico_dff = pd.DataFrame(mexico_df.iloc[:,1:16])
mexico_dff.columns = ['tp1_delta','PC1','PC2','PC3','PC4','PC5','PC6','PC7', \
'PC8', 'PC9', 'PC10', 'PC11', 'PC12', 'PC13', 'PC14']
#binary assignment for positive/negative values
for i in range(0,155):
if(mexico_dff.iloc[i,0] > 0):
mexico_dff.iloc[i,0] = "pos"
else:
mexico_dff.iloc[i,0] = "neg"
#up movement vs. down movement classification set up
up = np.asarray([1,0])
down = np.asarray([0,1])
mexico_dff['tp1_delta'] = mexico_dff['tp1_delta'].map({"pos": up, "neg": down})
#Break into training and test data
#data: independent values
#labels: classification
mexico_train_DNN1data = mexico_dff.iloc[0:150, 1:15]
mexico_train_DNN1labels = mexico_dff.iloc[0:150, 0]
mexico_test_DNN1data = mexico_dff.iloc[150:156, 1:15]
mexico_test_DNN1labels = mexico_dff.iloc[150:156, 0]
#Construct numpy arrays for test data
temptrain = []
for i in range(0, len(mexico_train_DNN1labels)):
temptrain.append(mexico_train_DNN1labels.iloc[i])
temptrainFIN = np.array(temptrain, dtype = np.float32)
temptest = []
for i in range(0, len(mexico_test_DNN1labels)):
temptest.append(mexico_test_DNN1labels.iloc[i])
temptestFIN = np.array(temptest, dtype = np.float32)
#set up NumPy arrays
mTrainDat = np.array(mexico_train_DNN1data, dtype = np.float32)
mTrainLab = temptrainFIN
mTestDat = np.array(mexico_test_DNN1data, dtype = np.float32)
mTestLab = temptestFIN
這樣做可以讓我看起來像以下數據：
#Independent value output
mTestDat
Out[289]:
array([[-0.08404002, -3.07483053, 0.41106853, ..., -0.08682428,
0.32954004, -0.36451185],
[-0.31538665, -2.23493481, 1.97653472, ..., 0.35220796,
0.09061374, -0.59035355],
[ 0.44257978, -3.04786181, -0.6633662 , ..., 1.34870672,
0.43879321, 0.26306254],
...,
[ 2.38574553, 0.09045095, -0.09710167, ..., 1.20889878,
0.00937434, -0.06398607],
[ 1.68626559, 0.65349185, 0.23625408, ..., -1.16267788,
0.45464727, -1.14916229],
[ 1.58263958, 0.1223636 , -0.12084256, ..., 0.7947616 ,
-0.47359121, 0.28013545]], dtype=float32)
#Classification labels (up or down movement) output
mTestLab
Out[290]:
array([[ 0., 1.],
[ 0., 1.],
[ 0., 1.],
[ 1., 0.],
[ 0., 1.],
[ 1., 0.],
........
[ 1., 0.],
[ 0., 1.],
[ 0., 1.],
[ 0., 1.]], dtype=float32)
下距該給定設置教程後，我可以儘可能運行代碼在classifier.train（）函數之前停止運行，並給了我以下錯誤：
# Specify that all features have real-value data
feature_columns = [tf.feature_column.numeric_column("x", shape=[mexico_train_DNN1data.shape[1]])]
# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
hidden_units=[10, 20, 10],
optimizer = tf.train.AdamOptimizer(0.01),
n_classes=2) #representing either an up or down movement
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x = {"x": mTrainDat},
y = mTrainLab,
num_epochs = None,
shuffle = True)
#Now, we train the model
classifier.train(input_fn=train_input_fn, steps = 2000)
File "Source\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\canned\head.py", line 174, in _check_labels
(static_shape,))
ValueError: labels shape must be [batch_size, labels_dimension], got (128, 2).
我不知道爲什麼我遇到這個錯誤，任何幫助表示讚賞。