Question

我希望获得模型预测from __future__ import print_function from keras.models import Sequential from keras.layers import Dense import keras import numpy as np model = Sequential() model.add(Dense(2000, input_dim=3072, activation='relu')) model.add(Dense(500, activation='relu')) model.add(Dense(66, activation='softmax')) model.fit(x_train,y_train, epochs=100, batch_size=128) scores = model.evaluate(x_train, y_train) print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

标签的准确性

predictions = model.predict(x_test)

现在我希望获得预测的准确性

  print("\n%s: %.2f%%" % (model.metrics_names[1], predictions*100))

我尝试过：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-262-edbcf292f31c> in <module>()
----> 1 print("\n%s: %.2f%%" % (model.metrics_names[1], predictions*100))

TypeError: float argument required, not numpy.ndarray

我收到了以下错误：

WITH DATA AS (SELECT '<soap:Envelope xmlns:soap="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema">
   <soap:Body>
      <GetDeliveryReportResponse xmlns="MyTest WebService">
         <GetDeliveryReportResult>919748021520500 -3-4/6/2017 1:18:49 PM,919400159619-3-4/6/2017 1:19:20 PM,||rc_smsSND_NUMB-11-4/6/2017 6:10:00 PM,||rc_smsSND_NUMB-11-4/6/2017 6:11:00 PM</GetDeliveryReportResult>
      </GetDeliveryReportResponse>
   </soap:Body>
</soap:Envelope>' xml FROM dual)

SELECT did
    FROM data,
          xmltable(XMLNamespaces ('http://schemas.xmlsoap.org/soap/envelope/'
                                   AS "soap"),
                   '/soap:Envelope/soap:Body/GetDeliveryReportResponse'
                   PASSING XMLTYPE(xml)
                   COLUMNS
                     did Varchar2(1000) PATH 'GetDeliveryReportResult');

Answer 1

model.predict生成的numpy.array与float完全不同。您可能尝试使用print(predictions)进行打印，但在这种情况下，使用带float的格式化字符串绝对不起作用。尝试：

print("\n%s:" % (model.metrics_names[1]))
print(100 * predictions)

或

print("\n%s: %s" % (model.metrics_names[1], np.array_str(predictions*100)))

或者x_test只有一个案例：

print("\n%s: %.2f%%" % (model.metrics_names[1], predictions[0]*100))

获得模型在预测上的准确性

1 个答案: