本文整理汇总了Python中Helper.extract_features_from_new_data方法的典型用法代码示例。如果您正苦于以下问题：Python Helper.extract_features_from_new_data方法的具体用法？Python Helper.extract_features_from_new_data怎么用？Python Helper.extract_features_from_new_data使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Helper的用法示例。
在下文中一共展示了Helper.extract_features_from_new_data方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: run
# 需要导入模块: import Helper [as 别名]
# 或者: from Helper import extract_features_from_new_data [as 别名]
def run(featureRepresentation='image', glcm_distance=1, glcm_isMultidirectional=False):
'''
Apply a CNN on the grain_images dataset and print test accuracies.
That is, train it on training data and test it on test data.
'''
train_data, train_targets, test_data, expected = Helper.extract_features_from_new_data(featureRepresentation, glcm_distance, glcm_isMultidirectional, train_size=0.5)
Helper.serialize("../Datasets/grain_glcm_d1_a4_2_new.data", (train_data, train_targets, test_data, expected))
# Build Classifier
classifier = skflow.TensorFlowEstimator(model_fn=multilayer_conv_model, n_classes=2,
steps=500, learning_rate=0.05, batch_size=128)
classifier.fit(train_data, train_targets)
# Assess
predictions = classifier.predict(test_data)
accuracy = metrics.accuracy_score(expected, predictions)
confusion_matrix = metrics.confusion_matrix(expected, predictions)
print("Confusion matrix:\n%s" % confusion_matrix)
print('Accuracy: %f' % accuracy)示例2: main
# 需要导入模块: import Helper [as 别名]
# 或者: from Helper import extract_features_from_new_data [as 别名]
def main():
#dataset = extract_features_from_old_data(featureRepresentation='glcm', glcm_distance=1, glcm_isMultidirectional=True)
#Helper.serialize("../Datasets/old_data.data", dataset)
dataset = Helper.extract_features_from_new_data(featureRepresentation='glcm', glcm_distance=1, glcm_isMultidirectional=True, train_size=0.75)
Helper.serialize("../Datasets/new_data_glcm_d1_a4_75_25.data", dataset)
build_model('glcm', dataset_file="../Datasets/new_data_glcm_d1_a4_75_25.data", iters=4, glcm_isMultidirectional=True)