使用 TensorFlow 模型评估输入的简单方法？

Question

在这里，我有一个使用生成的数据训练的增强决策树，并保存为est：from sklearn.datasets import make_blobsimport pandas as pdimport tensorflow as tf#creates an input function for a tf modeldef make_input_fn(X, Y, n_epochs=None, shuffle=True, verbose=False):    batch_len = len(Y)    def input_fn():        dataset = tf.data.Dataset.from_tensor_slices((dict(X), Y))        if shuffle:            dataset = dataset.shuffle(batch_len)        # For training, cycle thru dataset as many times as need (n_epochs=None).        dataset = dataset.repeat(n_epochs)        #dividing data into batches        dataset = dataset.batch(batch_len)        return dataset    return input_fn#making datatrainX, trainY = make_blobs(n_samples=10, centers=2, n_features=3, random_state=0)#xValstrainX = pd.DataFrame(trainX)trainX.columns = ['feature{}'.format(num) for num in trainX.columns]#yValstrainY = pd.DataFrame(trainY)trainY.columns = ['flag']# Defining input functiontrain_input_fn = make_input_fn(trainX, trainY)#defining tf feature columnsfeature_columns=[]for feature_name in list(trainX.columns):    feature_columns.append(tf.feature_column.numeric_column(feature_name,dtype=tf.float32))    #creating the estimatorn_batches = 1est = tf.estimator.BoostedTreesClassifier(feature_columns, n_batches_per_layer=n_batches)est.train(train_input_fn, max_steps=10)我想使用该模型根据一行训练数据进行预测以用于测试目的；像这样的事情：res = est.predict(trainX.loc[0])但是，我很难弄清楚如何去做。

Answer 1

您必须像训练时一样创建输入函数。

代码：

def my_input_fn(features, batch_size=256):

    """An input function for prediction."""

    # Convert the inputs to a Dataset without labels.

    return tf.data.Dataset.from_tensor_slices(dict(features)).batch(batch_size)

testX = pd.DataFrame(trainX.loc[0]).T

predictions = est.predict(

    input_fn=lambda: my_input_fn(testX))

预测将为您提供一个生成器对象。你必须迭代它才能获得预测

for pred_dict in predictions:

    class_id = pred_dict['class_ids'][0]

    probability = pred_dict['probabilities'][class_id]

    print(class_id, probability)

class_id是预测的ID。

请注意，pred_dict 还包含其他信息。

以下是 pred_dict 中包含的信息：

{'all_class_ids': array([0, 1]),

 'all_classes': array([b'0', b'1'], dtype=object),

 'class_ids': array([0], dtype=int64),

 'classes': array([b'0'], dtype=object),

 'logistic': array([0.17926924], dtype=float32),

 'logits': array([-1.5213063], dtype=float32),

 'probabilities': array([0.82073075, 0.17926925], dtype=float32)}