Question

我正在尝试构建一个使用sampled_softmax_loss的模型，而我似乎无法获得为该函数正确塑造的输入张量。这是一个示例，我可以告诉它应该与文档匹配，但抛出此异常：

ValueError：Shape必须是等级2，但是等级为1 'sampled_softmax_loss / LogUniformCandidateSampler'（op： 'LogUniformCandidateSampler'）输入形状：[？]。

代码：

import tensorflow as tf
import numpy as np
f1 = np.random.randint(low = 0, high = 4,size = 100)
labels = np.random.randint(low = 0, high = 5,size = 100)


f1_t = tf.feature_column.categorical_column_with_vocabulary_list('f1', vocabulary_list = [0,1,2,3])
base_columns = [f1_t]
feat_dict = {'f1' : f1}

def my_model_fn(
            features,
            labels,
            mode,
            params): 
    logits = tf.feature_column.linear_model(features, base_columns,units = params["n_classes"])
    if mode == tf.estimator.ModeKeys.PREDICT:
        predictions = {
            'probabilities': tf.nn.softmax(logits),
        }
        return tf.estimator.EstimatorSpec(mode, predictions=predictions)

    weights = [v for v in tf.global_variables() if v.name == 'linear_model/f1/weights:0'][0]
    biases = [v for v in tf.global_variables() if v.name == 'linear_model/bias_weights:0'][0]

    if mode == "train":
        loss = tf.nn.sampled_softmax_loss(
          weights=tf.transpose(weights),
          biases=biases,
          labels=labels,
          inputs=logits,
          num_classes = 5,
          num_sampled= 11,
            num_true=1,
          partition_strategy="div")
    elif mode == "eval": 
        None
    #    implement later


    if mode == tf.estimator.ModeKeys.EVAL:
        return tf.estimator.EstimatorSpec(
           mode,
           loss=loss,
        )

    optimizer = tf.train.FtrlOptimizer(learning_rate=.1,l2_regularization_strength=0.1)#AdagradOptimizer(0.001)
    train_op = optimizer.minimize(
       loss,
       global_step=tf.train.get_global_step())

    return tf.estimator.EstimatorSpec(
       mode,
       loss=loss,
       train_op=train_op)


classifier = tf.estimator.Estimator(
        model_fn = my_model_fn,
        params = {
            "feature_columns" : base_columns,
            "n_classes" : 5

        })


classifier.train(
input_fn = tf.estimator.inputs.numpy_input_fn(feat_dict,
                                                      labels,
                                                      batch_size = 3,
                                                      num_epochs=2,
                                                      shuffle=True))

如果有人能给我一些指示，我会永远欠你虚拟啤酒。

Answer 1

标签应采用[batch, one_hot]形状，例如[100, 6]

sampled_softmax_loss抱怨畸形张量（但似乎不是）

1 个答案: