Question

我正在尝试使用python SDk和tensorflow进行sagemaker上的测试分类。我可以修改此https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras/abalone.py并运行它，但是当我更改拱形以包含嵌入图层时，我收到错误

＆＃34; Fetch参数不能解释为Tensor。（Tensor Tensor（＆＃34;第一层/嵌入：0＆＃34;，shape =（*，），dtype = float32_ref）不是此图的元素。＆＃34; < / p>

当我将其作为独立模型运行时，它运行完美。这里是独立模型的拱门

model = Sequential()
model.add(Embedding(len(word_index) + 1,
                        EMBEDDING_DIM,
                        weights=[embedding_matrix],
                        input_length=MAX_SEQUENCE_LENGTH,
                        trainable=False))

model.add(Conv1D(64, kernel_size=10, padding='same', activation='relu'))
model.add(Conv1D(64, kernel_size=15, padding='same', activation='selu'))
model.add(Conv1D(128, kernel_size=15, padding='same', activation='relu'))
model.add(Conv1D(64, kernel_size=25, padding='same', activation='softmax'))
model.add(Conv1D(128, kernel_size=15, padding='same', activation='relu'))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dense(2, activation='softmax'))

这是sagemaker的model_fn：

embedding = tf.keras.layers.Embedding(len(word_index) + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=MAX_SEQUENCE_LENGTH,
                            trainable=False, name='first-layer')(features[INPUT_TENSOR_NAME])

first = tf.keras.layers.Conv1D(64, kernel_size=10, padding='same', activation='relu')(embedding)
second = tf.keras.layers.Conv1D(64, kernel_size=15, padding='same', activation='relu')(first)
 third = tf.keras.layers.Conv1D(128, kernel_size=15, padding='same', activation='relu')(second)
 fourth = tf.keras.layers.Conv1D(64, kernel_size=25, padding='same', activation='softmax')(third)
 fifth = tf.keras.layers.Conv1D(128, kernel_size=15, padding='same', activation='relu')(fourth)
sixth = tf.keras.layers.BatchNormalization()(fifth)

output = tf.keras.layers.Flatten()(sixth)
output_layer = tf.keras.layers.Dense(2, activation='softmax'))(output)

输入维度或值没有问题，如果我只用简单的密集层拱形替换这个拱门，代码就可以完美运行。

我已经尝试过解决方案了 TensorFlow: The tensor is not the element of this graph但我收到了新错误

输入图和图层图不一样：Tensor（＆＃34; random_shuffle_queue_DequeueMany：1＆＃34;，shape =（128,200），dtype = float32，device = / device：CPU：0）不是来自传入的图表。*

嵌入层产生不能被解释为sagemaker中的Tensor？

0 个答案: