我正在尝试使用Tensorflow 2.0.0的Keras和Tensorflow Datasets API来预测从多维输入到多维输出。
我在python 3.6.9上使用tensorflow 2.0.0和tensorflow-datasets 1.3.0。
下面是我的示例代码,我也在a Colab notebook上复制了它,你可以运行它:
import tensorflow as tf
data = [[1,2],[11,22]]
label = [[3,4,5], [33,44,55]]
dataset = tf.data.Dataset.from_tensor_slices((data,label))
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(3))
model.compile('adam','mse',metrics=['mse'])
model.fit(dataset, validation_data=dataset)在这个示例代码中,我试图预测[1,2]->[3,4,5]和[11,22]->[33,44,55]。然而,我得到了错误:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/tensorflow-2.0.0/python3.6/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1609 try:
-> 1610 c_op = c_api.TF_FinishOperation(op_desc)
1611 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimensions must be equal, but are 2 and 3 for 'loss/output_1_loss/SquaredDifference' (op: 'SquaredDifference') with input shapes: [2,3], [3,1].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
29 frames
/tensorflow-2.0.0/python3.6/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1611 except errors.InvalidArgumentError as e:
1612 # Convert to ValueError for backwards compatibility.
-> 1613 raise ValueError(str(e))
1614
1615 return c_op
ValueError: Dimensions must be equal, but are 2 and 3 for 'loss/output_1_loss/SquaredDifference' (op: 'SquaredDifference') with input shapes: [2,3], [3,1].发布于 2019-12-12 04:37:27
根据问题的thushv89's comment,在数据集上使用batch可以修复代码。原始代码比这复杂得多,但使用batch修复了它。
import tensorflow as tf
data = [[1,2],[11,22]]
label = [[3,4,5], [33,44,55]]
dataset = tf.data.Dataset.from_tensor_slices((data,label)).batch(2)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(3))
model.compile('adam','mse',metrics=['mse'])
model.fit(dataset, validation_data=dataset)https://stackoverflow.com/questions/59276713
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