在Keras/Tensorflow中使用部分形状的三维稀疏输入给出了误差
在Keras/Tensorflow中使用部分形状的三维稀疏输入给出了误差
提问于 2022-04-06 17:22:57
我试图创建一个密集的神经网络，其中我的输入是一个稀疏的三维矩阵。当转换为稠密矩阵时，形状为(2, None, n) (其中n是许多特性，并且是固定的)。当我使用密集的输入时，我的keras体系结构工作得很好。但是，为了节省内存，我尝试使用稀疏张量作为输入。
这是我的密码
import numpy as np
import tensorflow as tf
input = tf.keras.Input(batch_shape=(2,None,5))
x = tf.keras.layers.Dropout(0.1)(input)
x = tf.keras.layers.Dense(1)(x)
model = tf.keras.models.Model(inputs=[input], outputs=[x])
model.compile(loss='mse')
print(model.summary())
dummy_input = np.random.random((2,10,5))
dummy_sp = tf.sparse.from_dense(dummy_input)
dummy_output= np.random.random((2,10,1))
model.fit(x=dummy_sp, y=dummy_output, epochs=1)当我使用x=dummy_input时，上面的代码可以正常工作。然而，当我切换到稀疏输入dummy_sp时，我会得到以下错误
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(2, None, 5)] 0
dropout (Dropout) (2, None, 5) 0
dense (Dense) (2, None, 1) 6
=================================================================
Total params: 6
Trainable params: 6
Non-trainable params: 0
_________________________________________________________________
None
Traceback (most recent call last):
File "c:\Users\099391\OneDrive\Documents\Projects\NP_E-QSI\srcc\test.py", line 17, in <module>
model.fit(x=dummy_sp, y=dummy_output, epochs=1)
File "C:\Users\099391\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\099391\Anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py", line 1147, in autograph_handler
raise e.ag_error_metadata.to_exception(e)
TypeError: in user code:
File "C:\Users\099391\Anaconda3\lib\site-packages\keras\engine\training.py", line 1021, in train_function *
return step_function(self, iterator)
File "C:\Users\099391\Anaconda3\lib\site-packages\keras\engine\training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\099391\Anaconda3\lib\site-packages\keras\engine\training.py", line 1000, in run_step **
outputs = model.train_step(data)
File "C:\Users\099391\Anaconda3\lib\site-packages\keras\engine\training.py", line 859, in train_step
y_pred = self(x, training=True)
File "C:\Users\099391\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
TypeError: Exception encountered when calling layer "dropout" (type Dropout).
Failed to convert elements of SparseTensor(indices=Tensor("DeserializeSparse:0", shape=(None, 3), dtype=int64), values=Tensor("model/Cast:0", shape=(None,), dtype=float32), dense_shape=Tensor("stack:0", shape=(3,), dtype=int64)) to Tensor. Consider casting elements to a supported type. See https://www.tensorflow.org/api_docs/python/tf/dtypes for supported TF dtypes.
Call arguments received:
• inputs=<tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x000002423CD78E20>
• training=True回答 1
Stack Overflow用户
回答已采纳
发布于 2022-04-06 17:48:25
基于tf-文件，即使将sparse参数设置为False，只要输入数据是稀疏矩阵，仍然可以传递sparse张量。
稀疏:指定要创建的占位符是否稀疏的布尔值。只有“衣衫褴褛”和“稀疏”中的一个才是真实的。注意，如果稀疏是假的，稀疏张量仍然可以传递到输入--它们将被加密，默认值为0.。
input = tf.keras.Input(batch_shape=(2,None,5), sparse=False)
x = tf.keras.layers.Dropout(0.1)(input)
x = tf.keras.layers.Dense(1)(x)
model = tf.keras.models.Model(inputs=[input], outputs=[x])
print(model.summary())
# OK 更新
基于下面的评论。
class toDense(keras.layers.Layer):
def call(self, input):
if isinstance(input, tf.sparse.SparseTensor):
return tf.sparse.to_dense(input)
return input将其放置在Input之后和上述模型的Dropout层之前。喜欢,
input = tf.keras.Input(batch_shape=(2,None,5))
x = toDense()(input)
x = tf.keras.layers.Dropout(0.1)(x)
...接下来，你可以
dummy_input = np.random.random((2,10,5))
dummy_sp = tf.sparse.from_dense(dummy_input)
dummy_output= np.random.random((2,10,1))
model(dummy_sp).shape
>> TensorShape([2, 10, 1])
model.compile(loss='mse')
model.fit(x=dummy_sp, y=dummy_output, epochs=1)
1/1 [=====] - 0s 416ms/step - loss: 0.2746页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/71771137
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