另一个“在检查目标时出错:期望density_2具有形状(4，)，但是得到具有形状(1，)的阵列". [英] Yet another "Error when checking target: expected dense_2 to have shape (4,) but got array with shape (1,)"

问题描述

我正在Python 3中使用Keras.我遇到的问题似乎与许多其他问题相似，尽管我没有看到如何设置该问题，但我能告诉我的最好情况是我可能需要使用Flatten().参数正确.我收到错误消息:

I'm using Keras in Python 3. The issue I'm having seems to be similar to many others, and the best I can tell I might need to use Flatten(), though I am not seeing how to set the parameters correctly. I get the error:

ValueError:检查目标时出错:预期density_2具有形状 (4，)但形状为(1，)

ValueError: Error when checking target: expected dense_2 to have shape (4,) but got array with shape (1,)

我的数据还不是图像，但它们是我转为数据帧的序列.

My data is not of images (yet) but they are sequences I've turned in to data frames.

model = Sequential()
model.add(Dense(30, input_dim=16, activation='relu'))
model.add(Dense(len(TheBinsizeList), activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])


model.fit(Train_Columns, TrainTarget_Columns.to_frame(), epochs=100, batch_size=64)

print(Train_Columns.shape)
# Gives a value of (1627, 16)


print((TrainTarget_Columns.to_frame()).shape)
# Gives a value of (1627,1)

现在TrainTarget_Columns的值是以下两个元组的1627:

Now the value of TrainTarget_Columns are 1627 of these two tuples:

(1494，3)(1080，2)(1863，2)(919，4)(1700，2)(710，4)(1365，4)(1815，3)(1477，2)(1618) ，1)...

(1494, 3) (1080, 2) (1863, 2) (919, 4) (1700, 2) (710, 4) (1365, 4) (1815, 3) (1477, 2) (1618, 1)...

主题编号是每个小管中的第一个条目，第二个条目是作为训练目标的值.

The subject number is the first entry in each of the tuble, and the second entry is the value that is the training target.

虽然我看到将density_2中的TheBinsizeList从4更改为2会导致期望的形状从(4，)变为(2，)，但我看不到如何正确使用Flatten(如果需要)格式化值.

While I see that changing TheBinsizeList from 4 to 2 in dense_2 causes the expected shape to go from (4,) to (2,) I don't see how to use Flatten (if that is what is needed) to correctly format the values.

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 30)                510       
_________________________________________________________________
dense_2 (Dense)              (None, 4)                 124       
=================================================================
Total params: 634
Trainable params: 634
Non-trainable params: 0

感谢您的任何帮助.

推荐答案

考虑到您的模型摘要，模型期望输入形状为(batch_size, 16)且目标形状为(batch_size, 4).

Considering your model summary, the model expects an input of shape (batch_size, 16) and a target of shape (batch_size, 4).

如果目标的形状为(1627,1)，则说明您有问题.

If your target's shape is (1627,1) there is your problem.

解决方案:将其更改为一个热门变量(例如使用tf.one_hot(y, n_classes))，错误应消失

Solution: Change it to a one hot variable (e.g. using tf.one_hot(y, n_classes)) and the error should disappear

import numpy as np
import tensorflow as tf

input_dim = 16
hidden_dim = 30
n_classes = 4

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(hidden_dim, input_dim=input_dim, activation='relu'))
model.add(tf.keras.layers.Dense(n_classes, input_dim=hidden_dim, activation='relu'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

X = np.random.randn(100, input_dim)
y = np.random.randint(0, n_classes, size=(100,))

model.fit(X, y)
# ValueError: Shapes (None, 1) and (None, 4) are incompatible

y = tf.one_hot(y, n_classes)
model.fit(X, y)
# Works !

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