ValueError:两个形状中的尺寸1必须相等，但分别为10和1 [英] ValueError: Dimension 1 in both shapes must be equal, but are 10 and 1

问题描述

我试图借助MNIST数据集来实现shap DeepExplainer(DeepSHAP)模块.但是我收到以下错误:

I was trying to implement shap DeepExplainer (DeepSHAP) module with the help of MNIST Dataset. But i am getting the following error:

ValueError:两个形状中的尺寸1必须相等，但必须为10和1.形状为[?，10]和[?，1].对于渐变形状为[7，dense_2_1/Softmax_grad/gradients/gradients_7/dense_2_1/Softmax_grad/truediv_grad/Select_1(op:'Select')，输入形状为[?，1]，[?, 10]，[?, 10].

ValueError: Dimension 1 in both shapes must be equal, but are 10 and 1. Shapes are [?,10] and [?,1]. for gradients_7/dense_2_1/Softmax_grad/gradients/gradients_7/dense_2_1/Softmax_grad/truediv_grad/Select_1 (op: 'Select') with input shapes: [?,1], [?,10], [?,10].

该错误在该行中: shap_values = e.shap_values(x_test [1:5])

完整代码:

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
             activation='relu',
             input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
          optimizer=keras.optimizers.Adadelta(),
          metrics=['accuracy'])

model.fit(x_train, y_train,
      batch_size=batch_size,
      epochs=epochs,
      verbose=1,
      validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

import shap
import numpy as np

# select a set of background examples to take an expectation over
background = x_train[np.random.choice(x_train.shape[0], 100, replace=False)]

# explain predictions of the model on three images
e = shap.DeepExplainer(model, background)
shap_values = e.shap_values(x_test[1:5])
shap.image_plot(shap_values, -x_test[1:5])

推荐答案

您正在使用哪个Keras版本?您可以从代码 keras .__ version __

Which Keras version you are using? You can print from your code keras.__version__

当我在 Tensorflow 下导入keras时，您的代码按预期工作.主要变化之一是使用 adam 优化器，该优化器在您的情况下效果更好，因为它可以在几个时期内提高准确性.我尝试了 Adadelta ，但是它正在缓慢优化.自己检查性能.

When I imported keras under Tensorflow, your code worked as expected. One of the major change is using adam optimizer which works better in your case as it is able to improve accuracy within few epochs. I tried Adadelta but it was slowly optimizing. check the performance yourself.

检查以下代码是否有小的修改(不多).我所做的只是在 keas 2.3.1 抛出其他错误时在 Tensorflow 下导入模块.

Check the following code for small modifications (not much). All i did was to import modules under Tensorflow as keas 2.3.1 was throwing some other error.

from __future__ import print_function
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras import backend as K

batch_size = 256
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),activation='relu', input_shape=input_shape,))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=tf.keras.losses.categorical_crossentropy,
          optimizer=tf.keras.optimizers.Adam(0.001),#tf.keras.optimizers.Adadelta(),
          metrics=['accuracy'])

model.fit(x_train, y_train,
          steps_per_epoch = x_train.shape[0]//batch_size,
      batch_size=batch_size,
      epochs=epochs,
      verbose=1,
      validation_data=(x_test, y_test),validation_steps=x_test.shape[0]//batch_size)
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

这篇关于ValueError:两个形状中的尺寸1必须相等，但分别为10和1的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！