基本CNN分类模型有UnimplementedError:图执行错误:

Question

我从书中尝试了一个CNN应用于MNIST数据分类的示例代码:

from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28, 1)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation = 'relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64, (3,3), activation = 'relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation = 'relu'))
model.add(layers.Dense(10, activation = 'softmax'))
model.summary()
#Test this model on mnist
from keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000,28,28,1))
train_images = train_images.astype('float32')/255
test_images = test_images.reshape((10000,28,28,1))
test_images = test_images.astype('float32')/255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

代码应该是正确的，但是当我运行代码时出现错误:

UnimplementedError:图执行错误:

我认为问题可能是由于不同版本的tensorflow(我的tensorflow是2.8，而样本代码是在tensorflow 2.0中运行)。谁能告诉我如何解决这个问题?

Answer 1

看起来你需要在单个数字上执行形状而不是所有颗粒

[示例]:

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.utils import to_categorical
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSets
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
ds = tfds.load('mnist', split='train', shuffle_files=True)
ds = ds.shuffle(1024).batch(64).prefetch(tf.data.experimental.AUTOTUNE)
assert isinstance(ds, tf.data.Dataset)
for example in ds.take(1):
image, label = example["image"], example["label"]
#################################################
image = tf.cast( image, dtype=tf.float32 )
image = tf.math.divide_no_nan( image, 255 )
#################################################

train_images = image
train_labels = to_categorical(label)
test_labels = to_categorical(label)
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28, 1)))
model.add(tf.keras.layers.MaxPooling2D((2,2)))
model.add(tf.keras.layers.Conv2D(64, (3,3), activation = 'relu'))
model.add(tf.keras.layers.MaxPooling2D((2,2)))
model.add(tf.keras.layers.Conv2D(64, (3,3), activation = 'relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation = 'relu'))
model.add(tf.keras.layers.Dense(10, activation = 'softmax'))
model.summary()
model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy', metrics = ['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)

[Output]:输入图片描述