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一、修改源码加上如下两条代码

import os
os.environ["KERAS_BACKEND"] = "torch"

二、源码修改如下

import os
os.environ["KERAS_BACKEND"] = "torch"import numpy as np
import keras
from keras import layers
from keras.utils import to_categorical# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)# Load the data and split it between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()# Scale images to the [0, 1] range
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255
# Make sure images have shape (28, 28, 1)
x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
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 = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)batch_size = 128
epochs = 3model = keras.Sequential([layers.Input(shape=input_shape),layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),layers.MaxPooling2D(pool_size=(2, 2)),layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),layers.MaxPooling2D(pool_size=(2, 2)),layers.Flatten(),layers.Dropout(0.5),layers.Dense(num_classes, activation="softmax"),]
)model.summary()model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
)model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1
)score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])

三、Keras3 minist 训练22秒结束，训练过程截图

四、Keras3 minist 源码截图