使用Python实现,输出不使用批处理和使用批处理方法处理mnist数据集的执行时长
时间: 2024-05-21 14:12:25 浏览: 16
如果您使用Python实现,输出不使用批处理和使用批处理方法处理mnist数据集的执行时长,则可以使用如下代码:
不使用批处理:
import time
from keras.datasets import mnist
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from keras import backend as K
# set image dimensions
img_rows, img_cols = 28, 28
# load mnist dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# reshape data and convert labels to one-hot encoding
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)
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
# create and compile model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# train model
start_time = time.time()
model.fit(x_train, y_train, batch_size=32, epochs=10, verbose=1)
end_time = time.time()
# evaluate model
score = model.evaluate(x_test, y_test, verbose=0)
# print results and timing
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('Training time:', end_time - start_time, 'seconds')
使用批处理:
import time
from keras.datasets import mnist
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
from keras import backend as K
# set image dimensions
img_rows, img_cols = 28, 28
# load mnist dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# reshape data and convert labels to one-hot encoding
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)
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
# create and compile model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# train model with batch processing
start_time = time.time()
model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1)
end_time = time.time()
# evaluate model
score = model.evaluate(x_test, y_test, verbose=0)
# print results and timing
print('Test loss:', score[0])
print('Test accuracy:', score[1])
print('Training time:', end_time - start_time, 'seconds')
以上代码分别实现了不使用批处理和使用批处理方法处理mnist数据集的训练,并计算了训练时间,您可以直接复制代码并运行。