Keras 实现手写字识别（代码）

最新推荐文章于 2025-05-29 21:48:04 发布

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最新推荐文章于 2025-05-29 21:48:04 发布

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分类专栏： Keras 文章标签： Keras 手写字识别

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本文介绍了一个使用Keras搭建的简单神经网络模型进行MNIST手写数字识别的任务。该模型包含多个全连接层，并采用了Dropout来防止过拟合。通过调整参数如学习率和Dropout比例，实现了较好的训练集准确率（98%）和测试集准确率（95%）。文中还对比了不同层数对模型性能的影响。

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import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers import Conv2D, MaxPooling2D, Flatten
from keras.optimizers import SGD, Adam
from keras.utils import np_utils
from keras.datasets import mnist
# categorical_crossentropy

def load_data():
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    number = 10000
    x_train = x_train[0:number]
    y_train = y_train[0:number]
    x_train = x_train.reshape(number, 28 * 28)
    x_test = x_test.reshape(x_test.shape[0], 28 * 28)
    x_train = x_train.astype('float32')
    x_test = x_test.astype('float32')
    # convert class vectors to binary class matrices
    y_train = np_utils.to_categorical(y_train, 10)
    y_test = np_utils.to_categorical(y_test, 10)
    x_train = x_train
    x_test = x_test
    # 加上x_test=np.random.normal(x_test)
    x_test=np.random.normal(x_test)
    x_train = x_train / 255
    x_test = x_test / 255
    return (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test) = load_data()

model = Sequential()
model.add(Dense(input_dim=28*28, units=689, activation='relu'))
# 添加droput,正常设为0.5,这里trian的精度很高，故而设的高一些，为0.7；
# droput削弱trian的overfitting；
model.add(Dropout(0.5))
model.add(Dense(units=689, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=689, activation='relu'))
model.add(Dropout(0.5))

# for i in range(10):
#          model.add(Dense(units=689, activation='relu'))


model.add(Dense(units=10, activation='softmax'))

# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.1), metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=10, epochs=20)

result = model.evaluate(x_train, y_train, batch_size=10000)
print('\nTrain Acc', result[1])

result = model.evaluate(x_test, y_test, batch_size=10000)
print('\nTest Acc', result[1])

result = model.predict(x_test)


# 结果 10层网络，train acc = 96%；test acc = 92%
#     3层网络，train acc = 99%；test acc = 96%
# 增加了droput，3层网络，train acc = 98%； test acc = 95%