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大小: 4.91MB文件類型: .zip金幣: 2下載: 0 次發(fā)布日期: 2023-10-28
- 語言: 其他
- 標簽: 神經(jīng)網(wǎng)絡??深度學習??keras??
資源簡介
神經(jīng)網(wǎng)絡入門代碼,keras實現(xiàn),MNIST數(shù)據(jù)集識別,詳情見博客:http://blog.csdn.net/adamshan/article/details/79004784

代碼片段和文件信息
from?__future__?import?print_function
import?keras
from?keras.datasets?import?mnist
from?keras.models?import?Sequential
from?keras.layers?import?Dense
from?keras.optimizers?import?SGD
from?matplotlib?import?pyplot?as?plt
def?read_data(num_classes):
????#?the?data?shuffled?and?split?between?train?and?test?sets
????(x_train?y_train)?(x_test?y_test)?=?mnist.load_data()
????x_train?=?x_train.reshape(60000?784)
????x_test?=?x_test.reshape(10000?784)
????x_train?=?x_train.astype(‘float32‘)
????x_test?=?x_test.astype(‘float32‘)
????x_train?/=?255
????x_test?/=?255
????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)
????return?x_train?y_train?x_test?y_test
def?model(x_train?y_train?x_test?y_test?batch_size?epochs?num_classes):
????model?=?Sequential()
????model.add(Dense(15?activation=‘relu‘?input_shape=(784)))
????model.add(Dense(num_classes?activation=‘softmax‘))
????model.summary()
????model.compile(loss=‘categorical_crossentropy‘
??????????????????optimizer=SGD(lr=0.01)
??????????????????metrics=[‘accuracy‘])
????history?=?model.fit(x_train?y_train
????????????????????????batch_size=batch_size
????????????????????????epochs=epochs
????????????????????????verbose=1
????????????????????????validation_data=(x_test?y_test))
????###?print?the?keys?contained?in?the?history?object
????print(history.history.keys())
????plot_training(history=history)
????model.save(‘model.json‘)
????score?=?model.evaluate(x_test?y_test?verbose=0)
????print(‘Test?loss:‘?score[0])
????print(‘Test?accuracy:‘?score[1])
def?plot_training(history):
????###?plot?the?training?and?validation?loss?for?each?epoch
????plt.plot(history.history[‘loss‘])
????plt.plot(history.history[‘val_loss‘])
????plt.title(‘model?mean?squared?error?loss‘)
????plt.ylabel(‘mean?squared?error?loss‘)
????plt.xlabel(‘epoch‘)
????plt.legend([‘training?set‘?‘validation?set‘]?loc=‘upper?right‘)
????plt.show()
def?show_samples(samples?labels):
????plt.figure(figsize=(12?12))
????for?i?in?range(len(samples)):
????????plt.subplot(4?4?i+1)
????????plt.imshow(samples[i]?cmap=‘gray‘)
????????plt.title(labels[i])
????plt.show()
if?__name__?==?‘__main__‘:
????batch_size?=?128
????num_classes?=?10
????epochs?=?20
????x_train?y_train?x_test?y_test?=?read_data(num_classes)
????model(x_train?y_train?x_test?y_test?batch_size?epochs?num_classes)
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2018-01-08?16:49??code\
?????文件?????5382352??2018-01-08?16:49??code\model.json
?????文件????????2588??2018-01-08?16:49??code\model.py
?????目錄???????????0??2018-01-08?16:49??code\.ipynb_checkpoints\
?????文件??????330403??2018-01-08?16:49??code\.ipynb_checkpoints\Unti
?????文件??????330403??2018-01-08?16:49??code\model.ipynb
?????目錄???????????0??2018-01-08?16:49??code\.idea\
?????目錄???????????0??2018-01-08?16:49??code\.idea\inspectionProfiles\
?????文件?????????562??2018-01-08?16:49??code\.idea\inspectionProfiles\Project_Default.xm
?????文件?????????459??2018-01-08?16:49??code\.idea\code.iml
?????文件???????12139??2018-01-08?16:49??code\.idea\workspace.xm
?????文件?????????260??2018-01-08?16:49??code\.idea\modules.xm
?????文件?????????209??2018-01-08?16:49??code\.idea\misc.xm
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