| 12345678910111213141516171819202122232425262728293031323334 |
- import numpy as np
- from keras.callbacks import EarlyStopping
- from keras.models import Sequential
- from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten
- from sklearn.metrics import confusion_matrix
- def build_cnn_network():
- model = Sequential()
- model.add(Conv1D(32, kernel_size=5, strides=1,
- activation='relu',
- input_shape=(122, 1)))
- model.add(MaxPooling1D(pool_size=2, strides=2))
- model.add(Conv1D(64, 5, activation='relu'))
- model.add(MaxPooling1D(pool_size=2))
- model.add(Flatten())
- model.add(Dense(1000, activation='relu'))
- model.add(Dense(5, activation='softmax'))
- model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
- return model
- def run_cnn_network(train, target, epochs):
- cnn = build_cnn_network()
- train = np.expand_dims(train, axis=2)
- early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=0, mode='auto')
- cnn.fit(x=train, y=target, epochs=epochs, validation_split=0.1, batch_size=128, callbacks=[early_stopping])
- return cnn
- def evaluate_cnn_network(cnn, test, test_target, flag):
- loss, accuracy = cnn.evaluate(np.expand_dims(test, axis=2), test_target, batch_size=None, verbose=1)
- # print('[-]( 测试集: ' + flag + ' ) 测试损失为：' + str(loss) + ' 准确度为：' + str(accuracy))
- return loss, accuracy
|
