数据挖掘-心跳信号分类预测
class Net1(K.Model):
def init(self):
super(Net1, self).init()
self.conv1 = Conv1D(filters=16, kernel_size=3, padding='same', activation='relu', input_shape = (205, 1))
self.conv2 = Conv1D(filters=32, kernel_size=3, dilation_rate=2, padding='same', activation='relu')
self.conv3 = Conv1D(filters=64, kernel_size=3, dilation_rate=2, padding='same', activation='relu')
self.conv4 = Conv1D(filters=64, kernel_size=5, dilation_rate=2, padding='same', activation='relu')
self.max_pool1 = MaxPool1D(pool_size=3, strides=2, padding='same')
self.conv5 = Conv1D(filters=128, kernel_size=5, dilation_rate=2, padding='same', activation='relu')
self.conv6 = Conv1D(filters=128, kernel_size=5, dilation_rate=2, padding='same', activation='relu')
self.max_pool2 = MaxPool1D(pool_size=3, strides=2, padding='same')
self.dropout = Dropout(0.5)
self.flatten = Flatten()
self.fc1 = Dense(units=256, activation='relu')
self.fc21 = Dense(units=16, activation='relu')
self.fc22 = Dense(units=256, activation='sigmoid')
self.fc3 = Dense(units=4, activation='softmax')
def call(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.max_pool1(x)
x = self.conv5(x)
x = self.conv6(x)
x = self.max_pool2(x)
x = self.dropout(x)
x = self.flatten(x)
x1 = self.fc1(x)
x2 = self.fc22(self.fc21(x))
x = self.fc3(x1+x2)
return x
print(predictions_nn1(k_x_train))
