我想使用 Conv1D 将扩张卷积网络制作为顺序数据集。
所以我尝试使用 Boston 数据集进行 Conv1D。
from tensorflow.python.keras.layers import Conv1D, MaxPooling2D
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Input, Dense
from tensorflow.python.keras.models import Model
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
boston = load_boston()
df = pd.DataFrame(boston.data,columns=boston.feature_names)
df['target']= boston.target
y = df['target']
X = df.drop(columns=['target'])
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=2)
def NN_model(data= X_train):
#data = np.expand_dims(data, axis=2)
inputs = Input(((data.shape)))
x = Conv1D(2,2,dilation_rate=2,padding="same", activation="relu")(inputs)
x = Flatten()(x)
x = Dense(2048, activation="relu")(x)
predictions = Dense(1)(x) …