我在尝试重建一篇论文中提到的卷积模型，但遇到了一些问题。
这是论文中模型的架构：
这是我用Python和TensorFlow尝试的代码：
X = dfy = dataset['attack_map']X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.30)X_train = np.array(X_train)X_test = np.array(X_test)y_train = np.array(y_train)y_test = np.array(y_test)X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)shape = (X_train.shape[1], X_train.shape[2])## 第一块model1 = tf.keras.Sequential()model1.add(tf.keras.layers.InputLayer(input_shape = shape))model1.add(tf.keras.layers.Conv1D(filters = 32, kernel_size = 2, strides = 1))model1.add(tf.keras.layers.Activation('relu'))model1.add(tf.keras.layers.MaxPooling1D(pool_size = 2, strides = 1))model1.add(tf.keras.layers.Flatten())model1.add(tf.keras.layers.Dropout(.5))## 第二块model2 = tf.keras.Sequential()model2.add(tf.keras.layers.InputLayer(input_shape = shape))model2.add(tf.keras.layers.Conv1D(filters = 32, kernel_size = 4, strides = 1))model2.add(tf.keras.layers.Activation('relu'))model2.add(tf.keras.layers.MaxPooling1D(pool_size = 4, strides = 1))model2.add(tf.keras.layers.Flatten())model2.add(tf.keras.layers.Dropout(.5))## 第三块model3 = tf.keras.Sequential()model3.add(tf.keras.layers.InputLayer(input_shape = shape))model3.add(tf.keras.layers.Conv1D(filters = 32, kernel_size = 8, strides = 1))model3.add(tf.keras.layers.Activation('relu'))model3.add(tf.keras.layers.MaxPooling1D(pool_size = 8, strides = 1))model3.add(tf.keras.layers.Flatten())model3.add(tf.keras.layers.Dropout(.5))model3.add(tf.keras.layers.Dense(256))model3.add(tf.keras.layers.Dropout(.5))model3.add(tf.keras.layers.Dense(5))model3.add(tf.keras.layers.Softmax())model = tf.keras.Sequential() model.add(tf.keras.layers.Concatenate([model1, model2, model3]))model.add(tf.keras.layers.Dense(5))model.add(tf.keras.layers.Softmax())model.compile(loss = 'sparse_categorical_crossentropy', optimizer = "adam", metrics = ['accuracy'])callback = tf.keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0, patience=10, verbose=1, mode="auto", baseline=None, restore_best_weights=False),start = time.perf_counter()model.fit(X_train, y_train, epochs=EPOCHS, callbacks=[callback])elapsed = time.perf_counter() - startmodel.evaluate(X_test, y_test, verbose=2)print('Elapsed %.3f seconds.' % elapsed)我得到的错误是：
ValueError: A `Concatenate` layer should be called on a list of at least 2 inputs我的想法是连接这些块。这可能是一个愚蠢的问题，但我最近一直在使用TensorFlow。
有什么建议吗？谢谢
回答：
你应该在Keras中使用函数式API。这样你可以很容易地通过以下伪代码实现它：
h1=tf.keras.layers.Conv1D(filters = 32, kernel_size = 4, strides = 1)(inp)h1=tf.keras.layers.Activation('relu')(h)h1=tf.keras.layers.Flatten()(h)h1=tf.keras.layers.Dense(5)(h)out1=tf.keras.layers.Softmax()(h).........h2=tf.keras.layers.Dense(5)(h)out2=tf.keras.layers.Softmax()(h).........h3=tf.keras.layers.Dense(5)(h)out3=tf.keras.layers.Softmax()(h)基本上，out值代表不同结果密集层的输出。（模型）
—
h=tf.keras.layers.concatenate([out1,out2,out3])h=tf.keras.layers.Dense(5)(h)R_out=tf.keras.layers.Softmax()(h)model=tf.keras.Model(inputs=inp,outputs=R_out)