我尝试使用Tensorflow2创建一个用于信号处理应用的类似u-net的神经网络（自动编码器）结构。然而，在训练模型时遇到了一些问题。
这是我定义的神经网络结构。
def build_unet(input_shape, n_filters_list = [16, 32]): inputs = Input(shape=input_shape) print("in", inputs) contraction = {} for f in n_filters_list: x = Conv1D(f, 5, activation='relu', kernel_initializer='he_normal', padding='same')(inputs) x = Dropout(0.1)(x) x = Conv1D(f, 5, activation='relu', kernel_initializer='he_normal', padding='same')(x) contraction[f'conv{f}'] = x x = MaxPooling1D(pool_size=4,strides=2)(x) print("enc", x) inputs = x c5 = Conv1D(160, 5, activation='relu', kernel_initializer='he_normal', padding='same')(inputs) c5 = Dropout(0.2)(c5) c5 = Conv1D(160, 5, activation='relu', kernel_initializer='he_normal', padding='same')(c5) print("c5",c5) inputs = c5 print(inputs) for i,f in zip([0,0],reversed(n_filters_list)): x = Conv1DTranspose(f, 4 + i, 2)(inputs) print("dec",x) x = concatenate([x, contraction[f'conv{f}']]) x = Conv1D(f, 5, activation='relu', kernel_initializer='he_normal', padding='same')(x) x = Dropout(0.2)(x) x = Conv1D(f, 5, activation='relu', kernel_initializer='he_normal', padding='same')(x) inputs = x outputs = Conv1D(filters=1, kernel_size=3, activation="tanh", padding="same")(inputs) print("out",outputs) return Model(inputs=inputs, outputs=outputs)它可以像往常一样编译。
model = build_unet(input_shape=(3490,1))model.compile(optimizer="Adam", loss='mean_squared_error')我还尝试查看模型摘要。然而，它显示了意外的结果，因为它显示我只有2层。
当我尝试按以下方式训练我的模型时：
history = model.fit(training_generator, validation_data=validation_generator, epochs=100)它出现了这个错误：
ValueError: in user code:File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function * return step_function(self, iterator)File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,))File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step ** outputs = model.train_step(data)File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 859, in train_step y_pred = self(x, training=True)File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler raise e.with_traceback(filtered_tb) from NoneFile "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 228, in assert_input_compatibility raise ValueError(f'Input {input_index} of layer "{layer_name}" 'ValueError: Exception encountered when calling layer "model_1" (type Functional).Input 0 of layer "conv1d_77" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, None)Call arguments received: • inputs=tf.Tensor(shape=(None, None), dtype=float32) • training=True • mask=None有谁能提供我这个意外模型摘要和错误的原因，以及我该如何修复这个问题吗？
非常感谢。
回答：
您多次覆盖了inputs变量，所以当您在这里创建模型时Model(inputs = inputs, outputs = outputs)，您的inputs变量实际上并不包含Input(shape=input_shape)。
您可以基本保持代码不变，但添加一个额外的变量来记住初始输入层，像这样initial_input = inputs = Input(shape=input_shape)，然后在创建模型时使用initial_input变量，像这样Model(inputs = initial_input, outputs = outputs)
