我在TensorFlow中尝试训练一个1D CNN模型，输入数据的形状为(14400,1)，但我收到了一个错误，指出输入形状与模型不兼容。我已经确保我的输入数据具有正确的形状。我使用的是TensorFlow版本2.3.0

批次片段（每批32个示例，数据形状 – (14400,1)，标签形状 – (1,1)）

batch:  0Data shape:  (32, 14400, 1) (32, 1, 1)batch:  1Data shape:  (32, 14400, 1) (32, 1, 1)batch:  2Data shape:  (32, 14400, 1) (32, 1, 1)batch:  3Data shape:  (32, 14400, 1) (32, 1, 1)batch:  4Data shape:  (32, 14400, 1) (32, 1, 1)batch:  5Data shape:  (32, 14400, 1) (32, 1, 1)

CNN模型

model = Sequential()model.add(Conv1D(128, kernel_size=5, activation='relu', input_shape=(14400,1)))model.add(BatchNormalization())model.add(Dropout(.2))model.add(Conv1D(32, kernel_size=5, activation='relu'))model.add(BatchNormalization())model.add(Dropout(.2))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(.2))model.add(Dense(64, activation='relu'))model.add(Dropout(.2))model.add(Dense(32, activation='relu'))model.add(Dropout(.2))model.add(Dense(1, activation='sigmoid'))model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])model.summary()

模型摘要

Model: "sequential_11"_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================conv1d_19 (Conv1D)           (None, 14396, 128)        768       _________________________________________________________________batch_normalization_10 (Batc (None, 14396, 128)        512       _________________________________________________________________dropout_40 (Dropout)         (None, 14396, 128)        0         _________________________________________________________________conv1d_20 (Conv1D)           (None, 14392, 32)         20512     _________________________________________________________________batch_normalization_11 (Batc (None, 14392, 32)         128       _________________________________________________________________dropout_41 (Dropout)         (None, 14392, 32)         0         _________________________________________________________________flatten_8 (Flatten)          (None, 460544)            0         _________________________________________________________________dense_32 (Dense)             (None, 128)               58949760  _________________________________________________________________dropout_42 (Dropout)         (None, 128)               0         _________________________________________________________________dense_33 (Dense)             (None, 64)                8256      _________________________________________________________________dropout_43 (Dropout)         (None, 64)                0         _________________________________________________________________dense_34 (Dense)             (None, 32)                2080      _________________________________________________________________dropout_44 (Dropout)         (None, 32)                0         _________________________________________________________________dense_35 (Dense)             (None, 1)                 33        =================================================================Total params: 58,982,049Trainable params: 58,981,729Non-trainable params: 320_________________________________________________________________

导致错误的代码

history = model.fit(train_ds, validation_data=val_ds, epochs=10)

错误信息

ValueError: in user code:    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *        return step_function(self, iterator)    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **        outputs = model.distribute_strategy.run(run_step, args=(data,))    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica        return self._call_for_each_replica(fn, args, kwargs)    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica        return fn(*args, **kwargs)    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **        outputs = model.train_step(data)    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:747 train_step        y_pred = self(x, training=True)    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__        self.name)    /data/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_spec.py:168 assert_input_compatibility        layer_name + ' is incompatible with the layer: '    ValueError: Input 0 of layer sequential_11 is incompatible with the layer: its rank is undefined, but the layer requires a defined rank.

我非常感激您的帮助。

回答：

我已经解决了我的问题。问题出在我使用tf.data.Dataset.from_generator函数构建的自定义生成器上。由于我没有指定数据和标签的输出形状，这些形状被定义为未知，网络的输入层无法确定数据的形状。