ValueError: Input 0 of layer "sequential_52" is incompatible with the layer: expected shape=(None, 660, 12), found shape=(None, 12)

Question

I am currently building a CNN that does binary classification, I first do feature extraction using wavelet transform then pass that output to the model. But I'm getting the below error constantly.

train_labels shape: (660,) (labels)

train_data shape: (660, 12) where (num of samples, features)

I've tried:

1. add a new dimension to the dataset using np.newaxis but it produces cardinality errors

2. Data cardinality is ambiguous: x sizes: 1 y sizes: 660; i reshape the labels then but that's inefficient since then the model maps to 660 classes instead of 2.

   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 None
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
        raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
   
    ValueError: Input 0 of layer "sequential_52" is incompatible with the layer: expected shape=(None, 660, 12), found shape=(None, 12)
   

My code:

  model = Sequential()
  model.add(Conv1D((16), (1), input_shape= (660, 12) ,name = 'Conv1')) #yes
  model.add(BatchNormalization())
  model.add(Activation('relu'))
  model.add(Conv1D(32, (1),name = 'Conv2'))#yes
  model.add(Activation('relu'))#yes
  model.add(Dense(256, name = 'FC2'))#yes
  model.add(Activation('relu'))#yes
  model.add(Dropout(0.25))#yes
  model.add(Dropout(0.5))#yes
  model.add(Dense(1, activation = 'sigmoid'))#yes
  sgd = SGD()

  model.compile(loss='binary_crossentropy',optimizer=sgd,metrics=['accuracy'])

Answer 1

I reproduced your model and used model.summary() to take a closer look at the data shape at the different layers. Are you sure you want to have the shape (None,660,1) at the output?

Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
Conv1 (Conv1D)               (None, 660, 16)           208       
_________________________________________________________________
batch_normalization_5 (Batch (None, 660, 16)           64        
_________________________________________________________________
activation_15 (Activation)   (None, 660, 16)           0         
_________________________________________________________________
Conv2 (Conv1D)               (None, 660, 32)           544       
_________________________________________________________________
activation_16 (Activation)   (None, 660, 32)           0         
_________________________________________________________________
FC2 (Dense)                  (None, 660, 256)          8448      
_________________________________________________________________
activation_17 (Activation)   (None, 660, 256)          0         
_________________________________________________________________
dropout_8 (Dropout)          (None, 660, 256)          0         
_________________________________________________________________
dropout_9 (Dropout)          (None, 660, 256)          0         
_________________________________________________________________
dense_7 (Dense)              (None, 660, 1)            257       
=================================================================
Total params: 9,521
Trainable params: 9,489
Non-trainable params: 32
_________________________________________________________________

If you want to do a one output binary classification I suggest that you use a Flatten-layer or a MaxPool1D-layer somewhere before the final layer.