Conv1D input shape for

Question

I am training a CNN on a classification problem. The input shape is a number with x_train.shape = (6352,) and I have 10 classes.

I built this model:

# Add a Conv1D layer
input_shape=(6352,1)

model = keras.Sequential()

model.add(keras.layers.Conv1D(16, kernel_size=3, activation='relu', input_shape=input_shape))
model.add(keras.layers.MaxPooling1D(pool_size=3))

model.add(keras.layers.Conv1D(32, kernel_size=2, activation='relu'))  
model.add(keras.layers.MaxPooling1D(pool_size=3))

model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(64, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(10, activation='softmax'))

model.summary()

but when I try to fit the model, I get this:

WARNING:tensorflow:Model was constructed with shape (None, 6352, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 6352, 1), dtype=tf.float32, name='conv1d_3_input'), name='conv1d_3_input', description="created by layer 'conv1d_3_input'"), but it was called on an input with incompatible shape (None,).

 ValueError: Exception encountered when calling layer 'sequential_3' (type Sequential).
    
    Input 0 of layer "conv1d_3" is incompatible with the layer: expected min_ndim=3, found ndim=1. Full shape received: (None,)
    
    Call arguments received by layer 'sequential_3' (type Sequential):
      • inputs=tf.Tensor(shape=(None,), dtype=float32)
      • training=True
      • mask=None

How to know the correct input shape and how to track the shape through the layers so that the model can work?

I have tried several input shapes but nothing is working