ValueError: Input 0 of layer "sequential_8" is in compatible with the layer - 深度学习 model

Question

I am attempting to setup my first deep learning sequential model with a small test dataset.

Unfortunately, I get the following error message when I call model.fit():

ValueError: Input 0 of layer "sequential_8" is incompatible with the layer: expected shape=(None, 160, 4000), found shape=(32, 4000)

My model is as follows

num_of_classes = 2
input_shape = (1,4000)

y_train_cat = keras.utils.to_categorical(y_train, num_of_classes)
y_test_cat = keras.utils.to_categorical(y_test, num_of_classes)



model = Sequential()
model.add(Conv1D(filters=10, kernel_size=5, input_shape=(160, 4000)))
model.add(MaxPool1D(pool_size=5))
model.add(Flatten())
model.add(Dense(1000, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

The data is of the following dimensions

x_train.shape is (160, 4000)


y_train_cat is (160, 2)

There are two classes.

Thank you for reading this far and your help in advance

Answer 1

When you give a layer the shape, is supposed to be the same of a single sample... change

model.add(Conv1D(filters=10, kernel_size=5, input_shape=(160, 4000)))

to

model.add(Conv1D(filters=10, kernel_size=5, input_shape=(4000,1)))

and it should work fine

Edit:
You probably also need to reshape your input to add a dimension:

x_train = np.expand_dims(x_train, 2)

Explanation:
consider a single element, a 1D convolution "slides" a 1D filter over your 1D element, however you can assume to have multiple channels, thus the leading "1" in the shape of the input