为序列无效参数构建/训练一维CNN

Question

I'm building and training a CNN for a sequence, and have been using RNN's successfully, but am running into issues with CNN. Here's the code, cnn1 is first (more complex model), tried getting a simpler one to fit and getting errors on both:

The shapes are as follows:

xtrain (5206, 19, 4)
ytrain (5206, 4)
xvalid (651, 19, 4)
yvalid (651, 4)
xtest (651, 19, 4)
ytest (651, 4)

I've tried just about every combination of kernel sizes and nodes I can think of, tried 2 different model builds.

    model_cnn1.add(keras.layers.Conv1D(32, (4), activation='relu'))
    model_cnn1.add(keras.layers.MaxPooling1D((4)))
    model_cnn1.add(keras.layers.Conv1D(32, (4), activation='relu'))
    model_cnn1.add(keras.layers.MaxPooling1D((4)))
    model_cnn1.add(keras.layers.Conv1D(32, (4), activation='relu'))
    model_cnn1.add(keras.layers.Dense(4))


    model_cnn2 = keras.models.Sequential([
    keras.layers.Conv1D(100,(4),input_shape=(19,4),activation='relu'),
    keras.layers.MaxPooling1D(4),
    keras.layers.Dense(4)
])


    model_cnn2.compile(loss='mse',optimizer='adam',metrics= ['mse','accuracy'])

    model_cnn2.fit(X_train_tf,y_train_tf,epochs=25)

Output is 1/25 epochs, not entirely run, then on cnn1 I receive some variation of (final line):

ValueError: Negative dimension size caused by subtracting 4 from 1 for 'max_pooling1d_26/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,32]

on cnn2 (simpler) I get error (final line):

InvalidArgumentError: Incompatible shapes: [32,4,4] vs. [32,4]
[[{{node metrics_6/mse/SquaredDifference}}]] [Op:__inference_keras_scratch_graph_6917]

In general, is there some rule I should be following here for kernels/nodes/etc? I always seem to get these errors on the shape. I'm hoping after I build a model of each type I'll understand the ins and outs--no pun intended--but it's driving me crazy!

I've tried every combination of

Answer 1

You can read up on the docs of the Conv1D and MaxPooling1D to read that these layers change the output shape depending on the value for strides . In your case you can keep the output shape for Conv1D equal by specifying a padding. MaxPooling1D changes the output shape by definition. With strides = 4 , the output shape will be 4 times smaller in fact. I'd suggest carefully reading the docs to figure out exactly what happens and learning about the underlying theory of CNNs as to why this happens.