重塑Keras的1D numpy阵列

Question

I'm using keras, and when I try model.fit it throws an error because the X_Train and Y_Train inputs have incompatible shapes.

The data I have is a system of 10 inputs and 1 output. And I'm using 9 iterations of the data as a test, so I have a list of 9 vectors with shape [10, 1] so, understandable, X_Train.shape = [9, 10, 1]. My output is a list of 9 values which makes Y_Train.shape = [9,1]. Yet I get this error:

tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [9,1] vs. [9,10,1]

I asume the correct shape of the Y_Train vector must be [9, 1, 1] but cannot find a way to shape it so.

Based on this I have two questions: Is [9, 1, 1] the correct expected shape according to my description of the problem? and how do I make it conform to that expected shape?

Answer 1

The input shape that passes through a computation graph in keras is of the type:

(?, x.shape[1], x.shape[2], ....)   #As seen in model.summary()

The first ? is the channel for passing your samples (rows in your dataset). You can pass them in batches, so thats something that you define while fitting the model itself.

When setting the shape of the layers however, you set it as

(x.shape[1], x.shape[2], ....)

Keras automatically adds the first channel at the start for the batches. So, if each row in your dataset is a 1-D array of length 10. Then,

## For keras functional API
inp = Input((10,))

## For keras sequential API
model = Sequential([
    Dense(32, input_shape=(10,))
])

If you are working with say a 3-D dataset, where each 'row' or sample in your dataset is a 2-D array of (10,10) shape:

## For keras functional API
inp = Input((10,10))

## For keras sequential API
model = Sequential([
    Dense(32, input_shape=(10,10))
])

Specific to your question, since you have a list of 9 arrays of the shape (10,1). You should simply ignore the 9, since that is what gets pass on the first channel as (?, 10, 1). So define your input shapes as just (10,) or (10,1)