TensorFlow CNN Incompatible Shapes: 4D input shape

Question

I have sample data in the form: Data[n][31][31][5][2] with:

• "[n]" being the sample
• "[31][31]" being the array of data points
• "[5]" being the number of bits within that data point
• and "[2]" being one-hot encoding of the bits (eg a bit of 1 would be [1, 0] and a zero [0, 1])

The output is intended to either be a [5][2] or a [10] array of values which is validated against another [5][2] or [10] array. When trying to build the model, I get the following error:

 "ValueError: Shapes (None, 5, 2) and (None, 10) are incompatible"

The model code looks like this: (with train_m[n][31][31][5][2], tr_m[5][2], check_m[n][31][31][5][2], cr_m[5][2] being training data and expected output followed by validation data and expected output.)

model = Sequential([
    Conv2D(num_filters, filter_size, input_shape=(31, 31, 5, 2)),
    Flatten(),
    Dense(10, activation='relu'),
])


model.compile(
  'adam',
  loss='categorical_crossentropy',
  metrics=['accuracy'],
)

model.summary()
model.fit(
    train_m,
    tr_m,
    epochs=(100),
    validation_data=(check_m, cr_m),
    verbose=0
)

As the [5][2] outputs are one-hotted, I'm uncertain if they can be made to a [10] matrix while still being interpreted correctly. Further, would there be any way to make the dense layer to a [5][2]?

The full error can be seen here. I felt it would be awfully long to include in rawtext here.

If there's anything more that's needed, please let me know - I'm still very new to working with TensorFlow.

Answer 1

Your label shapes are (5,2) but network output is (10,) so this is confusing. Both output shape and label shape should be the same. use:

tf.keras.layers.Reshape((5,2))

after the Dense layer. you'll be fine