keras模型预测不适合，这意味着什么？

Question

I see the following example code on tensorflow 2.0 API

model = Sequential()
model.add(Embedding(1000, 64, input_length=10))
# the model will take as input an integer matrix of size (batch,
# input_length).
# the largest integer (i.e. word index) in the input should be no larger
# than 999 (vocabulary size).
# now model.output_shape == (None, 10, 64), where None is the batch
# dimension.

input_array = np.random.randint(1000, size=(32, 10))

model.compile('rmsprop', 'mse')
output_array = model.predict(input_array)
assert output_array.shape == (32, 10, 64)

I have used keras API for a few days, compile, fit and then predict is my way.

What does above example mean without fit step?

Answer 1

It represents the use of initialized parameters in the model without fit() . This example is just to illustrate the return shape of Embedding layer .