關於 tf.keras.layers.Embedding

tf.keras.layers.Embedding

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)

輸入只能是整數型的，如果已經normal過的數據不能作爲embedding的輸入