input:(input_dim,output_dim,input_length=None)
output:(input_length,output_dim)
import tensorflow as tf from tensorflow.keras import layers
model = tf.keras.Sequential()
model.add(layers.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.
import numpy as np
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)
