Keras Embedding layer output dimensionality

I am confused with the output dimensions specified in the embedding layer in this code snippet

from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.layers import Dense
from keras.models import Sequential
from keras.layers import Embedding, SimpleRNN

max_features = 10000
maxlen = 500
batch_size = 32

print('Loading data...')
(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=max_features)

print(len(input_train), 'train sequences')
print(len(input_test), 'test sequences')
print('Pad sequences (samples x time)')

input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)

print('input_train shape:', input_train.shape)
print('input_test shape:', input_test.shape)

print(input_train)

model = Sequential()
model.add(Embedding(max_features, 32))
model.add(SimpleRNN(32))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

Since the max_features is 10000, shouldn't the Embedding have an output dimensionality of 10000?

Solution

max_features is the number of words, not the dimensionality. In your embedding layer you have 10000 words that are each represented as an embedding with dimension 32.