在keras中输出中间层是如何工作的？

from keras.models import Model
from keras.layers import Input, Dense
from keras.datasets import mnist
from keras.utils import to_categorical

# 加载数据
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784).astype('float32') / 255
x_test = x_test.reshape(10000, 784).astype('float32') / 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 构建模型
inputs = Input(shape=(784,))
x = Dense(128, activation='relu')(inputs)
x = Dense(64, activation='relu')(x)
outputs = Dense(10, activation='softmax')(x)

model = Model(inputs=inputs, outputs=outputs)

# 获取中间层的输出
intermediate_layer_model = Model(inputs=model.input, outputs=model.layers[1].output)

# 输出中间层的输出
intermediate_output = intermediate_layer_model.predict(x_train)
print(intermediate_output.shape)