import h5py
 
def print_keras_wegiths(weight_file_path):
  f = h5py.File(weight_file_path) # 讀取weights h5文件返回File類
  try:
    if len(f.attrs.items()):
      print("{} contains: ".format(weight_file_path))
      print("Root attributes:")
    for key, value in f.attrs.items():
      print(" {}: {}".format(key, value)) # 輸出儲存在File類中的attrs信息，一般是各層的名稱
 
    for layer, g in f.items(): # 讀取各層的名稱以及包含層信息的Group類
      print(" {}".format(layer))
      print("  Attributes:")
      for key, value in g.attrs.items(): # 輸出儲存在Group類中的attrs信息，一般是各層的weights和bias及他們的名稱
        print("   {}: {}".format(key, value)) 
 
      print("  Dataset:")
      for name, d in g.items(): # 讀取各層儲存具體信息的Dataset類
        print("   {}: {}".format(name, d.value.shape)) # 輸出儲存在Dataset中的層名稱和權重，也可以打印dataset的attrs，但是keras中是空的
        print("   {}: {}".format(name. d.value))
  finally:
    f.close()

import keras
from keras.models import Model, load_model
from keras.layers import Input, Dense
from keras.optimizers import RMSprop
import numpy as np

inputs = Input((1,))
dense_1 = Dense(10, activation='relu')(inputs)
dense_2 = Dense(10, activation='relu')(dense_1)
dense_3 = Dense(10, activation='relu')(dense_2)
outputs = Dense(10)(dense_3)

model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=RMSprop(), loss='mse')
model.save('test.h5')

loaded_model = load_model('test.h5')
loaded_model.layers.pop()
loaded_model.layers.pop()

new_model = Model(inputs=inputs, outputs=dense_1)
new_model.compile(optimizer=RMSprop(), loss='mse')
new_model.set_weights(loaded_model.get_weights())

new_model.summary()
new_model.save('test_complete.h5')