tensorflow keras保存和加载模型

Question

我已经运行了此示例，尝试保存模型时出现以下错误。

import tensorflow as tf
import h5py
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer='adam',
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])

model.fit(x_train, y_train, epochs=2)
val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss, val_acc)

model.save('model.h5')

new_model = tf.keras.models.load_model('model.h5')

我收到此错误：

Traceback (most recent call last):
File "/home/zneic/PycharmProjects/test/venv/test.py", line 23, in <module>
model.save('model.h5')
File "/home/zneic/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py", line 1359, in save
'Currently `save` requires model to be a graph network. Consider '
NotImplementedError: Currently `save` requires model to be a graph network. Consider using `save_weights`, in order to save the weights of the model.

Answer 1

您的权重似乎没有保存或加载回会话中。 您可以尝试分别保存图形和权重并分别加载吗？

model_json = model.to_json()
with open("model.json", "w") as json_file:
    json_file.write(model_json)

model.save_weights("model.h5")

然后，您可以加载它们：

def loadModel(jsonStr, weightStr):
    json_file = open(jsonStr, 'r')
    loaded_nnet = json_file.read()
    json_file.close()

    serve_model = tf.keras.models.model_from_json(loaded_nnet)
    serve_model.load_weights(weightStr)

    serve_model.compile(optimizer=tf.train.AdamOptimizer(),
                        loss='categorical_crossentropy',
                        metrics=['accuracy'])
    return serve_model

model = loadModel('model.json', 'model.h5')

Answer 2

我有同样的问题，我解决了。 我不知道为什么，但是可以。 您可以这样修改：

model = tf.keras.Sequential([
  layers.Flatten(input_shape=(28, 28)),
  layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)),
  layers.Dropout(0.2),
  layers.Dense(10, activation=tf.nn.softmax)
])