Question

尝试执行model.save(save_path)时出现错误。我正在使用tensorflow 2.0 alpha

文件 “ /home/eugenekim/virtualenvs/ds-pip/local/lib/python3.6/site-packages/tensorflow/python/util/serialization.py”，第69行，在get_json_type中引发TypeError（'Not JSON Serializable：'，obj）TypeError：（'Not JSON Serializable：'， b'\ n \ x04Mean \ x12 \ x04Mean \ x1a“ item_1 / embedding_lookup / Identity_2 \ x1a \ x16Mean / reduction_indices * \ n \ n \ x04Tidx \ x12 \ x020 \ x03 * \ x0f \ n \ tkeep_dims \ x12 \ x02（\ x00 * \ x07 \ n \ x01T \ x12 \ x020 \ x01'）

相关代码如下（我的最佳猜测）。对于输入（这是ID列表），我将获得每个的嵌入并将其平均。我将它们作为连接图层的功能之一。

 import copy
 import tensorflow as tf
 import pandas as pd
 from tensorflow.keras.layers import Input, Embedding, concatenate, Dense, Flatten
 from tensorflow import feature_column
 from tensorflow.python.keras.engine import training_utils


 def df_to_dataset(dataframe, shuffle=True, batch_size=32):
   dataframe = dataframe.copy()
   labels = dataframe.pop('target')
   d = dict(dataframe)

   ds = tf.data.Dataset.from_tensor_slices((d, labels))
   if shuffle:
     ds = ds.shuffle(buffer_size=len(dataframe))
   ds = ds.batch(batch_size)
   return ds


 def get_model():
     first_input = Input(shape = (1,), name='first_input')
     second_input = Input(shape = (1,), name='second_input' )
     embedding_layer = Embedding(input_dim=10, output_dim=3, input_length=1)

     first_input_encoded = embedding_layer(first_input)
     first_input_encoded = tf.keras.layers.Reshape((3,))(first_input_encoded)


     selected = embedding_layer(second_input)
     item_average = tf.reduce_mean(selected, axis=1, keepdims=True)
     second_input_encoded = tf.keras.layers.Reshape((3,))(item_average)


     o = concatenate([first_input_encoded, second_input_encoded])
     o = Dense(1)(o)

     inputs = [first_input, second_input]
     model = tf.keras.models.Model(inputs=inputs, outputs=o)
     return model


 df = pd.DataFrame(
     [[3,[4,2,3],5, 'boy', 0],
     [2,[6,1,2],7, 'girl', 1]], columns=['first_input', 'second_input', 'child_month_young', 'child_gender_young', 'target'])

 train_ds = df_to_dataset(df)
 val_ds = df_to_dataset(df)

 model = get_model()

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


 model.fit(
     train_ds,
     validation_data=val_ds,
     epochs=1
 )

 print(model.summary())

 # tf.keras.models.save_model(model, 'test.h5')
 model.save('test.h5')

tf.keras.model无法序列化

0 个答案: