将 keras 模型另存为 .h5 [英] Save keras model as .h5

问题描述

我想将我训练好的 keras 模型保存为 .h5 文件.应该是直截了当的.简短示例:

I want to save my trained keras model as .h5 file. Should be straight forward. Short example:

#%%
import tensorflow as tf
import numpy as np
from tensorflow.keras.callbacks import ModelCheckpoint
import matplotlib.pyplot as plt

print('TF version: ',tf.__version__)

#%%
#########################
# BATCH SIZE
BATCH_SIZE=100
########################

# create training data
X_train_set = np.random.random(size=(10000,10))
y_train_set = np.random.random(size=(10000))

# create validation data
X_val_set = np.random.random(size=(100,10))
y_val_set = np.random.random(size=(100))

# convert np.array to dataset
train_dataset = tf.data.Dataset.from_tensor_slices((X_train_set, y_train_set))
val_dataset = tf.data.Dataset.from_tensor_slices((X_val_set, y_val_set))

# batching
train_dataset=train_dataset.batch(BATCH_SIZE)
val_dataset = val_dataset.batch(BATCH_SIZE)

# set up the model
my_model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(10,)),
    tf.keras.layers.Dense(100, activation='relu'),
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(1)
])

#%%
# custom optimizer with learning rate
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
    initial_learning_rate=1e-2,
    decay_steps=10000,
    decay_rate=0.9)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)


# compile the model
my_model.compile(optimizer=optimizer,loss='mse')

# define a checkpoint
checkpoint = ModelCheckpoint('./tf.keras_test',
                             monitor='val_loss',
                             verbose=1,
                             save_best_only=True,
                             mode='min',
                             save_freq='epoch')

callbacks = [checkpoint]

#%%
# train with datasets
history= my_model.fit(train_dataset,
             validation_data=val_dataset,
             #validation_steps=100,
             #callbacks=callbacks,
             epochs=10)

# save as .h5
my_model.save('my_model.h5',save_format='h5')

然而，my_model.save 给了我一个 TypeError:

However, my_model.save gives me a TypeError:

Traceback (most recent call last):
  File "/home/max/.local/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 3343, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-11-a369340a62e1>", line 1, in <module>
    my_model.save('my_model.h5',save_format='h5')
  File "/home/max/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/network.py", line 975, in save
    signatures, options)
  File "/home/max/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/save.py", line 112, in save_model
    model, filepath, overwrite, include_optimizer)
  File "/home/max/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 109, in save_model_to_hdf5
    save_weights_to_hdf5_group(model_weights_group, model_layers)
  File "/home/max/.local/lib/python3.6/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 631, in save_weights_to_hdf5_group
    param_dset = g.create_dataset(name, val.shape, dtype=val.dtype)
  File "/usr/local/lib/python3.6/dist-packages/h5py/_hl/group.py", line 143, in create_dataset
    if '/' in name:
TypeError: a bytes-like object is required, not 'str'

不确定是什么问题...是 TF2 问题吗?使用 TF1.X 保存为 .h5 从来没有问题，仍然可以将其保存为 .pb 图形.但是，我想将其作为 .h5.

Not sure what's the problem... Is it a TF2 issue? Never had problems saving as .h5 with TF1.X and still can save it as .pb graph. However, I'd like to have it as .h5.

推荐答案

所以这似乎是aa bug 在 h5py 库中，它应该接受一个 bytes 或一个 unicode str，但失败了一个 str 实例.它应该在下一个版本中修复.

So this seems to be a a bug in the h5py library, it should accept a bytes or a unicode str, but fails with a str instance. It should be fixed in the next release.

您可以在本地安装中降级 h5py 版本，它应该可以解决问题.这个问题是 3.0.0 版本引入的，所以早期的版本应该可以.

You could downgrade the h5py version in your local installation and it should work around the problem. The problem was introduced by version 3.0.0, so earlier versions should work.

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