我正在使用tensorflow.keras来构建具有3个密集层的简单神经网络。我能够成功训练9000个时期的模型，并获得0.0496的均方误差（MSE）。无论恢复模型，它都会在大约57 MSE开始训练。
这可能表明模型权重未成功加载，但是从头开始重新训练过程（不加载先前保存的权重）时，MSE的开始时间约为+9000。
编辑：
我的下面的代码：
from __future__ import absolute_import, division, print_function
import pathlib
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import model_from_json
from tensorflow.keras.models import load_model
print(tf.__version__)
dataset_path = 'D:\\data.csv'
checkpoint_model_json_path = 'modelBackup/model.json'
checkpoint_weights_h5_path = 'modelBackup/weights00009000.h5'
resume_from_checkpoint = True
print('reading dataset...')
column_names = ['paircode','x1o','x1h','x1l','x1c','x1v','x2o','x2h','x2l','x2c','x2v','x3o','x3h','x3l','x3c','x3v','x4o','x4h','x4l','x4c','x4v','x5o','x5h','x5l','x5c','x5v','x6o','x6h','x6l','x6c','x6v','x7o','x7h','x7l','x7c','x7v','x8o','x8h','x8l','x8c','x8v','x9o','x9h','x9l','x9c','x9v','x10o','x10h','x10l','x10c','x10v','x11o','x11h','x11l','x11c','x11v','x12o','x12h','x12l','x12c','x12v','x13o','x13h','x13l','x13c','x13v','x14o','x14h','x14l','x14c','x14v','x15o','x15h','x15l','x15c','x15v','x16o','x16h','x16l','x16c','x16v','x17o','x17h','x17l','x17c','x17v','x18o','x18h','x18l','x18c','x18v','x19o','x19h','x19l','x19c','x19v','x20o','x20h','x20l','x20c','x20v','x21o','x21h','x21l','x21c','x21v','x22o','x22h','x22l','x22c','x22v','x23o','x23h','x23l','x23c','x23v','x24o','x24h','x24l','x24c','x24v','x25o','x25h','x25l','x25c','x25v','x26o','x26h','x26l','x26c','x26v','x27o','x27h','x27l','x27c','x27v','x28o','x28h','x28l','x28c','x28v','x29o','x29h','x29l','x29c','x29v','x30o','x30h','x30l','x30c','x30v','x31o','x31h','x31l','x31c','x31v','x32o','x32h','x32l','x32c','x32v','x33o','x33h','x33l','x33c','x33v','x34o','x34h','x34l','x34c','x34v','x35o','x35h','x35l','x35c','x35v','x36o','x36h','x36l','x36c','x36v','x37o','x37h','x37l','x37c','x37v','x38o','x38h','x38l','x38c','x38v','x39o','x39h','x39l','x39c','x39v','x40o','x40h','x40l','x40c','x40v','x41o','x41h','x41l','x41c','x41v','x42o','x42h','x42l','x42c','x42v','x43o','x43h','x43l','x43c','x43v','x44o','x44h','x44l','x44c','x44v','x45o','x45h','x45l','x45c','x45v','x46o','x46h','x46l','x46c','x46v','x47o','x47h','x47l','x47c','x47v','x48o','x48h','x48l','x48c','x48v','x49o','x49h','x49l','x49c','x49v','x50o','x50h','x50l','x50c','x50v','nextclose']
dataset = pd.read_csv(dataset_path, names=column_names,
na_values = "?", comment='\t',
sep=",", skipinitialspace=True, skiprows = [0])
print('printing dataset tail...')
print(dataset.tail())
train_dataset = dataset.sample(frac=0.8,random_state=0)
test_dataset = dataset.drop(train_dataset.index)
train_labels = train_dataset.pop('nextclose')
test_labels = test_dataset.pop('nextclose')
def norm(x):
return x
# return (x - train_stats['mean']) / train_stats['std']
print('normalizing dataset...')
normed_train_data = norm(train_dataset)
normed_test_data = norm(test_dataset)
def build_model():
print('building the model')
model = keras.Sequential([
layers.Dense(512, activation=tf.nn.relu, input_shape=[len(train_dataset.keys())]),
layers.Dense(512, activation=tf.nn.relu), layers.Dense(256, activation=tf.nn.relu),
layers.Dense(1)
])
return model
def load_model_():
print('loading the model')
# load json and create model
json_file = open(checkpoint_model_json_path, 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights(checkpoint_weights_h5_path)
print("Loaded model from disk")
return loaded_model
if resume_from_checkpoint:
model = load_model_()
else:
model = build_model()
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mean_absolute_error', 'mean_squared_error', 'accuracy'])
model.summary()
print('testing 10 widthed batch...')
example_batch = normed_train_data[:10]
example_result = model.predict(example_batch)
print(example_result)
def plot_history(history):
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
plt.figure()
plt.xlabel('Epoch')
plt.ylabel('Mean Abs Error [nextclose]')
plt.plot(hist['epoch'], hist['mean_absolute_error'],
label='Train Error')
plt.plot(hist['epoch'], hist['val_mean_absolute_error'],
label = 'Val Error')
plt.ylim([0,5])
plt.legend()
plt.figure()
plt.xlabel('Epoch')
plt.ylabel('Mean Square Error [$nextclose^2$]')
plt.plot(hist['epoch'], hist['mean_squared_error'],
label='Train Error')
plt.plot(hist['epoch'], hist['val_mean_squared_error'],
label = 'Val Error')
plt.ylim([0,20])
plt.legend()
plt.show()
print('fitting the model...')
mc = keras.callbacks.ModelCheckpoint('weights{epoch:08d}.h5', save_weights_only=True, period=500)
print('saving the model...')
model_json = model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
history = model.fit(
normed_train_data, train_labels,
epochs=1, validation_split = 0.2, verbose=2,
batch_size=100000, callbacks=[mc])
print('evaluating the model...')
loss, mae, mse, accuracy = model.evaluate(normed_test_data, test_labels, verbose=0)
print("Testing set Mean Abs Error: {:5.2f} nextclose".format(mae))
print("Testing set Accuracy: {:5.2f} nextclose".format(accuracy))
输出：
1.13.1
reading dataset...
printing dataset tail...
paircode x1o x1h x1l x1c x1v x2o x2h x2l x2c x2v x3o x3h ... x48c x48v x49o x49h x49l x49c x49v x50o x50h x50l x50c x50v nextclose
381045 50 112.606 112.622 112.606 112.619 0.0 112.580 112.581 112.561 112.575 0.0 112.601 112.612 ... 112.118 0.0 112.083 112.090 112.079 112.087 0.0 112.025 112.033 112.023 112.032 0.0 112.033
381046 50 112.580 112.581 112.561 112.575 0.0 112.601 112.612 112.598 112.599 0.0 112.581 112.599 ... 112.087 0.0 112.025 112.033 112.023 112.032 0.0 112.031 112.034 112.031 112.033 0.0 112.141
381047 50 112.601 112.612 112.598 112.599 0.0 112.581 112.599 112.580 112.593 0.0 112.548 112.548 ... 112.032 0.0 112.031 112.034 112.031 112.033 0.0 112.142 112.149 112.140 112.141 0.0 112.157
381048 50 112.581 112.599 112.580 112.593 0.0 112.548 112.548 112.540 112.542 0.0 112.551 112.565 ... 112.033 0.0 112.142 112.149 112.140 112.141 0.0 112.161 112.161 112.157 112.157 0.0 112.121
381049 50 112.548 112.548 112.540 112.542 0.0 112.551 112.565 112.551 112.565 0.0 112.564 112.577 ... 112.141 0.0 112.161 112.161 112.157 112.157 0.0 112.121 112.129 112.121 112.121 0.0 112.140
[5 rows x 252 columns]
normalizing dataset...
loading the model
WARNING:tensorflow:From C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-04-05 12:10:15.520118: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Loaded model from disk
WARNING:tensorflow:From C:\Program Files\Python36\lib\site-packages\tensorflow\python\keras\utils\losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 512) 129024
_________________________________________________________________
dense_1 (Dense) (None, 512) 262656
_________________________________________________________________
dense_2 (Dense) (None, 256) 131328
_________________________________________________________________
dense_3 (Dense) (None, 1) 257
=================================================================
Total params: 523,265
Trainable params: 523,265
Non-trainable params: 0
_________________________________________________________________
testing 10 widthed batch...
[[106.244064]
[ 76.667534]
[ 82.01627 ]
[ 79.776405]
[116.600204]
[ 95.28444 ]
[ 76.96633 ]
[118.25993 ]
[120.39911 ]
[108.5381 ]]
fitting the model...
saving the model...
Train on 243872 samples, validate on 60968 samples
WARNING:tensorflow:From C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
- 6s - loss: 56.9330 - mean_absolute_error: 5.3921 - mean_squared_error: 56.9330 - acc: 0.0000e+00 - val_loss: 38.9868 - val_mean_absolute_error: 6.1875 - val_mean_squared_error: 38.9868 - val_acc: 0.0000e+00
evaluating the model...
Testing set Mean Abs Error: 6.19 nextclose
Testing set Accuracy: 0.00 nextclose
谢谢。
答案 0 :(得分：0)
您可以简单地构建模型，编译模型并保留随机初始化的权重以开始训练。接下来，要继续训练：构建模型，进行编译，然后重新加载保存的权重。
答案 1 :(得分：0)
for resume training you should not run the full code , just run this :
model= load_model('model.h5')
history = model.fit(normed_train_data, train_labels, epochs=1, v
validation_split = 0.2, verbose=2,
batch_size=128, callbacks=[mc])`
However you shoud edit this:
mc = keras.callbacks.ModelCheckpoint('weights{epoch:08d}.h5', save_weights_only=True, period=100)
into this:
mc = keras.callbacks.ModelCheckpoint('weights{epoch:08d}.h5', save_weights_only=False, period=100)
答案 2 :(得分：0)
This is a bug fixed currently in tensorflow-gpu-nighlybuild 2.0 as mentioned here.