我正在使用Keras在Mac OSX上用虚拟数据训练MobileNet结构。我设置了nump.random
和tensorflow.set_random_seed
,但由于某些原因,我无法获得可重复的结果:每次重新运行代码时,我都会得到不同的结果。为什么?这不是因为GPU,因为我在一台带有Radeon图形卡的MacBook Pro 2017上运行,因此Tensorflow不能利用它。代码是这样运行的:
python Keras_test.py
所以这不是国家的问题(我没有使用Jupyter或IPython:每次运行代码时都应该重置环境)。
编辑:我通过在导入Keras之前设置所有种子来更改了我的代码。结果仍然不确定,但结果的方差比之前小得多。这非常奇怪。
当前模型非常小(就深度神经网络而言),并非微不足道,它不需要GPU运行,在现代笔记本电脑上可以在几分钟内训练,因此重复我的实验对任何人来说都是可行的。我邀请您这样做:我很想了解不同系统之间变化水平的情况。
import numpy as np
# random seeds must be set before importing keras & tensorflow
my_seed = 512
np.random.seed(my_seed)
import random
random.seed(my_seed)
import tensorflow as tf
tf.set_random_seed(my_seed)
# now we can import keras
import keras.utils
from keras.applications import MobileNet
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
import os
height = 224
width = 224
channels = 3
epochs = 10
num_classes = 10
# Generate dummy data
batch_size = 32
n_train = 256
n_test = 64
x_train = np.random.random((n_train, height, width, channels))
y_train = keras.utils.to_categorical(np.random.randint(num_classes, size=(n_train, 1)), num_classes=num_classes)
x_test = np.random.random((n_test, height, width, channels))
y_test = keras.utils.to_categorical(np.random.randint(num_classes, size=(n_test, 1)), num_classes=num_classes)
# Get input shape
input_shape = x_train.shape[1:]
# Instantiate model
model = MobileNet(weights=None,
input_shape=input_shape,
classes=num_classes)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Viewing Model Configuration
model.summary()
# Model file name
filepath = 'model_epoch_{epoch:02d}_loss_{loss:0.2f}_val_{val_loss:.2f}.hdf5'
# Define save_best_only checkpointer
checkpointer = ModelCheckpoint(filepath=filepath,
monitor='val_acc',
verbose=1,
save_best_only=True)
# Let's fit!
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
callbacks=[checkpointer])
像往常一样,这是我的Python、Keras和Tensorflow版本:
python -c 'import keras; import tensorflow; import sys; print(sys.version, 'keras.__version__', 'tensorflow.__version__')'
/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
('2.7.15 |Anaconda, Inc.| (default, May 1 2018, 18:37:05) \n[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]', '2.1.6', '1.8.0')
这是运行代码多次得出的一些结果:您可以看到,代码在10次epochs中保存最佳模型(最佳验证准确率)并使用具有描述性的文件名,因此通过比较不同运行之间的文件名可以判断结果的变异性。
model_epoch_01_loss_2.39_val_3.28.hdf5
model_epoch_01_loss_2.39_val_3.54.hdf5
model_epoch_01_loss_2.40_val_3.47.hdf5
model_epoch_01_loss_2.41_val_3.08.hdf5