Google Colab上的TensorFlow-keras可重复性问题

Question

我有一个简单的代码可以在Google Colab上运行(我使用CPU模式(：

import numpy as np
import pandas as pd
## LOAD DATASET
datatrain = pd.read_csv("gdrive/My Drive/iris_train.csv").values
xtrain = datatrain[:,:-1]
ytrain = datatrain[:,-1]
datatest = pd.read_csv("gdrive/My Drive/iris_test.csv").values
xtest = datatest[:,:-1]
ytest = datatest[:,-1]
import tensorflow as tf
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.utils import to_categorical
## SET ALL SEED
import os
os.environ['PYTHONHASHSEED']=str(66)
import random
random.seed(66)
np.random.seed(66)
tf.set_random_seed(66)
from tensorflow.keras import backend as K
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)
## MAIN PROGRAM
ycat = to_categorical(ytrain) 
# build model
model = tf.keras.Sequential()
model.add(Dense(10, input_shape=(4,)))
model.add(Activation("sigmoid"))
model.add(Dense(3))
model.add(Activation("softmax"))
#choose optimizer and loss function
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
# train
model.fit(xtrain, ycat, epochs=15, batch_size=32)
#get prediction
classes = model.predict_classes(xtest)
#get accuration
accuration = np.sum(classes == ytest)/len(ytest) * 100

我已经阅读了该设置，以在此处使用带有TensorFlow后端的Keras创建可重复性代码，然后将所有代码放在同一单元格中。但是，每次运行该单元格(使用shift + enter运行单元格(时，结果(例如损失(总是不同的。

在我的情况下，可以复制以上代码的结果，如果仅：

1. 我使用" Runtime">"重新启动并运行全部"或，
运行
2. 我将该代码放在一个文件中，然后使用命令行(python3 file.py(运行它

我想念的是可以使结果可重现的情况下不重新启动运行时吗？

Answer 1

您还应该在Dense层中修复kernel_initializer的种子。因此，您的模型将就像：

model = tf.keras.Sequential()
model.add(Dense(10, kernel_initializer=keras.initializers.glorot_uniform(seed=66), input_shape=(4,)))
model.add(Activation("sigmoid"))
model.add(Dense(3, kernel_initializer=keras.initializers.glorot_uniform(seed=66)))
model.add(Activation("softmax"))

Answer 2

我尝试了网络上的大多数解决方案，并且以下代码对我有用：

seed=0
import os
os.environ['PYTHONHASHSEED'] = str(seed)
# For working on GPUs from "TensorFlow Determinism"
os.environ["TF_DETERMINISTIC_OPS"] = str(seed)
import numpy as np
np.random.seed(seed)
import random
random.seed(seed)
import tensorflow as tf
tf.random.set_seed(seed)

请注意，您应该在每次运行之前调用此代码(至少对我来说(

如果您想在CPU上运行代码：

    seed=0
    import os
    os.environ['PYTHONHASHSEED'] = str(seed)
    # For working on GPUs from "TensorFlow Determinism"
    os.environ['CUDA_VISBLE_DEVICE'] = ''
    import numpy as np
    np.random.seed(seed)
    import random
    random.seed(seed)
    import tensorflow as tf
    tf.random.set_seed(seed)

Answer 3

我试图使用keras和google colab(CPU(可重复地获得Tensorflow 2.0，并具有类似于@malioboro上面描述的IRIS数据集处理的版本。这似乎有效 - 可能很有用：

# Install TensorFlow
try:
  # %tensorflow_version only exists in Colab.
  %tensorflow_version 2.x
except Exception:
  pass
# Setup repro section from Keras FAQ with TF1 to TF2 adjustments
import numpy as np
import tensorflow as tf
import random as rn
# The below is necessary for starting Numpy generated random numbers
# in a well-defined initial state.
np.random.seed(42)
# The below is necessary for starting core Python generated random numbers
# in a well-defined state.
rn.seed(12345)
# Force TensorFlow to use single thread.
# Multiple threads are a potential source of non-reproducible results.
# For further details, see: https://stackoverflow.com/questions/42022950/
session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1,
                                        inter_op_parallelism_threads=1)
# The below tf.set_random_seed() will make random number generation
# in the TensorFlow backend have a well-defined initial state.
# For further details, see:
# https://www.tensorflow.org/api_docs/python/tf/set_random_seed
tf.compat.v1.set_random_seed(1234)
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)
tf.compat.v1.keras.backend.set_session(sess)
# Rest of code follows ...
# Some adopted from: https://janakiev.com/notebooks/keras-iris/
# Some adopted from the question.
#
# Load Data
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder, StandardScaler
iris = load_iris()
X = iris['data']
y = iris['target']
names = iris['target_names']
feature_names = iris['feature_names']
# One hot encoding
enc = OneHotEncoder()
Y = enc.fit_transform(y[:, np.newaxis]).toarray()
# Scale data to have mean 0 and variance 1 
# which is importance for convergence of the neural network
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Split the data set into training and testing
X_train, X_test, Y_train, Y_test = train_test_split(
    X_scaled, Y, test_size=0.5, random_state=2)
n_features = X.shape[1]
n_classes = Y.shape[1]
## MAIN PROGRAM
from tensorflow.keras.layers import Dense, Activation 
# build model
model = tf.keras.Sequential()
model.add(Dense(10, input_shape=(4,)))
model.add(Activation("sigmoid"))
model.add(Dense(3))
model.add(Activation("softmax"))
#choose optimizer and loss function
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
# train
model.fit(X_train, Y_train, epochs=20, batch_size=32)
#get prediction
classes = model.predict_classes(X_test)