根据我正在关注的 Tensorflow 教程，以下代码应该给出可重现的结果，因此可以检查练习是否正确完成。 Tensorflow版本是2.11.0。
import tensorflow as tf
import numpy as np
class MyDenseLayer(tf.keras.layers.Layer):
def __init__(self, n_output_nodes):
super(MyDenseLayer, self).__init__()
self.n_output_nodes = n_output_nodes
def build(self, input_shape):
d = int(input_shape[-1])
# Define and initialize parameters: a weight matrix W and bias b
# Note that parameter initialization is random!
self.W = self.add_weight("weight", shape=[d, self.n_output_nodes]) # note the dimensionality
self.b = self.add_weight("bias", shape=[1, self.n_output_nodes]) # note the dimensionality
print("Weight matrix is {}".format(self.W))
print("Bias vector is {}".format(self.b))
def call(self, x):
z = tf.add(tf.matmul(x, self.W), self.b)
y = tf.sigmoid(z)
return y
# Since layer parameters are initialized randomly, we will set a random seed for reproducibility
tf.random.set_seed(1)
layer = MyDenseLayer(3)
layer.build((1,2))
print(layer.call(tf.constant([[1.0,2.0]], tf.float32, shape=(1,2))))
但是，每次运行的结果都不同，因为
buildtf.random.set_seed两次运行的完整输出：
treuss@foo:~/python/tensorflow$ python lab1_1.py
2023-03-26 21:31:16.896212: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-26 21:31:18.021094: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.023462: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.023634: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.023976: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-26 21:31:18.024324: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.024471: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.024617: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.455771: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.455946: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.456125: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:18.456257: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4656 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1
Weight matrix is <tf.Variable 'weight:0' shape=(2, 3) dtype=float32, numpy=
array([[ 0.9970403 , -0.672126 , -0.00545013],
[ 0.5411365 , -0.8570848 , 0.5970814 ]], dtype=float32)>
Bias vector is <tf.Variable 'bias:0' shape=(1, 3) dtype=float32, numpy=array([[-0.9100063, 0.7671951, -0.9659226]], dtype=float32)>
tf.Tensor([[0.7630197 0.16532896 0.5554683 ]], shape=(1, 3), dtype=float32)
treuss@foo:~/python/tensorflow$ python lab1_1.py
2023-03-26 21:31:21.245548: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-26 21:31:22.372605: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.375021: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.375175: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.375521: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-26 21:31:22.375840: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.375960: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.376067: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.801768: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.801935: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.802112: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2023-03-26 21:31:22.802213: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 4656 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1
Weight matrix is <tf.Variable 'weight:0' shape=(2, 3) dtype=float32, numpy=
array([[ 0.72208846, 0.34211397, 0.04753423],
[ 0.48018157, 0.9557345 , -0.19968122]], dtype=float32)>
Bias vector is <tf.Variable 'bias:0' shape=(1, 3) dtype=float32, numpy=array([[ 0.31122065, -0.81101143, -0.7763765 ]], dtype=float32)>
tf.Tensor([[0.8801311 0.80885255 0.24449256]], shape=(1, 3), dtype=float32)
您会看到此行为，因为在 TF 2.7 之后，Keras 将
tf.random.uniformtf.keras.initializers.xxxglorot_uniformself.add_weight长话短说，您能做的最好的事情就是使用
tf.keras.utils.set_random_seed()tf.random.set_seed()这里有过相关讨论。