minval=0,maxval=1,dtype=dtype))) _, indices = tf.math.top_k(unnormalized_log_probs + z,k=K) return indices #random number generation rand_generator=tf.random.Generator.from_seed
# 重置Tensorflow图模型ops.reset_default_graph()# Create graph sessionsess = tf.Session()# set batch size for trainingbatch_size =100# make results reproducibleseed =3np.random.seed(seed) tf.set_random_seed(seed)# 将所有数据分割成训练集80%测试集20%train_indices = np.random.choice(len(x_...
# Set for reproducible results seed=99 np.random.seed(seed) tf.set_random_seed(seed) # Split data into train/test = 80%/20% train_indices=np.random.choice(len(x_vals),round(len(x_vals)*0.8), replace=False) test_indices=np.array(list(set(range(len(x_vals)))-set(train_indices))...
# Set for reproducible results seed = 99 np.random.seed(seed) tf.set_random_seed(seed) # Split data into train/test = 80%/20% train_indices = np.random.choice(len(x_vals), round(len(x_vals)*0.8), replace=False) test_indices = np.array(list(set(range(len(x_vals))) - set(tr...
seed = 13 np.random.seed(seed) tf.set_random_seed(seed) # Create variables for linear regression A = tf.Variable(tf.random_normal(shape=[1,1])) b = tf.Variable(tf.random_normal(shape=[1,1])) # Declare model operations model_output = tf.add(tf.matmul(x_data, A), b) ...
np.random.seed(seed) tf.set_random_seed(seed) # Declare batch size batch_size = 50 # Initialize placeholders x_data =tf.placeholder(shape=[None, 3], dtype=tf.float32) y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32) ...
tf.set_random_seed(seed)# Create variables for linear regressionA = tf.Variable(tf.random_normal(shape=[1,1])) b = tf.Variable(tf.random_normal(shape=[1,1]))# Declare model operationsmodel_output = tf.add(tf.matmul(x_data, A), b)### Loss Functions### Select appropriate loss ...
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt sess = tf.Session() tf.set_random_seed(5) np.random.seed(42) 现在我们需要声明我们的批量大小,模型变量,数据和占位符来输入数据。我们的计算图将包括将我们的正态分布数据输入到两个相似的神经网络中,这两个神经网络的区别仅...
首先，应该使用两个可选属性seed和seed2声明op；请参阅random_ops.cc中的现有op。然后，在Python语言中...
seed: An int for random seed at the op level. It works together with the seed at global graph level together to determine the random number generation. See `tf.set_random_seed`. Returns: A tensor of indices with shape [batch_size, list_size, 2]. The returned ...