如何记录（使用SummaryWriter，例如TensorBoard）张量的单个标量元素Variable？例如，如何记录网络中给定层或节点的各个权重？
在我的示例代码中，我将一个通用的前馈神经网络投入使用以进行简单的线性回归，并希望（在这种情况下）随着学习的进行记录孤立隐藏层中孤立节点的权重。
例如，我可以在会话期间显式获取这些值
sess.run(layer_weights)[0][i][0]
对于第i-th 个权重，其中layer_weights是权重Variables 的列表；但我不知道如何记录相应的标量值。如果我尝试
w1 = tf.slice(layer_weights[0], [0], [1])[0]
tf.scalar_summary('w1', w1)
或者
w1 = layer_weights[0][1][0]
tf.scalar_summary('w1', w1)
我明白了
ValueError：形状 (5, 1) 必须具有等级 1
如何从 TensorFlow 记录单个标量值Variable？
from __future__ import (absolute_import, print_function, division, unicode_literals)
import numpy as np
import tensorflow as tf
# Basic model parameters as external flags
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_float('network_nodes', [5, 1], 'The number of nodes in each layer, including input and output.')
flags.DEFINE_float('epochs', 250, 'Epochs to run')
flags.DEFINE_float('learning_rate', 0.15, 'Initial learning rate.')
flags.DEFINE_string('data_dir', './data', 'Directory to hold training and test data.')
flags.DEFINE_string('train_dir', './_tmp/train', 'Directory to log training (and the network def).')
flags.DEFINE_string('test_dir', './_tmp/test', 'Directory to log testing.')
def variable_summaries(var, name):
with tf.name_scope("summaries"):
mean = tf.reduce_mean(var)
tf.scalar_summary('mean/' + name, mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
tf.scalar_summary('sttdev/' + name, stddev)
tf.scalar_summary('max/' + name, tf.reduce_max(var))
tf.scalar_summary('min/' + name, tf.reduce_min(var))
tf.histogram_summary(name, var)
def add_layer(input_tensor, input_dim, output_dim, neuron_fn, layer_name):
with tf.name_scope(layer_name):
with tf.name_scope("weights"):
weights = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1))
with tf.name_scope("biases"):
biases = tf.Variable(tf.constant(0.1, shape=[output_dim]))
with tf.name_scope('activations'):
with tf.name_scope('weighted_inputs'):
weighted_inputs = tf.matmul(input_tensor, weights) + biases
tf.histogram_summary(layer_name + '/weighted_inputs', weighted_inputs)
output = neuron_fn(weighted_inputs)
return output, weights, biases
def make_ff_network(nodes, input_activation, hidden_activation_fn=tf.nn.sigmoid, output_activation_fn=tf.nn.softmax):
layer_activations = [input_activation]
layer_weights = []
layer_biases = []
n_layers = len(nodes)
for l in range(1, n_layers):
a, w, b = add_layer(layer_activations[l - 1], nodes[l - 1], nodes[l],
output_activation_fn if l == n_layers - 1 else hidden_activation_fn,
'output_layer' if l == n_layers - 1 else 'hidden_layer' + (
'_{}'.format(l) if n_layers > 3 else ''))
layer_activations += [a]
layer_weights += [w]
layer_biases += [b]
with tf.name_scope('output'):
net_activation = tf.identity(layer_activations[-1], name='network_activation')
return net_activation, layer_weights, layer_biases
# Inputs and outputs
with tf.name_scope('data'):
x = tf.placeholder(tf.float32, shape=[None, FLAGS.network_nodes[0]], name='inputs')
y_ = tf.placeholder(tf.float32, shape=[None, FLAGS.network_nodes[-1]], name='correct_outputs')
# Network structure
y, layer_weights, layer_biases = make_ff_network(FLAGS.network_nodes, x, output_activation_fn=tf.identity)
# Metrics and operations
with tf.name_scope('accuracy'):
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.square(y - y_))
# NONE OF THESE WORK:
#w1 = tf.slice(layer_weights[0], [0], [1])[0]
#tf.scalar_summary('w1', w1)
#w1 = layer_weights[0][1][0]
#tf.scalar_summary('w1', w1)
tf.scalar_summary('loss', loss)
train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(loss)
# Logging
train_writer = tf.train.SummaryWriter(FLAGS.train_dir, tf.get_default_graph())
test_writer = tf.train.SummaryWriter(FLAGS.test_dir)
merged = tf.merge_all_summaries()
W = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
train_x = np.random.rand(100000, FLAGS.network_nodes[0])
train_y = np.array([np.dot(W, train_x.T)+ 6.0]).T
test_x = np.random.rand(1000, FLAGS.network_nodes[0])
test_y = np.array([np.dot(W, test_x.T)+ 6.0]).T
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for ep in range(FLAGS.epochs):
sess.run(train_step, feed_dict={x: train_x, y_: train_y})
summary = sess.run(merged, feed_dict={x: test_x, y_: test_y})
test_writer.add_summary(summary, ep+1)
# THESE WORK
print('w1 = {}'.format(sess.run(layer_weights)[0][0][0]))
print('w2 = {}'.format(sess.run(layer_weights)[0][1][0]))
print('w3 = {}'.format(sess.run(layer_weights)[0][2][0]))
print('w4 = {}'.format(sess.run(layer_weights)[0][3][0]))
print('w5 = {}'.format(sess.run(layer_weights)[0][4][0]))
print(' b = {}'.format(sess.run(layer_biases)[0][0]))
