我尝试过但没有得到结果,但是当按如下方式使用时,得到了结果
import tensorboardcolab as tb
tbc = tb.TensorBoardColab()
之后,打开输出中的链接。
import tensorflow as tf
import numpy as np
明确创建一个Graph对象
graph = tf.Graph()
with graph.as_default()
完整的例子:
with tf.name_scope("variables"):
# Variable to keep track of how many times the graph has been run
global_step = tf.Variable(0, dtype=tf.int32, name="global_step")
# Increments the above `global_step` Variable, should be run whenever the graph is run
increment_step = global_step.assign_add(1)
# Variable that keeps track of previous output value:
previous_value = tf.Variable(0.0, dtype=tf.float32, name="previous_value")
# Primary transformation Operations
with tf.name_scope("exercise_transformation"):
# Separate input layer
with tf.name_scope("input"):
# Create input placeholder- takes in a Vector
a = tf.placeholder(tf.float32, shape=[None], name="input_placeholder_a")
# Separate middle layer
with tf.name_scope("intermediate_layer"):
b = tf.reduce_prod(a, name="product_b")
c = tf.reduce_sum(a, name="sum_c")
# Separate output layer
with tf.name_scope("output"):
d = tf.add(b, c, name="add_d")
output = tf.subtract(d, previous_value, name="output")
update_prev = previous_value.assign(output)
# Summary Operations
with tf.name_scope("summaries"):
tf.summary.scalar('output', output) # Creates summary for output node
tf.summary.scalar('product of inputs', b, )
tf.summary.scalar('sum of inputs', c)
# Global Variables and Operations
with tf.name_scope("global_ops"):
# Initialization Op
init = tf.initialize_all_variables()
# Collect all summary Ops in graph
merged_summaries = tf.summary.merge_all()
# Start a Session, using the explicitly created Graph
sess = tf.Session(graph=graph)
# Open a SummaryWriter to save summaries
writer = tf.summary.FileWriter('./Graph', sess.graph)
# Initialize Variables
sess.run(init)
def run_graph(input_tensor):
"""
Helper function; runs the graph with given input tensor and saves summaries
"""
feed_dict = {a: input_tensor}
output, summary, step = sess.run([update_prev, merged_summaries, increment_step], feed_dict=feed_dict)
writer.add_summary(summary, global_step=step)
# Run the graph with various inputs
run_graph([2,8])
run_graph([3,1,3,3])
run_graph([8])
run_graph([1,2,3])
run_graph([11,4])
run_graph([4,1])
run_graph([7,3,1])
run_graph([6,3])
run_graph([0,2])
run_graph([4,5,6])
# Writes the summaries to disk
writer.flush()
# Flushes the summaries to disk and closes the SummaryWriter
writer.close()
# Close the session
sess.close()
# To start TensorBoard after running this file, execute the following command:
# $ tensorboard --logdir='./improved_graph'
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在Google Colab上训练TensorFlow模型时,有什么方法可以使用TensorBoard?