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import tensorflow as tf
from tensorflow.python.client import timeline
import pylab
import numpy as np
import os
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
tf.logging.set_verbosity(tf.logging.INFO)
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tf.reset_default_graph()
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sess = tf.Session()
print(sess)
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from datetime import datetime
version = int(datetime.now().strftime("%s"))
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num_samples = 100000
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import numpy as np
x_train = np.random.rand(num_samples).astype(np.float32)
print(x_train)
noise = np.random.normal(scale=0.01, size=len(x_train))
y_train = x_train * 0.1 + 0.3 + noise
print(y_train)
pylab.plot(x_train, y_train, '.')
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import pylab
x_test = np.random.rand(len(x_train)).astype(np.float32)
print(x_test)
noise = np.random.normal(scale=.01, size=len(x_train))
y_test = x_test * 0.1 + 0.3 + noise
print(y_test)
pylab.plot(x_test, y_test, '.')
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with tf.device("/cpu:0"):
W = tf.get_variable(shape=[], name='weights')
print(W)
b = tf.get_variable(shape=[], name='bias')
print(b)
x_observed = tf.placeholder(shape=[None],
dtype=tf.float32,
name='x_observed')
print(x_observed)
y_pred = W * x_observed + b
print(y_pred)
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learning_rate = 0.025
with tf.device("/cpu:0"):
y_observed = tf.placeholder(shape=[None], dtype=tf.float32, name='y_observed')
print(y_observed)
loss_op = tf.reduce_mean(tf.square(y_pred - y_observed))
optimizer_op = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer_op.minimize(loss_op)
print("Loss Scalar: ", loss_op)
print("Optimizer Op: ", optimizer_op)
print("Train Op: ", train_op)
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with tf.device("/cpu:0"):
init_op = tf.global_variables_initializer()
print(init_op)
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sess.run(init_op)
print("Initial random W: %f" % sess.run(W))
print("Initial random b: %f" % sess.run(b))
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def test(x, y):
return sess.run(loss_op, feed_dict={x_observed: x, y_observed: y})
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test(x_train, y_train)
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loss_summary_scalar_op = tf.summary.scalar('loss', loss_op)
loss_summary_merge_all_op = tf.summary.merge_all()
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train_summary_writer = tf.summary.FileWriter('/root/tensorboard/linear/cpu/%s/train' % version,
graph=tf.get_default_graph())
test_summary_writer = tf.summary.FileWriter('/root/tensorboard/linear/cpu/%s/test' % version,
graph=tf.get_default_graph())
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%%time
from tensorflow.python.client import timeline
with tf.device("/cpu:0"):
run_metadata = tf.RunMetadata()
max_steps = 401
for step in range(max_steps):
if (step < max_steps - 1):
test_summary_log, _ = sess.run([loss_summary_merge_all_op, loss_op], feed_dict={x_observed: x_test, y_observed: y_test})
train_summary_log, _ = sess.run([loss_summary_merge_all_op, train_op], feed_dict={x_observed: x_train, y_observed: y_train})
else:
test_summary_log, _ = sess.run([loss_summary_merge_all_op, loss_op], feed_dict={x_observed: x_test, y_observed: y_test})
train_summary_log, _ = sess.run([loss_summary_merge_all_op, train_op], feed_dict={x_observed: x_train, y_observed: y_train},
options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
with open('timeline-cpu.json', 'w') as trace_file:
trace_file.write(trace.generate_chrome_trace_format(show_memory=True))
if step % 10 == 0:
print(step, sess.run([W, b]))
train_summary_writer.add_summary(train_summary_log, step)
train_summary_writer.flush()
test_summary_writer.add_summary(test_summary_log, step)
test_summary_writer.flush()
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pylab.plot(x_train, y_train, '.', label="target")
pylab.plot(x_train, sess.run(y_pred,
feed_dict={x_observed: x_train,
y_observed: y_train}),
".",
label="predicted")
pylab.legend()
pylab.ylim(0, 1.0)
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import os
optimize_me_parent_path = '/root/models/optimize_me/linear/cpu'
saver = tf.train.Saver()
os.system('rm -rf %s' % optimize_me_parent_path)
os.makedirs(optimize_me_parent_path)
unoptimized_model_graph_path = '%s/unoptimized_cpu.pb' % optimize_me_parent_path
print(unoptimized_model_graph_path)
tf.train.write_graph(sess.graph_def,
'.',
unoptimized_model_graph_path,
as_text=False)
model_checkpoint_path = '%s/model.ckpt' % optimize_me_parent_path
saver.save(sess,
save_path=model_checkpoint_path)
print(model_checkpoint_path)
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print(optimize_me_parent_path)
os.listdir(optimize_me_parent_path)
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sess.close()
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%%bash
summarize_graph --in_graph=/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from google.protobuf import text_format
from tensorflow.core.framework import graph_pb2
def convert_graph_to_dot(input_graph, output_dot, is_input_graph_binary):
graph = graph_pb2.GraphDef()
with open(input_graph, "rb") as fh:
if is_input_graph_binary:
graph.ParseFromString(fh.read())
else:
text_format.Merge(fh.read(), graph)
with open(output_dot, "wt") as fh:
print("digraph graphname {", file=fh)
for node in graph.node:
output_name = node.name
print(" \"" + output_name + "\" [label=\"" + node.op + "\"];", file=fh)
for input_full_name in node.input:
parts = input_full_name.split(":")
input_name = re.sub(r"^\^", "", parts[0])
print(" \"" + input_name + "\" -> \"" + output_name + "\";", file=fh)
print("}", file=fh)
print("Created dot file '%s' for graph '%s'." % (output_dot, input_graph))
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input_graph='/root/models/optimize_me/linear/cpu/unoptimized_cpu.pb'
output_dot='/root/notebooks/unoptimized_cpu.dot'
convert_graph_to_dot(input_graph=input_graph, output_dot=output_dot, is_input_graph_binary=True)
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%%bash
dot -T png /root/notebooks/unoptimized_cpu.dot \
-o /root/notebooks/unoptimized_cpu.png > /tmp/a.out
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from IPython.display import Image
Image('/root/notebooks/unoptimized_cpu.png', width=1024, height=768)
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