Learning how to use tensorflow, first tutorial code on mandelbrot set below
# Import libraries for simulation
import tensorflow as tf
import numpy as np
# Imports for visualization
import PIL.Image
from io import BytesIO
from IPython.display import Image, display
def DisplayFractal(a, fmt='jpeg'):
"""Display an array of iteration counts as a
colorful picture of a fractal."""
a_cyclic = (6.28*a/20.0).reshape(list(a.shape)+[1])
img = np.concatenate([10+20*np.cos(a_cyclic),
30+50*np.sin(a_cyclic),
155-80*np.cos(a_cyclic)], 2)
img[a==a.max()] = 0
a = img
a = np.uint8(np.clip(a, 0, 255))
f = BytesIO()
PIL.Image.fromarray(a).save(f, fmt)
display(Image(data=f.getvalue()))
sess = tf.InteractiveSession()
# Use NumPy to create a 2D array of complex numbers
Y, X = np.mgrid[-1.3:1.3:0.005, -2:1:0.005]
Z = X+1j*Y
xs = tf.constant(Z.astype(np.complex64))
zs = tf.Variable(xs)
ns = tf.Variable(tf.zeros_like(xs, tf.float32))
tf.global_variables_initializer().run()
# Compute the new values of z: z^2 + x
zs_ = zs*zs + xs
# Have we diverged with this new value?
not_diverged = tf.abs(zs_) < 4
# Operation to update the zs and the iteration count.
#
# Note: We keep computing zs after they diverge! This
# is very wasteful! There are better, if a little
# less simple, ways to do this.
#
step = tf.group(
zs.assign(zs_),
ns.assign_add(tf.cast(not_diverged, tf.float32))
)
for i in range(200): step.run()
DisplayFractal(ns.eval())
returns this on shell
<IPython.core.display.Image at 0x7fcdee1da810>
It doesn't display the image and I'd prefer if it saved the image.
How can I save the result as an image?
Scipy has an easy image save function! https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.misc.imsave.html
You should try this:
import scipy.misc
scipy.misc.imsave('mandelbrot.png',ns.eval())
I hope this works! Regardless, let me know!
Related
I am trying to optimize my Random forest regression model using a particle swarm optimizer to minimize the prediction error. But getting this error:UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U33'), dtype('<U33')) -> None
Can anyone please help me with this. I really appreciate any help you can provide.
My dataset contains 24 independent variables (X) and one dependent variable (y).
CODE:
import numpy as np
import pandas as pd
import re
import os
import random
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import animation, rc
%matplotlib inline
from matplotlib.animation import FuncAnimation
from sklearn.model_selection import cross_val_predict, train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, mean_absolute_percentage_error
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state=1)
from sklearn.ensemble import RandomForestRegressor
RFR = RandomForestRegressor()
RFR.fit(X_train, y_train)
######################### Particle swarm optimization ##################################
#### error rate
#### The objective is to find minimum error
def fitness_function(xtest, ytest):
# Prediction
ypred = RFR.predict(X_test)
error = mean_squared_error(y_test, ypred)
#r2 = r2_score(ytest, ypred)
return f'The error is: {error}'
from matplotlib import animation
import random
####### velocity #######################################
def update_velocity(particle, velocity, pbest, gbest, w_min=0.5, max=1.0, c=0.1):
# Initialise new velocity array
num_particle = len(particle)
new_velocity = np.array([0.2 for i in range(num_particle)])
# Randomly generate r1, r2 and inertia weight from normal distribution
r1 = random.uniform(0,max)
r2 = random.uniform(0,max)
w = random.uniform(w_min,max)
c1 = c
c2 = c
# Calculate new velocity
for i in range(num_particle):
new_velocity[i] = w*velocity[i] + c1*r1*(pbest[i]-particle[i])+c2*r2*(gbest[i]-particle[i])
return new_velocity
############## update position ##############
def update_position(particle, velocity):
# Move particles by adding velocity
new_particle = particle + velocity
return new_particle
######################################### PSO Main function ###################################
def pso_2d(population, dimension, position_min, position_max, generation, fitness_criterion):
# Initialization
# Population
particles = [[np.random.uniform(position_min[0:24], position_max[0 :24]) for j in range(population)] for i in range(dimension)] # generating random particle position
# Particle's best position
pbest_position = particles # personal best position
# Fitness
pbest_fitness = [fitness_function(p[0:24],p[1]) for p in particles] # personal best fitness
# Index of the best particle
# np.reshape(np.ravel(p[0]), (2, 31))
gbest_index = np.argmin(pbest_fitness)
# Global best particle position
gbest_position = pbest_position[gbest_index]
# Velocity (starting from 0 speed)
velocity = [[0.0 for j in range(dimension)] for i in range(population)]
# Loop for the number of generation
for t in range(generation):
# Stop if the average fitness value reached a predefined success criterion
if np.average(pbest_fitness) <= fitness_criterion:
break
else:
for n in range(population):
# Update the velocity of each particle
velocity[n] = update_velocity(particles[n], velocity[n], pbest_position[n], gbest_position)
# Move the particles to new position
particles[n] = update_position(particles[n], velocity[n])
# Calculate the fitness value
pbest_fitness = [fitness_function(p[0:24],p[1]) for p in particles]
# Find the index of the best particle
gbest_index = np.argmin(pbest_fitness)
# Update the position of the best particle
gbest_position = pbest_position[gbest_index]
# Print the results
print('Global Best Position: ', gbest_position)
print('Best Fitness Value: ', min(pbest_fitness))
print('Average Particle Best Fitness Value: ', np.average(pbest_fitness))
print('Number of Generation: ', t)
position_min = [-0.44306155, -0.52971118, -0.10311188, -0.60053201, -0.78198029,
-0.37737778, -0.14371436, -0.01623235, -0.88660182, -0.06182274,
-0.30084403, -0.98080838, -0.11787062, -0.84172055, -0.709991 ,
-0.9841236 , -0.32976052, -0.26586302, -0.87641669, -0.23728611,
-0.08874495, -0.03091284, -0.29987714, -0.96795309]
position_max = [0.44306155, 0.52971118, 0.10311188, 0.60053201, 0.78198029,
0.37737778, 0.14371436, 0.01623235, 0.88660182, 0.06182274,
0.30084403, 0.98080838, 0.11787062, 0.84172055, 0.709991 ,
0.9841236 , 0.32976052, 0.26586302, 0.87641669, 0.23728611,
0.08874495, 0.03091284, 0.29987714, 0.96795309]
pso_2d(100, 24, position_min, position_max, 400, 10e-4)
Output: UFuncTypeError: ufunc 'add' did not contain a loop with signature matching types (dtype('<U33'), dtype('<U33')) -> None
I would like to use tensorflow (version 2) to use gaussian process regression
to fit some data and I found the google colab example online here [1].
I have turned some of this notebook into a minimal example that is below.
Sometimes the code fails with the following error when using MCMC to marginalize the hyperparameters: and I was wondering if anyone has seen this before or knows how to get around this?
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input matrix is not invertible.
[[{{node mcmc_sample_chain/trace_scan/while/body/_168/smart_for_loop/while/body/_842/dual_averaging_step_size_adaptation___init__/_one_step/transformed_kernel_one_step/mh_one_step/hmc_kernel_one_step/leapfrog_integrate/while/body/_1244/leapfrog_integrate_one_step/maybe_call_fn_and_grads/value_and_gradients/value_and_gradient/gradients/leapfrog_integrate_one_step/maybe_call_fn_and_grads/value_and_gradients/value_and_gradient/PartitionedCall_grad/PartitionedCall/gradients/JointDistributionNamed/log_prob/JointDistributionNamed_log_prob_GaussianProcess/log_prob/JointDistributionNamed_log_prob_GaussianProcess/get_marginal_distribution/Cholesky_grad/MatrixTriangularSolve}}]] [Op:__inference_do_sampling_113645]
Function call stack:
do_sampling
[1] https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Gaussian_Process_Regression_In_TFP.ipynb#scrollTo=jw-_1yC50xaM
Note that some of code below is a bit redundant but it should
in some sections but it should be able to reproduce the error.
Thanks!
import time
import numpy as np
import tensorflow.compat.v2 as tf
import tensorflow_probability as tfp
tfb = tfp.bijectors
tfd = tfp.distributions
tfk = tfp.math.psd_kernels
tf.enable_v2_behavior()
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
#%pylab inline
# Configure plot defaults
plt.rcParams['axes.facecolor'] = 'white'
plt.rcParams['grid.color'] = '#666666'
#%config InlineBackend.figure_format = 'png'
def sinusoid(x):
return np.sin(3 * np.pi * x[..., 0])
def generate_1d_data(num_training_points, observation_noise_variance):
"""Generate noisy sinusoidal observations at a random set of points.
Returns:
observation_index_points, observations
"""
index_points_ = np.random.uniform(-1., 1., (num_training_points, 1))
index_points_ = index_points_.astype(np.float64)
# y = f(x) + noise
observations_ = (sinusoid(index_points_) +
np.random.normal(loc=0,
scale=np.sqrt(observation_noise_variance),
size=(num_training_points)))
return index_points_, observations_
# Generate training data with a known noise level (we'll later try to recover
# this value from the data).
NUM_TRAINING_POINTS = 100
observation_index_points_, observations_ = generate_1d_data(
num_training_points=NUM_TRAINING_POINTS,
observation_noise_variance=.1)
def build_gp(amplitude, length_scale, observation_noise_variance):
"""Defines the conditional dist. of GP outputs, given kernel parameters."""
# Create the covariance kernel, which will be shared between the prior (which we
# use for maximum likelihood training) and the posterior (which we use for
# posterior predictive sampling)
kernel = tfk.ExponentiatedQuadratic(amplitude, length_scale)
# Create the GP prior distribution, which we will use to train the model
# parameters.
return tfd.GaussianProcess(
kernel=kernel,
index_points=observation_index_points_,
observation_noise_variance=observation_noise_variance)
gp_joint_model = tfd.JointDistributionNamed({
'amplitude': tfd.LogNormal(loc=0., scale=np.float64(1.)),
'length_scale': tfd.LogNormal(loc=0., scale=np.float64(1.)),
'observation_noise_variance': tfd.LogNormal(loc=0., scale=np.float64(1.)),
'observations': build_gp,
})
x = gp_joint_model.sample()
lp = gp_joint_model.log_prob(x)
print("sampled {}".format(x))
print("log_prob of sample: {}".format(lp))
# Create the trainable model parameters, which we'll subsequently optimize.
# Note that we constrain them to be strictly positive.
constrain_positive = tfb.Shift(np.finfo(np.float64).tiny)(tfb.Exp())
amplitude_var = tfp.util.TransformedVariable(
initial_value=1.,
bijector=constrain_positive,
name='amplitude',
dtype=np.float64)
length_scale_var = tfp.util.TransformedVariable(
initial_value=1.,
bijector=constrain_positive,
name='length_scale',
dtype=np.float64)
observation_noise_variance_var = tfp.util.TransformedVariable(
initial_value=1.,
bijector=constrain_positive,
name='observation_noise_variance_var',
dtype=np.float64)
trainable_variables = [v.trainable_variables[0] for v in
[amplitude_var,
length_scale_var,
observation_noise_variance_var]]
# Use `tf.function` to trace the loss for more efficient evaluation.
#tf.function(autograph=False, experimental_compile=False)
def target_log_prob(amplitude, length_scale, observation_noise_variance):
return gp_joint_model.log_prob({
'amplitude': amplitude,
'length_scale': length_scale,
'observation_noise_variance': observation_noise_variance,
'observations': observations_
})
# Now we optimize the model parameters.
num_iters = 1000
optimizer = tf.optimizers.Adam(learning_rate=.01)
# Store the likelihood values during training, so we can plot the progress
lls_ = np.zeros(num_iters, np.float64)
for i in range(num_iters):
with tf.GradientTape() as tape:
loss = -target_log_prob(amplitude_var, length_scale_var,
observation_noise_variance_var)
grads = tape.gradient(loss, trainable_variables)
optimizer.apply_gradients(zip(grads, trainable_variables))
lls_[i] = loss
print('Trained parameters:')
print('amplitude: {}'.format(amplitude_var._value().numpy()))
print('length_scale: {}'.format(length_scale_var._value().numpy()))
print('observation_noise_variance: {}'.format(observation_noise_variance_var._value().numpy()))
num_results = 100
num_burnin_steps = 50
sampler = tfp.mcmc.TransformedTransitionKernel(
tfp.mcmc.HamiltonianMonteCarlo(
target_log_prob_fn=target_log_prob,
step_size=tf.cast(0.1, tf.float64),
num_leapfrog_steps=8),
bijector=[constrain_positive, constrain_positive, constrain_positive])
adaptive_sampler = tfp.mcmc.DualAveragingStepSizeAdaptation(
inner_kernel=sampler,
num_adaptation_steps=int(0.8 * num_burnin_steps),
target_accept_prob=tf.cast(0.75, tf.float64))
initial_state = [tf.cast(x, tf.float64) for x in [1., 1., 1.]]
# Speed up sampling by tracing with `tf.function`.
#tf.function(autograph=False, experimental_compile=False)
def do_sampling():
return tfp.mcmc.sample_chain(
kernel=adaptive_sampler,
current_state=initial_state,
num_results=num_results,
num_burnin_steps=num_burnin_steps,
trace_fn=lambda current_state, kernel_results: kernel_results)
t0 = time.time()
samples, kernel_results = do_sampling()
t1 = time.time()
print("Inference ran in {:.2f}s.".format(t1-t0))
This can happen if you have multiple index points that are very close, so you might consider using np.linspace or just doing some post filtering of your random draw. I would also suggest a bit bigger epsilon, maybe 1e-6.
I'm trying to learn tensorflow from working examples online but came across the example where i'm literally wondered how it works. Can any explain the maths behind this particular function of tensorflow and how [ns] get its value out of boolean data type.
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
Y, X = np.mgrid[-2.3:2.3:0.005, -5:5:0.005]
Z = X+1j*Y
c = tf.constant(Z, np.complex64)#.astype(np.complex64))
zs = tf.Variable(c)
ns = tf.Variable(tf.zeros_like(c, tf.float32))
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
zs_ = zs*zs + c
not_diverged = tf.abs(zs_) > 4
step = tf.group(zs.assign(zs_),
ns.assign_add(tf.cast(not_diverged, tf.float32)))
nx = tf.reduce_sum(ns)
zx = tf.reduce_sum(zs_)
cx = tf.reduce_sum(c)
zf = tf.reduce_all(not_diverged)
for i in range(200):
step.run()
print(sess.run([nx,zx,cx,zf]))
plt.imshow(ns.eval())
plt.show()
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# this defines the complex plane
Y, X = np.mgrid[-2.3:2.3:0.005, -5:5:0.005]
Z = X+1j*Y
c = tf.constant(Z, np.complex64)
# tensors are immutable in tensorflow,
# but variabels arent, so use variable
# to update values later on
zs = tf.Variable(c)
# ns will keep count of what has diverged
ns = tf.Variable(tf.zeros_like(c, tf.float32))
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# mandlebrot set M is defined as
# c \in M \iff |P_c^n(0)| <= 2 \iff abs(P_c^n(0)) <= 4
# where P_c(z) = z^2 + c
# the variable name is confusing, as it is actually
# the opposite, I renamed it below
zs_ = zs*zs + c
diverged = tf.abs(zs_) > 4
# ns gets its value as a bool casted to a float
# is given by True \mapsto 1., False \mapsto 0.
# the assign add just says, add tf.cast(diverged, tf.float32)
# to the variabel ns, and assign that value to the variable
step = tf.group(
zs.assign(zs_),
ns.assign_add(tf.cast(diverged, tf.float32)))
# here we iterate n to whatever we like
# each time we are moving further along the
# sequence P^n_c(0), which must be bounded
# in a disk of radius 2 to be in M
for i in range(200):
step.run()
# anywhere with value > 0 in the plot is not in the Mandlebrot set
# anywhere with value = 0 MIGHT be in the Mandlebrot set
# we don't know for sure if it is in the set,
# because we can only ever take n to be some
# finite number. But to be in the Mandlebrot set, it has
# to be bounded for all n!
plt.imshow(ns.eval())
plt.show()
I am trying to speed up the conversion of select tfrecords to a series of python dictionaries. Here's what I have. Initially the CPU utilization spikes, but then goes to almost zero, suggesting my code is not working correctly.
My goal is to have 3 dictionaries saved and pickled. There are 14,000+ tfrecord files (2 gigs appx). At the current rate, it will take about 84 hours to run on a single process.
Are there any problems with my use of manage dicts
import glob
import tensorflow as tf
import cPickle
import numpy as np
from tqdm import tqdm
import collections
from multiprocessing import Process, Manager, Pool
def get_multihot_encoding(example_label):
enc = np.zeros(10)
for label in example_label:
if label in lookup.values():
index = lookup_inverted[label]
enc[index] = 1
return list(enc)
# Set-up MultiProcessing
manager = Manager()
audio_embeddings_dict = manager.dict()
audio_labels_dict = manager.dict()
audio_multihot_dict = manager.dict()
sess = tf.Session()
# The iterable which gets passed to the function
all_tfrecord_filenames = glob.glob('/Users/jeff/features/audioset_v1_embeddings/unbal_train/*.tfrecord')
def process_tfrecord(tfrecord):
for idx, example in enumerate(tf.python_io.tf_record_iterator(tfrecord)):
tf_example = tf.train.Example.FromString(example)
vid_id = tf_example.features.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8')
example_label = list(np.asarray(tf_example.features.feature['labels'].int64_list.value))
# Non zero intersect of 2 sets is True - only create dict entries if this is true!
if set(example_label) & label_filters:
print(set(example_label) & label_filters, " Is the intersection of the two")
tf_seq_example = tf.train.SequenceExample.FromString(example)
n_frames = len(tf_seq_example.feature_lists.feature_list['audio_embedding'].feature)
audio_frame = []
for i in range(n_frames):
audio_frame.append(tf.cast(tf.decode_raw(
tf_seq_example.feature_lists.feature_list['audio_embedding'].feature[i].bytes_list.value[0],tf.uint8)
,tf.float32).eval(session=sess))
audio_embeddings_dict[vid_id] = audio_frame
audio_labels_dict[vid_id] = example_label
audio_multihot_dict[vid_id] = get_multihot_encoding(example_label)
#print(get_multihot_encoding(example_label), "Is the encoded label")
if idx % 100 == 0:
print ("Saving dictionary at loop: {}".format(idx))
cPickle.dump(audio_embeddings_dict, open('audio_embeddings_dict_unbal_train_multi_{}.pkl'.format(idx), 'wb'))
cPickle.dump(audio_multihot_dict, open('audio_multihot_dict_bal_untrain_multi_{}.pkl'.format(idx), 'wb'))
cPickle.dump(audio_multihot_dict, open('audio_labels_unbal_dict_multi_{}.pkl'.format(idx), 'wb'))
pool = Pool(50)
result = pool.map(process_tfrecord, all_tfrecord_filenames)
Can someone give a example on how to use tensorboard visualize numpy array value?
There is a related question here, I don't really get it.
Tensorboard logging non-tensor (numpy) information (AUC)
For example,
If I have
for i in range(100):
foo = np.random.rand(3,2)
How can I keep tracking the distribution of foo using tensorboard for 100 iterations? Can someone give a code example?
Thanks.
For simple values (scalar), you can use this recipe
summary_writer = tf.train.SummaryWriter(FLAGS.logdir)
summary = tf.Summary()
summary.value.add(tag=tagname, simple_value=value)
summary_writer.add_summary(summary, global_step)
summary_writer.flush()
As far as using array, perhaps you can add 6 values in a sequence, ie
for value in foo:
summary.value.add(tag=tagname, simple_value=value)
Another (simplest) way is just using placeholders. First, you can make a placeholder for your numpy array shape.
# Some place holders for summary
summary_reward = tf.placeholder(tf.float32, shape=(), name="reward")
tf.summary.scalar("reward", summary_reward)
Then, just call session.run the merged summary with the feed_dict.
# Summary
summ = tf.summary.merge_all()
...
s = sess.run(summ, feed_dict={summary_reward: reward})
writer.add_summary(s, i)
if you install this package via pip install tensorboard-pytorch it becomes as straightforward as it can get:
import numpy as np
from tensorboardX import SummaryWriter
writer = SummaryWriter()
for i in range(50):
writer.add_histogram("moving_gauss", np.random.normal(i, i, 1000), i, bins="auto")
writer.close()
Will generate the corresponding histogram data in the runs directory:
Found a way to work around, create a variable and assign the value of numpy array to the variable, use tensorboard to track the variable
mysummary_writer = tf.train.SummaryWriter("./tmp/test/")
a = tf.Variable(tf.zeros([3,2]), name="a")
sum1 = tf.histogram_summary("nparray1", a)
summary_op = tf.merge_all_summaries()
sess = tf.Session()
sess.run(tf.initialize_all_variables())
for ii in range(10):
foo = np.random.rand(3, 2)
assign_op = a.assign(foo)
summary, _ = sess.run([summary_op, assign_op])
mysummary_writer.add_summary(tf.Summary.FromString(summary), global_step=ii)
mysummary_writer.flush()
sess = tf.Session()
writer = tf.summary.FileWriter('tensorboard_test')
var = tf.Variable(0.0,trainable=False,name='loss')
sess.run(var.initializer)
summary_op = tf.summary.scalar('scalar1',var)
for value in array:
sess.run(var.assign(value))
summary = sess.run(summary_op)
writer.add_summary(summary,i)
It works, but slow.
You could define a function like this (taken from gyglim's gist):
def add_histogram(writer, tag, values, step, bins=1000):
"""
Logs the histogram of a list/vector of values.
From: https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
"""
# Create histogram using numpy
counts, bin_edges = np.histogram(values, bins=bins)
# Fill fields of histogram proto
hist = tf.HistogramProto()
hist.min = float(np.min(values))
hist.max = float(np.max(values))
hist.num = int(np.prod(values.shape))
hist.sum = float(np.sum(values))
hist.sum_squares = float(np.sum(values ** 2))
# Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
# See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
# Therefore we drop the start of the first bin
bin_edges = bin_edges[1:]
# Add bin edges and counts
for edge in bin_edges:
hist.bucket_limit.append(edge)
for c in counts:
hist.bucket.append(c)
# Create and write Summary
summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
writer.add_summary(summary, step)
And then add to the summary writer like this:
add_histogram(summary_writer, "Histogram_Name", your_numpy_array, step)
You can plot the vector with matplotlib, convert the plot to numpy array along the lines of
https://stackoverflow.com/a/35362787/10873169, and then add it to Tensorboard as image
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter("runs/example")
for step in range(1, 10):
# Time-dependent vector we want to plot
example_vector = np.sin(np.arange(100) / step)
# Plot it in matplotlib first. Default DPI doesn't look good in Tensorboard
fig = Figure(figsize=(5, 2), dpi=200)
canvas = FigureCanvasAgg(fig)
fig.gca().plot(example_vector)
canvas.draw()
# Get the image as a string of bytes
image_as_string = np.frombuffer(canvas.tostring_rgb(), dtype='uint8')
# Need to reshape to (height, width, channels)
target_shape = canvas.get_width_height()[::-1] + (3,)
reshaped_image = image_as_string.reshape(target_shape)
# Write to Tensorboard logs
writer.add_image("example_vector", reshaped_image,
dataformats="HWC", global_step=step)
writer.close()