Skip to content

Instantly share code, notes, and snippets.

@mchirico
Last active April 2, 2021 06:56
Show Gist options
  • Save mchirico/bcc376fb336b73f24b29 to your computer and use it in GitHub Desktop.
Save mchirico/bcc376fb336b73f24b29 to your computer and use it in GitHub Desktop.
Tensorflow: working with tensorboard, CSV, and saving results
#!/usr/bin/env python
import tensorflow as tf
import numpy as np
from numpy import genfromtxt
# Build Example Data is CSV format, but use Iris data
from sklearn import datasets
from sklearn.model_selection import train_test_split
import sklearn
def buildDataFromIris():
iris = datasets.load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.33, random_state=42)
f=open('cs-training.csv','w')
for i,j in enumerate(X_train):
k=np.append(np.array(y_train[i]),j )
f.write(",".join([str(s) for s in k]) + '\n')
f.close()
f=open('cs-testing.csv','w')
for i,j in enumerate(X_test):
k=np.append(np.array(y_test[i]),j )
f.write(",".join([str(s) for s in k]) + '\n')
f.close()
# Convert to one hot
def convertOneHot(data):
y=np.array([int(i[0]) for i in data])
y_onehot=[0]*len(y)
for i,j in enumerate(y):
y_onehot[i]=[0]*(y.max() + 1)
y_onehot[i][j]=1
return (y,y_onehot)
buildDataFromIris()
data = genfromtxt('cs-training.csv',delimiter=',') # Training data
test_data = genfromtxt('cs-testing.csv',delimiter=',') # Test data
x_train=np.array([ i[1::] for i in data])
y_train,y_train_onehot = convertOneHot(data)
x_test=np.array([ i[1::] for i in test_data])
y_test,y_test_onehot = convertOneHot(test_data)
# A number of features, 4 in this example
# B = 3 species of Iris (setosa, virginica and versicolor)
A=data.shape[1]-1 # Number of features, Note first is y
B=len(y_train_onehot[0])
tf_in = tf.placeholder("float", [None, A]) # Features
tf_weight = tf.Variable(tf.zeros([A,B]))
tf_bias = tf.Variable(tf.zeros([B]))
tf_softmax = tf.nn.softmax(tf.matmul(tf_in,tf_weight) + tf_bias)
# Training via backpropagation
tf_softmax_correct = tf.placeholder("float", [None,B])
tf_cross_entropy = -tf.reduce_sum(tf_softmax_correct*tf.log(tf_softmax))
# Train using tf.train.GradientDescentOptimizer
tf_train_step = tf.train.GradientDescentOptimizer(0.01).minimize(tf_cross_entropy)
# Add accuracy checking nodes
tf_correct_prediction = tf.equal(tf.argmax(tf_softmax,1), tf.argmax(tf_softmax_correct,1))
tf_accuracy = tf.reduce_mean(tf.cast(tf_correct_prediction, "float"))
# Recreate logging dir
import shutil, os, sys
TMPDir='./tenIrisSave'
try:
shutil.rmtree(TMPDir)
except:
print "Tmp Dir did not exist"
os.mkdir(TMPDir, 0755 )
# Initialize and run
sess = tf.Session()
#sess = tf.InteractiveSession()
init = tf.initialize_all_variables()
sess.run(init)
# Build the summary operation based on the TF collection of Summaries.
tf.train.write_graph(sess.graph_def, TMPDir + '/logsd','graph.pbtxt')
#acc = tf.scalar_summary("Accuracy:", tf_accuracy)
tf.scalar_summary("Accuracy:", tf_accuracy)
tf.histogram_summary('weights', tf_weight)
tf.histogram_summary('bias', tf_bias)
tf.histogram_summary('softmax', tf_softmax)
tf.histogram_summary('accuracy', tf_accuracy)
summary_op = tf.merge_all_summaries()
#summary_writer = tf.train.SummaryWriter('./tenIrisSave/logs',graph_def=sess.graph_def)
summary_writer = tf.train.SummaryWriter(TMPDir + '/logs',sess.graph_def)
# This will not work. You need the full path.
# tensorboard --logdir=./tenIrisSave/ # BAD!
# tensorboard --logdir=$(pwd)/tenIrisSave/ # Good!
# This is for saving all our work
saver = tf.train.Saver([tf_weight,tf_bias])
print("...")
# Run the training
k=[]
saved=0
for i in range(100):
sess.run(tf_train_step, feed_dict={tf_in: x_train, tf_softmax_correct: y_train_onehot})
# Print accuracy
result = sess.run(tf_accuracy, feed_dict={tf_in: x_test, tf_softmax_correct: y_test_onehot})
print "Run {},{}".format(i,result)
k.append(result)
summary_str = sess.run(summary_op,feed_dict={tf_in: x_test, tf_softmax_correct: y_test_onehot})
summary_writer.add_summary(summary_str, i)
if result == 1 and saved == 0:
saved=1
print "saving"
saver.save(sess,"./tenIrisSave/saveOne")
k=np.array(k)
print(np.where(k==k.max()))
print "Max: {}".format(k.max())
print "\nTo see the output, run the following:"
print "tensorboard --logdir=$(pwd)/tenIrisSave"
@mchirico
Copy link
Author

Apparently you have to give the full path when running tensorboard

tensorboard --logdir=$(pwd)/tenIrisSave

This will give you the following tensorboards

screenshot 2015-11-22 07 32 19

screenshot 2015-11-22 07 34 47

screenshot 2015-11-22 07 35 53

@mchirico
Copy link
Author

Note...you'll probably want to restore and run the saved value...here's a link to the code that will do that.

https://gist.github.com/mchirico/cce03212eda66e654c54

@bhaavanmerchant
Copy link

Wouldn't

tf_weight = tf.Variable(tf.random_normal([A,B], stddev=0.01))
tf_bias = tf.Variable(tf.random_normal([B], stddev=0.01))
``` be better as it would do random initialization instead of zeros?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment