tensorboard_basic.py

'''
Graph and Loss visualization using Tensorboard.
This example is using the MNIST database of handwritten digits
(http://yann.lecun.com/exdb/mnist/)
'''
from __future__ import print_function

import tensorflow as tf 

# import mnist data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)


# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1
logs_path = '/tmp/tensorflow_logs/example'


# tf Graph Input
# mnist data image of shape 28*28=784
x = tf.placeholder(tf.float32, [None, 784], name='InputData')
# 0-9 digits recognition => 10 classes
y = tf.placeholder(tf.float32, [None, 10], name='LabelData')

# Set model weights
W = tf.Variable(tf.zeros([784, 10]), name='Weights')
b = tf.Variable(tf.zeros([10]), name='Bias')



# Construct model and encapsulating all ops into scopes, making
# Tensorboard's Graph visualization more convenient
with tf.name_scope('Model'):
	#model
	pred = tf.nn.softmax(tf.matmul(x, W) + b)