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shapesorter.py
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shapesorter.py
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import tensorflow as tf
import sys
import numpy
# Number of classes is 2 (squares and triangles)
nClass=2
# Simple model (set to True) or convolutional neural network (set to False)
simpleModel=True
# Dimensions of image (pixels)
height=32
width=32
# Function to tell TensorFlow how to read a single image from input file
def getImage(filename):
# convert filenames to a queue for an input pipeline.
filenameQ = tf.train.string_input_producer([filename],num_epochs=None)
# object to read records
recordReader = tf.TFRecordReader()
# read the full set of features for a single example
key, fullExample = recordReader.read(filenameQ)
# parse the full example into its' component features.
features = tf.parse_single_example(
fullExample,
features={
'image/height': tf.FixedLenFeature([], tf.int64),
'image/width': tf.FixedLenFeature([], tf.int64),
'image/colorspace': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
'image/channels': tf.FixedLenFeature([], tf.int64),
'image/class/label': tf.FixedLenFeature([],tf.int64),
'image/class/text': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
'image/format': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
'image/filename': tf.FixedLenFeature([], dtype=tf.string,default_value=''),
'image/encoded': tf.FixedLenFeature([], dtype=tf.string, default_value='')
})
# now we are going to manipulate the label and image features
label = features['image/class/label']
image_buffer = features['image/encoded']
# Decode the jpeg
with tf.name_scope('decode_jpeg',[image_buffer], None):
# decode
image = tf.image.decode_jpeg(image_buffer, channels=3)
# and convert to single precision data type
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# cast image into a single array, where each element corresponds to the greyscale
# value of a single pixel.
# the "1-.." part inverts the image, so that the background is black.
image=tf.reshape(1-tf.image.rgb_to_grayscale(image),[height*width])
# re-define label as a "one-hot" vector
# it will be [0,1] or [1,0] here.
# This approach can easily be extended to more classes.
label=tf.stack(tf.one_hot(label-1, nClass))
return label, image
# associate the "label" and "image" objects with the corresponding features read from
# a single example in the training data file
label, image = getImage("data/train-00000-of-00001")
# and similarly for the validation data
vlabel, vimage = getImage("data/validation-00000-of-00001")
# associate the "label_batch" and "image_batch" objects with a randomly selected batch---
# of labels and images respectively
imageBatch, labelBatch = tf.train.shuffle_batch(
[image, label], batch_size=100,
capacity=2000,
min_after_dequeue=1000)
# and similarly for the validation data
vimageBatch, vlabelBatch = tf.train.shuffle_batch(
[vimage, vlabel], batch_size=100,
capacity=2000,
min_after_dequeue=1000)
# interactive session allows inteleaving of building and running steps
sess = tf.InteractiveSession()
# x is the input array, which will contain the data from an image
# this creates a placeholder for x, to be populated later
x = tf.placeholder(tf.float32, [None, width*height])
# similarly, we have a placeholder for true outputs (obtained from labels)
y_ = tf.placeholder(tf.float32, [None, nClass])
if simpleModel:
# run simple model y=Wx+b given in TensorFlow "MNIST" tutorial
print "Running Simple Model y=Wx+b"
# initialise weights and biases to zero
# W maps input to output so is of size: (number of pixels) * (Number of Classes)
W = tf.Variable(tf.zeros([width*height, nClass]))
# b is vector which has a size corresponding to number of classes
b = tf.Variable(tf.zeros([nClass]))
# define output calc (for each class) y = softmax(Wx+b)
# softmax gives probability distribution across all classes
y = tf.nn.softmax(tf.matmul(x, W) + b)
else:
# run convolutional neural network model given in "Expert MNIST" TensorFlow tutorial
# functions to init small positive weights and biases
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
# set up "vanilla" versions of convolution and pooling
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
print "Running Convolutional Neural Network Model"
nFeatures1=32
nFeatures2=64
nNeuronsfc=1024
# use functions to init weights and biases
# nFeatures1 features for each patch of size 5x5
# SAME weights used for all patches
# 1 input channel
W_conv1 = weight_variable([5, 5, 1, nFeatures1])
b_conv1 = bias_variable([nFeatures1])
# reshape raw image data to 4D tensor. 2nd and 3rd indexes are W,H, fourth
# means 1 colour channel per pixel
# x_image = tf.reshape(x, [-1,28,28,1])
x_image = tf.reshape(x, [-1,width,height,1])
# hidden layer 1
# pool(convolution(Wx)+b)
# pool reduces each dim by factor of 2.
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
# similarly for second layer, with nFeatures2 features per 5x5 patch
# input is nFeatures1 (number of features output from previous layer)
W_conv2 = weight_variable([5, 5, nFeatures1, nFeatures2])
b_conv2 = bias_variable([nFeatures2])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
# denseley connected layer. Similar to above, but operating
# on entire image (rather than patch) which has been reduced by a factor of 4
# in each dimension
# so use large number of neurons
# check our dimensions are a multiple of 4
if (width%4 or height%4):
print "Error: width and height must be a multiple of 4"
sys.exit(1)
W_fc1 = weight_variable([(width/4) * (height/4) * nFeatures2, nNeuronsfc])
b_fc1 = bias_variable([nNeuronsfc])
# flatten output from previous layer
h_pool2_flat = tf.reshape(h_pool2, [-1, (width/4) * (height/4) * nFeatures2])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# reduce overfitting by applying dropout
# each neuron is kept with probability keep_prob
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# create readout layer which outputs to nClass categories
W_fc2 = weight_variable([nNeuronsfc, nClass])
b_fc2 = bias_variable([nClass])
# define output calc (for each class) y = softmax(Wx+b)
# softmax gives probability distribution across all classes
# this is not run until later
y=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# measure of error of our model
# this needs to be minimised by adjusting W and b
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
# define training step which minimises cross entropy
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# argmax gives index of highest entry in vector (1st axis of 1D tensor)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
# get mean of all entries in correct prediction, the higher the better
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# run the session
# initialize the variables
sess.run(tf.global_variables_initializer())
# start the threads used for reading files
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess,coord=coord)
# start training
nSteps=1000
for i in range(nSteps):
batch_xs, batch_ys = sess.run([imageBatch, labelBatch])
# run the training step with feed of images
if simpleModel:
train_step.run(feed_dict={x: batch_xs, y_: batch_ys})
else:
train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})
if (i+1)%100 == 0: # then perform validation
# get a validation batch
vbatch_xs, vbatch_ys = sess.run([vimageBatch, vlabelBatch])
if simpleModel:
train_accuracy = accuracy.eval(feed_dict={
x:vbatch_xs, y_: vbatch_ys})
else:
train_accuracy = accuracy.eval(feed_dict={
x:vbatch_xs, y_: vbatch_ys, keep_prob: 1.0})
print("step %d, training accuracy %g"%(i+1, train_accuracy))
# finalise
coord.request_stop()
coord.join(threads)