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MS_conv.py
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MS_conv.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 31 15:13:57 2017
@author: mulugetasemework
"""
# encoding: UTF-8
# Copyright 2016 Google.com
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Download images and labels into mnist.test (10K images+labels) and mnist.train (60K images+labels)
#mnist = mnist_data.read_data_sets("data", one_hot=True, reshape=False, validation_size=0)
# neural network structure for this sample:
#
# · · · · · · · · · · (input data, 1-deep) X [batch, imageSize1, imageSize1, 1]
# @ @ @ @ @ @ @ @ @ @ -- conv. layer 5x5x1=>4 stride 1 W1 [5, 5, 1, 4] B1 [4]
# ∶∶∶∶∶∶∶∶∶∶∶∶∶∶∶∶∶∶∶ Y1 [batch, imageSize1, imageSize1, 4]
# @ @ @ @ @ @ @ @ -- conv. layer 5x5x4=>8 stride 2 W2 [5, 5, 4, 8] B2 [8]
# ∶∶∶∶∶∶∶∶∶∶∶∶∶∶∶ Y2 [batch, 14, 14, 8]
# @ @ @ @ @ @ -- conv. layer 4x4x8=>12 stride 2 W3 [4, 4, 8, 12] B3 [12]
# ∶∶∶∶∶∶∶∶∶∶∶ Y3 [batch, 7, 7, 12] => reshaped to YY [batch, 7*7*12]
# \x/x\x\x/ -- fully connected layer (relu) W4 [7*7*12, 200] B4 [200]
# · · · · Y4 [batch, 200]
# \x/x\x/ -- fully connected layer (softmax) W5 [200, 10] B5 [10]
# · · · Y [batch, 10]
#from tensorflow.examples.tutorials.mnist import input_data as mnist_data
#import cv2
try:
import tensorflow as tf
except:
import tf
#print("Tensorflow version " + tf.__version__)
tf.set_random_seed(0.0)
import numpy as np
import os
import matplotlib.pyplot as plt
runfile('/Users/.../Phyton/processDataAndSetup.py', wdir='/Users/mulugetasemework/Dropbox/Phyton')
# input X: imageSize1ximageSize1 grayscale images, the first dimension (None) will index the images in the mini-batch
X = tf.placeholder(tf.float32, [None, imageSize1, imageSize1,1])
# correct answers will go here
Y_ = tf.placeholder(tf.float32, [None, n_classes])
# variable learning rate
lr = tf.placeholder(tf.float32)
# three convolutional layers with their channel counts, and a
# fully connected layer (tha last layer has n_classes softmax neurons)
K = 4 # first convolutional layer output depth
L = 8 # second convolutional layer output depth
M = 12 # third convolutional layer
N = 200 # fully connected layer
W1 = tf.Variable(tf.truncated_normal([5, 5, 1, K], stddev=0.1)) # 5x5 patch, 1 input channel, K output channels
#B1 = tf.Variable(tf.ones([K])/n_classes)
B1 = tf.Variable(tf.ones([K])/10)
W2 = tf.Variable(tf.truncated_normal([5, 5, K, L], stddev=0.1))
B2 = tf.Variable(tf.ones([L])/10)
W3 = tf.Variable(tf.truncated_normal([4, 4, L, M], stddev=0.1))
B3 = tf.Variable(tf.ones([M])/10)
W4 = tf.Variable(tf.truncated_normal([int(imageSize1/4) * int(imageSize1/4) * M, N], stddev=0.1))
B4 = tf.Variable(tf.ones([N])/10)
W5 = tf.Variable(tf.truncated_normal([N, 1], stddev=0.1))
B5 = tf.Variable(tf.ones([n_classes])/n_classes)
# The model
stride = 1 # output is imageSize1ximageSize1
Y1 = tf.nn.relu(tf.nn.conv2d(X, W1, strides=[1, stride, stride, 1], padding='SAME') + B1)
stride = 2 # output is 14x14
Y2 = tf.nn.relu(tf.nn.conv2d(Y1, W2, strides=[1, stride, stride, 1], padding='SAME') + B2)
stride = 2 # output is 7x7
Y3 = tf.nn.relu(tf.nn.conv2d(Y2, W3, strides=[1, stride, stride, 1], padding='SAME') + B3)
# reshape the output from the third convolution for the fully connected layer
YY = tf.reshape(Y3, shape=[-1, int(imageSize1/4) * int(imageSize1/4) * M])
Y4 = tf.nn.relu(tf.matmul(YY, W4) + B4)
Ylogits = tf.matmul(Y4, W5) + B5
Y = tf.nn.softmax(Ylogits)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)
cross_entropy = tf.reduce_mean(cross_entropy)*100
# accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# matplotlib visualisation
allweights = tf.concat([tf.reshape(W1, [-1]), tf.reshape(W2, [-1]), tf.reshape(W3, [-1]), tf.reshape(W4, [-1]), tf.reshape(W5, [-1])], 0)
allbiases = tf.concat([tf.reshape(B1, [-1]), tf.reshape(B2, [-1]), tf.reshape(B3, [-1]), tf.reshape(B4, [-1]), tf.reshape(B5, [-1])], 0)
# training step, the learning rate is a placeholder
train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
max_learning_rate = 0.01
min_learning_rate = 0.0001
decay_speed = 2#round(epochs/10)
def training_step(i, update_train_data, update_test_data, update_valid_data):
thisCountTr = return_counterUpdateTr()
start = thisCountTr[-1]
end = start + batch_size
batch_X,batch_Y = train_features[start:end], train_labels[start:end]
batch_X = np.reshape( batch_X,[len(batch_X),imageSize1,imageSize1,-1])
# learning rate decay
max_learning_rate = 0.01
min_learning_rate = 0.0001
decay_speed = 2#round(epochs/10)
learning_rate = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i/decay_speed)
if update_train_data:
a, cost, w, b = sess.run([accuracy, cross_entropy, allweights, allbiases], {X: batch_X, Y_: batch_Y })
print(str(i) + ": |--------- " + str(a) + " --- " + str(cost) +
" --- <-Training accuracy:" + " <- loss: " + " : epoch " +
str(i*100//len(train_features)+1) + " (lr:" + str(learning_rate) + ")")
return_train_cost(cost)
return_train_accuracy(a,i,testEvery)
if TransormTrainingData==1:
if end <= len(train_features_trans):
batch_X_trans,batch_Y_trans = train_features_trans[start:end], train_labels_trans[start:end]
batch_X_trans= np.reshape( batch_X,[len(batch_X_trans),imageSize1,imageSize1,-1])
a_trans, cost_trans, w, b = sess.run([accuracy, cross_entropy, allweights, allbiases], {X: batch_X_trans, Y_: batch_Y_trans })
return_train_cost_trans(cost_trans)
return_train_accuracy_trans(a_trans)
if update_valid_data and doNotValidate == 0:
startV = i
end = startV + 1
batch_X_valid,batch_Y_valid = valid_features[startV:end], valid_labels[startV:end]
batch_X_valid = np.reshape(batch_X_valid,[len(batch_X_valid),imageSize1,imageSize1,-1])
a, valid_cost, w, b = sess.run([accuracy, cross_entropy, allweights, allbiases], {X: batch_X_valid, Y_: batch_Y_valid})
print(str(i) + ":*** Validation accuracy:" + str(a) + " loss: " + str(valid_cost) + " (lr:" + str(learning_rate) + ")")
return_valid_cost(valid_cost)
return_valid_accuracy(a,i)
if update_test_data:# and i > 0:
thisCount = return_counterUpdate()
startTst = thisCount[-1]
end = startTst + test_batch_size
if end <=len( test_features):
batch_X_test,test_labels2 = test_features[startTst:end], test_labels[startTst:end]
test_features2 = np.reshape(batch_X_test,[len(batch_X_test),imageSize1,imageSize1,-1])
a, cost = sess.run([accuracy, cross_entropy ], {X: test_features2, Y_: test_labels2 })
print(str(i) + ": ********* epoch " + str(i*100//len(test_features2)+1) +
" ********* test accuracy:" + str(a) + " test loss: " + str(cost))
return_test_cost(cost)
return_test_accuracy(a,i)
if test_thiscode==1:
test_labels3 = swapped_test_labels[startTst:end]
aS, ctestS = sess.run([accuracy, cross_entropy ], {X: test_features2, Y_: test_labels3 })
return_test_costS(ctestS)
return_test_accuracyS(aS,i)
if test_shuffled == 1:
thisCount = return_counterUpdate_shuff_test()
startTst_shuff = thisCount[-1]
end_shuff = startTst_shuff + test_batch_size
if end_shuff <= len(test_features):
test_labels_reversed = test_labels.iloc[::-1]
test_features_reversed = test_features[::-1]
batch_X_shuff,batch_Y_shuff = test_features_reversed[startTst_shuff: end_shuff], test_labels [startTst: end_shuff]
batch_X_shuff = np.reshape(batch_X_shuff,[len(batch_X_shuff),imageSize1,imageSize1,-1])
aS_shuff, ctestS_shuff = sess.run([accuracy, cross_entropy ], {X: (batch_X_shuff), Y_: (batch_Y_shuff) })
return_test_cost_shuff(ctestS_shuff)
return_test_accuracy_shuff(aS_shuff,i)
thisCount = return_counterUpdate_trans()
startTst_trans = thisCount[-1]
end_trans = startTst_trans + test_batch_size_trans
if end_trans <= len(test_features_trans):
batch_X_test_trans,test_labels2_trans = test_features_trans[startTst_trans:end_trans], test_labels_trans[startTst_trans: end_trans]
test_features2_trans = np.reshape(batch_X_test_trans,[len(batch_X_test_trans),imageSize1,imageSize1,-1])
a_trans, cost_trans = sess.run([accuracy, cross_entropy ], {X: test_features2_trans, Y_: test_labels2_trans })
return_test_cost_trans(cost_trans)
return_test_accuracy_trans(a_trans,i,testEvery_trans)
sess.run(train_step, {X: batch_X, Y_: batch_Y, lr: learning_rate})
for i in range(epochs): training_step(i, i , i % testEvery == 0, i % validateEvery==0)
runfile('/Users/.../Phyton/plotDLs.py', wdir='/Users/.../Phyton')
mainTitle2='Conv--' + 'TransformTrainingData:' + str(TransormTrainingData
) +'.svg'
mainTitle='Convolutional'+ ' ******* Translate: ' + str(translateImage
)+ ' Rotate: ' + str(rotateImage)+ ' Affine: ' + str(affineOrNot
)+ ' Perspective: ' + str(perspectiveOrNot)+ ' Warp: ' + str(WarpOrNot
) + ' keepDataLength: ' + str(keepDataSize
) + ' TransformTrainingData: ' + str(TransormTrainingData
) + ' \n max_learning_rate : ' + str(max_learning_rate
)+ ' min_learning_rate: ' + str(min_learning_rate) + ' decay_speed: ' + str(decay_speed)
figDir="/Users/mulugetasemework/Documents/Python/"
figname= mainTitle+'.svg'
f.suptitle(mainTitle,size=7 )
plt.subplots_adjust(left=0.1, wspace=0.2, top=0.7, bottom=0.2)
f.show()
os.chdir(figDir)
#plt.savefig(mainTitle2, format='svg', dpi=1200)