-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathMS_5LReLU_LRdecay_DO.py
252 lines (182 loc) · 10 KB
/
MS_5LReLU_LRdecay_DO.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
#!/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.
# neural network with 5 layers
#
# · · · · · · · · · · (input data, flattened pixels) X [batch, imageSize1*imageSize1] # imageSize1*imageSize1 = imageSize1*imageSize1
# \x/x\x/x\x/x\x/x\x/ ✞ -- fully connected layer (relu+dropout) W1 [imageSize1*imageSize1, 200] B1[200]
# · · · · · · · · · Y1 [batch, 200]
# \x/x\x/x\x/x\x/ ✞ -- fully connected layer (relu+dropout) W2 [200, 100] B2[100]
# · · · · · · · Y2 [batch, 100]
# \x/x\x/x\x/ ✞ -- fully connected layer (relu+dropout) W3 [100, 60] B3[60]
# · · · · · Y3 [batch, 60]
# \x/x\x/ ✞ -- fully connected layer (relu+dropout) W4 [60, 30] B4[30]
# · · · Y4 [batch, 30]
# \x/ -- fully connected layer (softmax) W5 [30, 10] B5[10]
# · Y5 [batch, 10]
import math
try:
import tensorflow as tf
except:
import tf
tf.set_random_seed(0.0)
import numpy as np
import os
import matplotlib.pyplot as plt
runfile('/Users/.../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 ])
# correct answers will go here
Y_ = tf.placeholder(tf.float32, [None, n_classes])
# variable learning rate
lr = tf.placeholder(tf.float32)
# Probability of keeping a node during dropout = 1.0 at test time (no dropout) and 0.75 at training time
pkeep = tf.placeholder(tf.float32)
# five layers and their number of neurons (tha last layer has n_classes softmax neurons)
L = 200
M = 100
N = 60
O = 30
# Weights initialised with small random values between -0.2 and +0.2
# When using RELUs, make sure biases are initialised with small *positive* values for example 0.1 = tf.ones([K])/10
W1 = tf.Variable(tf.truncated_normal([imageSize1*imageSize1,L], stddev=0.1)) # imageSize1*imageSize1 = imageSize1 * imageSize1
B1 = tf.Variable(tf.ones([L])/10)
W2 = tf.Variable(tf.truncated_normal([L, M], stddev=0.1))
B2 = tf.Variable(tf.ones([M])/10)
W3 = tf.Variable(tf.truncated_normal([M, N], stddev=0.1))
B3 = tf.Variable(tf.ones([N])/10)
W4 = tf.Variable(tf.truncated_normal([N, O], stddev=0.1))
B4 = tf.Variable(tf.ones([O])/10)
W5 = tf.Variable(tf.truncated_normal([O, 1], stddev=0.1))
B5 = tf.Variable(tf.zeros([n_classes]))
# The model, with dropout at each layer
XX = tf.reshape(X, [-1, imageSize1*imageSize1])
Y1 = tf.nn.relu(tf.matmul(XX, W1) + B1)
Y1d = tf.nn.dropout(Y1, pkeep)
Y2 = tf.nn.relu(tf.matmul(Y1d, W2) + B2)
Y2d = tf.nn.dropout(Y2, pkeep)
Y3 = tf.nn.relu(tf.matmul(Y2d, W3) + B3)
Y3d = tf.nn.dropout(Y3, pkeep)
Y4 = tf.nn.relu(tf.matmul(Y3d, W4) + B4)
Y4d = tf.nn.dropout(Y4, pkeep)
Ylogits = tf.matmul(Y4d, 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
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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)
# You can call this function in a loop to train the model, 100 images at a time
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]
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)
# compute training values
if update_train_data:
a, Train_cost, w, b = sess.run([accuracy, cross_entropy, allweights, allbiases], {X: batch_X, Y_: batch_Y, pkeep: 1.0})
print(str(i) + ": |--------- " + str(a) + " --- " + str(Train_cost) +
" --- <-Training accuracy:" + " <- loss: " + " : epoch " +
str(i*100//len(train_features)+1) + " (lr:" + str(learning_rate) + ")")
return_train_cost(Train_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]
a_trans, Train_cost_trans, w_trans, b_trans = sess.run([accuracy, cross_entropy, allweights, allbiases], {X: batch_X_trans, Y_: batch_Y_trans, pkeep: 1.0})
return_train_cost_trans(Train_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]
a, valid_cost, w, b = sess.run([accuracy, cross_entropy, allweights, allbiases], {X: batch_X_valid, Y_: batch_Y_valid, pkeep: 1.0})
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:
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]
a, cost = sess.run([accuracy, cross_entropy ], {X: batch_X_test, Y_: test_labels2, pkeep: 1.0})
print(str(i) + ": ********* epoch " + str(i*100//len(test_features)+1) +
" ********* test accuracy:" + str(a) + " test loss: " + str(cost))
return_test_cost(cost)
return_test_accuracy(a,i)
if test_thiscode==1:
test_labels3 = np.array(swapped_test_labels[startTst:end])
aS, ctestS = sess.run([accuracy, cross_entropy ], {X: batch_X_test, Y_: test_labels3, pkeep: 1.0})
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[startTst_shuff: end_shuff], test_labels_reversed[startTst: end_shuff]
aS_shuff, ctestS_shuff = sess.run([accuracy, cross_entropy ], {X: (batch_X_shuff), Y_: (batch_Y_shuff), pkeep: 1.0})
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]
a_trans, cost_trans = sess.run([accuracy, cross_entropy ], {X: batch_X_test_trans, Y_: test_labels2_trans, pkeep: 1.0})
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, pkeep: 0.75, lr: learning_rate})
for i in range(epochs): training_step(i, i , i % testEvery == 0, i % validateEvery==0)
runfile('/Users/.../plotDLs.py', wdir='/Users/.../Phyton')
mainTitle2='5L_ReLU_lrDecay_DO--' + 'TransformTrainingData:' + str(TransormTrainingData
) +'.svg'
mainTitle='five_layers_relu_lrdecay_dropout '+ ' ******* 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)