-
Notifications
You must be signed in to change notification settings - Fork 0
/
CNN.py
232 lines (215 loc) · 7.74 KB
/
CNN.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
import theano
from theano import tensor as T
from theano.tensor.nnet import conv2d
from theano.tensor.signal import pool
from MLP import HiddenLayer
from logistic import LogisticRegression
import logistic
import six.moves.cPickle as pickle
import numpy
import pandas as pd
theano.config.floatX = 'float32'
rng = numpy.random.RandomState(23455)
class LeNetConvPoolLayer(object):
def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
assert image_shape[1] == filter_shape[1]
fan_in = numpy.prod(filter_shape[1:])
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) //
numpy.prod(poolsize))
W_bound = numpy.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
numpy.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
conv_out = conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
input_shape=image_shape
)
pooled_out = pool.pool_2d(
input=conv_out,
ds=poolsize,
ignore_border=True
)
self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
self.params = [self.W, self.b]
self.input = input
class CNN_data(object):
def __init__(self,input):
self.layer0 = pickle.load(open('cnnlayer0.pkl'))
self.layer1 = pickle.load(open('cnnlayer1.pkl'))
self.layer2 = pickle.load(open('cnnlayer2.pkl'))
self.logRegressionLayer = pickle.load(open('cnnlayer3.pkl'))
self.negative_log_likelihood = (
self.logRegressionLayer.negative_log_likehood
)
self.errors = self.logRegressionLayer.errors
self.params = self.layer0.params + self.layer1.params + self.layer2.params + self.logRegressionLayer.params
self.input = input
class CNN(object):
def __init__(self, rng, input, n_hidden_out, n_out,nkerns,batch_size):
self.layer0 = LeNetConvPoolLayer(
rng,
input= input.reshape((batch_size, 1, 28, 28)),
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
self.layer1 = LeNetConvPoolLayer(
rng,
input=self.layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0] , 5, 5),
poolsize=(2, 2)
)
self.layer2 = HiddenLayer(
rng,
input=self.layer1.output.flatten(2),
n_in=nkerns[1] * 4 * 4,
n_out=n_hidden_out,
activation=T.tanh
)
self.logRegressionLayer = LogisticRegression(
input = self.layer2.output,
n_in = n_hidden_out,
n_out = n_out
)
self.negative_log_likelihood = (
self.logRegressionLayer.negative_log_likehood
)
self.errors = self.logRegressionLayer.errors
self.params = self.layer0.params + self.layer1.params + self.layer2.params + self.logRegressionLayer.params
self.input = input
def test_cnn(nkerns=(6,12),learning_rate=0.01,n_epochs=10,
batch_size=60, n_hidden=500,n_out=10):
print('... data prepare')
dataset = logistic.load_my_train_data()
train_set_x, train_set_y = dataset[0]
test_set_x, test_set_y = dataset[1]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
print('... building the model')
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
rng = numpy.random.RandomState(1234)
# layer0_input = x.reshape((batch_size, 1, 28, 28))
cnn = CNN(
rng=rng,
input=x,
n_hidden_out = n_hidden,
n_out = n_out,
nkerns = nkerns,
batch_size=batch_size
)
cost = (
cnn.negative_log_likelihood(y)
)
test_model = theano.function(
inputs=[index],
outputs=cnn.errors(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
}
)
gparams = [T.grad(cost, param) for param in cnn.params]
updates = [
(param_i, param_i - learning_rate * grad_i)
for param_i, grad_i in zip(cnn.params, gparams)
]
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]
}
)
print('... training the model')
done_looping = False
epoch = 0
best_test_score = 1
while (epoch < n_epochs) and (not done_looping):
epoch += 1
for minibatch_index in range(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
print minibatch_avg_cost
test_losses = [test_model(i) for i in range(n_test_batches)]
test_score = numpy.mean(test_losses)
print ('epoch %i testscore %f %%') % (epoch, test_score)
if test_score < best_test_score:
print(
(
' epoch %i, minibatch %i/%i, test error of'
' best model %f %%'
) %
(
epoch,
minibatch_index + 1,
n_train_batches,
test_score * 100.
)
)
best_test_score = test_score
with open('cnnlayer0.pkl', 'wb') as f:
pickle.dump(cnn.layer0, f)
with open('cnnlayer1.pkl', 'wb') as f:
pickle.dump(cnn.layer1, f)
with open('cnnlayer2.pkl', 'wb') as f:
pickle.dump(cnn.layer2, f)
with open('cnnlayer3.pkl', 'wb') as f:
pickle.dump(cnn.logRegressionLayer, f)
def predict():
cnnlayer0 = pickle.load(open('cnnlayer0.pkl'))
cnnlayer1 = pickle.load(open('cnnlayer1.pkl'))
cnnlayer2 = pickle.load(open('cnnlayer2.pkl'))
cnnlayer3 = pickle.load(open('cnnlayer3.pkl'))
lout0 = theano.function(
inputs=[cnnlayer0.input],
outputs=cnnlayer0.output
)
lout1 = theano.function(
inputs=[cnnlayer1.input],
outputs=cnnlayer1.output
)
lout2 = theano.function(
inputs=[cnnlayer2.input],
outputs=cnnlayer2.output
)
lout3 = theano.function(
inputs=[cnnlayer3.input],
outputs=cnnlayer3.y_pred
)
test_set = logistic.load_my_test_data()
test_set_x = test_set.get_value()
#lout0_v = loutEnd(test_set_x)
#lout1_v = lout1(test_set_x)
#print test_set_x.shape
test_set_x = test_set_x.reshape(28000L, 1, 28, 28)
print test_set_x.shape
#print type(test_set_x)
test = T.lmatrix(name='test')
lout0_v = lout0(test_set_x)
lout1_v = lout1(lout0_v)
lout_1_vv =lout1_v.reshape(28000,640)
lout2_v = lout2(lout_1_vv)
# predicted_value = lout3(lout2_v)
predicted_values = lout3(lout2_v)
#print predicted_values.dtype
ids = numpy.arange(predicted_values.shape[0]+1)
#print ids.dtype
# print predicted_values
df = pd.DataFrame({"ImageId": ids[1:], "Label": predicted_values})
#print df
df.to_csv('submission.csv', index=False, index_label=True)
if __name__ == '__main__':
test_cnn()
#predict()