-
Notifications
You must be signed in to change notification settings - Fork 1
/
mnist_maxout.py
230 lines (198 loc) · 8.18 KB
/
mnist_maxout.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 numpy as np
import theano
import theano.tensor as T
from blocks.bricks import Softmax, Linear
from blocks.initialization import Constant, Uniform
from blocks.algorithms import GradientDescent
from blocks.roles import WEIGHT, BIAS
from blocks.graph import ComputationGraph
from blocks.filter import VariableFilter
from blocks.extensions import FinishAfter, Printing
from blocks.extensions.monitoring import DataStreamMonitoring
from blocks.model import Model
from fuel.streams import DataStream
from fuel.schemes import SequentialScheme
from blocks.main_loop import MainLoop
import operator
from blocks.roles import PARAMETER
#from batch_normalize import ConvolutionalLayer, ConvolutionalActivation, Linear
# change for cpu tests
from blocks.bricks.conv import ConvolutionalSequence
from convolution_stride import ConvolutionalLayer, Maxout_
from blocks.bricks.conv import Flattener
from blocks.bricks import Linear
from blocks.bricks.cost import MisclassificationRate
from blocks.algorithms import Momentum, RMSProp, Scale
#from fuel.datasets.hdf5 import H5PYDataset
#from fuel.transformers import Flatten
floatX = theano.config.floatX
#import h5py
from contextlib import closing
#from momentum import Momentum_dict
#import re
from maxout_extension import Clip_param
from fuel.datasets import MNIST
def errors(p_y_given_x, y):
"""Return a float representing the number of errors in the minibatch
over the total number of examples of the minibatch ; zero one
loss over the size of the minibatch
:type y: theano.tensor.TensorType
:param y: corresponds to a vector that gives for each example the
correct label
"""
y = T.cast(y, 'int8')
y_pred = T.argmax(p_y_given_x, axis=1)
y_pred = y_pred.dimshuffle((0, 'x'))
y_pred = T.cast(y_pred, 'int8')
# check if y has same dimension of y_pred
if y.ndim != y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', y_pred.type)
)
# check if y is of the correct datatype
if y.dtype.startswith('int'):
# the T.neq operator returns a vector of 0s and 1s, where 1
# represents a mistake in prediction
return T.mean(T.neq(y_pred, y), dtype=floatX)
else:
raise NotImplementedError()
def init_param(params, name, value):
if name in params:
param_i = params[name]
shape = param_i.get_value().shape
#print (name, shape, value.shape)
param_i.set_value((value.reshape(shape)).astype(floatX))
else:
raise Exception("unknown parameter")
def build_params(cnn_layer, mlp_layer):
params = []
names = []
input = T.tensor4()
x = T.matrix()
for i, layer in zip(range(len(cnn_layer)), cnn_layer):
param_layer = VariableFilter(roles=[WEIGHT, BIAS])(ComputationGraph(layer.apply(input).sum()).variables)
for p in param_layer:
p.name = "layer_"+str(i)+"_"+p.name
names.append(p.name)
params.append(p)
for i, layer in zip(range(len(mlp_layer)), mlp_layer):
param_layer= VariableFilter(roles=[WEIGHT, BIAS])(ComputationGraph(layer.apply(x).sum()).variables)
for p in param_layer:
p.name = "layer_"+str(i+len(cnn_layer))+"_"+p.name
names.append(p.name)
params.append(p)
return params, names
def maxout_mnist_test():
# if it is working
# do a class
x = T.tensor4('features')
y = T.imatrix('targets')
batch_size = 128
# maxout convolutional layers
# layer0
filter_size = (8, 8)
activation = Maxout_(num_pieces=2).apply
pooling_size = 4
pooling_step = 2
pad = 0
image_size = (28, 28)
num_channels = 1
num_filters = 48
layer0 = ConvolutionalLayer(activation, filter_size, num_filters,
pooling_size=(pooling_size, pooling_size),
pooling_step=(pooling_step, pooling_step),
pad=pad,
image_size=image_size,
num_channels=num_channels,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01),
name="layer_0")
layer0.initialize()
num_filters = 48
filter_size = (8,8)
pooling_size = 4
pooling_step = 2
pad = 3
image_size = (layer0.get_dim('output')[1],
layer0.get_dim('output')[2])
num_channels = layer0.get_dim('output')[0]
layer1 = ConvolutionalLayer(activation, filter_size, num_filters,
pooling_size=(pooling_size, pooling_size),
pooling_step=(pooling_step, pooling_step),
pad=pad,
image_size=image_size,
num_channels=num_channels,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01),
name="layer_1")
layer1.initialize()
num_filters = 24
filter_size=(5,5)
pooling_size = 2
pooling_step = 2
pad = 3
activation = Maxout_(num_pieces=4).apply
image_size = (layer1.get_dim('output')[1],
layer1.get_dim('output')[2])
num_channels = layer1.get_dim('output')[0]
layer2 = ConvolutionalLayer(activation, filter_size, num_filters,
pooling_size=(pooling_size, pooling_size),
pooling_step=(pooling_step, pooling_step),
pad = pad,
image_size=image_size,
num_channels=num_channels,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01),
name="layer_2")
layer2.initialize()
conv_layers = [layer0, layer1, layer2]
output_conv = x
for layer in conv_layers :
output_conv = layer.apply(output_conv)
output_conv = Flattener().apply(output_conv)
mlp_layer = Linear(54, 10,
weights_init=Uniform(width=0.01),
biases_init=Uniform(width=0.01), name="layer_5")
mlp_layer.initialize()
output_mlp = mlp_layer.apply(output_conv)
params, names = build_params(conv_layers, [mlp_layer])
cost = Softmax().categorical_cross_entropy(y.flatten(), output_mlp)
cost.name = 'cost'
cg_ = ComputationGraph(cost)
weights = VariableFilter(roles=[WEIGHT])(cg_.variables)
cost = cost + 0.001*sum([sum(p**2) for p in weights])
cg = ComputationGraph(cost)
error_rate = errors(output_mlp, y)
error_rate.name = 'error'
# training
step_rule = RMSProp(0.01, 0.9)
#step_rule = Momentum(0.2, 0.9)
train_set = MNIST('train')
test_set = MNIST("test")
data_stream = DataStream.default_stream(
train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size))
data_stream_monitoring = DataStream.default_stream(
train_set, iteration_scheme=SequentialScheme(train_set.num_examples, batch_size))
data_stream_test =DataStream.default_stream(
test_set, iteration_scheme=SequentialScheme(test_set.num_examples, batch_size))
algorithm = GradientDescent(cost=cost, params=cg.parameters,
step_rule=step_rule)
monitor_train = DataStreamMonitoring(
variables=[cost, error_rate], data_stream=data_stream_monitoring, prefix="train")
monitor_valid = DataStreamMonitoring(
variables=[cost, error_rate], data_stream=data_stream_test, prefix="test")
extensions = [ monitor_train,
monitor_valid,
FinishAfter(after_n_epochs=50),
Printing(every_n_epochs=1)
]
main_loop = MainLoop(data_stream=data_stream,
algorithm=algorithm, model = Model(cost),
extensions=extensions)
main_loop.run()
from blocks.serialization import dump
with closing(open('../data_mnist/maxout', 'w')) as f:
dump(vae, f)
if __name__ == '__main__':
maxout_mnist_test()