-
Notifications
You must be signed in to change notification settings - Fork 0
/
r2d2_paper.py
381 lines (307 loc) · 19.4 KB
/
r2d2_paper.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
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
""" Code for the R2D2 algorithm and network definitions. """
from __future__ import print_function
import numpy as np
import sys
import tensorflow as tf
try:
import special_grads
except KeyError as e:
print('WARN: Cannot define MaxPoolGrad, likely already defined for this version of tensorflow: %s' % e,
file=sys.stderr)
from tensorflow.python.platform import flags
from utils import mse, xent, conv_block, normalize
FLAGS = flags.FLAGS
class R2D2_paper:
"""This class implements the R2D2 algorithm as proposed in "Meta-learning with differentiable closed-form solvers"
Attributes:
dim_input: An integer equal to the (flattened) input size
dim_hidden: A list of integers which contains the number of (output) channels in each layer
dim_output: An integer equal to the (flattened) output size
img_size: An integer equal to the length of one side of a square image
classification: A boolean representing whether a classification is being carried out or not
construct_weights: A function that constructs the weights for the model to be used
forward: A function that defines the TensorFlow structure for a forward pass, building on the weights from construct_weights
loss_func: Loss function to be used, xent = cross entropy for classification, mse = mean squared error for regression
update_lr: A float for the base learning learning rate
meta_lr: A float for the meta learning rate, is part of the TensorFlow graph and can thus be modified when feeding
test_num_updates: An integer equal to the amount of finetuning steps that should be taken during training and testing
activation: Activation function to be used in the neural layers
dropout: A float signifying the percentage of neurons to be dropped out
channels: An integer equal to the number of channels in the input (image)
"""
def __init__(self, dim_input=1, dim_output=1, test_num_updates=5):
"""Inits an R2D2 model
Most attributes for this class are determined based on the
arguments passed through the FLAGS.
Currently this class supports the following datasets:
miniImagenet, CIFAR-FS
After initialization the function construct_model() should
always be called. This is required before the TensorFlow graph
can be feeded with data and operations can be executed.
Args:
dim_input: An integer equal to the (flattened) input size
dim_output: An integer equal to the (flattened) output size
test_num_updates: An integer equal to the amount of finetuning steps that should be taken during training and testing
Raises:
ValueError: The dataset specified is not recognized
"""
self.dim_input = dim_input
self.dim_output = dim_output
self.update_lr = FLAGS.update_lr
self.meta_lr = tf.placeholder_with_default(FLAGS.meta_lr, ())
self.classification = False
self.test_num_updates = test_num_updates
if FLAGS.datasource == 'sinusoid':
self.dim_hidden = [40, 40]
self.loss_func = mse
self.forward = self.forward_fc
self.construct_weights = self.construct_fc_weights
elif FLAGS.datasource == 'omniglot' or FLAGS.datasource == 'miniimagenet' or FLAGS.datasource == 'cifarfs':
self.loss_func = xent
self.classification = True
if FLAGS.conv:
self.dropout = 0.0
if FLAGS.model == 'r2d2':
self.activation = lambda x: tf.nn.leaky_relu(x, alpha=0.1)
self.dim_hidden = [96, 192, 384, 512]
if FLAGS.datasource == 'miniimagenet':
self.dropout = 0.1
elif FLAGS.datasource == 'cifarfs':
self.dropout = 0.4
elif FLAGS.model == 'maml':
self.activation = tf.nn.relu
self.dim_hidden = [32, 32, 32, 32]
self.forward = self.forward_conv
self.construct_weights = self.construct_conv_weights
else:
self.dim_hidden = [256, 128, 64, 64]
self.forward=self.forward_fc
self.construct_weights = self.construct_fc_weights
# Determine amount of channels to use
if FLAGS.datasource == 'miniimagenet' or FLAGS.datasource == 'cifarfs':
self.channels = 3
else:
self.channels = 1
# Compute image width (=height)
self.img_size = int(np.sqrt(self.dim_input/self.channels)) # dim input is length of totally flattened image
else:
raise ValueError('Unrecognized data source.')
def construct_model(self, input_tensors=None, prefix='metatrain_'):
"""Constructs the TensorFlow graph, and defines operations
This function contains the meta-learning model in TensorFlow,
and its operation definitions. Refer to the paper for algorithm
details.
Args:
input_tensors: tensorflow queues with inputs for base-training (a), and inputs for meta-training (b). Not required.
prefix: string with values {'metatrain_','metaval_'} to a model for training or evaluation respectively
"""
# This function constructs the model, and defines the ops. The ops are not called yet! That happens in session.run(...)
# a: training data for inner gradient, b: test data for meta gradient
if input_tensors is None:
self.inputa = tf.placeholder(tf.float32)
self.inputb = tf.placeholder(tf.float32)
self.labela = tf.placeholder(tf.float32)
self.labelb = tf.placeholder(tf.float32)
else: # Directly couple input tensors from tf queue to object variables
self.inputa = input_tensors['inputa']
self.inputb = input_tensors['inputb']
self.labela = input_tensors['labela']
self.labelb = input_tensors['labelb']
with tf.variable_scope('model', reuse=None) as training_scope:
# Use a seed for reproducibility
tf.set_random_seed(1)
if 'weights' in dir(self):
# weights were already initialized during some training, reuse those
training_scope.reuse_variables()
weights = self.weights
else:
# Define the weights
# this is done when construct_model is called
self.weights = weights = self.construct_weights()
# outputbs[i] and lossesb[i] is the output and loss after i+1 gradient updates
lossesa, outputas, lossesb, outputbs, labelas, labelbs = [], [], [], [], [], []
accuraciesa, accuraciesb = [], []
num_updates = max(self.test_num_updates, FLAGS.num_updates)
outputbs = [[]]*num_updates
lossesb = [[]]*num_updates
accuraciesb = [[]]*num_updates
def task_baselearn(inp, reuse=True):
""" Finetune on one task in the meta-batch. """
inputa, inputb, labela, labelb = inp
task_outputbs, task_lossesb = [], []
if self.classification:
task_accuraciesb = []
task_outputa = self.forward(inputa, weights, reuse=reuse) # only reuse on the first iter
task_lossa = self.loss_func(task_outputa, labela)
## Pass through CNN
x = self.forward_conv_CNN(inputa, weights, reuse=True)
# Linear Regression with Woodbury Identity using training set (a) to determine new weights for linear regressor
xT = tf.transpose(x)
xxT = tf.matmul(x,xT)
fast_weights = dict(zip(weights.keys(), [weights[key] for key in weights.keys()])) # Copy current weights
fast_weights['stop_w5'] = tf.matmul(tf.matmul(xT,tf.linalg.inv(xxT + weights['lr_lambda'] * tf.eye(tf.shape(xxT)[0],tf.shape(xxT)[1]))),labela)
# for dropout
if FLAGS.train:
is_training = True
else:
is_training = False
# MAML line 8: calculate output/loss on test set (b), internally does LR conversion with scale alpha and bias beta
output = self.forward(inputb, fast_weights, reuse=True, is_training=True)
task_outputbs.append(output)
task_lossesb.append(self.loss_func(output, labelb))
task_output = [task_outputa, task_outputbs, task_lossa, task_lossesb]
# When classification, extend the output with accuracies
if self.classification:
task_accuracya = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputa), 1), tf.argmax(labela, 1))
for j in range(num_updates):
task_accuraciesb.append(tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(task_outputbs[j]), 1), tf.argmax(labelb, 1)))
task_output.extend([task_accuracya, task_accuraciesb])
return task_output
if FLAGS.norm is not 'None': # to initialize BatchNorm variables, run the base_learning step on task 0
unused = task_baselearn((self.inputa[0], self.inputb[0], self.labela[0], self.labelb[0]), False)
out_dtype = [tf.float32, [tf.float32]*num_updates, tf.float32, [tf.float32]*num_updates]
if self.classification: # accuracies are also stored in the case of classification
out_dtype.extend([tf.float32, [tf.float32]*num_updates])
# Executes fine tuning for ALL TASKS in meta batch: the input queues are formatted in a way to contain multiple tasks
result = tf.map_fn(task_baselearn, elems=(self.inputa, self.inputb, self.labela, self.labelb), dtype=out_dtype, parallel_iterations=FLAGS.meta_batch_size)
if self.classification:
outputas, outputbs, lossesa, lossesb, accuraciesa, accuraciesb = result
else:
outputas, outputbs, lossesa, lossesb = result
## Performance & Optimization
if 'train' in prefix:
# in meta-training: execute full base learning and meta learning
self.total_loss1 = total_loss1 = tf.reduce_sum(lossesa) / tf.to_float(FLAGS.meta_batch_size)
self.total_losses2 = total_losses2 = [tf.reduce_sum(lossesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
self.outputas, self.outputbs = outputas, outputbs
if self.classification:
self.total_accuracy1 = total_accuracy1 = tf.reduce_sum(accuraciesa) / tf.to_float(FLAGS.meta_batch_size)
self.total_accuracies2 = total_accuracies2 = [tf.reduce_sum(accuraciesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
self.pretrain_op = tf.train.AdamOptimizer(self.meta_lr).minimize(total_loss1)
if FLAGS.metatrain_iterations > 0: # FLAGS.metatrain_iterations = how many times to execute
# This is the meta optimizer
optimizer = tf.train.AdamOptimizer(self.meta_lr)
# Compute gradients after num_updates
self.gvs = gvs = optimizer.compute_gradients(self.total_losses2[FLAGS.num_updates-1])
# Gradients are clipped by [-10,10] to avoid gradient explosion
if FLAGS.datasource == 'miniimagenet' or FLAGS.datasource == 'cifarfs':
gvs = [(tf.clip_by_value(grad, -10, 10), var) for grad, var in gvs if grad is not None]
# update parameters
self.metatrain_op = optimizer.apply_gradients(gvs)
else:
# in meta-validation: execute only base learning (fine tuning) and validate
self.metaval_total_loss1 = total_loss1 = tf.reduce_sum(lossesa) / tf.to_float(FLAGS.meta_batch_size)
self.metaval_total_losses2 = total_losses2 = [tf.reduce_sum(lossesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
if self.classification:
self.metaval_total_accuracy1 = total_accuracy1 = tf.reduce_sum(accuraciesa) / tf.to_float(FLAGS.meta_batch_size)
self.metaval_total_accuracies2 = total_accuracies2 =[tf.reduce_sum(accuraciesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]
# For diagnostic purposes
self.test_accuraciesa = accuraciesa
self.test_accuraciesb = [accuraciesb[j] for j in range(num_updates)]
self.test_outputas = outputas
self.test_outputbs = outputbs
self.labelas = self.labela
self.labelbs = self.labelb
## Summaries
tf.summary.scalar(prefix+'Pre-update loss', total_loss1)
if self.classification:
tf.summary.scalar(prefix+'Pre-update accuracy', total_accuracy1)
for j in range(num_updates):
tf.summary.scalar(prefix+'Post-update loss, step ' + str(j+1), total_losses2[j])
if self.classification:
tf.summary.scalar(prefix+'Post-update accuracy, step ' + str(j+1), total_accuracies2[j])
def construct_conv_weights(self):
""" R2D2 model weights initialziation:
4 conv blocks:
- 3x3 convolutions
- batch-normalization
- 2x2 max pooling
- leaky relu (factor 0.1)
- filters: [96, 192, 384, 512]
- dropout on layer 3 and 4
"""
weights = {}
dtype = tf.float32
conv_initializer = tf.contrib.layers.xavier_initializer_conv2d(dtype=dtype)
fc_initializer = tf.contrib.layers.xavier_initializer(dtype=dtype)
k = 3
# CNN weights
weights['conv1'] = tf.get_variable('conv1', [k, k, self.channels, self.dim_hidden[0]], initializer=conv_initializer, dtype=dtype)
weights['b1'] = tf.Variable(tf.zeros([self.dim_hidden[0]]))
weights['conv2'] = tf.get_variable('conv2', [k, k, self.dim_hidden[0], self.dim_hidden[1]], initializer=conv_initializer, dtype=dtype)
weights['b2'] = tf.Variable(tf.zeros([self.dim_hidden[1]]))
weights['conv3'] = tf.get_variable('conv3', [k, k, self.dim_hidden[1], self.dim_hidden[2]], initializer=conv_initializer, dtype=dtype)
weights['b3'] = tf.Variable(tf.zeros([self.dim_hidden[2]]))
weights['conv4'] = tf.get_variable('conv4', [k, k, self.dim_hidden[2], self.dim_hidden[3]], initializer=conv_initializer, dtype=dtype)
weights['b4'] = tf.Variable(tf.zeros([self.dim_hidden[3]]))
# RR weights
# assumes max pooling, flat_dim is concatenated flattened output of layer 3 and 4
flat_dim = 51200
if FLAGS.model == 'maml':
if FLAGS.datasource == 'miniimagenet':
flat_dim = 4000
elif FLAGS.datasource == 'cifarfs':
flat_dim = 640
else:
if FLAGS.datasource == 'miniimagenet':
flat_dim = 51200
elif FLAGS.datasource == 'cifarfs':
flat_dim = 8192
weights['stop_w5'] = tf.get_variable('stop_w5', [flat_dim, self.dim_output], initializer=fc_initializer)
# hyperparameters of base learner, to be learnt in outer loop together with CNN parameters
weights['lr_lambda'] = tf.Variable(tf.zeros(1, dtype = dtype))
weights['lr_alpha'] = tf.Variable(tf.zeros(1, dtype = dtype))
weights['lr_beta'] = tf.Variable(tf.zeros(1, dtype = dtype))
return weights
def forward_conv(self, inp, weights, reuse=False, scope='', is_training=False):
"""R2D2 model forward specification
This is only to be used in the meta-learning step, during base training the direct solution for LR is used.
It consists of:
- Feature extractor CNN part, concatenate flattened outputs of layer 3 and 4
- Linear regression prediction on concatenated flattened outputs of layer 3 and 4 with scale and bias adjust for cross-entropy loss
Args:
inp:
weights: Model weights to be used for forward prediction
reuse: A boolean which defines whether or not to reuse the batch normalization initialization
scope: TensorFlow Variable scope to be used
is_training: A boolean signifying whether we are training or not, relevant for dropout
"""
out = self.forward_conv_CNN(inp, weights, reuse=reuse, scope=scope, is_training=is_training)
out = self.forward_conv_lr(out, weights, reuse=reuse, scope=scope, is_training=is_training)
return out
def forward_conv_CNN(self, inp, weights, reuse=False, scope='', is_training=False):
""" R2D2 model specification, CNN part:
4 conv blocks:
- 3x3 convolutions
- batch-normalization
- 2x2 max pooling
- leaky relu (factor 0.1)
- filters: [96, 192, 384, 512]
- dropout on layer 3 and 4
- concatenate flattened outputs of layer 3 and 4
Args:
<see forward_conv()>
"""
channels = self.channels
inp = tf.reshape(inp, [-1, self.img_size, self.img_size, channels])
hidden1 = conv_block(inp, weights['conv1'], weights['b1'], reuse, scope+'0', activation = self.activation)
hidden2 = conv_block(hidden1, weights['conv2'], weights['b2'], reuse, scope+'1', activation = self.activation)
hidden3 = conv_block(hidden2, weights['conv3'], weights['b3'], reuse, scope+'2', activation = self.activation)
hidden3 = tf.layers.dropout(hidden3, rate=self.dropout, training=is_training)
hidden4 = conv_block(hidden3, weights['conv4'], weights['b4'], reuse, scope+'3', activation = self.activation)
hidden4 = tf.layers.dropout(hidden4, rate=self.dropout, training=is_training)
# Flattening of blocks 3 and 4
hidden3 = tf.reshape(hidden3, [-1, np.prod([int(dim) for dim in hidden3.get_shape()[1:]])])
hidden4 = tf.reshape(hidden4, [-1, np.prod([int(dim) for dim in hidden4.get_shape()[1:]])])
# Concatenate
flatconcat34 = tf.concat([hidden3, hidden4], axis=1) # keep batched (axis 0), concatenate columns (axis 1)
return flatconcat34
def forward_conv_lr(self, inp, weights, reuse=False, scope='', is_training=False):
""" R2D2 model specification, Linear Regression part:
- Take in concatenated flattened outputs of layer 3 and 4
- Perform linear regression prediction, with meta parameters scale alpha, and bias beta
Args:
<see forward_conv()>
"""
W = weights['stop_w5']
return tf.multiply(weights['lr_alpha'],tf.matmul(inp, W)) + tf.multiply(weights['lr_beta'],tf.ones(shape=[inp.get_shape()[0], self.dim_output], dtype=tf.float32))