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deepModel_meta.py
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deepModel_meta.py
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import tensorflow as tf
import numpy as np
import tensorflow.contrib.layers as tcl
import sys
from utils import *
#sys.path.append("./YellowFin/tuner_utils")
#from yellowfin import YFOptimizer
class DeepModel():
def __init__(self, particle_size, model_input_size, num_class, dropout_rate=0.5, finetune=False, maml=False):
self.maml = maml
if self.maml:
self.update_lr = 0.01
self.meta_lr = 0.01
self.weights = {}
self.particle_size = particle_size
self.batch_size = model_input_size[0]
self.num_col = model_input_size[1]
self.num_row = model_input_size[2]
self.num_channel = model_input_size[3]
self.num_class = num_class
self.global_step = tf.Variable(0, trainable=False)
#self.arch = 'default'
self.arch = 'improved'
#self.arch = 'deeper'
#self.arch = 'resnet'
self.dropout_rate = dropout_rate
self.finetune = finetune
if self.arch == 'default':
self.learning_rate = 0.01
self.learning_rate_decay = 0.95
self.decay_steps = 400
self.momentum = 0.9
else:
self.learning_rate = 0.01
self.learning_rate_decay = 0.95
self.decay_steps = 800
self.momentum = 0.9
self.verbose = False
self.build_graph()
def __variable(self, name, shape, stddev, wd, trainable=True):
var = tf.get_variable(name, shape,
initializer = tf.truncated_normal_initializer(stddev=stddev, seed = 1234), trainable=trainable)
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def build_graph(self):
if self.arch == 'default':
ks = [9, 5, 3, 2] # 9,5,3,2
elif self.arch == 'improved':
ks = [5, 5, 3, 2] # 9,5,3,2
self.ks = ks
elif self.arch == 'deeper':
ks = [3,3,3,3,3,3]
elif self.arch == 'resnet':
num_block = 1
#ks = [3, 3, 3, 3] # 9,5,3,2
if self.arch == 'deeper':
fm = [1, 8, 16, 32, 32, 32, 64]
elif self.arch == 'resnet':
fm = [1, 8, 16, 32]
else:
fm = [1, 8, 16, 32, 64]
self.w = {}
self.b = {}
self.data = tf.placeholder('float32', [None, self.num_col, self.num_row, self.num_channel], name='data')
#self.label = tf.placeholder('float32', shape=(self.batch_size,2))
self.label = tf.placeholder('int64', shape=(self.batch_size,))
self.is_train = tf.placeholder('int64', None, name='is_train')
if self.is_train == 1:
phase = True
else:
phase = False
data = self.data
####### CONV LAYER
if self.arch == 'resnet':
with tf.variable_scope('conv0'):
data = conv_bn_relu_layer(data, [3, 3, 1, fm[1]], 1, phase)
# total layers = 1 + 2n + 2n + 2n + 1 = 6n + 2
for i in range(len(fm)-1):
for j in range(num_block):
with tf.variable_scope('conv%d_%d'%(i,j)):
data = residual_block(data, fm[i+1], first_block = i==0 and j==0, phase=phase)
bn_layer = batch_normalization_layer(data, fm[-1], phase=phase)
relu_layer = tf.nn.relu(bn_layer)
if self.verbose:
print ("begore global pool:", relu_layer.get_shape())
global_pool = tf.reduce_mean(relu_layer, [1, 2])
data = global_pool
if self.verbose:
print ("after global pool:", data.get_shape())
else:
if not self.finetune:
for i in range(len(ks)):
self.w[i] = self.__variable('w%d'%i, shape=[ks[i], ks[i], fm[i], fm[i+1]], stddev=0.05, wd=0.0)
self.b[i] = tf.get_variable('b%d'%i, [fm[i+1]], initializer=tf.constant_initializer(0.0))
if self.maml:
self.weights['w'+str(i)] = self.w[i]
self.weights['b'+str(i)] = self.b[i]
else:
for i in range(len(ks)): #NOTE:if finetune, only train the highest conv layer
trainable = True if i == len(ks)-1 else False
self.w[i] = self.__variable('w%d'%i, shape=[ks[i], ks[i], fm[i], fm[i+1]], stddev=0.05, wd=0.0, trainable=trainable)
self.b[i] = tf.get_variable('b%d'%i, [fm[i+1]], initializer=tf.constant_initializer(0.0), trainable=trainable)
for i in range(len(ks)):
if self.arch == 'deeper':
stride = 1 if i%2==0 else 2
conv = tf.nn.conv2d(data, self.w[i], strides=[1,stride,stride,1], padding='VALID')
else:
conv = tf.nn.conv2d(data, self.w[i], strides=[1,1,1,1], padding='VALID')
if self.arch == 'default':
relu = tf.nn.relu(tf.nn.bias_add(conv, self.b[i]))
else:
relu = lrelu(tf.nn.bias_add(conv, self.b[i]))
if self.arch == 'deeper':
data = relu
else:
pool = tf.nn.max_pool(relu, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')
data = pool
if self.verbose:
print data.get_shape()
####### FC LAYER
#shape = data.get_shape().as_list()
#dim = reduce(lambda x,y: x*y, shape)
if self.arch == 'deeper':
dim = fm[-1] * 5 * 5
self.w_fc1 = self.__variable('wfc1', shape=[dim, self.num_class], stddev=0.05, wd=0.0005)
self.b_fc1 = tf.get_variable('bfc1', [self.num_class], initializer=tf.constant_initializer(0.0))
elif self.arch == 'resnet':
dim = fm[-1] * 1 * 1 # 64x16x16 -> global pool -> 64x1x1
self.w_fc1 = self.__variable('wfc1', shape=[dim, self.num_class], stddev=0.05, wd=0.0005)
self.b_fc1 = tf.get_variable('bfc1', [self.num_class], initializer=tf.constant_initializer(0.0))
else:
dim = fm[-1] * 2 * 2
self.w_fc1 = self.__variable('wfc1', shape=[dim, 64], stddev=0.05, wd=0.0005)
self.b_fc1 = tf.get_variable('bfc1', [64], initializer=tf.constant_initializer(0.0))
self.w_fc2 = self.__variable('wfc2', shape=[64, self.num_class], stddev=0.05, wd=0.0005)
self.b_fc2 = tf.get_variable('bfc2', [self.num_class], initializer=tf.constant_initializer(0.0))
if self.maml:
self.weights['w_fc1'] = self.w_fc1
self.weights['b_fc1'] = self.b_fc1
self.weights['w_fc2'] = self.w_fc2
self.weights['b_fc2'] = self.b_fc2
#hid = tf.reshape(pool4, [self.batch_size, -1])
hid = tf.reshape(data, [self.batch_size, -1])
if self.is_train == 1:
hid = tf.nn.dropout(hid, self.dropout_rate)
#fc1 = tf.nn.relu(tf.matmul(hid, self.w_fc1) + self.b_fc1)
if self.arch == 'default':
fc1 = tf.nn.relu(tf.matmul(hid, self.w_fc1) + self.b_fc1)
fc2 = tf.matmul(fc1, self.w_fc2) + self.b_fc2
elif self.arch == 'improved':
fc1 = lrelu(tf.matmul(hid, self.w_fc1) + self.b_fc1)
fc2 = tf.matmul(fc1, self.w_fc2) + self.b_fc2
elif self.arch == 'deeper':
fc2 = tf.matmul(hid, self.w_fc1) + self.b_fc1
elif self.arch == 'resnet':
fc2 = tf.matmul(hid, self.w_fc1) + self.b_fc1
####### SOFTMAX LAYER
#cross_entropy = tf.nn.softmax_cross_entropy_with_logits(fc2, self.label, name='cross_entropy_all')
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(fc2, self.label, name='cross_entropy_all')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
self.loss = tf.add_n(tf.get_collection('losses'), name='loss')
self.learning_rate_op = tf.maximum(tf.train.exponential_decay(
self.learning_rate, self.global_step, self.decay_steps,
self.learning_rate_decay, staircase=True), 0.0001)
self.optim = tf.train.MomentumOptimizer(self.learning_rate_op, self.momentum).minimize(self.loss, global_step=self.global_step)
#self.optim = tf.train.MomentumOptimizer(0.01, self.momentum).minimize(self.loss, global_step=self.global_step)
#self.optim = YFOptimizer().minimize(self.loss, global_step=self.global_step)
#self.optim = tf.train.AdamOptimizer(self.learning_rate_op).minimize(self.loss, global_step=self.global_step)
self.prediction_op = tf.nn.softmax(fc2)
#tf.initialize_all_variables().run()
self.bdata = tf.placeholder('float32', [None, None, None, self.num_channel], name='bdata')
resize_size = (self.num_col, self.num_row)
#self.resize_op = tf.image.resize_bilinear(self.bdata, resize_size)
img = tf.image.resize_bilinear(self.bdata, resize_size)
axis = list(range(len(img.get_shape()) - 1))
mean, variance = tf.nn.moments(img, axis)
self.resize_op = tf.nn.batch_normalization(img, mean, variance, 0, 1, 0)
params_number = self.get_trainable_variable_number()
#if self.verbose:
print "parameter number ======================= ", params_number
def get_trainable_variable_number(self):
cnt = 0
for variable in tf.trainable_variables():
shape = variable.get_shape()
if self.verbose:
print(shape)
var_params = 1
for dim in shape:
var_params *= dim.value
if self.verbose:
print (var_params)
cnt += var_params
return cnt
def train_batch(self, data, label, sess):
data = sess.run(self.resize_op, feed_dict={self.bdata: data})
_, loss_value, learning_rate, prediction = sess.run(
[self.optim, self.loss, self.learning_rate_op, self.prediction_op],
feed_dict={self.data: data, self.label: label, self.is_train: 1}
)
return loss_value, learning_rate, prediction
def metatrain_batch(self, data, label, sess):
#data: [batch_size, num_samples, dim_input]
data = sess.run(self.resize_op, feed_dict={self.bdata: data})
_, loss_value, learning_rate, prediction = sess.run(
[self.optim, self.loss, self.learning_rate_op, self.prediction_op],
feed_dict={self.data: data, self.label: label, self.is_train: 1}
)
#task_outputa = self.forward(data, self.weights, reuse=reuse)
#task_lossa = self.loss_func(task_outputa, label)
#Use Weights To Get Loss And Gradients
task_outputa = self.loss
grads = tf.gradients(task_outputa, list(self.weights.values()))
if self.stop_grad:
grads = [tf.stop_gradient(grad) for grad in grads]
gradients = dict(zip(self.weights.keys(), grads))
fast_weights = dict(zip(self.weights.keys(), [self.weights[key] - self.update_lr*gradients[key] for key in self.weights.keys()]))
#output = self.forward(inputb, fast_weights, reuse=True)
#task_outputbs.append(output)
#task_lossesb.append(self.loss_func(output, labelb))
return loss_value, learning_rate, prediction
def forward(inp, weights, reuse=False):
for i in range(len(self.ks)):
conv = tf.nn.conv2d(data, weights['w'+str(i)], strides=[1,1,1,1], padding='VALID')
relu = lrelu(tf.nn.bias_add(conv, weights['b'+str(i)]))
data = tf.nn.max_pool(relu, ksize=[1,2,2,1], strides=[1,2,2,1], padding='VALID')
hid = tf.reshape(data, [self.batch_size, -1])
if self.is_train == 1:
hid = tf.nn.dropout(hid, self.dropout_rate)
fc1 = lrelu(tf.matmul(hid, weights['w_fc1']) + weights['b_fc1'])
fc2 = tf.matmul(fc1, weights['w_fc2']) + weights['b_fc2']
def evaluation(self, data, sess, label=None):
size = len(data)
predictions = np.ndarray(shape=(size, self.num_class), dtype=np.float32)
#resize_size = (self.num_col, self.num_row)
#data = tf.image.resize_bilinear(data, resize_size).eval()
#data = sess.run(self.resize_op, feed_dict={self.bdata: data})
for begin in xrange(0, size, self.batch_size):
end = begin + self.batch_size
if end <= size:
batch_data = data[begin:end]
if batch_data[0].shape != batch_data[-1].shape:
predictions[begin:end, :] = label[begin:end]
continue
#batch_data = tf.image.resize_bilinear(batch_data, resize_size).eval()
#mean = batch_data.mean()
#std = batch_data.std()
#batch_data = (batch_data - mean) / std
batch_data = sess.run(self.resize_op, feed_dict={self.bdata: batch_data})
predictions[begin:end, :] = sess.run(
self.prediction_op,
feed_dict={self.data: batch_data})
else:
batch_data = data[-self.batch_size:]
if batch_data[0].shape != batch_data[-1].shape:
predictions[begin:end, :] = label[begin:end]
continue
#batch_data = tf.image.resize_bilinear(batch_data, resize_size).eval()
#mean = batch_data.mean()
#std = batch_data.std()
#batch_data = (batch_data - mean) / std
batch_data = sess.run(self.resize_op, feed_dict={self.bdata: batch_data})
batch_predictions = sess.run(
self.prediction_op,
feed_dict={self.data: batch_data})
predictions[begin:, :] = batch_predictions[(begin-(size-self.batch_size)):, :]
return predictions