def define_network(self, image):
with tf.name_scope("Block1"):
conv1_1 = ConvLayer(image, 3, 64, name="conv1_1")
conv1_2 = ConvLayer(conv1_1, 64, 64, name="conv1_2")
pool1 = MaxPoolLayer(conv1_2, name='pool1')
with tf.name_scope("Block2"):
conv2_1 = ConvLayer(pool1, 64, 128, name="conv2_1")
conv2_2 = ConvLayer(conv2_1, 128, 128, name="conv2_2")
pool2 = MaxPoolLayer(conv2_2, name='pool2')
with tf.name_scope("Block3"):
conv3_1 = ConvLayer(pool2, 128, 256, name="conv3_1")
conv3_2 = ConvLayer(conv3_1, 256, 256, name="conv3_2")
conv3_3 = ConvLayer(conv3_2, 256, 256, name="conv3_3")
conv3_4 = ConvLayer(conv3_3, 256, 256, name="conv3_4")
pool3 = MaxPoolLayer(conv3_4, name='pool3')
with tf.name_scope("Block4"):
conv4_1 = ConvLayer(pool3, 256, 512, name="conv4_1")
conv4_2 = ConvLayer(conv4_1, 512, 512, name="conv4_2")
conv4_3 = ConvLayer(conv4_2, 512, 512, name="conv4_3")
conv4_4 = ConvLayer(conv4_3, 512, 512, name="conv4_4")
pool4 = MaxPoolLayer(conv4_4, name='pool4')
with tf.name_scope("DenseBlock"):
fc6 = DenseLayer(pool4, 1024, name='fc6')
drop_6 = DropoutLayer(fc6, dropout_rate=self.p)
fc7 = DenseLayer(drop_6, 1024, name='fc7')
drop_7 = DropoutLayer(fc7, dropout_rate=self.p)
return drop_7
def step_lstm(x_t,mask,h_tm1_1,c_tm1_1,h_tm1_2,c_tm1_2,h_tm1_3,c_tm1_3):
[h_t_1,c_t_1,y_t_1]=layer1.run(x_t,h_tm1_1,c_tm1_1)
dl1=DropoutLayer(rng,input=y_t_1,prob=0.5,is_train=is_train,mask=mask)
[h_t_2,c_t_2,y_t_2]=layer2.run(dl1.output,h_tm1_2,c_tm1_2)
[h_t_3,c_t_3,y_t_3]=layer3.run(y_t_2,h_tm1_3,c_tm1_3)
y = T.dot(y_t_3, self.W_hy) + self.b_y
return [h_t_1,c_t_1,h_t_2,c_t_2,h_t_3,c_t_3,y]
def affine_relu_conv(self, args, layers, channels, filter_size, dropout,
name_prefix):
#TODO: treat initialization as hyperparameter, but don't regularize?
layers.append(ScaleLayer(args, layers[-1],
name=name_prefix + '_scale'))
self.add_params_to_self(args, layers[-1])
#no regularization on biases
layers.append(BiasLayer(args, layers[-1], name=name_prefix + '_shift'))
self.add_params_to_self(args, layers[-1])
layers.append(
NonlinearityLayer(layers[-1],
nonlinearity=rectify,
name=name_prefix + '_relu'))
#TODO: regularization here is disconnected
layers.append(
Conv2DLayer(args,
layers[-1],
channels,
filter_size,
pad='same',
W=lasagne.init.HeNormal(gain='relu'),
b=None,
nonlinearity=None,
name=name_prefix + '_conv'))
self.add_params_to_self(args, layers[-1])
if dropout: #TODO:
#layers.append(DropoutLayer(layers[-1], dropout))
layers.append(DropoutLayer(layers[-1], dropout))
#TODO: add Gaussian noise? Better to put after BN?
return layers[-1]
def main2():
dnn = DNN(input=28 * 28,
layers=[DropoutLayer(160, LQ),
Layer(10, LCE)],
eta=0.05,
lmbda=1) # 98%
dnn.initialize_rand()
train, test, vadilation = load_mnist_simple()
f_names = [f'mnist_expaned_k0{i}.pkl.gz' for i in range(50)]
shuffle(f_names)
for f_name in f_names:
print(f_name)
with timing("load"):
raw_data = load_data(f_name)
with timing("shuffle"):
shuffle(raw_data)
with timing("reshape"):
data = [(x.reshape((784, 1)), y)
for x, y in islice(raw_data, 100000)]
del raw_data
with timing("learn"):
dnn.learn(data)
del data
print('TEST:', dnn.test(test))
def addDropoutLayer(self, **kwargs):
"""
Add Dropout layer.
"""
input_layer = self.input_layer if not self.all_layers \
else self.all_layers[-1]
self.n_dropout_layers += 1
name = "dropout%i" % self.n_dropout_layers
new_layer = DropoutLayer(input_layer, name=name, **kwargs)
self.all_layers += (new_layer, )
def main():
train, test, vadilation = load_mnist_simple()
# x, y = train[0]
# print("x: ", x.shape)
# print("y: ", y)
with timing(f""):
# dnn = DNN(input=28 * 28, layers=[Layer(30, LQ), Layer(10, LCE)], eta=0.05) # 96%
# dnn = DNN(input=28 * 28, layers=[Layer(30, LQ), Layer(10, SM)], eta=0.001) # 68%
# dnn = DNN(input=28 * 28, layers=[Layer(100, LQ), Layer(10, LCE)], eta=0.05, lmbda=5) # 98%
# dnn = DNN(input=28 * 28, layers=[DropoutLayer(100, LQ), Layer(10, LCE)], eta=0.05) # 97.5%
dnn = DNN(input=28 * 28, layers=[DropoutLayer(160, LQ), Layer(10, LCE)], eta=0.05, lmbda=3)
dnn.initialize_rand()
dnn.learn(train, epochs=30, test=vadilation, batch_size=29)
print('test:', dnn.test(test))
print(dnn.stats())
def __init__(self, num_input=256, num_hidden=[512,512], num_output=256, clip_at=0.0, scale_norm=0.0):
X = T.fmatrix()
Y = T.imatrix()
lr = T.fscalar()
alpha = T.fscalar()
reg = T.fscalar()
dropout_prob = T.fscalar()
self.num_input = num_input
self.num_hidden = num_hidden
self.num_output = num_output
self.clip_at = clip_at
self.scale_norm = scale_norm
inputs = InputLayer(X, name='inputs')
num_prev = num_input
prev_layer = inputs
self.layers = [inputs]
if type(num_hidden) is types.IntType:
lstm = LSTMLayer(num_prev, num_hidden, input_layers=[prev_layer], name="lstm", go_backwards=False)
num_prev = num_hidden
prev_layer = lstm
self.layers.append(prev_layer)
prev_layer = DropoutLayer(prev_layer, dropout_prob=dropout_prob)
self.layers.append(prev_layer)
FC = FullyConnectedLayer(num_prev, num_output, input_layers=[prev_layer], name="yhat")
self.layers.append(FC)
Y_hat = FC.output()
# change to probilities
Y_hat = T.nnet.softmax(Y_hat)
params = get_params(self.layers)
caches = make_caches(params)
updates, grads = momentum(loss, params, lr, reg)
self.train_func = theano.function([X, Y, lr, reg, dropout_prob, alpha], loss, updates=updates, allow_input_downcast=True)
self.predict_sequence_func = theano.function([X, dropout_prob], [Y_hat], allow_input_downcast=True)
def image_repr(self, x, rand, config):
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(
input=layer1_input,
image_shape=(3, 227, 227, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4,
padsize=0,
group=1,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=True,
lib_conv=lib_conv,
)
layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(
input=convpool_layer1.output,
image_shape=(96, 27, 27, batch_size),
filter_shape=(96, 5, 5, 256),
convstride=1,
padsize=2,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.1,
lrn=True,
lib_conv=lib_conv,
)
layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(
input=convpool_layer2.output,
image_shape=(256, 13, 13, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1,
padsize=1,
group=1,
poolsize=1,
poolstride=0,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
)
layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(
input=convpool_layer3.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1,
padsize=1,
group=2,
poolsize=1,
poolstride=0,
bias_init=0.1,
lrn=False,
lib_conv=lib_conv,
)
layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(
input=convpool_layer4.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1,
padsize=1,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
)
layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = MaxoutLayer(input=fc_layer6_input, n_in=9216, n_out=4096)
layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096)
fc_layer7 = MaxoutLayer(input=dropout_layer6.output,
n_in=4096,
n_out=4096)
layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
#dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096)
# Rename weight types so that weights can be shared
new_weight_types = []
counter_W = 0
counter_b = 0
for w in weight_types:
if w == 'W':
new_weight_types.append('W' + str(counter_W))
counter_W += 1
elif w == 'b':
new_weight_types.append('b' + str(counter_b))
counter_b += 1
weight_types = new_weight_types
return fc_layer7, layers, params, weight_types
def main_graph(self, trained_model, scope, emb_dim, gru, rnn_dim, rnn_num, drop_out=0.5, emb=None):
if trained_model is not None:
param_dic = {'nums_chars': self.nums_chars, 'nums_tags': self.nums_tags, 'crf': self.crf, 'emb_dim': emb_dim,
'gru': gru, 'rnn_dim': rnn_dim, 'rnn_num': rnn_num, 'drop_out': drop_out, 'buckets_char': self.buckets_char,
'ngram': self.ngram, 'is_space': self.is_space, 'sent_seg': self.sent_seg, 'emb_path': self.emb_path,
'tag_scheme': self.tag_scheme}
#print param_dic
f_model = open(trained_model, 'w')
pickle.dump(param_dic, f_model)
f_model.close()
# define shared weights and variables
dr = tf.placeholder(tf.float32, [], name='drop_out_holder')
self.drop_out = dr
self.drop_out_v = drop_out
self.emb_layer = EmbeddingLayer(self.nums_chars + 20, emb_dim, weights=emb, name='emb_layer')
if self.ngram is not None:
ng_embs = [None for _ in range(len(self.ngram))]
for i, n_gram in enumerate(self.ngram):
self.gram_layers.append(EmbeddingLayer(n_gram + 5000 * (i + 2), emb_dim, weights=ng_embs[i], name= str(i + 2) + 'gram_layer'))
with tf.variable_scope('BiRNN'):
if gru:
fw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
bw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
else:
fw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim, state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim, state_is_tuple=True)
if rnn_num > 1:
fw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([fw_rnn_cell]*rnn_num, state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([bw_rnn_cell]*rnn_num, state_is_tuple=True)
output_wrapper = TimeDistributed(HiddenLayer(rnn_dim * 2, self.nums_tags, activation='linear', name='hidden'), name='wrapper')
#define model for each bucket
for idx, bucket in enumerate(self.buckets_char):
if idx == 1:
scope.reuse_variables()
t1 = time()
input_v = tf.placeholder(tf.int32, [None, bucket], name='input_' + str(bucket))
self.input_v.append([input_v])
emb_set = []
word_out = self.emb_layer(input_v)
emb_set.append(word_out)
if self.ngram is not None:
for i in range(len(self.ngram)):
input_g = tf.placeholder(tf.int32, [None, bucket], name='input_g' + str(i) + str(bucket))
self.input_v[-1].append(input_g)
gram_out = self.gram_layers[i](input_g)
emb_set.append(gram_out)
if len(emb_set) > 1:
emb_out = tf.concat(2, emb_set)
else:
emb_out = emb_set[0]
emb_out = DropoutLayer(dr)(emb_out)
emb_out = tf.unpack(emb_out)
rnn_out = BiLSTM(rnn_dim, fw_cell=fw_rnn_cell, bw_cell=bw_rnn_cell, p=dr, name='BiLSTM' + str(bucket), scope='BiRNN')(emb_out, input_v)
output = output_wrapper(rnn_out)
output_c = tf.pack(output, axis=1)
self.output.append([output_c])
self.output_.append([tf.placeholder(tf.int32, [None, bucket], name='tags' + str(bucket))])
self.bucket_dit[bucket] = idx
print 'Bucket %d, %f seconds' % (idx + 1, time() - t1)
assert len(self.input_v) == len(self.output) and len(self.output) == len(self.output_) and len(self.output) == len(self.counts)
self.params = tf.trainable_variables()
self.saver = tf.train.Saver()
def __init__(self, rng, params, cost_function='mse', optimizer=RMSprop):
lr = params["lr"]
batch_size = params["batch_size"]
sequence_length = params["seq_length"]
# minibatch)
X = T.matrix(name="input", dtype=dtype) # batch of sequence of vector
Y = T.matrix(name="output", dtype=dtype) # batch of sequence of vector
is_train = T.iscalar(
'is_train'
) # pseudo boolean for switching between training and prediction
#CNN global parameters.
subsample = (1, 1)
p_1 = 0.5
border_mode = "same"
cnn_batch_size = batch_size
pool_size = (2, 2)
#Layer1: conv2+pool+drop
filter_shape = (128, 1, 10, 10)
input_shape = (cnn_batch_size, 1, 144, 176
) #input_shape= (samples, channels, rows, cols)
input = X.reshape(input_shape)
c1 = ConvLayer(rng,
input,
filter_shape,
input_shape,
border_mode,
subsample,
activation=nn.relu)
p1 = PoolLayer(c1.output,
pool_size=pool_size,
input_shape=c1.output_shape)
dl1 = DropoutLayer(rng, input=p1.output, prob=p_1, is_train=is_train)
#Layer2: conv2+pool
subsample = (1, 1)
filter_shape = (256, p1.output_shape[1], 3, 3)
c2 = ConvLayer(rng,
dl1.output,
filter_shape,
p1.output_shape,
border_mode,
subsample,
activation=nn.relu)
p2 = PoolLayer(c2.output,
pool_size=pool_size,
input_shape=c2.output_shape)
#Layer3: conv2+pool
filter_shape = (256, p2.output_shape[1], 3, 3)
c3 = ConvLayer(rng,
p2.output,
filter_shape,
p2.output_shape,
border_mode,
subsample,
activation=nn.relu)
p3 = PoolLayer(c3.output,
pool_size=pool_size,
input_shape=c3.output_shape)
#Layer4: conv2+pool
filter_shape = (128, p3.output_shape[1], 3, 3)
c4 = ConvLayer(rng,
p3.output,
filter_shape,
p3.output_shape,
border_mode,
subsample,
activation=nn.relu)
p4 = PoolLayer(c4.output,
pool_size=pool_size,
input_shape=c4.output_shape)
#Layer5: hidden
n_in = reduce(lambda x, y: x * y, p4.output_shape[1:])
x_flat = p4.output.flatten(2)
h1 = HiddenLayer(rng, x_flat, n_in, 1024, activation=nn.relu)
#Layer6: hidden
lreg = LogisticRegression(rng, h1.output, 1024, params['n_output'])
self.output = lreg.y_pred
self.params = c1.params + c2.params + c3.params + c4.params + h1.params + lreg.params
cost = get_err_fn(self, cost_function, Y)
L2_reg = 0.0001
L2_sqr = theano.shared(0.)
for param in self.params:
L2_sqr += (T.sum(param[0]**2) + T.sum(param[1]**2))
cost += L2_reg * L2_sqr
_optimizer = optimizer(cost, self.params, lr=lr)
self.train = theano.function(inputs=[X, Y, is_train],
outputs=cost,
updates=_optimizer.getUpdates(),
allow_input_downcast=True)
self.predictions = theano.function(inputs=[X, is_train],
outputs=self.output,
allow_input_downcast=True)
self.n_param = count_params(self.params)
def __init__(self, config):
self.config = config
batch_size = config['batch_size']
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
layers = []
params = []
weight_types = []
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x1 = T.ftensor4('x1')
x2 = T.ftensor4('x2')
y = T.lvector('y') # The ground truth to be compared with will go here
rand1 = T.fvector('rand1')
rand2 = T.fvector('rand2')
print '... building the model'
if flag_datalayer:
data_layerA = DataLayer(input=x1,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config['rand_crop'])
layer1A_input = data_layerA.output
else:
layer1A_input = x1
if flag_datalayer:
data_layerB = DataLayer(input=x2,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config['rand_crop'])
layer1B_input = data_layerB.output
else:
layer1B_input = x2
fc_layer2_input = T.concatenate(
(T.flatten(layer1A_input.dimshuffle(3, 0, 1, 2),
2), T.flatten(layer1B_input.dimshuffle(3, 0, 1, 2), 2)),
axis=1)
fc_layer2 = FCLayer(input=fc_layer2_input, n_in=154587 * 2, n_out=4096)
layers.append(fc_layer2)
params += fc_layer2.params
weight_types += fc_layer2.weight_type
dropout_layer2 = DropoutLayer(fc_layer2.output, n_in=4096, n_out=4096)
fc_layer3 = FCLayer(input=dropout_layer2.output, n_in=4096, n_out=4096)
layers.append(fc_layer3)
params += fc_layer3.params
weight_types += fc_layer3.weight_type
dropout_layer3 = DropoutLayer(fc_layer3.output, n_in=4096, n_out=4096)
# Final softmax layer
softmax_layer3 = SoftmaxLayer(
input=dropout_layer3.output, n_in=4096,
n_out=2) # Only a single binary output is required!
layers.append(softmax_layer3)
params += softmax_layer3.params
weight_types += softmax_layer3.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer3.negative_log_likelihood(y)
self.errors = softmax_layer3.errors(y)
self.errors_top_5 = softmax_layer3.errors_top_x(y, 5)
self.x1 = x1
self.x2 = x2
self.y = y
self.rand1 = rand1
self.rand2 = rand2
self.layers = layers
self.params = params
self.weight_types = weight_types
self.batch_size = batch_size
def main_graph(self, trained_model, scope, emb_dim, gru, rnn_dim, rnn_num, drop_out=0.5, rad_dim=30, emb=None,
ngram_embedding=None, pixels=None, con_width=None, filters=None, pooling_size=None):
"""
:param trained_model:
:param scope:
:param emb_dim:
:param gru:
:param rnn_dim:
:param rnn_num:
:param drop_out:
:param rad_dim: n
:param emb:
:param ngram_embedding: 预训练 ngram embeddig 文件
:param pixels:
:param con_width:
:param filters:
:param pooling_size:
:return:
"""
# trained_model: 模型存储路径
if trained_model is not None:
param_dic = {'nums_chars': self.nums_chars, 'nums_tags': self.nums_tags, 'tag_scheme': self.tag_scheme,
'graphic': self.graphic, 'pic_size': self.pic_size, 'word_vec': self.word_vec,
'radical': self.radical, 'crf': self.crf, 'emb_dim': emb_dim, 'gru': gru, 'rnn_dim': rnn_dim,
'rnn_num': rnn_num, 'drop_out': drop_out, 'filter_size': con_width, 'filters': filters,
'pooling_size': pooling_size, 'font': self.font, 'buckets_char': self.buckets_char,
'ngram': self.ngram}
print "RNN dimension is %d" % rnn_dim
print "RNN number is %d" % rnn_num
print "Character embedding size is %d" % emb_dim
print "Ngram embedding dimension is %d" % emb_dim
# 存储模型超参数
if self.metric == 'All':
# rindex() 返回子字符串 str 在字符串中最后出现的位置
# 截取模型文件名
pindex = trained_model.rindex('/') + 1
for m in self.all_metrics:
f_model = open(trained_model[:pindex] + m + '_' + trained_model[pindex:], 'w')
pickle.dump(param_dic, f_model)
f_model.close()
else:
f_model = open(trained_model, 'w')
pickle.dump(param_dic, f_model)
f_model.close()
# define shared weights and variables
dr = tf.placeholder(tf.float32, [], name='drop_out_holder')
self.drop_out = dr
self.drop_out_v = drop_out
# 字向量层
# 为什么字符数要加 500 ？
# emb_dim 是每个字符的特征向量维度，可以通过命令行参数设置
# weights 表示预训练的字向量，可以通过命令行参数设置
if self.word_vec:
self.emb_layer = EmbeddingLayer(self.nums_chars + 500, emb_dim, weights=emb, name='emb_layer')
# 偏旁部首向量
# 依照《康熙字典》，共有 214 个偏旁部首。
# 只用了常见汉字的偏旁部首，非常见汉字和非汉字的偏旁部首用其他两个特殊符号代替，
# 所以共有 216 个偏旁部首
if self.radical:
self.radical_layer = EmbeddingLayer(216, rad_dim, name='radical_layer')
if self.ngram is not None:
if ngram_embedding is not None:
assert len(ngram_embedding) == len(self.ngram)
else:
ngram_embedding = [None for _ in range(len(self.ngram))]
for i, n_gram in enumerate(self.ngram):
self.gram_layers.append(EmbeddingLayer(n_gram + 1000 * (i + 2), emb_dim, weights=ngram_embedding[i],
name=str(i + 2) + 'gram_layer'))
wrapper_conv_1, wrapper_mp_1, wrapper_conv_2, wrapper_mp_2, wrapper_dense, wrapper_dr = \
None, None, None, None, None, None
if self.graphic:
# 使用图像信息，需要用到 CNN
self.input_p = []
assert pixels is not None and filters is not None and pooling_size is not None and con_width is not None
self.pixels = pixels
pixel_dim = int(math.sqrt(len(pixels[0])))
wrapper_conv_1 = TimeDistributed(Convolution(con_width, 1, filters, name='conv_1'), name='wrapper_c1')
wrapper_mp_1 = TimeDistributed(Maxpooling(pooling_size, pooling_size, name='pooling_1'), name='wrapper_p1')
p_size_1 = toolbox.down_pool(pixel_dim, pooling_size)
wrapper_conv_2 = TimeDistributed(Convolution(con_width, filters, filters, name='conv_2'), name='wrapper_c2')
wrapper_mp_2 = TimeDistributed(Maxpooling(pooling_size, pooling_size, name='pooling_2'), name='wrapper_p2')
p_size_2 = toolbox.down_pool(p_size_1, pooling_size)
wrapper_dense = TimeDistributed(
HiddenLayer(p_size_2 * p_size_2 * filters, 100, activation='tanh', name='conv_dense'), name='wrapper_3')
wrapper_dr = TimeDistributed(DropoutLayer(self.drop_out), name='wrapper_dr')
with tf.variable_scope('BiRNN'):
if gru:
fw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
bw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
else:
fw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim, state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim, state_is_tuple=True)
if rnn_num > 1:
fw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([fw_rnn_cell] * rnn_num, state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([bw_rnn_cell] * rnn_num, state_is_tuple=True)
# 隐藏层，输入是前向 RNN 的输出加上 后向 RNN 的输出，所以输入维度为 rnn_dim * 2
# 输出维度即标签个数
output_wrapper = TimeDistributed(
HiddenLayer(rnn_dim * 2, self.nums_tags[0], activation='linear', name='hidden'),
name='wrapper')
# define model for each bucket
# 每一个 bucket 中的句子长度不一样，所以需要定义单独的模型
# bucket: bucket 中的句子长度
for idx, bucket in enumerate(self.buckets_char):
if idx == 1:
# scope 是 tf.variable_scope("tagger", reuse=None, initializer=initializer)
# 只需要设置一次 reuse，后面就都 reuse 了
scope.reuse_variables()
t1 = time()
# 输入的句子，one-hot 向量
# shape = （batch_size, 句子长度）
input_sentences = tf.placeholder(tf.int32, [None, bucket], name='input_' + str(bucket))
self.input_v.append([input_sentences])
emb_set = []
if self.word_vec:
# 根据 one-hot 向量查找对应的字向量
# word_out: shape=(batch_size, 句子长度，字向量维度（64）)
word_out = self.emb_layer(input_sentences)
emb_set.append(word_out)
if self.radical:
# 嵌入偏旁部首信息，shape = (batch_size, 句子长度)
input_radicals = tf.placeholder(tf.int32, [None, bucket], name='input_r' + str(bucket))
self.input_v[-1].append(input_radicals)
radical_out = self.radical_layer(input_radicals)
emb_set.append(radical_out)
if self.ngram is not None:
for i in range(len(self.ngram)):
input_g = tf.placeholder(tf.int32, [None, bucket], name='input_g' + str(i) + str(bucket))
self.input_v[-1].append(input_g)
gram_out = self.gram_layers[i](input_g)
emb_set.append(gram_out)
if self.graphic:
input_p = tf.placeholder(tf.float32, [None, bucket, pixel_dim * pixel_dim])
self.input_p.append(input_p)
pix_out = tf.reshape(input_p, [-1, bucket, pixel_dim, pixel_dim, 1])
conv_out_1 = wrapper_conv_1(pix_out)
pooling_out_1 = wrapper_mp_1(conv_out_1)
conv_out_2 = wrapper_conv_2(pooling_out_1)
pooling_out_2 = wrapper_mp_2(conv_out_2)
assert p_size_2 == pooling_out_2[0].get_shape().as_list()[1]
pooling_out = tf.reshape(pooling_out_2, [-1, bucket, p_size_2 * p_size_2 * filters])
pooling_out = tf.unstack(pooling_out, axis=1)
graphic_out = wrapper_dense(pooling_out)
graphic_out = wrapper_dr(graphic_out)
emb_set.append(graphic_out)
if self.window_size > 1:
padding_size = int(np.floor(self.window_size / 2))
word_padded = tf.pad(word_out, [[0, 0], [padding_size, padding_size], [0, 0]], 'CONSTANT')
Ws = []
for q in range(1, self.window_size + 1):
Ws.append(tf.get_variable("W_%d" % q, shape=[q * emb_dim, self.filters_number]))
b = tf.get_variable("b", shape=[self.filters_number])
z = [None for _ in range(0, bucket)]
for q in range(1, self.window_size + 1):
for i in range(padding_size, bucket + padding_size):
low = i - int(np.floor((q - 1) / 2))
high = i + int(np.ceil((q + 1) / 2))
x = word_padded[:, low, :]
for j in range(low + 1, high):
x = tf.concat(values=[x, word_padded[:, j, :]], axis=1)
z_iq = tf.tanh(tf.nn.xw_plus_b(x, Ws[q - 1], b))
if z[i - padding_size] is None:
z[i - padding_size] = z_iq
else:
z[i - padding_size] = tf.concat([z[i - padding_size], z_iq], axis=1)
z = tf.stack(z, axis=1)
values, indices = tf.nn.top_k(z, sorted=False, k=emb_dim)
# highway layer
X = tf.unstack(word_out, axis=1)
Conv_X = tf.unstack(values, axis=1)
X_hat = []
W_t = tf.get_variable("W_t", shape=[emb_dim, emb_dim])
b_t = tf.get_variable("b_t", shape=[emb_dim])
for x, conv_x in zip(X, Conv_X):
T_x = tf.sigmoid(tf.nn.xw_plus_b(x, W_t, b_t))
X_hat.append(tf.multiply(conv_x, T_x) + tf.multiply(x, 1 - T_x))
X_hat = tf.stack(X_hat, axis=1)
emb_set.append(X_hat)
if len(emb_set) > 1:
# 各种字向量直接 concat 起来（字向量、偏旁部首、n-gram、图像信息等）
emb_out = tf.concat(axis=2, values=emb_set)
else:
emb_out = emb_set[0]
# rnn_out 是前向 RNN 的输出和后向 RNN 的输出 concat 之后的值
rnn_out = BiLSTM(rnn_dim, fw_cell=fw_rnn_cell, bw_cell=bw_rnn_cell, p=dr,
name='BiLSTM' + str(bucket), scope='BiRNN')(self.highway(emb_out, "tag"), input_sentences)
# 应用全连接层，Wx+b 得到最后的输出
output = output_wrapper(rnn_out)
# 为什么要 [output] 而不是 output 呢？
self.output.append([output])
self.output_.append([tf.placeholder(tf.int32, [None, bucket], name='tags' + str(bucket))])
self.bucket_dit[bucket] = idx
# language model
lm_rnn_dim = rnn_dim
with tf.variable_scope('LM-BiRNN'):
if gru:
lm_fw_rnn_cell = tf.nn.rnn_cell.GRUCell(lm_rnn_dim)
lm_bw_rnn_cell = tf.nn.rnn_cell.GRUCell(lm_rnn_dim)
else:
lm_fw_rnn_cell = tf.nn.rnn_cell.LSTMCell(lm_rnn_dim, state_is_tuple=True)
lm_bw_rnn_cell = tf.nn.rnn_cell.LSTMCell(lm_rnn_dim, state_is_tuple=True)
if rnn_num > 1:
lm_fw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([lm_fw_rnn_cell] * rnn_num, state_is_tuple=True)
lm_bw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([lm_bw_rnn_cell] * rnn_num, state_is_tuple=True)
lm_rnn_output = BiLSTM(lm_rnn_dim, fw_cell=lm_fw_rnn_cell,
bw_cell=lm_bw_rnn_cell, p=dr,
name='LM-BiLSTM' + str(bucket),
scope='LM-BiRNN')(self.highway(emb_set[0]), input_sentences)
lm_output_wrapper = TimeDistributed(
HiddenLayer(lm_rnn_dim * 2, self.nums_chars + 2, activation='linear', name='lm_hidden'),
name='lm_wrapper')
lm_final_output = lm_output_wrapper(lm_rnn_output)
self.lm_predictions.append([lm_final_output])
self.lm_groundtruthes.append([tf.placeholder(tf.int32, [None, bucket], name='lm_targets' + str(bucket))])
print 'Bucket %d, %f seconds' % (idx + 1, time() - t1)
assert \
len(self.input_v) == len(self.output) and \
len(self.output) == len(self.output_) and \
len(self.lm_predictions) == len(self.lm_groundtruthes) and \
len(self.output) == len(self.counts)
self.params = tf.trainable_variables()
self.saver = tf.train.Saver()
def train_net(config, private_config):
# UNPACK CONFIGS
(train_videos_spatial_jhmdb,val_videos_spatial_jhmdb,train_videos_temporal_jhmdb,val_videos_temporal_jhmdb,
train_targets,val_targets,
train_labels_jhmdb,val_labels_jhmdb) = unpack_configs_jhmdb(config,gpu_id=private_config['gpu_id'])
# print('val_len',len(val_videos_spatial_jhmdb),'train_len',len(train_videos_spatial_jhmdb))
if config['modal']=='rgb':
train_videos = list(train_videos_spatial_jhmdb)
test_videos = list(val_videos_spatial_jhmdb)
else:
train_videos = list(train_videos_temporal_jhmdb)
test_videos = list(val_videos_temporal_jhmdb)
print('jhmdb_len',len(train_videos),len(train_labels_jhmdb))#,len(tr_video_length_jhmdb))
flag_para_load =config['para_load']
gpu_send_queue = private_config['queue_gpu_send']
gpu_recv_queue = private_config['queue_gpu_recv']
# pycuda and zmq set up
drv.init()
dev = drv.Device(int(private_config['gpu'][-1]))
ctx = dev.make_context()
sock_gpu = zmq.Context().socket(zmq.PAIR)
if private_config['flag_client']:
sock_gpu.connect('tcp://*****:*****@ iter = ', num_iter
print 'training cost:', cost_ij,'cost_nll:',cost_nll,'cost_attention:',cost_att
if config['print_train_error']:
error_ij = train_error()
gpu_send_queue.put(error_ij)
that_error = gpu_recv_queue.get()
error_ij = (error_ij + that_error) / 2.
if private_config['flag_verbose']:
print 'training error rate:', error_ij
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
if count%20 == 0:
e = time.time()
print "time per 20 iter:", (e - s)
# ############### Test on Validation Set ##################
DropoutLayer.SetDropoutOff()
this_val_error, this_val_loss = get_test_error(config,
shared_x, shared_mask, shared_y,shared_target,shared_use_noise,
shared_conv,test_videos, val_labels_jhmdb,
flag_para_load,
batch_size,num_seq, validate_model_lstm,train_model,
send_queue=load_send_queue, recv_queue=load_recv_queue)
# report validation stats
gpu_send_queue.put(this_val_error)
that_val_error = gpu_recv_queue.get()
this_val_error = (this_val_error + that_val_error) / 2.
gpu_send_queue.put(this_val_loss)
that_val_loss = gpu_recv_queue.get()
this_val_loss = (this_val_loss + that_val_loss) / 2.
if private_config['flag_verbose']:
print('epoch %i: test loss of jhmdb %f ' %
(epoch, this_val_loss))
print('epoch %i: test error of jhmdb %f %%' %
(epoch, this_val_error * 100.))
val_record.append([this_val_error, this_val_loss])
if private_config['flag_save']:
np.save(config['weights_dir'] + 'test_record_jhmdb.npy', val_record)
DropoutLayer.SetDropoutOn()
###########################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx,
val_record, learning_rate)
# Save Weights, only one of them will do
if private_config['flag_save'] :
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
print('Optimization complete.')
def main_graph(self, trained_model, scope, emb_dim, gru, rnn_dim, rnn_num, drop_out=0.5, rad_dim=30, emb=None, ng_embs=None, pixels=None, con_width=None, filters=None, pooling_size=None):
if trained_model is not None:
param_dic = {}
param_dic['nums_chars'] = self.nums_chars
param_dic['nums_tags'] = self.nums_tags
param_dic['tag_scheme'] = self.tag_scheme
param_dic['graphic'] = self.graphic
param_dic['pic_size'] = self.pic_size
param_dic['word_vec'] = self.word_vec
param_dic['radical'] = self.radical
param_dic['crf'] = self.crf
param_dic['emb_dim'] = emb_dim
param_dic['gru'] = gru
param_dic['rnn_dim'] = rnn_dim
param_dic['rnn_num'] = rnn_num
param_dic['drop_out'] = drop_out
param_dic['filter_size'] = con_width
param_dic['filters'] = filters
param_dic['pooling_size'] = pooling_size
param_dic['font'] = self.font
param_dic['buckets_char'] = self.buckets_char
param_dic['ngram'] = self.ngram
#print param_dic
f_model = open(trained_model, 'w')
pickle.dump(param_dic, f_model)
f_model.close()
# define shared weights and variables
dr = tf.placeholder(tf.float32, [], name='drop_out_holder')
self.drop_out = dr
self.drop_out_v = drop_out
if self.word_vec:
self.emb_layer = EmbeddingLayer(self.nums_chars + 500, emb_dim, weights=emb, name='emb_layer')
if self.radical:
self.radical_layer = EmbeddingLayer(216, rad_dim, name='radical_layer')
if self.ngram is not None:
if ng_embs is not None:
assert len(ng_embs) == len(self.ngram)
else:
ng_embs = [None for _ in range(len(self.ngram))]
for i, n_gram in enumerate(self.ngram):
self.gram_layers.append(EmbeddingLayer(n_gram + 1000 * (i + 2), emb_dim, weights=ng_embs[i], name= str(i + 2) + 'gram_layer'))
wrapper_conv_1, wrapper_mp_1, wrapper_conv_2, wrapper_mp_2, wrapper_dense, wrapper_dr = None, None, None, None, None, None
if self.graphic:
self.input_p = []
assert pixels is not None and filters is not None and pooling_size is not None and con_width is not None
self.pixels = pixels
pixel_dim = int(math.sqrt(len(pixels[0])))
wrapper_conv_1 = TimeDistributed(Convolution(con_width, 1, filters, name='conv_1'), name='wrapper_c1')
wrapper_mp_1 = TimeDistributed(Maxpooling(pooling_size, pooling_size, name='pooling_1'), name='wrapper_p1')
p_size_1 = toolbox.down_pool(pixel_dim, pooling_size)
wrapper_conv_2 = TimeDistributed(Convolution(con_width, filters, filters, name='conv_2'), name='wrapper_c2')
wrapper_mp_2 = TimeDistributed(Maxpooling(pooling_size, pooling_size, name='pooling_2'), name='wrapper_p2')
p_size_2 = toolbox.down_pool(p_size_1, pooling_size)
wrapper_dense = TimeDistributed(HiddenLayer(p_size_2 * p_size_2 * filters, 100, activation='tanh', name='conv_dense'), name='wrapper_3')
wrapper_dr = TimeDistributed(DropoutLayer(self.drop_out), name='wrapper_dr')
with tf.variable_scope('BiRNN'):
if gru:
fw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
bw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
else:
fw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim, state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim, state_is_tuple=True)
if rnn_num > 1:
fw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([fw_rnn_cell]*rnn_num, state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell([bw_rnn_cell]*rnn_num, state_is_tuple=True)
output_wrapper = TimeDistributed(HiddenLayer(rnn_dim * 2, self.nums_tags[0], activation='linear', name='hidden'), name='wrapper')
#define model for each bucket
for idx, bucket in enumerate(self.buckets_char):
if idx == 1:
scope.reuse_variables()
t1 = time()
input_v = tf.placeholder(tf.int32, [None, bucket], name='input_' + str(bucket))
self.input_v.append([input_v])
emb_set = []
if self.word_vec:
word_out = self.emb_layer(input_v)
emb_set.append(word_out)
if self.radical:
input_r = tf.placeholder(tf.int32, [None, bucket], name='input_r' + str(bucket))
self.input_v[-1].append(input_r)
radical_out = self.radical_layer(input_r)
emb_set.append(radical_out)
if self.ngram is not None:
for i in range(len(self.ngram)):
input_g = tf.placeholder(tf.int32, [None, bucket], name='input_g' + str(i) + str(bucket))
self.input_v[-1].append(input_g)
gram_out = self.gram_layers[i](input_g)
emb_set.append(gram_out)
if self.graphic:
input_p = tf.placeholder(tf.float32, [None, bucket, pixel_dim*pixel_dim])
self.input_p.append(input_p)
pix_out = tf.reshape(input_p, [-1, bucket, pixel_dim, pixel_dim, 1])
pix_out = tf.unpack(pix_out, axis=1)
conv_out_1 = wrapper_conv_1(pix_out)
pooling_out_1 = wrapper_mp_1(conv_out_1)
conv_out_2 = wrapper_conv_2(pooling_out_1)
pooling_out_2 = wrapper_mp_2(conv_out_2)
assert p_size_2 == pooling_out_2[0].get_shape().as_list()[1]
pooling_out = tf.reshape(pooling_out_2, [-1, bucket, p_size_2 * p_size_2 * filters])
pooling_out = tf.unpack(pooling_out, axis=1)
graphic_out = wrapper_dense(pooling_out)
graphic_out = wrapper_dr(graphic_out)
emb_set.append(graphic_out)
if len(emb_set) > 1:
emb_out = tf.concat(2, emb_set)
emb_out = tf.unpack(emb_out)
else:
emb_out = emb_set[0]
rnn_out = BiLSTM(rnn_dim, fw_cell=fw_rnn_cell, bw_cell=bw_rnn_cell, p=dr, name='BiLSTM' + str(bucket), scope='BiRNN')(emb_out, input_v)
output = output_wrapper(rnn_out)
output_c = tf.pack(output, axis=1)
self.output.append([output_c])
self.output_.append([tf.placeholder(tf.int32, [None, bucket], name='tags' + str(bucket))])
self.bucket_dit[bucket] = idx
print 'Bucket %d, %f seconds' % (idx + 1, time() - t1)
assert len(self.input_v) == len(self.output) and len(self.output) == len(self.output_) and len(self.output) == len(self.counts)
self.params = tf.trainable_variables()
self.saver = tf.train.Saver()
def main_graph(self,
trained_model,
scope,
emb_dim,
gru,
rnn_dim,
rnn_num,
fnn_dim,
window_size,
drop_out=0.5,
rad_dim=30,
emb=None,
ng_embs=None,
pixels=None,
con_width=None,
filters=None,
pooling_size=None):
if trained_model is not None:
param_dic = {}
param_dic['nums_chars'] = self.nums_chars
param_dic['nums_tags'] = self.nums_tags
param_dic['tag_scheme'] = self.tag_scheme
param_dic['graphic'] = self.graphic
param_dic['pic_size'] = self.pic_size
param_dic['word_vec'] = self.word_vec
param_dic['radical'] = self.radical
param_dic['crf'] = self.crf
param_dic['emb_dim'] = emb_dim
param_dic['gru'] = gru
param_dic['rnn_dim'] = rnn_dim
param_dic['rnn_num'] = rnn_num
param_dic['fnn_dim'] = fnn_dim
param_dic['window_size'] = window_size
param_dic['drop_out'] = drop_out
param_dic['filter_size'] = con_width
param_dic['filters'] = filters
param_dic['pooling_size'] = pooling_size
param_dic['font'] = self.font
param_dic['buckets_char'] = self.buckets_char
param_dic['ngram'] = self.ngram
param_dic['mode'] = self.mode
#print param_dic
if self.metric == 'All':
pindex = trained_model.rindex('/') + 1
for m in self.all_metrics:
f_model = open(
trained_model[:pindex] + m + '_' +
trained_model[pindex:], 'w')
pickle.dump(param_dic, f_model)
f_model.close()
else:
f_model = open(trained_model, 'w')
pickle.dump(param_dic, f_model)
f_model.close()
# define shared weights and variables
dr = tf.placeholder(tf.float32, [], name='drop_out_holder')
self.drop_out = dr
self.drop_out_v = drop_out
#concat_emb_dim = emb_dim * 2
concat_emb_dim = 0
if self.word_vec:
self.emb_layer = EmbeddingLayer(self.nums_chars + 500,
emb_dim,
weights=emb,
name='emb_layer')
concat_emb_dim += emb_dim
if self.radical:
self.radical_layer = EmbeddingLayer(216,
rad_dim,
name='radical_layer')
concat_emb_dim += rad_dim
if self.ngram is not None:
if ng_embs is not None:
assert len(ng_embs) == len(self.ngram)
else:
ng_embs = [None for _ in range(len(self.ngram))]
for i, n_gram in enumerate(self.ngram):
self.gram_layers.append(
EmbeddingLayer(n_gram + 1000 * (i + 2),
emb_dim,
weights=ng_embs[i],
name=str(i + 2) + 'gram_layer'))
concat_emb_dim += emb_dim
wrapper_conv_1, wrapper_mp_1, wrapper_conv_2 = None, None, None
wrapper_mp_2, wrapper_dense, wrapper_dr = None, None, None
if self.graphic:
self.input_p = []
assert pixels is not None and filters is not None and pooling_size is not None and con_width is not None
self.pixels = pixels
pixel_dim = int(math.sqrt(len(pixels[0])))
wrapper_conv_1 = Convolution(con_width, 1, filters, name='conv_1')
wrapper_mp_1 = Maxpooling(pooling_size,
pooling_size,
name='pooling_1')
p_size_1 = toolbox.down_pool(pixel_dim, pooling_size)
wrapper_conv_2 = Convolution(con_width,
filters,
filters,
name='conv_2')
wrapper_mp_2 = Maxpooling(pooling_size,
pooling_size,
name='pooling_2')
p_size_2 = toolbox.down_pool(p_size_1, pooling_size)
wrapper_dense = HiddenLayer(p_size_2 * p_size_2 * filters,
100,
activation='tanh',
name='conv_dense')
wrapper_dr = DropoutLayer(self.drop_out)
concat_emb_dim += 100
fw_rnn_cell, bw_rnn_cell = None, None
if self.mode == 'RNN':
with tf.variable_scope('BiRNN'):
if gru:
fw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
bw_rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_dim)
else:
fw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim,
state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_dim,
state_is_tuple=True)
if rnn_num > 1:
fw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(
[fw_rnn_cell] * rnn_num, state_is_tuple=True)
bw_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(
[bw_rnn_cell] * rnn_num, state_is_tuple=True)
output_wrapper = HiddenLayer(rnn_dim * 2,
self.nums_tags[0],
activation='linear',
name='out_wrapper')
fnn_weights, fnn_bias = None, None
else:
with tf.variable_scope('FNN'):
fnn_weights = tf.get_variable(
'conv_w',
[2 * window_size + 1, concat_emb_dim, 1, fnn_dim])
fnn_bias = tf.get_variable(
'conv_b', [fnn_dim],
initializer=tf.constant_initializer(0.1))
output_wrapper = HiddenLayer(fnn_dim,
self.nums_tags[0],
activation='linear',
name='out_wrapper')
#define model for each bucket
for idx, bucket in enumerate(self.buckets_char):
if idx == 1:
scope.reuse_variables()
t1 = time()
input_v = tf.placeholder(tf.int32, [None, bucket],
name='input_' + str(bucket))
self.input_v.append([input_v])
emb_set = []
if self.word_vec:
word_out = self.emb_layer(input_v)
emb_set.append(word_out)
if self.radical:
input_r = tf.placeholder(tf.int32, [None, bucket],
name='input_r' + str(bucket))
self.input_v[-1].append(input_r)
radical_out = self.radical_layer(input_r)
emb_set.append(radical_out)
if self.ngram is not None:
for i in range(len(self.ngram)):
input_g = tf.placeholder(tf.int32, [None, bucket],
name='input_g' + str(i) +
str(bucket))
self.input_v[-1].append(input_g)
gram_out = self.gram_layers[i](input_g)
emb_set.append(gram_out)
if self.graphic:
input_p = tf.placeholder(tf.float32,
[None, bucket, pixel_dim * pixel_dim])
self.input_p.append(input_p)
pix_out = tf.reshape(input_p, [-1, pixel_dim, pixel_dim, 1])
conv_out_1 = wrapper_conv_1(pix_out)
pooling_out_1 = wrapper_mp_1(conv_out_1)
conv_out_2 = wrapper_conv_2(pooling_out_1)
pooling_out_2 = wrapper_mp_2(conv_out_2)
assert p_size_2 == pooling_out_2[0].get_shape().as_list()[1]
pooling_out = tf.reshape(
pooling_out_2, [-1, bucket, p_size_2 * p_size_2 * filters])
graphic_out = wrapper_dense(pooling_out)
graphic_out = wrapper_dr(graphic_out)
emb_set.append(graphic_out)
if len(emb_set) > 1:
emb_out = tf.concat(axis=2, values=emb_set)
else:
emb_out = emb_set[0]
if self.mode == 'RNN':
rnn_out = BiLSTM(rnn_dim,
fw_cell=fw_rnn_cell,
bw_cell=bw_rnn_cell,
p=dr,
name='BiLSTM' + str(bucket),
scope='BiRNN')(emb_out, input_v)
output = output_wrapper(rnn_out)
else:
emb_out = tf.pad(emb_out,
[[0, 0], [window_size, window_size], [0, 0]])
emb_out = tf.reshape(
emb_out, [-1, bucket + 2 * window_size, concat_emb_dim, 1])
conv_out = tf.nn.conv2d(emb_out,
fnn_weights, [1, 1, 1, 1],
padding='VALID') + fnn_bias
fnn_out = tf.nn.tanh(conv_out)
fnn_out = tf.reshape(fnn_out, [-1, bucket, fnn_dim])
output = output_wrapper(fnn_out)
self.output.append([output])
self.output_.append([
tf.placeholder(tf.int32, [None, bucket],
name='tags' + str(bucket))
])
self.bucket_dit[bucket] = idx
print 'Bucket %d, %f seconds' % (idx + 1, time() - t1)
assert len(self.input_v) == len(self.output) and len(self.output) == len(self.output_) \
and len(self.output) == len(self.counts)
self.params = tf.trainable_variables()
self.saver = tf.train.Saver()
def train_net(config):
# UNPACK CONFIGS
(flag_para_load, train_filenames, val_filenames, train_labels, val_labels,
img_mean) = unpack_configs(config)
# pycuda set up
drv.init()
dev = drv.Device(int(config['gpu'][-1]))
ctx = dev.make_context()
if flag_para_load:
# zmq set up
sock = zmq.Context().socket(zmq.PAIR)
sock.connect('tcp://*****:*****@ iter = ', num_iter
print 'training cost:', cost_ij
if config['print_train_error']:
print 'training error rate:', train_error()
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
############### Test on Validation Set ##################
DropoutLayer.SetDropoutOff()
this_validation_error, this_validation_loss = get_val_error_loss(
rand_arr,
shared_x,
shared_y,
val_filenames,
val_labels,
flag_para_load,
img_mean,
batch_size,
validate_model,
send_queue=load_send_queue,
recv_queue=load_recv_queue)
print('epoch %i: validation loss %f ' % (epoch, this_validation_loss))
print('epoch %i: validation error %f %%' %
(epoch, this_validation_error * 100.))
val_record.append([this_validation_error, this_validation_loss])
np.save(config['weights_dir'] + 'val_record.npy', val_record)
DropoutLayer.SetDropoutOn()
############################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx, val_record,
learning_rate)
# Save weights
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
print('Optimization complete.')
def set_dropout_off(self):
DropoutLayer.SetDropoutOff()
def train_net(config, private_config):
# UNPACK CONFIGS
(flag_para_load, flag_datalayer, train_filenames, val_filenames,
train_labels, val_labels, img_mean) = \
unpack_configs(config, ext_data=private_config['ext_data'],
ext_label=private_config['ext_label'])
gpu_send_queue = private_config['queue_gpu_send']
gpu_recv_queue = private_config['queue_gpu_recv']
# pycuda and zmq set up
drv.init()
dev = drv.Device(int(private_config['gpu'][-1]))
ctx = dev.make_context()
sock_gpu = zmq.Context().socket(zmq.PAIR)
if private_config['flag_client']:
sock_gpu.connect('tcp://*****:*****@ iter = ', num_iter
print 'training cost:', cost_ij
if config['print_train_error']:
error_ij = train_error()
gpu_send_queue.put(error_ij)
that_error = gpu_recv_queue.get()
error_ij = (error_ij + that_error) / 2.
if private_config['flag_verbose']:
print 'training error rate:', error_ij
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
############### Test on Validation Set ##################
DropoutLayer.SetDropoutOff()
this_val_error, this_val_loss = get_val_error_loss(
rand_arr,
shared_x,
shared_y,
val_filenames,
val_labels,
flag_datalayer,
flag_para_load,
batch_size,
validate_model,
send_queue=load_send_queue,
recv_queue=load_recv_queue)
# report validation stats
gpu_send_queue.put(this_val_error)
that_val_error = gpu_recv_queue.get()
this_val_error = (this_val_error + that_val_error) / 2.
gpu_send_queue.put(this_val_loss)
that_val_loss = gpu_recv_queue.get()
this_val_loss = (this_val_loss + that_val_loss) / 2.
if private_config['flag_verbose']:
print('epoch %i: validation loss %f ' % (epoch, this_val_loss))
print('epoch %i: validation error %f %%' %
(epoch, this_val_error * 100.))
val_record.append([this_val_error, this_val_loss])
if private_config['flag_save']:
np.save(config['weights_dir'] + 'val_record.npy', val_record)
DropoutLayer.SetDropoutOn()
############################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx, val_record,
learning_rate)
# Save Weights, only one of them will do
if private_config['flag_save']:
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
print('Optimization complete.')
def __init__(self, config, testMode):
self.config = config
batch_size = config['batch_size']
lib_conv = config['lib_conv']
useLayers = config['useLayers']
#imgWidth = config['imgWidth']
#imgHeight = config['imgHeight']
initWeights = config['initWeights'] #if we wish to initialize alexnet with some weights. #need to make changes in layers.py to accept initilizing weights
if initWeights:
weightsDir = config['weightsDir']
weightFileTag = config['weightFileTag']
prob_drop = config['prob_drop']
# ##################### BUILD NETWORK ##########################
x = T.ftensor4('x')
mean = T.ftensor4('mean')
#y = T.lvector('y')
print '... building the model'
self.layers = []
params = []
weight_types = []
if useLayers >= 1:
convpool_layer1 = ConvPoolLayer(input=x-mean,
image_shape=(3, None, None, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4, padsize=0, group=1,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=True,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_0'+weightFileTag, 'b_0'+weightFileTag]
)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
if useLayers >= 2:
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(96, None, None, batch_size), #change from 27 to appropriate value sbased on conv1's output
filter_shape=(96, 5, 5, 256),
convstride=1, padsize=2, group=2,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=True,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W0_1'+weightFileTag, 'W1_1'+weightFileTag, 'b0_1'+weightFileTag, 'b1_1'+weightFileTag]
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
if useLayers >= 3:
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(256, None, None, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=0,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_2'+weightFileTag, 'b_2'+weightFileTag]
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
if useLayers >= 4:
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(384, None, None, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1, padsize=1, group=2,
poolsize=1, poolstride=0,
bias_init=0.1, lrn=False,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W0_3'+weightFileTag, 'W1_3'+weightFileTag, 'b0_3'+weightFileTag, 'b1_3'+weightFileTag]
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
if useLayers >= 5:
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(384, None, None, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1, padsize=1, group=2,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W0_4'+weightFileTag, 'W1_4'+weightFileTag, 'b0_4'+weightFileTag, 'b1_4'+weightFileTag]
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
if useLayers >= 6:
fc_layer6_input = T.flatten(convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096, initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_5'+weightFileTag, 'b_5'+weightFileTag])
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
if testMode:
dropout_layer6 = fc_layer6
else:
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096, prob_drop=prob_drop)
if useLayers >= 7:
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096, initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_6'+weightFileTag, 'b_6'+weightFileTag])
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
if testMode:
dropout_layer6 = fc_layer7
else:
dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096, prob_drop=prob_drop)
if useLayers >= 8:
softmax_layer8 = SoftmaxLayer(input=dropout_layer7.output, n_in=4096, n_out=1000, initWeights=initWeights, weightsDir=weightsDir, weightFiles=['W_7'+weightFileTag, 'b_7'+weightFileTag])
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.output = self.layers[useLayers-1]
self.params = params
self.x = x
self.mean = mean
self.weight_types = weight_types
self.batch_size = batch_size
self.useLayers = useLayers
self.outLayer = self.layers[useLayers-1]
meanVal = np.load(config['mean_file'])
meanVal = meanVal[:, :, :, np.newaxis].astype('float32') #x is 4d, with 'batch' number of images. meanVal has only '1' in the 'batch' dimension. subtraction wont work.
meanVal = np.tile(meanVal,(1,1,1,batch_size))
self.meanVal = meanVal
#meanVal = np.zeros([3,imgHeight,imgWidth,2], dtype='float32')
if useLayers >= 8: #if last layer is softmax, then its output is y_pred
finalOut = self.outLayer.y_pred
else:
finalOut = self.outLayer.output
self.forwardFunction = theano.function([self.x, In(self.mean, value=meanVal)], [finalOut])
def train_net(config):
# UNPACK CONFIGS
(flag_para_load, train_filenames, val_filenames,
train_labels, val_labels, img_mean) = unpack_configs(config)
if flag_para_load:
# zmq set up
sock = zmq.Context().socket(zmq.PAIR)
sock.connect('tcp://*****:*****@iter " + str(count)
if count == 1:
s = time.time()
if count == 20:
e = time.time()
print "time per 20 iter:", (e - s)
logger.info("time per 20 iter: %f" % (e - s))
cost_ij = train_model_wrap(train_model, shared_x,
shared_y, rand_arr, img_mean,
count, minibatch_index,
minibatch_range, batch_size,
train_filenames, train_labels,
flag_para_load,
config['batch_crop_mirror'],
send_queue=load_send_queue,
recv_queue=load_recv_queue)
if num_iter % config['print_freq'] == 0:
#print 'training @ iter = ', num_iter
#print 'training cost:', cost_ij
logger.info("training @ iter = %i" % (num_iter))
logger.info("training cost: %lf" % (cost_ij))
if config['print_train_error']:
logger.info('training error rate: %lf' % train_error())
#print 'training error rate:', train_error()
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
############### Test on Validation Set ##################
#"""
DropoutLayer.SetDropoutOff()
# result_list = [ this_validation_error, this_validation_error_top5, this_validation_loss ]
# or
# result_list = [ this_validation_error, this_validation_loss ]
result_list = get_val_error_loss(
#this_validation_error, this_validation_loss = get_val_error_loss(
rand_arr, shared_x, shared_y,
val_filenames, val_labels,
flag_para_load, img_mean,
batch_size, validate_model,
send_queue=load_send_queue,
recv_queue=load_recv_queue,
flag_top_5=flag_top5)
logger.info(('epoch %i: validation loss %f ' %
(epoch, result_list[-1])))
#print('epoch %i: validation loss %f ' %
# (epoch, this_validation_loss))
if flag_top5:
logger.info(('epoch %i: validation error (top 1) %f %%, (top5) %f %%' %
(epoch, result_list[0] * 100., result_list[1] * 100.)))
else:
logger.info(('epoch %i: validation error %f %%' %
(epoch, result_list[0] * 100.)))
#print('epoch %i: validation error %f %%' %
# (epoch, this_validation_error * 100.))
val_record.append(result_list)
#val_record.append([this_validation_error, this_validation_loss])
np.save(config['weights_dir'] + 'val_record.npy', val_record)
DropoutLayer.SetDropoutOn()
############################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx,
val_record, learning_rate)
# Save weights
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
#"""
print('Optimization complete.')
def __init__(self, rng, params, cost_function='mse', optimizer=RMSprop):
lr = params["lr"]
n_lstm = params['n_hidden']
n_out = params['n_output']
batch_size = params["batch_size"]
sequence_length = params["seq_length"]
# minibatch)
X = T.tensor3() # batch of sequence of vector
Y = T.tensor3() # batch of sequence of vector
is_train = T.iscalar(
'is_train'
) # pseudo boolean for switching between training and prediction
#CNN global parameters.
subsample = (1, 1)
p_1 = 0.5
border_mode = "valid"
cnn_batch_size = batch_size * sequence_length
pool_size = (2, 2)
#Layer1: conv2+pool+drop
filter_shape = (64, 1, 9, 9)
input_shape = (cnn_batch_size, 1, 120, 60
) #input_shape= (samples, channels, rows, cols)
input = X.reshape(input_shape)
c1 = ConvLayer(rng,
input,
filter_shape,
input_shape,
border_mode,
subsample,
activation=nn.relu)
p1 = PoolLayer(c1.output,
pool_size=pool_size,
input_shape=c1.output_shape)
dl1 = DropoutLayer(rng, input=p1.output, prob=p_1)
retain_prob = 1. - p_1
test_output = p1.output * retain_prob
d1_output = T.switch(T.neq(is_train, 0), dl1.output, test_output)
#Layer2: conv2+pool
filter_shape = (128, p1.output_shape[1], 3, 3)
c2 = ConvLayer(rng,
d1_output,
filter_shape,
p1.output_shape,
border_mode,
subsample,
activation=nn.relu)
p2 = PoolLayer(c2.output,
pool_size=pool_size,
input_shape=c2.output_shape)
#Layer3: conv2+pool
filter_shape = (128, p2.output_shape[1], 3, 3)
c3 = ConvLayer(rng,
p2.output,
filter_shape,
p2.output_shape,
border_mode,
subsample,
activation=nn.relu)
p3 = PoolLayer(c3.output,
pool_size=pool_size,
input_shape=c3.output_shape)
#Layer4: hidden
n_in = reduce(lambda x, y: x * y, p3.output_shape[1:])
x_flat = p3.output.flatten(2)
h1 = HiddenLayer(rng, x_flat, n_in, 1024, activation=nn.relu)
n_in = 1024
rnn_input = h1.output.reshape((batch_size, sequence_length, n_in))
#Layer5: gru
self.n_in = n_in
self.n_lstm = n_lstm
self.n_out = n_out
self.W_xr = init_weight((self.n_in, self.n_lstm),
rng=rng,
name='W_xi',
sample='glorot')
self.W_hr = init_weight((self.n_lstm, self.n_lstm),
rng=rng,
name='W_hr',
sample='glorot')
self.b_r = init_bias(self.n_lstm, rng=rng, sample='zero')
self.W_xz = init_weight((self.n_in, self.n_lstm),
rng=rng,
name='W_xz',
sample='glorot')
self.W_hz = init_weight((self.n_lstm, self.n_lstm),
rng=rng,
name='W_hz',
sample='glorot')
self.b_z = init_bias(self.n_lstm, rng=rng, sample='zero')
self.W_xh = init_weight((self.n_in, self.n_lstm),
rng=rng,
name='W_xh',
sample='glorot')
self.W_hh = init_weight((self.n_lstm, self.n_lstm),
rng=rng,
name='W_hh',
sample='glorot')
self.b_h = init_bias(self.n_lstm, rng=rng, sample='zero')
self.W_hy = init_weight((self.n_lstm, self.n_out),
rng=rng,
name='W_hy',
sample='glorot')
self.b_y = init_bias(self.n_out, rng=rng, sample='zero')
self.params = [
self.W_xr, self.W_hr, self.b_r, self.W_xz, self.W_hz, self.b_z,
self.W_xh, self.W_hh, self.b_h, self.W_hy, self.b_y
]
def step_lstm(x_t, h_tm1):
r_t = T.nnet.sigmoid(
T.dot(x_t, self.W_xr) + T.dot(h_tm1, self.W_hr) + self.b_r)
z_t = T.nnet.sigmoid(
T.dot(x_t, self.W_xz) + T.dot(h_tm1, self.W_hz) + self.b_z)
h_t = T.tanh(
T.dot(x_t, self.W_xh) + T.dot((r_t * h_tm1), self.W_hh) +
self.b_h)
hh_t = z_t * h_t + (1 - z_t) * h_tm1
y_t = T.dot(hh_t, self.W_hy) + self.b_y
return [hh_t, y_t]
h0 = shared(np.zeros(shape=(batch_size, self.n_lstm),
dtype=dtype)) # initial hidden state
#(1, 0, 2) -> AxBxC to BxAxC
#(batch_size,sequence_length, n_in) >> (sequence_length, batch_size ,n_in)
#T.dot(x_t, self.W_xi)x_t=(sequence_length, batch_size ,n_in), W_xi= [self.n_in, self.n_lstm]
[h_vals,
y_vals], _ = theano.scan(fn=step_lstm,
sequences=rnn_input.dimshuffle(1, 0, 2),
outputs_info=[h0, None])
self.output = y_vals.dimshuffle(1, 0, 2)
self.params = c1.params + c2.params + c3.params + h1.params + self.params
cost = get_err_fn(self, cost_function, Y)
_optimizer = optimizer(cost, self.params, lr=lr)
self.train = theano.function(inputs=[X, Y, is_train],
outputs=cost,
updates=_optimizer.getUpdates(),
allow_input_downcast=True)
self.predictions = theano.function(inputs=[X, is_train],
outputs=self.output,
allow_input_downcast=True)
self.n_param = count_params(self.params)
def __init__(self,
input_size=(1, 28, 28),
activation_type=ActivationType.ReLU,
hidden_size=50,
output_size=10):
# basic paramters
self.__activation_type__ = activation_type
self.params = {}
self.layers = []
# set layers
channel_num = input_size[0]
for i, param in enumerate([
{
'filter_num': 16,
'filter_size': 3,
'pad': 1,
'stride': 1
},
{
'filter_num': 16,
'filter_size': 3,
'pad': 1,
'stride': 1
},
{
'filter_num': 32,
'filter_size': 3,
'pad': 1,
'stride': 1
},
{
'filter_num': 32,
'filter_size': 3,
'pad': 2,
'stride': 1
},
{
'filter_num': 64,
'filter_size': 3,
'pad': 1,
'stride': 1
},
{
'filter_num': 64,
'filter_size': 3,
'pad': 1,
'stride': 1
},
{
'pre_node_num': 64 * 4 * 4,
'next_node_num': hidden_size
},
{
'pre_node_num': hidden_size,
'next_node_num': output_size
},
]):
# layer 1 ~ 6 Convolution Layer & ReLU Layer
if i + 1 in range(1, 7):
# create convolution layer
convolution_layer = ConvolutionLayer(
index=i + 1,
activation_type=self.__activation_type__,
filter_num=param['filter_num'],
channel_num=channel_num,
filter_size=param['filter_size'],
stride=param['stride'],
padding=param['pad'])
self.layers.append(convolution_layer)
self.layers.append(
self.activationLayerFromType(activation_type, index=i + 1))
# layer 2, 4, 6 Pooling Layer
if i + 1 in (2, 4, 6):
self.layers.append(
PoolingLayer(index=i + 1, pool_h=2, pool_w=2,
stride=2))
# update next channel num
channel_num = convolution_layer.filter_num
layer = convolution_layer
# layer 7, 8 Hidden Layer & ReLU Layer & Dropout Layer
if i + 1 in (7, 8):
hidden_layer = HiddenLayer(
index=i + 1,
activation_type=self.__activation_type__,
pre_node_num=param['pre_node_num'],
next_node_num=param['next_node_num'])
self.layers.append(hidden_layer)
if i + 1 == 7:
self.layers.append(
self.activationLayerFromType(activation_type,
index=i + 1))
self.layers.append(DropoutLayer(index=i + 1,
dropout_ratio=0.5))
layer = hidden_layer
# set W,b
self.params['W{}'.format(i + 1)] = layer.W
self.params['b{}'.format(i + 1)] = layer.b
print('layer {} created'.format(i + 1))
if Config.IS_DEBUG:
print('W{} shape : {}'.format(
i + 1, self.params['W{}'.format(i + 1)].shape))
print('b{} shape : {}'.format(
i + 1, self.params['W{}'.format(i + 1)].shape))
# output created layer structures
for layer in self.layers:
print(layer.name)
# keep weight required layer indexes
self.weight_layer_indexes = []
for j, layer in enumerate(self.layers):
if isinstance(layer, (ConvolutionLayer, HiddenLayer)):
self.weight_layer_indexes.append(j)
self.debug('weight_layer_indexes {}'.format(self.weight_layer_indexes))
print('{} layers created'.format(len(self.layers)))
# last layer SoftmaxWithLoss Layer
self.lastLayer = SoftmaxWithLossLayer()
BranchedLayer([None, ConvLayer(64, [1, 7])]),
BranchedLayer([None, ConvLayer(96, 3, padding='valid')]),
MergeLayer(axis=3),
BranchedLayer([
ConvLayer(192, 3, strides=2, padding='valid'),
MaxPoolLayer(3, strides=2, padding='valid')
]),
MergeLayer(axis=3),
*([inception_a] * args.na), # x4
ConvLayer(1024, 3, strides=2), # reduction_a
*([inception_b] * args.nb), # x7
ConvLayer(1536, 3, strides=2), # reduction_b
*([inception_c] * args.nc), # x3
GlobalAvgPoolLayer(),
FlattenLayer(),
DropoutLayer(rate=args.drop_prob)
]
data_params = {
'na': args.na,
'nb': args.nb,
'nc': args.nc,
'batch_norm': batch_norm,
'drop_prob': args.drop_prob,
'augmentation': True
}
cnn = CNN(layers,
n_classes=n_classes,
batch_size=128,
l2_lambda=args.l2_lambda,
model.add_layer(
Convolution(32, (3, 3),
input_shape=(batch_size, X_tr.shape[1], X_tr.shape[2],
X_tr.shape[3]),
weight_initializer=NormalInitializer(std)))
model.add_layer(ReLuActivation())
model.add_layer(BatchNormalization())
model.add_layer(
Convolution(32, (3, 3),
weight_initializer=NormalInitializer(std),
padding='same'))
model.add_layer(ReLuActivation())
model.add_layer(MaxPool((2, 2)))
model.add_layer(Flatten())
model.add_layer(
Affine(100, weight_initializer=NormalInitializer(std), reg=reg))
model.add_layer(ReLuActivation())
model.add_layer(DropoutLayer(drop_rate=0.3))
model.add_layer(
Affine(n_classes, weight_initializer=NormalInitializer(std), reg=reg))
model.initialize(loss=CrossEntropyLoss(),
optimizer=Adam(learning_rate=0.001,
decay_fst_mom=0.9,
decay_sec_mom=0.999))
# with open('model_90_49.14262959724404', 'rb') as file:
# model = pickle.load(file)
model.fit(batch_size, X_tr, y_tr, n_epochs=100, metric=accuracy_metric)
def __init__(self, config):
ModelBase.__init__(self)
self.config = config
self.verbose = self.config['verbose']
self.name = 'alexnet'
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
n_softmax_out = config['n_softmax_out']
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4('x')
y = T.lvector('y')
rand = T.fvector('rand')
lr = T.scalar('lr')
if self.verbose: print 'AlexNet 2/16'
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x,
image_shape=(3, 256, 256, batch_size),
cropsize=227,
rand=rand,
mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(input=layer1_input,
image_shape=(3, 227, 227, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4,
padsize=0,
group=1,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=True,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(96, 27, 27, batch_size),
filter_shape=(96, 5, 5, 256),
convstride=1,
padsize=2,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.1,
lrn=True,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(256, 13, 13, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1,
padsize=1,
group=1,
poolsize=1,
poolstride=0,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1,
padsize=1,
group=2,
poolsize=1,
poolstride=0,
bias_init=0.1,
lrn=False,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1,
padsize=1,
group=2,
poolsize=3,
poolstride=2,
bias_init=0.0,
lrn=False,
lib_conv=lib_conv,
verbose=self.verbose)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input,
n_in=9216,
n_out=4096,
verbose=self.verbose)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output,
n_in=4096,
n_out=4096,
verbose=self.verbose)
fc_layer7 = FCLayer(input=dropout_layer6.output,
n_in=4096,
n_out=4096,
verbose=self.verbose)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output,
n_in=4096,
n_out=4096,
verbose=self.verbose)
softmax_layer8 = SoftmaxLayer(input=dropout_layer7.output,
n_in=4096,
n_out=n_softmax_out,
verbose=self.verbose)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.p_y_given_x = softmax_layer8.p_y_given_x
self.y_pred = softmax_layer8.y_pred
self.output = self.p_y_given_x
self.cost = softmax_layer8.negative_log_likelihood(y)
self.error = softmax_layer8.errors(y)
if n_softmax_out < 5:
self.error_top_5 = softmax_layer8.errors_top_x(y, n_softmax_out)
else:
self.error_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
# inputs
self.x = x
self.y = y
self.rand = rand
self.lr = lr
self.shared_x = theano.shared(
np.zeros(
(3, config['input_width'], config['input_height'],
config['file_batch_size']), # for loading large batch
dtype=theano.config.floatX),
borrow=True)
self.shared_y = theano.shared(np.zeros((config['file_batch_size'], ),
dtype=int),
borrow=True)
self.shared_lr = theano.shared(np.float32(config['learning_rate']))
# training related
self.base_lr = np.float32(config['learning_rate'])
self.step_idx = 0
self.mu = config['momentum'] # def: 0.9 # momentum
self.eta = config['weight_decay'] #0.0002 # weight decay
self.weight_types = weight_types
self.batch_size = batch_size
self.grads = T.grad(self.cost, self.params)
subb_ind = T.iscalar('subb') # sub batch index
#print self.shared_x[:,:,:,subb_ind*self.batch_size:(subb_ind+1)*self.batch_size].shape.eval()
self.subb_ind = subb_ind
self.shared_x_slice = self.shared_x[:, :, :, subb_ind *
self.batch_size:(subb_ind + 1) *
self.batch_size]
self.shared_y_slice = self.shared_y[subb_ind *
self.batch_size:(subb_ind + 1) *
self.batch_size]
def __init__(self,rng,params,cost_function='mse',optimizer = RMSprop):
lr=params["lr"]
n_lstm=params['n_hidden']
n_out=params['n_output']
batch_size=params["batch_size"]
sequence_length=params["seq_length"]
X = T.tensor3() # batch of sequence of vector
Y = T.tensor3() # batch of sequence of vector
is_train = T.iscalar('is_train') # pseudo boolean for switching between training and prediction
#CNN global parameters.
subsample=(1,1)
p_1=0.5
border_mode="valid"
cnn_batch_size=batch_size*sequence_length
pool_size=(2,2)
#Layer1: conv2+pool+drop
filter_shape=(64,1,9,9)
input_shape=(cnn_batch_size,1,120,60) #input_shape= (samples, channels, rows, cols)
input= X.reshape(input_shape)
c1=ConvLayer(rng, input,filter_shape, input_shape,border_mode,subsample, activation=nn.relu)
p1=PoolLayer(c1.output,pool_size=pool_size,input_shape=c1.output_shape)
dl1=DropoutLayer(rng,input=p1.output,prob=p_1,is_train=is_train)
#Layer2: conv2+pool
filter_shape=(128,p1.output_shape[1],3,3)
c2=ConvLayer(rng, dl1.output, filter_shape,p1.output_shape,border_mode,subsample, activation=nn.relu)
p2=PoolLayer(c2.output,pool_size=pool_size,input_shape=c2.output_shape)
#Layer3: conv2+pool
filter_shape=(128,p2.output_shape[1],3,3)
c3=ConvLayer(rng, p2.output,filter_shape,p2.output_shape,border_mode,subsample, activation=nn.relu)
p3=PoolLayer(c3.output,pool_size=pool_size,input_shape=c3.output_shape)
#Layer4: hidden
n_in= reduce(lambda x, y: x*y, p3.output_shape[1:])
x_flat = p3.output.flatten(2)
h1=HiddenLayer(rng,x_flat,n_in,1024,activation=nn.relu)
n_in=1024
rnn_input = h1.output.reshape((batch_size,sequence_length, n_in))
#Layer5: LSTM
self.n_in = n_in
self.n_lstm = n_lstm
self.n_out = n_out
self.W_hy = init_weight((self.n_lstm, self.n_out), rng=rng,name='W_hy', sample= 'glorot')
self.b_y = init_bias(self.n_out,rng=rng, sample='zero')
layer1=LSTMLayer(rng,0,self.n_in,self.n_lstm)
self.params = layer1.params
self.params.append(self.W_hy)
self.params.append(self.b_y)
def step_lstm(x_t,h_tm1,c_tm1):
[h_t,c_t,y_t]=layer1.run(x_t,h_tm1,c_tm1)
y = T.dot(y_t, self.W_hy) + self.b_y
return [h_t,c_t,y]
H = T.matrix(name="H",dtype=dtype) # initial hidden state
C = T.matrix(name="C",dtype=dtype) # initial hidden state
[h_t,c_t,y_vals], _ = theano.scan(fn=step_lstm,
sequences=[rnn_input.dimshuffle(1,0,2)],
outputs_info=[H, C, None])
self.output = y_vals.dimshuffle(1,0,2)
self.params =c1.params+c2.params+c3.params+h1.params+self.params
cost=get_err_fn(self,cost_function,Y)
L2_reg=0.0001
L2_sqr = theano.shared(0.)
for param in self.params:
L2_sqr += (T.sum(param ** 2))
cost += L2_reg*L2_sqr
_optimizer = optimizer(cost, self.params, lr=lr)
self.train = theano.function(inputs=[X,Y,is_train,H,C],outputs=[cost,h_t[-1],c_t[-1]],updates=_optimizer.getUpdates(),allow_input_downcast=True)
self.predictions = theano.function(inputs = [X,is_train,H,C], outputs = [self.output,h_t[-1],c_t[-1]],allow_input_downcast=True)
self.n_param=count_params(self.params)
def set_dropout_on(self):
DropoutLayer.SetDropoutOn()
def validate_performance(config):
# UNPACK CONFIGS
(flag_para_load, train_filenames, val_filenames, train_labels, val_labels,
img_mean) = unpack_configs(config)
if flag_para_load:
# pycuda and zmq set up
drv.init()
dev = drv.Device(int(config['gpu'][-1]))
ctx = dev.make_context()
sock = zmq.Context().socket(zmq.PAIR)
sock.connect('tcp://localhost:{0}'.format(config['sock_data']))
load_send_queue = config['queue_t2l']
load_recv_queue = config['queue_l2t']
else:
load_send_queue = None
load_recv_queue = None
import theano.sandbox.cuda
theano.sandbox.cuda.use(config['gpu'])
import theano
theano.config.on_unused_input = 'warn'
from layers import DropoutLayer
from alex_net import AlexNet, compile_models
import theano.misc.pycuda_init
import theano.misc.pycuda_utils
# # BUILD NETWORK ##
model = AlexNet(config)
layers = model.layers
batch_size = model.batch_size
# # COMPILE FUNCTIONS ##
(train_model, validate_model, train_error, learning_rate, shared_x,
shared_y, rand_arr, vels) = compile_models(model, config, flag_top_5=True)
print '... training'
if flag_para_load:
# pass ipc handle and related information
gpuarray_batch = theano.misc.pycuda_utils.to_gpuarray(
shared_x.container.value)
h = drv.mem_get_ipc_handle(gpuarray_batch.ptr)
sock.send_pyobj((gpuarray_batch.shape, gpuarray_batch.dtype, h))
load_send_queue.put(img_mean)
load_epoch = config['load_epoch']
load_weights(layers, config['weights_dir'], load_epoch)
DropoutLayer.SetDropoutOff()
this_validation_error, this_validation_error_top_5, this_validation_loss = \
get_val_error_loss(rand_arr, shared_x, shared_y,
val_filenames, val_labels,
flag_para_load,img_mean,
batch_size, validate_model,
send_queue=load_send_queue,
recv_queue=load_recv_queue,
flag_top_5=True)
print('validation error %f %%' % (this_validation_error * 100.))
print('top 5 validation error %f %%' %
(this_validation_error_top_5 * 100.))
print('validation loss %f ' % (this_validation_loss))
return this_validation_error, this_validation_loss
def main_graph(self,
trained_model,
scope,
emb_dim,
cell,
rnn_dim,
rnn_num,
drop_out=0.5,
emb=None):
if trained_model is not None:
param_dic = {
'nums_chars': self.nums_chars,
'nums_tags': self.nums_tags,
'crf': self.crf,
'emb_dim': emb_dim,
'cell': cell,
'rnn_dim': rnn_dim,
'rnn_num': rnn_num,
'drop_out': drop_out,
'buckets_char': self.buckets_char,
'ngram': self.ngram,
'is_space': self.is_space,
'sent_seg': self.sent_seg,
'emb_path': self.emb_path,
'tag_scheme': self.tag_scheme
}
#print param_dic
f_model = open(trained_model, 'w')
pickle.dump(param_dic, f_model)
f_model.close()
# define shared weights and variables
batch_size_h = tf.placeholder(tf.int32, [], name='batch_size_holder')
dr = tf.placeholder(tf.float32, [], name='drop_out_holder')
self.batch_size_h = batch_size_h
self.drop_out = dr
self.drop_out_v = drop_out
# pdb.set_trace()
self.emb_layer = EmbeddingLayer(self.nums_chars + 20,
emb_dim,
weights=emb,
name='emb_layer')
if self.ngram is not None:
ng_embs = [None for _ in range(len(self.ngram))]
for i, n_gram in enumerate(self.ngram):
self.gram_layers.append(
EmbeddingLayer(n_gram + 5000 * (i + 2),
emb_dim,
weights=ng_embs[i],
name=str(i + 2) + 'gram_layer'))
with tf.variable_scope('BiRNN'):
if cell == 'gru':
fw_rnn_cell = tf.contrib.rnn.GRUCell(rnn_dim) #forward
bw_rnn_cell = tf.contrib.rnn.GRUCell(rnn_dim) #backward
else:
fw_rnn_cell = tf.contrib.rnn.LSTMCell(rnn_dim,
state_is_tuple=True)
bw_rnn_cell = tf.contrib.rnn.LSTMCell(rnn_dim,
state_is_tuple=True)
if rnn_num > 1:
fw_rnn_cell = tf.contrib.rnn.MultiRNNCell([fw_rnn_cell] *
rnn_num,
state_is_tuple=True)
bw_rnn_cell = tf.contrib.rnn.MultiRNNCell([bw_rnn_cell] *
rnn_num,
state_is_tuple=True)
output_wrapper = HiddenLayer(rnn_dim * 2,
self.nums_tags,
activation='linear',
name='hidden')
#define model for each bucket
for idx, bucket in enumerate(self.buckets_char):
if idx == 1:
scope.reuse_variables()
t1 = time()
batch_size = self.real_batches[idx]
input_v1 = tf.placeholder(tf.int32, [None, bucket],
name='input_1' + str(bucket))
input_v2 = tf.placeholder(tf.int32, [None, bucket],
name='input_2' + str(bucket))
self.input_v1.append([input_v1])
self.input_v2.append([input_v2])
#output = None
output = []
for i in range(self.num_gpus):
with tf.device('/gpu:{}'.format(i)):
input_1 = input_v1[i * batch_size_h:(i + 1) * batch_size_h]
input_2 = input_v2[i * batch_size_h:(i + 1) * batch_size_h]
emb_set1 = []
emb_set2 = []
word_out1 = self.emb_layer(input_1)
word_out2 = self.emb_layer(input_2)
emb_set1.append(word_out1)
emb_set2.append(word_out2)
# if self.ngram is not None:
# for i in range(len(self.ngram)):
# input_g = tf.placeholder(tf.int32, [None, bucket], name='input_g' + str(i) + str(bucket))
# self.input_v[-1].append(input_g)
# gram_out = self.gram_layers[i](input_g)
# emb_set.append(gram_out)
if len(emb_set1) > 1:
emb_out1 = tf.concat(axis=2, values=emb_set1)
emb_out2 = tf.concat(axis=2, values=emb_set2)
else:
emb_out1 = emb_set1[0]
emb_out2 = emb_set2[0]
emb_out1 = DropoutLayer(dr)(emb_out1)
emb_out2 = DropoutLayer(dr)(emb_out2)
rnn_out = BiLSTM(rnn_dim,
fw_cell=fw_rnn_cell,
bw_cell=bw_rnn_cell,
p=dr,
name='BiLSTM' + str(bucket),
scope='BiRNN')(emb_out1, emb_out2,
input_v1)
output_g = output_wrapper(rnn_out)
# if output == None:
# output = output_g
# else:
# output = tf.concat([output,output_g],axis = 0)
#pdb.set_trace()
output.append(output_g)
self.output.append([output])
self.output_.append([
tf.placeholder(tf.int32, [None, bucket - 1],
name='tags' + str(bucket))
])
self.bucket_dit[bucket] = idx
print 'Bucket %d, %f seconds' % (idx + 1, time() - t1)
assert len(self.input_v1) == len(self.output)
self.params = tf.trainable_variables()
self.saver = tf.train.Saver()
def __init__(self, config):
self.config = config
batch_size = config['batch_size']
flag_datalayer = config['use_data_layer']
lib_conv = config['lib_conv']
# ##################### BUILD NETWORK ##########################
# allocate symbolic variables for the data
# 'rand' is a random array used for random cropping/mirroring of data
x = T.ftensor4('x')
y = T.ivector('y')
rand = T.fvector('rand')
print '... building the model'
self.layers = []
params = []
weight_types = []
if flag_datalayer:
data_layer = DataLayer(input=x, image_shape=(3, 256, 256,
batch_size),
cropsize=227, rand=rand, mirror=True,
flag_rand=config['rand_crop'])
layer1_input = data_layer.output
else:
layer1_input = x
convpool_layer1 = ConvPoolLayer(input=layer1_input,
image_shape=(3, 227, 227, batch_size),
filter_shape=(3, 11, 11, 96),
convstride=4, padsize=0, group=1,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=True,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer1)
params += convpool_layer1.params
weight_types += convpool_layer1.weight_type
convpool_layer2 = ConvPoolLayer(input=convpool_layer1.output,
image_shape=(96, 27, 27, batch_size),
filter_shape=(96, 5, 5, 256),
convstride=1, padsize=2, group=2,
poolsize=3, poolstride=2,
bias_init=0.1, lrn=True,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer2)
params += convpool_layer2.params
weight_types += convpool_layer2.weight_type
convpool_layer3 = ConvPoolLayer(input=convpool_layer2.output,
image_shape=(256, 13, 13, batch_size),
filter_shape=(256, 3, 3, 384),
convstride=1, padsize=1, group=1,
poolsize=1, poolstride=0,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer3)
params += convpool_layer3.params
weight_types += convpool_layer3.weight_type
convpool_layer4 = ConvPoolLayer(input=convpool_layer3.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 384),
convstride=1, padsize=1, group=2,
poolsize=1, poolstride=0,
bias_init=0.1, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer4)
params += convpool_layer4.params
weight_types += convpool_layer4.weight_type
convpool_layer5 = ConvPoolLayer(input=convpool_layer4.output,
image_shape=(384, 13, 13, batch_size),
filter_shape=(384, 3, 3, 256),
convstride=1, padsize=1, group=2,
poolsize=3, poolstride=2,
bias_init=0.0, lrn=False,
lib_conv=lib_conv,
)
self.layers.append(convpool_layer5)
params += convpool_layer5.params
weight_types += convpool_layer5.weight_type
fc_layer6_input = T.flatten(
convpool_layer5.output.dimshuffle(3, 0, 1, 2), 2)
fc_layer6 = FCLayer(input=fc_layer6_input, n_in=9216, n_out=4096)
self.layers.append(fc_layer6)
params += fc_layer6.params
weight_types += fc_layer6.weight_type
dropout_layer6 = DropoutLayer(fc_layer6.output, n_in=4096, n_out=4096)
fc_layer7 = FCLayer(input=dropout_layer6.output, n_in=4096, n_out=4096)
self.layers.append(fc_layer7)
params += fc_layer7.params
weight_types += fc_layer7.weight_type
dropout_layer7 = DropoutLayer(fc_layer7.output, n_in=4096, n_out=4096)
softmax_layer8 = SoftmaxLayer(
input=dropout_layer7.output, n_in=4096, n_out=1000)
self.layers.append(softmax_layer8)
params += softmax_layer8.params
weight_types += softmax_layer8.weight_type
# #################### NETWORK BUILT #######################
self.cost = softmax_layer8.negative_log_likelihood(y)
self.errors = softmax_layer8.errors(y)
self.errors_top_5 = softmax_layer8.errors_top_x(y, 5)
self.params = params
self.x = x
self.y = y
self.rand = rand
self.weight_types = weight_types
self.batch_size = batch_size
