def model_fn(features,labels,mode,params):
def cnn_encoder(inputs,reuse=False,training=True):
inputs = tf.expand_dims(inputs,axis=-1)
with tf.variable_scope("cnn_encoder",reuse=reuse) \
as cnnscope:
conv = tf.layers.conv1d(inputs=inputs,
reuse=reuse,
filters=32,
kernel_size=[2,3,4],
padding="same",
activation=tf.nn.relu)
pool = tf.reduce_max(input_tensor=conv,axis=-1)
dropout_pool = tf.layers.dropout(inputs=pool,
rate=0.2,training=training)
return dropout_pool
def lstm_encoder(inputs,reuse=True,training=True):
initializer = tf.contrib.layers.xavier_initializer()
lstm = tf.nn.rnn_cell.LSTMCell(300,initializer=initializer)
with tf.variable_scope("lstm_encoder",reuse=reuse) \
as lstmscope:
outputs,_ = tf.nn.bidirectional_dynamic_rnn(lstm,lstm,
inputs,
dtype=tf.float32,
scope=lstmscope)
output_fw,output_bw = outputs
concate_output = tf.concat([outputfw,output_bw],axis=-1)
return concate_output
embeddings = tf.Variable(params["embedding_initializer"],trainable=False)
sent1_embed = tf.nn.embedding_lookup(embeddings,features['sent1'])
sent2_embed = tf.nn.embedding_lookup(embeddings,features['sent2'])
training = (mode == tf.estimator.ModeKeys.TRAIN)
sent1_embed_drop = tf.layers.dropout(inputs=sent1_embed,\
rate=0.2,
training=training)
sent2_embed_drop = tf.layers.dropout(inputs=sent2_embed,
rate=0.2,
training=training)
sent1_cnn = cnn_encoder(sent1_embed_drop,False,training)
sent2_cnn = cnn_encoder(sent2_embed_drop,True,training)
sent1_lstm = lstm_encoder(sent1_embed_drop,False,training)
sent2_lstm = lstm_encoder(sent2_embed_drop,True,training)
concat_embed = tf.concate([sent1_cnn,sent1_embed,sent2_cnn,sent2_embed])
logits = tf.layers.dense(concat_embed,3,activation=tf.nn.relu)
if labels is not None:
labels = tf.reshape(labels,[-1,1])
optimizer = tf.train.AdamOptimizer()
def _train_op_fn(loss):
return optimizer.minimize(loss,tf.train.get_global_step())
return head.create_estimator_spec(
features=features,
labels=labels,
mode=mode,
logits=logits,
train_op_fn=_train_op_fn)
def call(self, words, features, hidden, object_seq):
'''
words shape: (batch_size, 1)
features shape: (batch_size, 64, embedding_dim)
object_seq: (batch_size, max_object_num)
'''
# apply attention later
# context_vector, attention_weights = self.attention(features, hidden)
# get the word embedding presentation
x = self.embedding(words) # ( batch_size, 1, embedding_dim)
# integrating knowledge
# (batch_size, max_object_num, embedding_dim)
object_embedding = self.embedding(object_seq)
similar_object_index = self.get_similar_object(
object_embedding, x) # (batch, 1, embedding_dim)
external_know = np.zeros(x.shape[0], 1)
for i, obj in enumerate(similar_object_index):
obj = self.tokenizer.index_word[object_seq[i, obj]]
scene, _ = get_knowledge(obj)
if(len(scene_info) != 0):
# no external knowledge
external_know[i] = self.tokenizer.word_index[scene[0]]
external_know = self.embedding(
external_know) # (batch, 1, embedding_dim)
external_know = tf.reshape(
external_know, (-1, external_know.shape[-1]))
# apply GRU
# output: (batch_size, 1, units) state: batch_size, units
output, state = self.gru(x)
# apply sentinel
s_t = self.sentinel(x, output, state) # (batch_size, units)
# apply attention
context_vector, attention_weights, beta_t = self.attention(
features, output, s_t) # context_vector: (batch_size, units)
# calculate c_hat_t
beta_t = tf.expand_dims(beta_t, axis=-1)
context_vector_new = beta_t * s_t + \
(1 - beta_t) * context_vector # (batch_size, units)
# alignment
x = tf.concat([tf.expand_dims(context_vector_new, 1),
output], axis=-1) # (batch_size, 1, units)
x = self.fc1(output) # (batch_size, max_length, units)
x = tf.reshape(x, (-1, x.shape[2])) # (batch_size * max_length, units)
x = self.fc2(tf.concate([x, external_know], axis=-1))
return x, output, attention_weights
def call(self, inputs, encoder_hidden_state, encoder_states, train=False):
if train:
initial_states = encoder_hidden_state
all_states = []
batch_size = initial_states[0][0].shape[0]
output = []
for current_word_index in range(inputs.shape[1]):
current_word = inputs[:, current_word_index]
current_word = tf.expand_dims(current_word, 1)
all_state = self.embedding(current_word)
current_initial_state = []
for lstm_layer, initial_state, ecoder_state in zip(
self.lstm_layers, initial_states, encoder_states):
context_vec = self.attention(
(tf.expand_dims(initial_state[0], 1), encoder_state),
0)
all_state = tf.concate([all_state, context_vec])
####### fix this
all_state, h, c = lstm_layer(all_state,
initial_state=initial_state)
current_initial_state.append((h, c))
context_vec = self.attention(
(tf.expand_dims(h, 1), encoder_states), 0)
all_state = tf.concat([context_vec, all_state], 2)
current_word = self.fully_connected(all_state)
output.append(current_word)
initial_states = current_initial_state
output = tf.concat(output, 1)
return output
else:
initial_states = encoder_hidden_state
all_states = []
batch_size = initial_states[0][0].shape[0]
current_word = np.ones((batch_size, 1))
output = []
for _ in range(self.max_length - 1):
all_state = self.embedding(current_word)
current_initial_state = []
for lstm_layer, initial_state in zip(self.lstm_layers,
initial_states):
all_state, h, c = lstm_layer(all_state,
initial_state=initial_state)
current_initial_state.append((h, c))
context_vec = self.attention(
(tf.expand_dims(h, 1), encoder_states), 0)
all_state = tf.concat([context_vec, all_state], 2)
current_word = self.fully_connected(all_state)
current_word = tf.argmax(current_word, axis=2)
output.append(current_word)
initial_states = current_initial_state
output = tf.concat(output, 1)
return output
def __init__(self, W_embedding, setting):
self.model_name = settings.model_name
self.title_len = settings.title_len
self.content_len = settings.content_len
self.filter_sizes = settings.filter_sizes
self.n_filter = settings.n_filter
self.n_filter_total = self.n_filter * len(self.filter_sizes)#256*5
self.n_class = settings.n_class
self.fc_hidden_size = settings.fc_hidden_size
self._global_step=tf.Variable(0,trainable=False,name='Global_Step')
self.update_emas=list()
#placeholders
self._tst=tf.placeholder(tf.bool)
self._keep_prob=tf.placeholder(tf.float32,[])
self._batch_size=tf.placeholder(tf.int32,[])
with tf.name_scope('Inputs'):
self._X1_inputs=tf.placeholder(tf.int64,[None,self.title_len],name='X1_inputs')
self._X2_inputs=tf.placeholder(tf.int64,[None,self.content_len],name='X2_inputs')
self._y_inputs=tf.placeholder(tf.float32,[None,self.n_class],name='y_inputs')
with tf.variable_scope('embedding'):
self.embedding=tf.get_variable(name='embedding',shape=W_embedding,
initializer=tf.constant_initializer(W_embedding),trainable=True)
self.embedding_size=W_embedding.shape[1]#1024
with tf.variable_scope('cnn_text'):
output_title=self.cnn_inference(self._X1_inputs, self.title_len)
with tf.variable_scope('cnn_content'):
output_content=self.cnn_inference(self._X2_inputs, self.content_len)
with tf.variable_scope('fc_bn_layer'):
output=tf.concate([output_title,output_content],axis=1)#batch_size*2560
W_fc=self.weight_variable([2*self.n_filter_total,self.fc_hidden_size],name='Weight_fc')
tf.summary.histogram('Weight_fc',W_fc)
h_fc=tf.matmul(output,W_fc,name='h_fc')#batch_size*fc_hidden_size
beta_fc=tf.Variable(tf.constant(0.1,tf.float32,shape=[self.fc_hidden_size],name='beta_fc'))
tf.summary.histogram('beta_fc',beta_fc)
fc_bn,update_ema_fc=self.batchnorm(h_fc,beta_fc,convolutional=False)
self.update_emas.append(update_ema_fc)
self.fc_bn_relu=tf.nn.relu(fc_bn,name='relu')
fc_bn_drop=tf.nn.dropout(self.fc_bn_relu,self.keep_prob)
with tf.variable_scope('out_layer'):
W_out=self.weight_variable([self.fc_hidden_size,self.n_class],name='Weight_out')
tf.summary.histogram('Weight_out',W_out)
b_out=self.bias_variable([self.n_class],name='bias_out')
tf.summary.histogram('bias_out',b_out)
self._y_pred=tf.nn.xw_plus_b(fc_bn_drop,W_out,b_out,name='y_pred')
with tf.name_scope('loss'):
self._loss=tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=self._y_pred,labels=self._y_inputs))
tf.summary.scalar('loss',self._loss)
self.saver=tf.train.Saver(max_to_keep=2)
def decode_bbox(self, conv_bbox, anchors, stride):
conv_shape = tf.shape(conv_bbox)
batch_size = conv_shape[0]
output_size = conv_shape[1]
anchor_per_scale = len(anchors)
conv_bbox = tf.reshape(conv_bbox,
(batch_size, output_size, output_size,
anchor_per_scale, 5 + self.num_class))
conv_bbox_raw_xy = conv_bbox[:, :, :, :, 0:2]
conv_bbox_raw_hw = conv_bbox[:, :, :, :, 2:4]
conv_bbox_raw_conf = conv_bbox[:, :, :, :, 4:5]
conv_bbox_raw_prob = conv_bbox[:, :, :, :, 5:]
#x = tf.tile(tf.range(13, dtype=tf.int32)[:, tf.newaxis], [1,13])
x = tf.range(13, dtype=tf.int32)
x = tf.expand_dims(x, axis=-1)
x = tf.tile(x, [1, 13])
#y = tf.tile(tf.range(13, dtype=tf.int32)[:, tf.newaxis], [1,13])
y = tf.range(13, dtype=tf.int32)
y = tf.expand_dims(y, axis=-1)
y = tf.tile(y, [1, 13])
#xy_grid = tf.concat([x[:, :, tf.newaxis], y[:, :, tf.newaxis]], axis=-1)
xy_grid = tf.concate(
[tf.expand_dims(x, axis=-1),
tf.expand_dims(y, axis=-1)], axis=-1)
xy_grid = tf.tile(xy_grid[tf.newaxis, :, :, tf.newaxis, :],
[batch_size, 1, 1, anchor_per_scale, 1])
xy_grid = tf.cast(xy_grid, tf.float32)
pred_xy = (tf.sigmoid(conv_bbox_raw_xy) + xy_grid) * stride
pred_hw = (tf.exp(conv_bbox_raw_hw) * anchors) * stride
pred_xywh = tf.concat([pred_xy, pred_hw], axis=-1)
pred_conf = tf.sigmoid(conv_bbox_raw_conf)
pred_prob = tf.sigmoid(conv_bbox_raw_prob)
result = tf.concat([pred_xywh, pred_conf, pred_prob], axis=-1)
return result
def run_head(self, proposals, stage):
"""
Args:
proposals: BoxProposals
stage: 0, 1, 2
Returns:
FastRCNNHead
Nx4, updated boxes
"""
reg_weights = tf.constant(cfg.CASCADE.BBOX_REG_WEIGHTS[stage],
dtype=tf.float32) # 创建cascade的权重，是持久化常量浮点数
pooled_feature = self.roi_func(proposals.boxes) # N,C,S,S
# FIXME
if roi_func_extra != None:
pooled_feature = tf.concate(
[pooled_feature,
self.roi_func_extra(proposals.boxes)], 0)
pooled_feature = self.scale_gradient(pooled_feature) # 这里不太理解为什么重新赋值
head_feature = self.fastrcnn_head_func('head', pooled_feature)
# 82-87不太理解.....
# changed by Paul
label_logits, box_logits = fastrcnn_outputs(
'outputs_new',
head_feature,
self.num_classes,
class_agnostic_regression=True)
head = FastRCNNHead(proposals, box_logits, label_logits, self.gt_boxes,
reg_weights)
refined_boxes = head.decoded_output_boxes_class_agnostic()
refined_boxes = clip_boxes(refined_boxes, self.image_shape2d)
# tf.stop_gradient：停止梯度计算；参数 - 张量 + 操作名称
return head, tf.stop_gradient(refined_boxes, name='output_boxes')
def generator(self, z, y=None):
with tensorflow.variable_scope('generator') as scope:
if not self.y_dim:
s_h, s_w = self.output_height, self.output_width
s_h2, s_w2 = conv_out_size_same(s_h,
2), conv_out_size_same(s_w, 2)
s_h4, s_w4 = conv_out_size_same(s_h2, 2), conv_out_size_same(
s_w2, 2)
s_h8, s_w8 = conv_out_size_same(s_h4, 2), conv_out_size_same(
s_w4, 2)
s_h16, s_w16 = conv_out_size_same(s_h8, 2), conv_out_size_same(
s_w8, 2)
self.z_, self.h0_w, self.h0_b = linear(z,
self.gf_dim * 8 *
s_h16 * s_w16,
'g_h0_lin',
with_w=True)
self.h0 = tensorflow.reshape(
self.z_, [-1, s_h16, s_w16, self.gf_dim * 8])
h0 = tensorflow.nn.relu(self.g_bn0(self.h0))
self.h1, self.h1_w, self.h1_b = deconv2d(
h0, [self.batch_size, s_h8, s_w8, self.gf_dim * 4],
name='g_h1',
with_w=True)
h1 = tensorflow.nn.relu(self.g_bn1(self.h1))
h2, self.h2_w, self.h2_b = deconv2d(
h1, [self.batch_size, s_h4, s_w4, self.gf_dim * 2],
name='g_h2',
with_w=True)
h2 = tensorflow.nn.relu(self.g_bn2(h2))
h3, self.h3_w, self.h3_b = deconv2d(
h2, [self.batch_size, s_h2, s_w2, self.gf_dim * 1],
name='g_h3',
with_w=True)
h3 = tensorflow.nn.relu(self.g_bn3(h3))
h4, self.h4_w, self.h4_b = deconv2d(
h3, [self.batch_size, s_h, s_w, self.c_dim],
name='g_h4',
with_w=True)
return tensorflow.nn.tanh(h4)
else:
s_h, s_w = self.output_height, self.output_width
s_h2, s_h4 = int(s_h / 2), int(s_h / 4)
s_w2, s_w4 = int(s_w / 2), int(s_w / 4)
yb = tensorflow.reshape(y, [self.batch_size, 1, 1, self.y_dim])
z = tensorflow.concate([z, y], 1)
h0 = tensorflow.nn.relu(
self.g_bn0(linear(z, self.gfc_dim, 'g_h0_lin')))
h0 = tensorflow.concat([h0, y], 1)
h1 = tensorflow.nn.relu(
self.g_bn1(
linear(h0, self.gf_dim * 2 * s_h4 * s_w4, 'g_h1_lin')))
h1 = tensorflow.reshape(
h1, [self.batch_size, s_h4, s_w4, self.gf_dim * 2])
h1 = conv_conv_concat(h1, yb)
h2 = tensorflow.nn.relu(
self.g_bn2(
deconv2d(
h1, [self.batch_size, s_h2, s_w2, self.gf_dim * 2],
name='g_h2')))
h2 = conv_conv_concat(h2, yb)
return tensorflow.nn.sigmoid(
deconv2d(h2, [self.batch_size, s_h, s_w, self.c_dim],
name='g_h3'))
def __init__(self, user_count, item_count, cate_count, cate_list):
'''
uij=(u, i, y, hist_i, sl)
self.i: uij[1],
self.y: uij[2],
self.hist_i: uij[3],
self.sl: uij[4],
self.lr: l
'''
# self.u = tf.placeholder(tf.int32,[None,],name='user')
self.i = tf.placeholder(tf.int32, [
None,
], name='item') #pos or neg id ,target
self.j = tf.placeholder(tf.int32, [
None,
], name='item_j')
self.y = tf.placeholder(tf.float32, [
None,
], name='label')
self.hist_i = tf.placeholder(tf.int32, [None, None], name='history_i')
self.s1 = tf.placeholder(tf.int32, [
None,
], name='sequence_lenght')
self.lr = tf.placeholder(tf.float64, name='learning_rate')
hidden_units = 32
# user_emb_w = tf.get_variable('user_emb_w',[user_count,hidden_units])
item_emb_w = tf.get_variable('item_emb_w',
[item_count, hidden_units // 2])
item_b = tf.get_variable('item_b', [item_count],
initializer=tf.constant_initializer(0.0))
cate_emb_w = tf.get_variable('cate_emb_w',
[cate_count, hidden_units // 2])
cate_list = tf.convert_to_tensor(cate_list, dtype=tf.int64)
# u_emb=tf.nn.embedding_lookup(user_emb_w,self.u)
# ic 是item到category的转换
self.ic = tf.gather(cate_list, self.i)
i_emb = tf.concate(values=[
tf.nn.embedding_lookup(item_emb_w, self.i),
tf.nn.embedding_lookup(cate_emb_w, self.ic)
],
axis=1)
i_b = tf.gather(item_b, self.i)
self.jc = tf.gather(cate_list, self.j)
j_emb = tf.concate(values=[
tf.nn.embedding_lookup(item_emb_w, self.j),
tf.nn.embedding_lookup(cate_emb_w, self.jc)
],
axis=1)
j_b = tf.gather(item_b, self.j)
self.hc = tf.gather(cate_list, self.hist_i) #相当于user behavior features
h_emb = tf.concat([
tf.nn.embedding_lookup(item_emb_w, self.hist_i),
tf.nn.embedding_lookup(cate_emb_w, self.hc)
],
axis=2)
hist = attention(i_emb, h_emb, self.s1)
hist = tf.layers.batch_normalization(inputs=hist)
hist = tf.reshape(hist, [-1, hidden_units])
hist = tf.layers.dense(hist, hidden_units)
u_emb = hist
# fcn begin
din_i = tf.concat([u_emb, i_emb], axis=-1)
din_i = tf.layers.batch_normalization(inputs=din_i, name='b1')
d_layer_1_i = tf.layers.dense(din_i, 80, activation=None, name='f1')
d_layer_1_i = dice(d_layer_1_i, name='dice_1_i')
d_layer_2_i = tf.layers.dense(d_layer_1_i,
40,
activation=None,
name='f2')
d_layer_2_i = dice(d_layer_2_i, name='dice_2_i')
d_layer_3_i = tf.layers.dense(d_layer_2_i,
1,
activation=None,
name='f3')
din_j = tf.concat([u_emb, j_emb], axis=-1)
din_j = tf.layers.batch_normalization(inputs=din_j,
name='b1',
reuse=True)
d_layer_1_j = tf.layers.dense(din_j,
80,
activation=None,
name='f1',
reuse=True)
d_layer_1_j = dice(d_layer_1_j, name='dice_1_j')
d_layer_2_j = tf.layers.dense(d_layer_1_j,
40,
activation=None,
name='f2',
reuse=True)
d_layer_2_j = dice(d_layer_2_j, name='dice_2_j')
d_layer_3_j = tf.layers.dense(d_layer_2_j,
1,
activation=None,
name='f3',
reuse=True)
d_layer_3_i = tf.reshape(d_layer_3_i, [-1])
d_layer_3_j = tf.reshape(d_layer_3_j, [-1])
x = i_b - j_b + d_layer_3_i - d_layer_3_j # [B]
self.logits = i_b + d_layer_3_i
# logits for all item:
u_emb_all = tf.expand_dims(u_emb, 1)
u_emb_all = tf.tile(u_emb_all, [1, item_count, 1])
all_emb = tf.concat(
[item_emb_w,
tf.nn.embedding_lookup(cate_emb_w, cate_list)],
axis=1)
all_emb = tf.expand_dims(all_emb, 0)
all_emb = tf.tile(all_emb, [512, 1, 1])
din_all = tf.concat([u_emb_all, all_emb], axis=-1)
din_all = tf.layers.batch_normalization(inputs=din_all,
name='b1',
reuse=True)
d_layer_1_all = tf.layers.dense(din_all,
80,
activation=None,
name='f1',
reuse=True)
d_layer_1_all = dice(d_layer_1_all, name='dice_1_all')
d_layer_2_all = tf.layers.dense(d_layer_1_all,
40,
activation=None,
name='f2',
reuse=True)
d_layer_2_all = dice(d_layer_2_all, name='dice_2_all')
d_layer_3_all = tf.layers.dense(d_layer_2_all,
1,
activation=None,
name='f3',
reuse=True)
d_layer_3_all = tf.reshape(d_layer_3_all, [-1, item_count])
self.logits_all = tf.sigmoid(item_b + d_layer_3_all)
# -- fcn end -------
self.mf_auc = tf.reduce_mean(tf.to_float(x > 0))
self.score_i = tf.sigmoid(i_b + d_layer_3_i)
self.score_j = tf.sigmoid(j_b + d_layer_3_j)
self.score_i = tf.reshape(self.score_i, [-1, 1])
self.score_j = tf.reshape(self.score_j, [-1, 1])
self.p_and_n = tf.concat([self.score_i, self.score_j], axis=-1)
# Step variable
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.global_epoch_step = \
tf.Variable(0, trainable=False, name='global_epoch_step')
self.global_epoch_step_op = \
tf.assign(self.global_epoch_step, self.global_epoch_step + 1)
# loss and train
self.loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=self.logits,
labels=self.y))
trainable_params = tf.trainable_variables()
self.train_op = tf.train.GradientDescentOptimizer(
learning_rate=self.lr).minimize(self.loss)
def build_train(make_obs_ph,
q_func,
num_actions,
optimizer,
grad_norm_clipping=None,
gamma=1.0,
double_q=True,
scope="deepq",
reuse=None,
param_noise=False,
param_noise_filter_func=None):
"""Creates the train function:
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInput
a function that takes a name and creates a placeholder of input with that name
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
num_actions: int
number of actions
reuse: bool
whether or not to reuse the graph variables
optimizer: tf.train.Optimizer
optimizer to use for the Q-learning objective.
grad_norm_clipping: float or None
clip gradient norms to this value. If None no clipping is performed.
gamma: float
discount rate.
double_q: bool
if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
In general it is a good idea to keep it enabled.
scope: str or VariableScope
optional scope for variable_scope.
reuse: bool or None
whether or not the variables should be reused. To be able to reuse the scope must be given.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
param_noise_filter_func: tf.Variable -> bool
function that decides whether or not a variable should be perturbed. Only applicable
if param_noise is True. If set to None, default_param_noise_filter is used by default.
Returns
-------
act: (tf.Variable, bool, float) -> tf.Variable
function to select and action given observation.
` See the top of the file for details.
train: (object, np.array, np.array, object, np.array, np.array) -> np.array
optimize the error in Bellman's equation.
` See the top of the file for details.
update_target: () -> ()
copy the parameters from optimized Q function to the target Q function.
` See the top of the file for details.
debug: {str: function}
a bunch of functions to print debug data like q_values.
"""
if param_noise:
act_f = build_act_with_param_noise(
make_obs_ph,
q_func,
num_actions,
scope=scope,
reuse=reuse,
param_noise_filter_func=param_noise_filter_func)
else:
act_f = build_act(make_obs_ph,
q_func,
num_actions,
scope=scope,
reuse=reuse)
with tf.variable_scope(scope, reuse=reuse):
# set up placeholders
obs_t_input = make_obs_ph("obs_t")
act_t_ph = tf.placeholder(tf.int32, [None], name="action")
rew_t_ph = tf.placeholder(tf.float32, [None], name="reward")
obs_tp1_input = make_obs_ph("obs_tp1")
done_mask_ph = tf.placeholder(tf.float32, [None], name="done")
importance_weights_ph = tf.placeholder(tf.float32, [None],
name="weight")
# q network evaluation
q_t, act_idxs_t = q_func(obs_t_input.get(), scope="q_func",
reuse=True) # reuse parameters from act
q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=tf.get_variable_scope().name +
"/q_func")
# target q network evalution
q_tp1, act_idxs_tp1 = q_func(obs_tp1_input.get(),
scope="target_q_func")
target_q_func_vars = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES,
scope=tf.get_variable_scope().name + "/target_q_func")
# q scores for actions which we know were selected in the given state.
q_t_selected = []
#act_t_ph_list = tf.unstack(act_t_ph, 0)
for (idx, value) in enumerate(q_t):
q_t_selected.append(
tf.gather(
value,
tf.where(
tf.equal(act_idxs_t[idx], tf.gather(act_t_ph,
idx)))[0]))
#q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)
q_t_selected = tf.concate(q_t_selected, axis=0)
# compute estimate of best possible value starting from state at t + 1
if double_q:
q_tp1_using_online_net, act_idxs_tp1_using_online_net = q_func(
obs_tp1_input.get(), scope="q_func", reuse=True)
q_tp1_best = []
for (idx, value) in enumerate(q_tp1_using_online_net):
q_tp1_best.append(
tf.gather(q_tp1[idx], tf.argmax(value, axis=0)))
#q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
q_tp1_best = tf.stack(q_tp1_best, axis=0)
else:
q_tp1_best = []
for value in q_tp1:
q_tp1_best.append(tf.reduce_max(value))
q_tp1_best = tf.stack(q_tp1_best, axis=0)
q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked
# compute the error (potentially clipped)
td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
errors = U.huber_loss(td_error)
weighted_error = tf.reduce_mean(importance_weights_ph * errors)
# compute optimization op (potentially with gradient clipping)
if grad_norm_clipping is not None:
gradients = optimizer.compute_gradients(weighted_error,
var_list=q_func_vars)
for i, (grad, var) in enumerate(gradients):
if grad is not None:
gradients[i] = (tf.clip_by_norm(grad,
grad_norm_clipping), var)
optimize_expr = optimizer.apply_gradients(gradients)
else:
optimize_expr = optimizer.minimize(weighted_error,
var_list=q_func_vars)
# update_target_fn will be called periodically to copy Q network to target Q network
update_target_expr = []
for var, var_target in zip(
sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_expr.append(var_target.assign(var))
update_target_expr = tf.group(*update_target_expr)
# Create callable functions
train = U.function(inputs=[
obs_t_input, act_t_ph, rew_t_ph, obs_tp1_input, done_mask_ph,
importance_weights_ph
],
outputs=td_error,
updates=[optimize_expr])
update_target = U.function([], [], updates=[update_target_expr])
q_values = U.function([obs_t_input], q_t)
return act_f, train, update_target, {'q_values': q_values}
def system_mat_1st_order_elec( fwd_model, img):
pp = fwd_model_parameters(fwd_model)
FC, FF1, FF2, CC1, CC2 = system_mat_fields(fwd_model, elec_imped=False)
FC_shape =FC.shape.as_list()
lFC = FC_shape[0]
n_dim = pp['n_dims']
dim_n_elem = n_dim*pp['n_elem']
elem_data = img['elem_data'].reshape(pp['n_elem'], 1)
if len(elem_data.shape)<3:
zc_ = np.zeros([pp['n_elec'], 1])
zc = np.zeros([0, 1])
for i in range(pp['n_elec']):
eleci = fwd_model['electrode'][i]
zc_[i] = eleci.z_contact
bdy_idx, bdy_area = find_electrode_bdy(pp['boundary'], pp['nodes'], eleci.nodes)
zc = np.vstack((zc, np.ones([bdy_idx[0].shape[0]*n_dim, 1], dtype=np.float32)*(1.0/zc_[i])))
# zc = np.tile(zc, [n_dim, 1])
elem_sigma = tf.contrib.kfac.utils.kronecker_product(tf.constant(elem_data, dtype=tf.float32), tf.ones([n_dim, 1], dtype=tf.float32))
#elem_sigma = tf.concat([elem_sigma, tf.ones([lFC-dim_n_elem, 1], dtype=tf.float32)], axis=0)
elem_sigma = tf.concat([elem_sigma, zc], axis=0)
es_indices = tf.cast(tf.matmul(tf.ones([2,1], dtype=tf.int32), tf.reshape(tf.range(lFC), [1, lFC])), tf.int64)
es_shape = [lFC, lFC]
ES = tf.SparseTensor(tf.transpose(es_indices), tf.squeeze(elem_sigma), es_shape)
else:
# ToDo: need to test for complex conductivity
if n_dim==2:
idx = np.arange(1, dim_n_elem+1, 2)
# [idx,idx+1,idx,idx+1]' [idx,idx,idx+1,idx+1]'
es_indices = tf.concate((tf.concat((idx,idx+1,idx,idx+1, np.arange(dim_n_elem+1, lFC)), axis=1).T,
tf.concat((idx,idx,idx+1,idx+1, np.arange(dim_n_elem+1, lFC)), axis=1).T),
axis=0)
es_data = tf.concat((elem_data.flatten('F').reshape(dim_n_elem, 1), np.ones([lFC - dim_n_elem, 1], dtype=np.complex64)), axis=0)
es_shape = [lFC, lFC]
ES = tf.SparseTensor(es_indices.T,
es_data.flatten('F'),
dense_shape=es_shape)
if n_dim==3:
idx = np.arange(1, dim_n_elem+1, 3)
es_indices = np.vstack(
(np.hstack((idx, idx+1, idx+2, idx, idx+1, idx+2, idx, idx+1, idx+2, np.arange(dim_n_elem+1, lFC))).T,
np.hstack((idx, idx, idx, idx+1, idx+1, idx+1, idx+2, idx+2, idx+2, np.arange(dim_n_elem+1, lFC))).T)
)
es_data = np.vstack((elem_data.flatten('F').reshape(dim_n_elem, 1), np.ones[lFC - dim_n_elem, 1])).astype(np.complex64)
es_shape = [lFC, lFC]
ES = tf.SparseTensor(es_indices.T,
es_data.flatten('F'),
dense_shape=es_shape)
FC_t = tf.transpose(FC)
E = tf.matmul(FC_t, tf.sparse_tensor_dense_matmul(ES, FC))
E = 0.5*tf.add(tf.transpose(E), E)
return E
def system_mat_1st_order( fwd_model, img=None):
pp = fwd_model_parameters(fwd_model)
FC, _,_,_,_ = system_mat_fields(fwd_model, elec_imped=True)
FC_shape =FC.shape.as_list()
lFC = FC_shape[0]
n_dim = pp['n_dims']
dim_n_elem = n_dim*pp['n_elem']
try:
elem_data = img['elem_data'].reshape(pp['n_elem'], 1)
except KeyError:
elem_data = np.ones([pp['n_elem'], 1], dtype=np.float32)
if len(elem_data.shape)<3:
elem_sigma = tf.contrib.kfac.utils.kronecker_product(tf.constant(elem_data, dtype=tf.float32), tf.ones([n_dim, 1], dtype=tf.float32))
elem_sigma = tf.concat([elem_sigma, tf.ones([lFC-dim_n_elem, 1], dtype=tf.float32)], axis=0)
es_indices = tf.cast(tf.matmul(tf.ones([2,1], dtype=tf.int32), tf.reshape(tf.range(lFC), [1, lFC])), tf.int64)
es_shape = [lFC, lFC]
ES = tf.SparseTensor(tf.transpose(es_indices), tf.squeeze(elem_sigma), es_shape)
else:
# ToDo: need to test for future use
if n_dim==2:
idx = np.arange(1, dim_n_elem+1, 2)
# [idx,idx+1,idx,idx+1]' [idx,idx,idx+1,idx+1]'
es_indices = tf.concate((tf.concat((idx,idx+1,idx,idx+1, np.arange(dim_n_elem+1, lFC)), axis=1).T,
tf.concat((idx,idx,idx+1,idx+1, np.arange(dim_n_elem+1, lFC)), axis=1).T),
axis=0)
es_data = tf.concat((elem_data.flatten('F').reshape(dim_n_elem, 1), np.ones([lFC - dim_n_elem, 1], dtype=np.complex64)), axis=0)
es_shape = [lFC, lFC]
ES = tf.SparseTensor(es_indices.T,
es_data.flatten('F'),
dense_shape=es_shape)
if n_dim==3:
idx = np.arange(1, dim_n_elem+1, 3)
es_indices = np.vstack(
(np.hstack((idx, idx+1, idx+2, idx, idx+1, idx+2, idx, idx+1, idx+2, np.arange(dim_n_elem+1, lFC))).T,
np.hstack((idx, idx, idx, idx+1, idx+1, idx+1, idx+2, idx+2, idx+2, np.arange(dim_n_elem+1, lFC))).T)
)
es_data = np.vstack((elem_data.flatten('F').reshape(dim_n_elem, 1), np.ones[lFC - dim_n_elem, 1])).astype(np.complex64)
es_shape = [lFC, lFC]
ES = tf.SparseTensor(es_indices.T,
es_data.flatten('F'),
dense_shape=es_shape)
FC_t = tf.transpose(FC)
E = tf.matmul(FC_t, tf.sparse_tensor_dense_matmul(ES, FC))
E = 0.5*tf.add(tf.transpose(E), E)
return E
class TextCNN():
"""
title: inputs->textcnn->output_title
content: inputs->textcnn->output_content
concat[output_title, output_content] -> fc+bn+relu -> sigmoid_entropy.
"""
def __init__(self, W_embedding, setting):
self.model_name = settings.model_name
self.title_len = settings.title_len
self.content_len = settings.content_len
self.filter_sizes = settings.filter_sizes
self.n_filter = settings.n_filter
self.n_filter_total = self.n_filter * len(self.filter_sizes)#256*5
self.n_class = settings.n_class
self.fc_hidden_size = settings.fc_hidden_size
self._global_step=tf.Variable(0,trainable=False,name='Global_Step')
self.update_emas=list()
#placeholders
self._tst=tf.placeholder(tf.bool)
self._keep_prob=tf.placeholder(tf.float32,[])
self._batch_size=tf.placeholder(tf.int32,[])
with tf.name_scope('Inputs'):
self._X1_inputs=tf.placeholder(tf.int64,[None,self.title_len],name='X1_inputs')
self._X2_inputs=tf.placeholder(tf.int64,[None,self.content_len],name='X2_inputs')
self._y_inputs=tf.placeholder(tf.float32,[None,self.n_class],name='y_inputs')
with tf.variable_scope('embedding'):
self.embedding=tf.get_variable(name='embedding',shape=W_embedding,
initializer=tf.constant_initializer(W_embedding),trainable=True)
self.embedding_size=W_embedding.shape[1]#1024
with tf.variable_scope('cnn_text'):
output_title=self.cnn_inference(self._X1_inputs, self.title_len)
with tf.variable_scope('cnn_content'):
output_content=self.cnn_inference(self._X2_inputs, self.content_len)
with tf.variable_scope('fc_bn_layer'):
output=tf.concate([output_title,output_content],axis=1)#batch_size*2560
W_fc=self.weight_variable([2*self.n_filter_total,self.fc_hidden_size],name='Weight_fc')
tf.summary.histogram('Weight_fc',W_fc)
h_fc=tf.matmul(output,W_fc,name='h_fc')#batch_size*fc_hidden_size
beta_fc=tf.Variable(tf.constant(0.1,tf.float32,shape=[self.fc_hidden_size],name='beta_fc'))
tf.summary.histogram('beta_fc',beta_fc)
fc_bn,update_ema_fc=self.batchnorm(h_fc,beta_fc,convolutional=False)
self.update_emas.append(update_ema_fc)
self.fc_bn_relu=tf.nn.relu(fc_bn,name='relu')
fc_bn_drop=tf.nn.dropout(self.fc_bn_relu,self.keep_prob)
with tf.variable_scope('out_layer'):
W_out=self.weight_variable([self.fc_hidden_size,self.n_class],name='Weight_out')
tf.summary.histogram('Weight_out',W_out)
b_out=self.bias_variable([self.n_class],name='bias_out')
tf.summary.histogram('bias_out',b_out)
self._y_pred=tf.nn.xw_plus_b(fc_bn_drop,W_out,b_out,name='y_pred')
with tf.name_scope('loss'):
self._loss=tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=self._y_pred,labels=self._y_inputs))
tf.summary.scalar('loss',self._loss)
self.saver=tf.train.Saver(max_to_keep=2)
@property
def tst(self):
return self._tst
@property
def keep_pro(self):
return self._keep_prob
@property
def batch_size(self):
return self._batch_size
@property
def global_step(self):
return self._global_step
@property
def X1_inputs(self):
return self._X1_inputs
@propery
def X2_inputs(self):
return self._X2_inputs
@property
def y_inputs(self):
return self._y_inputs
@property
def y_pred(self):
return self._y_pred
@propery
def loss(self):
return self._loss
def weight_variable(self,shape,name):
initial=tf.truncated_normal(shape,stddev=0.1)
return tf.Variable(initial,name=name)
def bias_variable(self,shape,name):
initial=tf.constant(0.1,shape=shape)
return tf.Variable(initial,name=name)
def batchnorm(self, Ylogits, offset, convolutional=False):
"""batchnormalization.
Args:
Ylogits: 1D向量或者是3D的卷积结果。
num_updates: 迭代的global_step
offset：表示beta，全局均值；在 RELU 激活中一般初始化为 0.1。
scale：表示lambda，全局方差；在 sigmoid 激活中需要，这 RELU 激活中作用不大。
m: 表示batch均值；v:表示batch方差。
bnepsilon：一个很小的浮点数，防止除以 0.
Returns:
Ybn: 和 Ylogits 的维度一样，就是经过 Batch Normalization 处理的结果。
update_moving_everages：更新mean和variance，主要是给最后的 test 使用。
"""
exp_moving_avg=tf.train.ExponentialMovingAverage(0.999,self._global_step)
bnepsilon=1e-5
if convolutional:
#以某一维度计算均值或方差
mean,variance=tf.nn.moments(Ylogits,[0,1,2])#只剩下卷积核那个维度的维数了
else:
mean,variance=tf.nn.moments(Ylogits,[0])
update_moving_averages=exp_moving_avg.apply([mean,variance])
tf.cond(self.tst,lambda: exp_moving_avg.average(mean), lambda: mean)
tf.cond(self.tst,lambda: exp_moving_avg.average(variance), lambda: variance)
Ybn=tf.nn.batch_normalization(Ylogits,m,v,offset,None,bnepsilon)
return Ybn,update_moving_averages
def cnn_inference(self,X_inputs,n_step):
"""TextCNN 模型。
Args:
X_inputs: tensor.shape=(batch_size, n_step)
Returns:
title_outputs: tensor.shape=(batch_size, self.n_filter_total)
"""
inputs=tf.nn.embedding_lookup(self.embedding, X_inputs)#batch_size, n_step,embedding_size
inputs=tf.expand_dims(input,-1)#batch_size, n_step,embedding_size,1
pooled_outputs=list()
for i,filter_size in enumerate(self.filter_sizes):
with tf.variable_scope('conv-maxpool-%s'%filter_size):
filter_shape=[filter_size,self.embedding_size,1,self.n_filter]
W_filter=self.weight_variable(shape=filter_shape,name='W_filter')
beta=self.bias_variable(shape=[self.n_filter],name='beta_filter')
tf.summary.histogram('beta',beta)
conv=tf.nn.conv2d(inputs,W_filter,strides=[1,1,1,1],padding='VALID',name='conv')
#激活层前面加BN,注意
conv_bn,update_emas=self.batchnorm(conv,beta,convolutional=True)
#激活层
h=tf.nn.relu(conv_bn,name="relu")
#池化层
pooled=tf.nn.max_pool(h,ksize=[1,n_step-filter_size+1,1,1],strides=[1,1,1,1],padding='VALID',name='pool')
pooled_outputs.append(pooled)#shape of pooled [1,1,1,X]
self.update_emas.append(update_emas)
h_pool=tf.concate(pooled_outputs,3)
h_pool_flat=tf.reshape(h_pool,[-1,self.n_filter_total])
return h_pool_flat#batch_size（一批有几条数据）*n_filter_total(一条数据可以获得的最大池化值)