def create_cell():
if self.dropout_keep_prob < 1.0:
single_cell = lambda: BasicLSTMCell(hidden_size)
hidden = MultiRNNCell(
[single_cell() for _ in range(num_layer)])
hidden = DropoutWrapper(
hidden,
input_keep_prob=self.dropout_keep_prob,
output_keep_prob=self.dropout_keep_prob)
else:
single_cell = lambda: BasicLSTMCell(hidden_size)
hidden = MultiRNNCell(
[single_cell() for _ in range(num_layer)])
return hidden
def __init__(self,
num_symbols,
num_embed_units,
num_units,
num_labels,
embed,
learning_rate=0.001,
max_gradient_norm=5.0):
self.texts = tf.placeholder(tf.int32, [None, None]) # shape: sentence*max_word
self.text_length = tf.placeholder(tf.int32, [None]) # shape: sentence
self.labels = tf.placeholder(tf.int32, [None]) # shape: sentence
self.keep_prob = tf.placeholder(tf.float32)
self.learning_rate = tf.Variable(float(learning_rate), trainable=False, dtype=tf.float32)
self.global_step = tf.Variable(0, trainable=False)
self.epoch = tf.Variable(0, trainable=False)
self.epoch_add_op = self.epoch.assign(self.epoch + 1)
# build the embedding table (index to vector)
self.embed = tf.get_variable('embed', dtype=tf.float32, initializer=embed)
self.embed_inputs = tf.nn.embedding_lookup(self.embed, self.texts) # shape: sentence*max_word*num_embed_units
fw_cell = DropoutWrapper(BasicLSTMCell(num_units), output_keep_prob=self.keep_prob)
bw_cell = DropoutWrapper(BasicLSTMCell(num_units), output_keep_prob=self.keep_prob)
middle_outputs, middle_states = bidirectional_dynamic_rnn(fw_cell, bw_cell, self.embed_inputs, self.text_length, dtype=tf.float32, scope="word_rnn")
middle_outputs = tf.concat(middle_outputs, 2) # shape: sentence*max_word*(2*num_units)
middle_inputs = tf.expand_dims(tf.reduce_max(middle_outputs, axis=1), 0) # shape: 1*sentence*(2*num_units)
top_cell = DropoutWrapper(BasicLSTMCell(num_units), output_keep_prob=self.keep_prob)
outputs, states = dynamic_rnn(top_cell, middle_inputs, dtype=tf.float32, scope="sentence_rnn")
self.outputs = outputs[0] # shape: sentence*num_units
logits = tf.layers.dense(self.outputs, num_labels)
self.loss = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.labels, logits=logits), name='loss')
mean_loss = self.loss / tf.cast(tf.shape(self.labels)[0], dtype=tf.float32)
self.predict_labels = tf.argmax(logits, 1, 'predict_labels', output_type=tf.int32)
self.accuracy = tf.reduce_sum(tf.cast(tf.equal(self.labels, self.predict_labels), tf.int32), name='accuracy')
self.params = tf.trainable_variables()
# calculate the gradient of parameters
opt = tf.train.AdamOptimizer(self.learning_rate)
gradients = tf.gradients(mean_loss, self.params)
clipped_gradients, self.gradient_norm = tf.clip_by_global_norm(gradients, max_gradient_norm)
self.update = opt.apply_gradients(zip(clipped_gradients, self.params), global_step=self.global_step)
self.saver = tf.train.Saver(max_to_keep=3, pad_step_number=True)
def model(input, vocab_size):
# 构建随机的词向量矩阵
# tf.get_variable(name, shape, initializer): name变量的名称，shape变量的维度，initializer变量初始化的方式
embeddings = tf.get_variable("embeddings", [vocab_size, embedding_size],
initializer=tf.truncated_normal_initializer)
embedded = tf.nn.embedding_lookup(embeddings, input)
# 将数据处理成LSTM的输入格式（时序）
rnn_input = tf.unstack(embedded,
max_document_length,
axis=1,
name="rnn-input")
# 定义LSTM
lstm_cell = BasicLSTMCell(20, forget_bias=1.0)
rnn_outputs, rnn_states = static_rnn(lstm_cell,
rnn_input,
dtype=tf.float32)
# predict
logits = tf.layers.dense(rnn_outputs[-1], num_classes)
predicted_labels = tf.argmax(logits, axis=1)
return predicted_labels, [embeddings, embedded, lstm_cell, logits]
def __init__(self, feature_size, max_video_length, num_classes, cell_size, use_lstm, learning_rate,
learning_rate_decay_factor, min_learning_rate, training_steps_per_epoch, max_gradient_norm,
keep_prob=0.5, is_training=False):
self.frame_feature_ph = tf.placeholder(tf.float32, [None, max_video_length, feature_size])
self.video_length_ph = tf.placeholder(tf.int32, [None])
self.video_label_ph = tf.placeholder(tf.int32, [None])
if is_training:
self.global_step = tf.Variable(0, trainable=False)
self.learning_rate = tf.maximum(
tf.train.exponential_decay(
learning_rate,
self.global_step,
training_steps_per_epoch,
learning_rate_decay_factor,
staircase=True),
min_learning_rate)
# Make RNN cells
cell = GRUCell(cell_size)
if use_lstm:
cell = BasicLSTMCell(cell_size, state_is_tuple=False)
# RNN
with tf.variable_scope('DynamicRNN'):
outputs, state = dynamic_rnn(cell=cell, inputs=self.frame_feature_ph, sequence_length=self.video_length_ph, dtype=tf.float32)
state = tf.nn.relu(state)
if is_training:
state = tf.nn.dropout(state, keep_prob=keep_prob)
if num_classes == 2:
with tf.variable_scope('Classification'):
logit = tf.contrib.layers.fully_connected(inputs=state, num_outputs=1, activation_fn=None) # [batch_size, 1]
self.logit = tf.squeeze(logit) # [batch_size]
if is_training:
video_label = tf.cast(x=self.video_label_ph, dtype=tf.float32)
self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=video_label, logits=self.logit))
else:
self.prediction = tf.cast(tf.greater(x=logit, y=0.5), tf.int32)
else:
with tf.variable_scope('Classification'):
self.logits = tf.contrib.layers.fully_connected(inputs=state, num_outputs=num_classes, activation_fn=None) # [batch_size, num_classes]
if is_training:
self.loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.video_label_ph, logits=self.logits))
else:
self.prediction = tf.argmax(logits, 1)
if is_training:
params = tf.trainable_variables()
gradients = tf.gradients(self.loss, params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients, max_gradient_norm)
self.train_op = tf.train.AdamOptimizer(self.learning_rate).apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step)
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=99999999)
def __init__(self, state_space_size, action_space_size, scope, trainer):
with tf.variable_scope(scope):
# Input
self.inputs = tf.placeholder(shape=[None, state_space_size], dtype=tf.float32)
# Recurrent network for temporal dependencies
lstm_cell = BasicLSTMCell(256, state_is_tuple=True)
c_init = np.zeros_like((1, lstm_cell.state_size.c), dtype=np.float32)
h_init = np.zeros_like((1, lstm_cell.state_size.h), dtype=np.float32)
self.state_init = [c_init, h_init]
c_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.c])
h_in = tf.placeholder(tf.float32, [1, lstm_cell.state_size.h])
self.state_in = (c_in, h_in)
state_in = LSTMStateTuple(c_in, h_in)
lstm_outputs, lstm_state = tf.nn.dynamic_rnn(lstm_cell, self.inputs,
initial_state=state_in,
sequence_length=tf.shape(self.inputs)[0],
time_major=False)
lstm_c, lstm_h = lstm_state
self.state_out = (lstm_c[:1, :], lstm_h[:1, :])
rnn_out = tf.reshape(lstm_outputs, [-1, 256])
# Output layers for policy and value estimations
self.policy = slim.fully_connected(rnn_out, action_space_size,
activation_fn=tf.nn.softmax,
weights_initializer=normalized_columns_initializer(0.01),
biases_initializer=None)
self.value = slim.fully_connected(rnn_out, 1,
activation_fn=None,
weights_initializer=normalized_columns_initializer(1.0),
biases_initializer=None)
# Only the worker network need ops for loss functions and gradient updating.
if scope != 'global':
self.actions = tf.placeholder(shape=[None], dtype=tf.int32)
self.actions_onehot = tf.one_hot(self.actions, action_space_size, dtype=tf.float32)
self.target_v = tf.placeholder(shape=[None], dtype=tf.float32)
self.advantages = tf.placeholder(shape=[None], dtype=tf.float32)
self.responsible_outputs = tf.reduce_sum(self.policy * self.actions_onehot, [1])
# Loss functions
self.value_loss = 0.5 * tf.reduce_sum(tf.square(self.target_v - tf.reshape(self.value, [-1])))
self.entropy = - tf.reduce_sum(self.policy * tf.log(self.policy))
self.policy_loss = -tf.reduce_sum(tf.log(self.responsible_outputs) * self.advantages)
self.loss = 0.5 * self.value_loss + self.policy_loss - self.entropy * 0.01
# Get gradients from local network using local losses
local_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)
self.gradients = tf.gradients(self.loss, local_vars)
self.var_norms = tf.global_norm(local_vars)
grads, self.grad_norms = tf.clip_by_global_norm(self.gradients, 40.0)
# Apply local gradients to global network
global_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'global')
self.apply_grads = trainer.apply_gradients(zip(grads, global_vars))
def __call__(self, img_ph, location_network, retina_sensor,
glimpse_network):
# lstm cell
cell = BasicLSTMCell(self.hidden_size)
# helper func for feeding glimpses to every step of lstm
# h_t_prev: a 2D tensor of shape (B, hidden_size). The hidden state vector for the previous timestep `t-1`.
loc_ts, mean_ts = [], []
## at time step t, location-->pths-->glimpse
def loop_function(h_prev, _):
# predict location from previous hidden state
loc_t, mean_t = location_network(h_prev)
loc_ts.append(loc_t)
mean_ts.append(mean_t)
# crop pths from image based on the predicted location
pths_t = retina_sensor(img_ph, loc_t)
# generate glimpse image from current pths_t and loc_t
glimpse = glimpse_network(pths_t, loc_t)
return glimpse
# lstm init h_t
init_state = cell.zero_state(self.batch_size, tf.float32)
# lstm inputs at every step
init_loc = tf.random_uniform((self.batch_size, self.loc_dim),
minval=-1,
maxval=1)
init_pths = retina_sensor(img_ph, init_loc)
init_glimpse = glimpse_network(init_pths, init_loc)
rnn_inputs = [init_glimpse]
rnn_inputs.extend([0] * self.num_glimpses)
# get hidden state of every step from lstm
h_ts, _ = rnn_decoder(rnn_inputs,
init_state,
cell,
loop_function=loop_function)
return loc_ts, mean_ts, h_ts
def __init__(self, frame_feature_ph, num_classes, cell_size, use_lstm=False):
self.frame_feature_ph = frame_feature_ph
cell = GRUCell(cell_size)
if use_lstm:
cell = BasicLSTMCell(cell_size, state_is_tuple=False)
with tf.variable_scope('DynamicRNN'):
outputs, state = dynamic_rnn(cell=cell, inputs=self.frame_feature_ph, dtype=tf.float32)
outputs = tf.nn.relu(outputs)
with tf.variable_scope('Classification'):
node_logit = tf.contrib.layers.fully_connected(inputs=outputs, num_outputs=num_classes, activation_fn=None)
logit = tf.nn.softmax(node_logit)
self.logit = tf.nn.softmax(tf.reduce_mean(node_logit,1))
self.node = tf.argmax(logit, 2)
self.prediction = tf.argmax(self.logit,1)
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=99999999)
def __init__(self, img_width, img_height, nb_locations,
glimpse_width, glimpse_height,
g_size, l_size, glimpse_output_size, loc_dim, time_dim, variance,
cell_size, nb_glimpses, nb_classes, learning_rate, learning_rate_decay_factor,
min_learning_rate, nb_training_batch, max_gradient_norm, is_training=False):
self.img_ph = tf.placeholder(tf.float32, [None, img_height, img_width])
self.lbl_ph = tf.placeholder(tf.int64, [None])
self.global_step = tf.Variable(0, trainable=False)
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / training_batch_num)
self.learning_rate = tf.maximum(tf.train.exponential_decay(
learning_rate, self.global_step,
nb_training_batch, # batch number
learning_rate_decay_factor,
# If the argument staircase is True,
# then global_step / decay_steps is an integer division
# and the decayed learning rate follows a staircase function.
staircase=True),
min_learning_rate)
cell = BasicLSTMCell(cell_size)
with tf.variable_scope('GlimpseNetwork'):
glimpse_network = GlimpseNetwork(img_width,
img_height,
glimpse_width,
glimpse_height,
loc_dim+time_dim,
g_size,
l_size,
glimpse_output_size,
nb_locations)
with tf.variable_scope('LocationNetwork'):
location_network = LocationNetwork(loc_dim=loc_dim*nb_locations+time_dim,
rnn_output_size=cell.output_size, # cell_size
variance=variance,
is_sampling=is_training)
# with tf.variable_scope('CNN'):
# cnn = CNN(nb_locations, glimpse_output_size)
# with tf.variable_scope('CDD'):
# cdd = CDD(glimpse_height, nb_locations*glimpse_output_size)
# Core Network
batch_size = tf.shape(self.img_ph)[0]
init_loc_1 = tf.random_uniform((batch_size, loc_dim), minval=-1, maxval=1)
init_loc_2 = tf.random_uniform((batch_size, loc_dim), minval=-1, maxval=1)
init_loc_3 = tf.random_uniform((batch_size, loc_dim), minval=-1, maxval=1)
init_t = tf.random_uniform((batch_size, loc_dim), minval=-1, maxval=1)
# shape: (batch_size, loc_dim), range: [-1,1)
init_state = cell.zero_state(batch_size, tf.float32)
self.init_glimpse = glimpse_network(self.img_ph, init_loc_1, init_loc_2, init_loc_3,
init_t)
# self.init_glimpse_cooperate = cnn(self.init_glimpse)
# self.imgs_ph, self.imgs_ph_re, self.h_fc1, self.conv_2d_1st, self.conv_2d_2nd, self.conv_2d_flat = cdd(self.init_glimpse)
rnn_inputs = [self.init_glimpse]
rnn_inputs.extend([0] * nb_glimpses)
locs, loc_means = [], []
def loop_function(prev, _):
loc, loc_mean = location_network(prev)
locs.append(loc)
loc_means.append(loc_mean)
glimpse = glimpse_network(self.img_ph, tf.reshape(loc[:,0],[-1,1]),
tf.reshape(loc[:, 1], [-1, 1]),
tf.reshape(loc[:, 2], [-1, 1]),
tf.reshape(loc[:, 3], [-1, 1]))
# glimpse_cooperate = cnn(glimpse)
return glimpse
rnn_outputs, _ = rnn_decoder(rnn_inputs, init_state, cell, loop_function=loop_function)
# Time independent baselines
with tf.variable_scope('Baseline'):
baseline_w = _weight_variable((cell.output_size, 1))
baseline_b = _bias_variable((1,))
baselines = []
for output in rnn_outputs[1:]:
baseline = tf.nn.xw_plus_b(output, baseline_w, baseline_b)
baseline = tf.squeeze(baseline)
baselines.append(baseline)
baselines = tf.stack(baselines) # [timesteps, batch_sz]
baselines = tf.transpose(baselines) # [batch_sz, timesteps]
# Classification. Take the last step only.
rnn_last_output = rnn_outputs[-1]
with tf.variable_scope('Classification'):
logit_w = _weight_variable((cell.output_size, nb_classes))
logit_b = _bias_variable((nb_classes,))
logits = tf.nn.xw_plus_b(rnn_last_output, logit_w, logit_b)
# self.prediction = tf.argmax(logits, 1)
self.softmax = tf.nn.softmax(logits)
self.pred = tf.argmax(self.softmax, 1)
self.accuracy = tf.reduce_mean(tf.cast(tf.equal(self.pred, self.lbl_ph), tf.float32))
if is_training:
# classification loss
self.cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.lbl_ph, logits=logits))
# RL reward
reward = tf.cast(tf.equal(self.pred, self.lbl_ph), tf.float32)
rewards = tf.expand_dims(reward, 1) # [batch_sz, 1]
rewards = tf.tile(rewards, (1, nb_glimpses)) # [batch_sz, timesteps]
advantages = rewards - tf.stop_gradient(baselines)
self.advantage = tf.reduce_mean(advantages)
logll = _log_likelihood(loc_means, locs, variance)
logllratio = tf.reduce_mean(logll * advantages)
self.reward = tf.reduce_mean(reward)
# baseline loss
self.baselines_mse = tf.reduce_mean(tf.square((rewards - baselines)))
# hybrid loss
self.loss = -logllratio + self.cross_entropy + self.baselines_mse
params = tf.trainable_variables()
gradients = tf.gradients(self.loss, params)
clipped_gradients, norm = tf.clip_by_global_norm(gradients, max_gradient_norm)
self.train_op = tf.train.AdamOptimizer(self.learning_rate).apply_gradients(
zip(clipped_gradients, params), global_step=self.global_step)
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=99999999)
def __init__(self,
img_size_width,
img_size_height,
CNN_patch_width,
CNN_patch_height,
CNN_patch_number,
patch_window_width,
patch_window_height,
g_size,
l_size,
glimpse_output_size,
loc_dim,
variance,
cell_size,
num_glimpses,
num_classes,
learning_rate,
learning_rate_decay_factor,
min_learning_rate,
training_batch_num,
max_gradient_norm,
last_lstm_size,
n_time_window,
is_training=False):
self.img_ph = tf.placeholder(tf.float32,
[None, img_size_width * img_size_height])
self.lbl_ph = tf.placeholder(tf.int64, [None])
self.global_step = tf.Variable(0, trainable=False)
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / training_batch_num)
self.learning_rate = tf.maximum(
tf.train.exponential_decay(
learning_rate,
self.global_step,
training_batch_num, # batch number
learning_rate_decay_factor,
# If the argument staircase is True,
# then global_step / decay_steps is an integer division
# and the decayed learning rate follows a staircase function.
staircase=True),
min_learning_rate)
cell = BasicLSTMCell(cell_size)
with tf.variable_scope('CNN'):
cnn_network = CNN(img_size_width, img_size_height, CNN_patch_width,
CNN_patch_height, CNN_patch_number)
with tf.variable_scope('GlimpseNetwork'):
glimpse_network = GlimpseNetwork(img_size_width, img_size_height,
patch_window_width,
patch_window_height, loc_dim,
g_size, l_size,
glimpse_output_size)
with tf.variable_scope('LocationNetwork'):
location_network = LocationNetwork(
loc_dim=loc_dim,
rnn_output_size=cell.output_size, # cell_size
variance=variance,
is_sampling=is_training)
# Core Network
self.img_ph = cnn_network(self.img_ph)
batch_size = tf.shape(self.img_ph)[0] # training_batch_size * M
init_loc = tf.random_uniform((batch_size, loc_dim),
minval=-1,
maxval=1)
# shape: (batch_size, loc_dim), range: [-1,1)
init_state = cell.zero_state(batch_size, tf.float32)
init_glimpse = glimpse_network(self.img_ph, init_loc)
rnn_inputs = [init_glimpse]
rnn_inputs.extend([0] * num_glimpses)
self.locs, loc_means = [], []
def loop_function(prev, _):
loc, loc_mean = location_network(prev)
self.locs.append(loc)
loc_means.append(loc_mean)
glimpse = glimpse_network(self.img_ph, loc)
return glimpse
rnn_outputs, _ = rnn_decoder(rnn_inputs,
init_state,
cell,
loop_function=loop_function)
# Time independent baselines
with tf.variable_scope('Baseline'):
baseline_w = _weight_variable((cell.output_size, 1))
baseline_b = _bias_variable((1, ))
baselines = []
for output in rnn_outputs[1:]:
baseline = tf.nn.xw_plus_b(output, baseline_w, baseline_b)
baseline = tf.squeeze(baseline)
baselines.append(baseline)
baselines = tf.stack(baselines) # [timesteps, batch_sz]
baselines = tf.transpose(baselines) # [batch_sz, timesteps]
# Classification. Take the last step only.
rnn_last_output = rnn_outputs[-1]
with tf.variable_scope('Classification'):
logit_w = _weight_variable((cell.output_size, num_classes))
logit_b = _bias_variable((num_classes, ))
logits = tf.nn.xw_plus_b(rnn_last_output, logit_w, logit_b)
self.prediction = tf.argmax(logits, 1)
self.softmax = tf.nn.softmax(logits)
with tf.variable_scope('LSTM_Classification'):
last_lstm_w_in = _weight_variable(
(cell.output_size, last_lstm_size))
last_lstm_b_in = _bias_variable((last_lstm_size, ))
last_lstm_in = tf.matmul(rnn_last_output,
last_lstm_w_in) + last_lstm_b_in
last_lstm_in = tf.reshape(last_lstm_in,
[-1, n_time_window, last_lstm_size])
if int((tf.__version__).split('.')[1]) < 12 and int(
(tf.__version__).split('.')[0]) < 1:
cell = tf.nn.rnn_cell.BasicLSTMCell(last_lstm_size,
forget_bias=1.0,
state_is_tuple=True)
else:
cell = tf.contrib.rnn.BasicLSTMCell(last_lstm_size)
# lstm cell is divided into two parts (c_state, h_state)
init_state_last_lstm = cell.zero_state(batch_size // n_time_window,
dtype=tf.float32)
lstm_outputs, final_state = tf.nn.dynamic_rnn(
cell,
last_lstm_in,
initial_state=init_state_last_lstm,
time_major=False)
last_lstm_w_out = _weight_variable((cell.output_size, num_classes))
last_lstm_b_out = _bias_variable((num_classes, ))
if int((tf.__version__).split('.')[1]) < 12 and int(
(tf.__version__).split('.')[0]) < 1:
lstm_outputs = tf.unpack(tf.transpose(
lstm_outputs, [1, 0, 2])) # states is the last outputs
else:
lstm_outputs = tf.unstack(tf.transpose(lstm_outputs,
[1, 0, 2]))
lstm_logits = tf.matmul(lstm_outputs[-1],
last_lstm_w_out) + last_lstm_b_out
lstm_logits = tf.reshape(tf.tile(lstm_logits, (1, n_time_window)),
[-1, num_classes])
self.lstm_prediction = tf.argmax(lstm_logits, 1)
self.lstm_softmax = tf.nn.softmax(lstm_logits)
if is_training:
# classification loss
self.cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.lbl_ph, logits=logits))
self.lstm_cross_entropy = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.lbl_ph, logits=lstm_logits))
# RL reward
reward = tf.cast(tf.equal(self.prediction, self.lbl_ph),
tf.float32)
rewards = tf.expand_dims(reward, 1) # [batch_sz, 1]
rewards = tf.tile(rewards,
(1, num_glimpses)) # [batch_sz, timesteps]
advantages = rewards - tf.stop_gradient(baselines)
self.advantage = tf.reduce_mean(advantages)
logll = _log_likelihood(loc_means, self.locs, variance)
logllratio = tf.reduce_mean(logll * advantages)
self.reward = tf.reduce_mean(reward)
# baseline loss
self.baselines_mse = tf.reduce_mean(
tf.square((rewards - baselines)))
# hybrid loss
self.loss = -logllratio + self.cross_entropy + self.baselines_mse + self.lstm_cross_entropy
params = tf.trainable_variables()
gradients = tf.gradients(self.loss, params)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.train_op = tf.train.AdamOptimizer(
self.learning_rate).apply_gradients(
zip(clipped_gradients, params),
global_step=self.global_step)
self.saver = tf.train.Saver(tf.global_variables(),
max_to_keep=99999999)
def __init__(self, kwd_voc_size, *args, **kwargs):
BasicLSTMCell.__init__(self, *args, **kwargs)
self.key_words_voc_size = kwd_voc_size
def single_cell():
return BasicLSTMCell(rnnHiddenSize, state_is_tuple=False)
def model(self):
self.train_mode = tf.placeholder(tf.bool)
self.train_dataset = self._generate_train_image_label()
self.test_dataset = self.generate_test_image_label()
self.valid_dataset = self.generate_valid_image_label()
self.iter = tf.data.Iterator.from_structure(
self.train_dataset.output_types, self.train_dataset.output_shapes)
self.train_init_op = self.iter.make_initializer(self.train_dataset)
self.test_init_op = self.iter.make_initializer(self.test_dataset)
self.valid_init_op = self.iter.make_initializer(self.valid_dataset)
self.images, self.labels = self.iter.get_next()
self.img_ph = tf.tile(self.images, [self.glimpse_times, 1, 1, 1])
self.lbl_ph = tf.tile(self.labels, [self.glimpse_times])
cell = BasicLSTMCell(self.lstm_cell_size)
init_state = cell.zero_state(tf.shape(self.img_ph)[0], tf.float32)
self.rgbs, self.rnn_outputs, self.locs, self.loc_means = self.rnn_decode(
init_state, cell, self.img_ph)
baselines = self.baseline_network(self.rnn_outputs)
logits, probs = self.prob_network(self.rnn_outputs[-1])
predict_label = tf.argmax(logits, 1, output_type=tf.int32)
if self.result_mode == 'single':
rewards = tf.cast(tf.equal(predict_label, self.lbl_ph), tf.float32)
rewards = tf.tile(tf.expand_dims(rewards, 1),
[1, self.num_glimpses])
self.test_label = tf.argmax(tf.reduce_mean(tf.reshape(
probs, [self.glimpse_times, -1, 200]),
axis=0),
1,
output_type=tf.int32)
self.test_acc = tf.reduce_mean(
tf.cast(tf.equal(self.test_label, self.labels), tf.float32))
self.m_test_acc = self.test_acc
else:
m_logits, m_probs = self.prob_network(tf.stack(self.rnn_outputs))
m_predict_label = tf.argmax(m_logits, -1, output_type=tf.int32)
m_lbl = tf.tile(tf.expand_dims(self.lbl_ph, 0),
[self.num_glimpses, 1])
rewards = tf.transpose(
tf.cast(tf.equal(m_predict_label, m_lbl), tf.float32), [1, 0])
self.classification_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=m_lbl, logits=m_logits))
self.test_label = tf.argmax(tf.reduce_mean(tf.reshape(
probs, [self.glimpse_times, -1, 200]),
axis=0),
1,
output_type=tf.int32)
self.test_acc = tf.reduce_mean(
tf.cast(tf.equal(self.test_label, self.labels), tf.float32))
m_test_label = tf.argmax(tf.reduce_mean(tf.reshape(
tf.reduce_mean(m_probs, 0), [self.glimpse_times, -1, 200]),
axis=0),
1,
output_type=tf.int32)
self.m_test_acc = tf.reduce_mean(
tf.cast(tf.equal(m_test_label, self.labels), tf.float32))
log_action_prob = self._log_likelihood(self.loc_means, self.locs,
self.variance)
if self.reinforce_mode == 'baseline':
advantages = tf.stop_gradient(rewards - baselines)
if self.result_mode == 'single':
self.classification_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.lbl_ph, logits=logits))
self.reinforce_loss = tf.reduce_mean(log_action_prob * advantages)
self.baselines_loss = tf.reduce_mean(
tf.square((tf.stop_gradient(rewards) - baselines)))
self.location_loss = self._loc_loss(self.loc_means)
self.loss = -self.reinforce_loss + self.classification_loss + self.baselines_loss
else:
advantages = rewards
if self.result_mode == 'single':
self.classification_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.lbl_ph, logits=logits))
self.reinforce_loss = tf.reduce_mean(log_action_prob * advantages)
self.baselines_loss = tf.constant(0)
self.location_loss = self._loc_loss(self.loc_means)
self.loss = -self.reinforce_loss + self.classification_loss
if self.mode == 'fine_tune':
params = tf.trainable_variables()
elif self.mode == 'origin':
params = [
param for param in tf.trainable_variables()
if 'resnet' not in param.name
]
else:
# params = tf.trainable_variables()
params = [
param for param in tf.trainable_variables()
if 'resnet' not in param.name
]
# params = [param for param in tf.trainable_variables() if 'resnet' not in param.name and
# 'glimpse_feature' not in param.name]
gradients = tf.gradients(self.loss, params)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, self.max_gradient_norm)
if self.train_method == 'Adam':
self.train_op = tf.train.AdamOptimizer(learning_rate=self.decay_learning_rate).\
apply_gradients(zip(clipped_gradients, params), global_step=self.global_step)
else:
self.train_op = tf.train.MomentumOptimizer(self.decay_learning_rate, 0.9). \
apply_gradients(zip(clipped_gradients, params), global_step=self.global_step)
self.reward = tf.reduce_mean(rewards)
self.advantage = tf.reduce_mean(advantages)
tf.summary.histogram(
'probs',
tf.reduce_sum(probs * tf.one_hot(self.lbl_ph, 200), axis=-1))
for param in params:
tf.summary.histogram(param.name, param)
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('reward', self.reward)
tf.summary.scalar('advantage', self.advantage)
tf.summary.scalar('classification_loss', self.classification_loss)
tf.summary.scalar('reinforce_loss', self.reinforce_loss)
tf.summary.scalar('baseline_loss', self.baselines_loss)
self.merged = tf.summary.merge_all()
self.test_multi_dataset = self.generate_test_multi_image_label()
self.multi_iter = tf.data.Iterator.from_structure(
self.test_multi_dataset.output_types,
self.test_multi_dataset.output_shapes)
self.test_multi_init_op = self.multi_iter.make_initializer(
self.test_multi_dataset)
self.multi_images, self.multi_labels = self.multi_iter.get_next()
self.multi_images = tf.reshape(self.multi_images, [-1, 224, 224, 3])
self.multi_img_ph = tf.tile(self.multi_images,
[self.glimpse_times, 1, 1, 1])
multi_init_state = cell.zero_state(
tf.shape(self.multi_img_ph)[0], tf.float32)
_, self.multi_rnn_outputs, __, ___ = self.rnn_decode(
multi_init_state, cell, self.multi_img_ph)
_, multi_probs = self.prob_network(self.multi_rnn_outputs[-1])
self.multi_test_labels = tf.reduce_mean(tf.reshape(
multi_probs, [self.glimpse_times, -1, 200]),
axis=0)
self.multi_test_labels = tf.reduce_mean(tf.reshape(
self.multi_test_labels, [-1, 10, 200]),
axis=1)
self.multi_test_labels = tf.argmax(self.multi_test_labels,
1,
output_type=tf.int32)
self.multi_accuracy = tf.reduce_mean(
tf.cast(tf.equal(self.multi_test_labels, self.multi_labels),
tf.float32))
# 容器，存放输入输出
datas_placeholder = tf.placeholder(tf.int32, [None, max_document_length])
labels_placeholder = tf.placeholder(tf.int32, [None])
# 词向量表
embeddings = tf.get_variable("embeddings", [vocab_size, embedding_size],
initializer=tf.truncated_normal_initializer)
# 将词索引号转换为词向量[None, max_document_length] => [None, max_document_length, embedding_size]
embedded = tf.nn.embedding_lookup(embeddings, datas_placeholder)
# 转换为LSTM的输入格式，要求是数组，数组的每个元素代表某个时间戳一个Batch的数据
rnn_input = tf.unstack(embedded, max_document_length, axis=1)
# 定义LSTM
lstm_cell = BasicLSTMCell(20, forget_bias=1.0)
rnn_outputs, rnn_states = static_rnn(lstm_cell, rnn_input, dtype=tf.float32)
#利用LSTM最后的输出进行预测
logits = tf.layers.dense(rnn_outputs[-1], num_classes)
predicted_labels = tf.argmax(logits, axis=1)
# 定义损失和优化器
losses = tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(
labels_placeholder, num_classes),
logits=logits)
mean_loss = tf.reduce_mean(losses)
optimizer = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(mean_loss)
def __init__(self,
img_channel,
img_size,
pth_size,
g_size,
l_size,
glimpse_output_size,
loc_dim,
variance,
cell_size,
num_glimpses,
num_classes,
learning_rate,
learning_rate_decay_factor,
min_learning_rate,
training_steps_per_epoch,
max_gradient_norm,
fc1_size,
base_channels,
output_dim,
is_training=False):
self.img_ph = tf.placeholder(tf.float32,
[None, img_size * img_size * img_channel])
self.lbl_ph = tf.placeholder(tf.float32, [None, output_dim])
self.global_step = tf.Variable(0, trainable=False)
self.learning_rate = tf.maximum(
tf.train.exponential_decay(learning_rate,
self.global_step,
training_steps_per_epoch,
learning_rate_decay_factor,
staircase=True), min_learning_rate)
cell = BasicLSTMCell(cell_size)
with tf.variable_scope('GlimpseNetwork'):
glimpse_network = GlimpseNetwork(img_channel, img_size, pth_size,
loc_dim, g_size, l_size,
glimpse_output_size)
with tf.variable_scope('Agent'):
# the agent is resposibale for select a windows and est a gain
with tf.variable_scope('LocationNetwork'):
location_network = LocationNetwork(
loc_dim=loc_dim,
rnn_output_size=cell.output_size,
variance=variance,
is_sampling=is_training)
with tf.variable_scope('WhiteBalanceNetwork'):
wb_network = WhiteBalanceNetwork(
rnn_output_size=cell.output_size, output_dim=output_dim)
if FLAGS.USE_CRITIC:
with tf.variable_scope('Critic'):
critic_network = CriticNetwork(fc1_size, base_channels)
# Core Network
batch_size = tf.shape(self.img_ph)[0]
init_loc = tf.random_uniform((batch_size, loc_dim),
minval=-1,
maxval=1)
init_state = cell.zero_state(batch_size, tf.float32)
init_glimpse = glimpse_network(self.img_ph, init_loc)
rnn_inputs = [init_glimpse]
rnn_inputs.extend([0] * num_glimpses)
locs, loc_means = [], []
gains = []
img_retouched = []
def _apply_gain(ill, loc, img, patch_wise=False):
if patch_wise:
retina = RetinaSensor(img_channel, img_size, pth_size)
pth = retina(img, loc, serial=False)
img = tf.reshape(
img, [tf.shape(img)[0], img_size, img_size, img_channel])
retouched_channel = []
for i in range(3):
tmp = pth[:, :, :, i]
tmp = tf.reshape(tmp, [tf.shape(tmp)[0], -1])
tmp_ill = tf.reshape(ill[:, i] / ill[:, 1],
[tf.shape(img)[0], 1])
tmp_ill = tf.tile(tmp_ill, [1, pth_size * pth_size])
tmp *= tmp_ill
retouched_channel.append(tmp)
retouched = tf.concat(retouched_channel, -1)
img[:,
round(img_size * loc[0]) -
pth_size:round(img_size * loc[0]) + pth_size,
round(img_size * loc[1]) -
pth_size:round(img_size * loc[1]) +
pth_size, :] = retouched
else:
img = tf.reshape(
img, [tf.shape(img)[0], img_size, img_size, img_channel])
retouched_channel = []
for i in range(3):
tmp = img[:, :, :, i]
tmp = tf.reshape(tmp, [tf.shape(tmp)[0], -1])
tmp_ill = tf.reshape(ill[:, i] / ill[:, 1],
[tf.shape(img)[0], 1])
tmp_ill = tf.tile(tmp_ill, [1, img_size * img_size])
tmp *= tmp_ill
retouched_channel.append(tmp)
img = tf.concat(retouched_channel, -1)
return img
def _loop_function(prev, _):
loc, loc_mean = location_network(prev)
locs.append(loc)
loc_means.append(loc_mean)
gain = wb_network(prev)
gains.append(gain)
if img_retouched:
img_retouched.append(_apply_gain(gain, loc, img_retouched[-1]))
glimpse = glimpse_network(img_retouched[-1], loc)
else:
img_retouched.append(_apply_gain(gain, loc, self.img_ph))
glimpse = glimpse_network(self.img_ph, loc)
return glimpse
rnn_outputs, _ = rnn_decoder(rnn_inputs,
init_state,
cell,
loop_function=_loop_function)
assert len(gains) == len(locs)
# Time independent baselines
with tf.variable_scope('Baseline'):
baseline_w = weight_variable((cell.output_size, 1))
baseline_b = bias_variable((1, ))
baselines = []
for output in rnn_outputs[1:]:
baseline = tf.nn.xw_plus_b(output, baseline_w, baseline_b)
baseline = tf.squeeze(baseline)
baselines.append(baseline)
baselines = tf.stack(baselines) # [timesteps, batch_sz]
baselines = tf.transpose(baselines) # [batch_sz, timesteps]
# Classification. Take the last step only.
rnn_last_output = rnn_outputs[-1]
with tf.variable_scope('Classification'):
logit_w = weight_variable((cell.output_size, num_classes))
logit_b = bias_variable((num_classes, ))
logits = tf.nn.xw_plus_b(rnn_last_output, logit_w, logit_b)
# batch_size *3
self.prediction = tf.nn.l2_normalize(logits, axis=1)
self.locations = locs
if is_training:
# angular loss
self.xent = get_angular_loss(self.prediction, self.lbl_ph)
tf.summary.scalar('xent', self.xent)
# RL reward
# reward shape [batchsize, 1]
if FLAGS.USE_CRITIC:
img_critic = tf.reshape(self.img_ph, [
tf.shape(self.img_ph)[0], img_size, img_size, img_channel
])
img_real = apply_gain(img_critic, self.lbl_ph)
img_real = tf.reshape(
img_real,
[tf.shape(img_real)[0], img_size, img_size, img_channel])
img_fake = apply_gain(img_critic, self.prediction)
img_fake = tf.reshape(
img_fake,
[tf.shape(img_fake)[0], img_size, img_size, img_channel])
real_logit = critic_network(img_real,
is_train=is_training,
reuse=False)
fake_logit = critic_network(img_fake,
is_train=is_training,
reuse=True)
rnn_fake_logits = []
for index_sequence in range(len(img_retouched)):
rnn_img_fake = tf.reshape(img_retouched[index_sequence], [
tf.shape(img_retouched[index_sequence])[0], img_size,
img_size, img_channel
])
rnn_fake_logit = critic_network(rnn_img_fake,
is_train=is_training,
reuse=True)
rnn_fake_logits.append(rnn_fake_logit)
rewards = tf.stop_gradient(
tf.convert_to_tensor(
rnn_fake_logits)) # shape (timesteps, batch_sz, 1)
rewards = tf.transpose(tf.squeeze(
rewards, 2)) # shape [batch_sz, timesteps]
self.c_loss = tf.reduce_mean(fake_logit - real_logit)
if FLAGS.grad_penalty < 0:
# use grad clip
gradients = tf.gradients(self.c_loss, theta_c)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.opt_c = tf.train.AdamOptimizer(
self.learning_rate).apply_gradients(
zip(clipped_gradients, params),
global_step=self.global_step)
else:
# Critic gradient norm and penalty
alpha_dist = tf.contrib.distributions.Uniform(low=0.,
high=1.)
alpha = alpha_dist.sample((batch_size, 1, 1, 1))
interpolated = img_real + alpha * (img_fake - img_real)
inte_logit = critic_network(images=interpolated,
is_train=is_training,
reuse=True)
gradients = tf.gradients(inte_logit, [
interpolated,
])[0]
gradient_norm = tf.sqrt(
1e-6 + tf.reduce_sum(gradients**2, axis=[1, 2, 3]))
gradient_penalty = FLAGS.grad_penalty * tf.reduce_mean(
tf.maximum(gradient_norm - 1.0, 0.0)**2)
self.c_loss += gradient_penalty
theta_c = tf.trainable_variables(scope='critic')
gradients = tf.gradients(self.c_loss, theta_c)
self.opt_c = tf.train.AdamOptimizer(
self.learning_rate).apply_gradients(
zip(gradients, theta_c), global_step=self.global_step)
else:
reward = tf.norm(self.prediction - self.lbl_ph, axis=1)
rewards = tf.expand_dims(reward, 1)
rewards = tf.tile(rewards,
(1, num_glimpses)) # [batch_sz, timesteps]
advantages = rewards - tf.stop_gradient(baselines)
self.advantage = tf.reduce_mean(advantages)
logll = log_likelihood(loc_means, locs, variance)
logllratio = tf.reduce_mean(logll * advantages)
self.reward = tf.reduce_mean(rewards)
tf.summary.scalar('reward', self.reward)
# baseline loss
self.baselines_mse = tf.reduce_mean(
tf.square((rewards - baselines)))
# hybrid loss
self.loss = -logllratio + self.xent + self.baselines_mse
tf.summary.scalar('loss', self.loss)
# exclude the variables in critic scope
params_all = tf.trainable_variables()
params = []
for var in params_all:
if not 'critic' in var.op.name:
params.append(var)
gradients = tf.gradients(self.loss, params)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.train_op = tf.train.AdamOptimizer(
self.learning_rate).apply_gradients(
zip(clipped_gradients, params),
global_step=self.global_step)
img = tf.reshape(
self.img_ph,
[tf.shape(self.img_ph)[0], img_size, img_size, img_channel])
tf.summary.image('input', img)
tf.summary.image('gt', apply_gain(img, self.lbl_ph))
tf.summary.image('est', apply_gain(img, self.prediction))
self.sum_total = tf.summary.merge_all()
self.saver = tf.train.Saver(tf.global_variables(),
max_to_keep=99999999)
def __init__(self,
img_shape,
pth_size,
g_size,
l_size,
glimpse_output_size,
loc_dim,
variance,
cell_size,
num_glimpses,
num_classes,
learning_rate,
learning_rate_decay_factor,
min_learning_rate,
training_steps_per_epoch,
max_gradient_norm,
is_training=False):
self.is_training = is_training
self.img_ph = tf.placeholder(
tf.float32, [None, img_shape[0], img_shape[1], img_shape[2]])
self.lbl_ph = tf.placeholder(tf.int64, [None])
self.global_step = tf.Variable(0, trainable=False)
self.learning_rate = tf.maximum(
tf.train.exponential_decay(learning_rate,
self.global_step,
training_steps_per_epoch,
learning_rate_decay_factor,
staircase=True), min_learning_rate)
cell = BasicLSTMCell(cell_size)
with tf.variable_scope('GlimpseNetwork'):
glimpse_network = GlimpseNetwork(img_shape, pth_size, loc_dim,
g_size, l_size,
glimpse_output_size)
with tf.variable_scope('LocationNetwork'):
location_network = LocationNetwork(
loc_dim=loc_dim,
rnn_output_size=cell.output_size,
variance=variance,
is_sampling=self.is_training)
# Core Network
batch_size = tf.shape(self.img_ph)[0]
init_loc = tf.random_uniform((batch_size, loc_dim),
minval=-1,
maxval=1)
init_state = cell.zero_state(batch_size, tf.float32)
init_glimpse = glimpse_network(self.img_ph, init_loc)
rnn_inputs = [init_glimpse]
rnn_inputs.extend([0] * num_glimpses)
locs, loc_means = [], []
def loop_function(prev, _):
loc, loc_mean = location_network(prev, self.is_training)
locs.append(loc)
loc_means.append(loc_mean)
glimpse = glimpse_network(self.img_ph, loc)
return glimpse
rnn_outputs, _ = rnn_decoder(rnn_inputs,
init_state,
cell,
loop_function=loop_function)
# to be displyed
self.locs = locs
# Time independent baselines
with tf.variable_scope('Baseline'):
baseline_w = _weight_variable((cell.output_size, 1))
baseline_b = _bias_variable((1, ))
baselines = []
for output in rnn_outputs[1:]:
baseline = tf.nn.xw_plus_b(output, baseline_w, baseline_b)
baseline = tf.squeeze(baseline)
baselines.append(baseline)
baselines = tf.stack(baselines) # [timesteps, batch_sz]
baselines = tf.transpose(baselines) # [batch_sz, timesteps]
# Classification. Take the last step only.
rnn_last_output = rnn_outputs[-1]
with tf.variable_scope('Classification'):
logit_w = _weight_variable((cell.output_size, num_classes))
logit_b = _bias_variable((num_classes, ))
logits = tf.nn.xw_plus_b(rnn_last_output, logit_w, logit_b)
self.prediction = tf.argmax(logits, 1)
self.softmax = tf.nn.softmax(logits)
if self.is_training:
# classification loss
#self.xent = focal_loss(logits, self.lbl_ph)#
self.xent = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.lbl_ph, logits=logits))
# RL reward
reward = tf.cast(tf.equal(self.prediction, self.lbl_ph),
tf.float32)
# reward = tf.multiply(tf.cast(tf.equal(self.prediction, self.lbl_ph), tf.float32),0.1) + tf.multiply(tf.cast(tf.multiply(self.prediction, self.lbl_ph), tf.float32),0.9)
rewards = tf.expand_dims(reward, 1) # [batch_sz, 1]
rewards = tf.tile(rewards,
(1, num_glimpses)) # [batch_sz, timesteps]
advantages = rewards - tf.stop_gradient(baselines)
self.advantage = tf.reduce_mean(advantages)
logll = _log_likelihood(loc_means, locs, variance)
logllratio = tf.reduce_mean(logll * advantages)
self.reward = tf.reduce_mean(reward)
# baseline loss
self.baselines_mse = tf.reduce_mean(
tf.square((rewards - baselines)))
# hybrid loss
self.loss = -logllratio + self.xent + self.baselines_mse
params = tf.trainable_variables()
gradients = tf.gradients(self.loss, params)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, max_gradient_norm)
self.train_op = tf.train.AdamOptimizer(
self.learning_rate).apply_gradients(
zip(clipped_gradients, params),
global_step=self.global_step)
self.saver = tf.train.Saver(tf.global_variables(),
max_to_keep=99999999)
def __init__(self,
config,
decay_step,
is_training=False,
is_translate=False):
# image means feed-in images: batch_size * img_size^2
# label: labels of images not one hot representation
self.config = config
self.decay_step = decay_step
self.is_training = is_training
self.is_translate = is_translate
# input data placeholders
with tf.name_scope('input'):
self.image = tf.placeholder(
tf.float32,
[None, config.input_img_size * config.input_img_size])
self.label = tf.placeholder(tf.int64, [None])
with tf.name_scope('image_translate'):
# translate MNIST data if need
if self.is_translate:
img = tf.reshape(self.image, [
tf.shape(self.image)[0], config.input_img_size,
config.input_img_size, 1
],
name='2D_2_4D')
self.proc_image = self._translate_image(img)
# reshape into 2D tensor: [batch_size, img_size^2]
# new_img_size = self.proc_image.get_shape().as_list()
# print(new_img_size)
self.proc_image = tf.reshape(self.proc_image, [
tf.shape(self.image)[0], config.img_size * config.img_size
],
name='4D_2_2D')
else:
self.proc_image = self.image
with tf.name_scope('global_step'):
self.global_step = tf.Variable(0, trainable=False)
# define learning rate
with tf.name_scope('learning_rate'):
self.learning_rate = tf.maximum(
tf.train.exponential_decay(config.learning_rate,
self.global_step,
decay_step,
config.decay_factor,
staircase=True),
config.min_learning_rate)
tf.summary.scalar("learning_rate", self.learning_rate)
# Glimpse Network
with tf.name_scope('glimpse_net'):
self.glimpse_network = GlimpseNetwork(
config=config, is_translate=self.is_translate)
# Actor Network
with tf.name_scope('actor_net'):
self.actor_network = ActorNetwork(config=config,
rnn_output_size=config.cell_size,
is_sampling=self.is_training)
# LSTM Network
with tf.name_scope('lstm'):
cell = BasicLSTMCell(config.cell_size, name='basic_lstm_cell')
with tf.name_scope('initialization'):
with tf.name_scope('batch_size'):
batch_size = tf.shape(self.image)[0]
with tf.name_scope('init_locs'):
init_locs = tf.random_uniform(
shape=[batch_size, config.loc_dim],
minval=-1,
maxval=1,
name='sampling')
with tf.name_scope('init_state'):
init_state = cell.zero_state(batch_size, tf.float32)
# transfer glimpse network output into 2D list
# rnn_inputs: 3D list [[batch_size, 256], ...]
with tf.name_scope('init_glimpse'):
init_glimpse = self.glimpse_network(
self.proc_image, init_locs)
with tf.name_scope('rnn_inputs'):
rnn_inputs = [init_glimpse]
rnn_inputs.extend([0] * config.num_glimpses)
with tf.name_scope('init_list'):
self.locs, self.loc_means, self.retina_reprsent = [], [], []
# with tf.name_scope('rnn_decoder'):
def loop_function(prev, _):
loc, loc_mean = self.actor_network(prev)
self.locs.append(loc)
self.loc_means.append(loc_mean)
glimpse = self.glimpse_network(self.proc_image, loc)
self.retina_reprsent.append(
self.glimpse_network.retina_sensor.retina_reprsent)
return glimpse
self.rnn_outputs, _ = rnn_decoder(rnn_inputs,
init_state,
cell,
loop_function=loop_function)
# Critic Network
with tf.name_scope('critic_net'):
self.critic_network = CriticNetwork(
config=config, rnn_output_size=cell.output_size)
# Classify Network
with tf.name_scope('classify_net'):
self.classify_network = ClassifyNetwork(
config=config, rnn_output_size=cell.output_size)
rnn_last_output = self.rnn_outputs[-1]
self.logits = self.classify_network(rnn_last_output)
with tf.name_scope('argmax'):
self.prediction = tf.argmax(self.logits, 1) # [batch_size]
with tf.name_scope('softmax'):
self.softmax = tf.nn.softmax(self.logits)
if is_training:
# hybrid loss: classification loss, RL reward, baseline loss
with tf.name_scope('total_loss'):
self.loss = self.total_loss()
tf.summary.scalar("total_loss", self.loss)
with tf.name_scope('train'):
var_list = tf.trainable_variables()
gradients = tf.gradients(self.loss, var_list)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, config.max_gradient_norm)
self.train_op = tf.train.AdamOptimizer(
self.learning_rate).apply_gradients(
zip(clipped_gradients, var_list),
global_step=self.global_step)
with tf.name_scope('merge'):
self.merged = tf.summary.merge_all()
# 词向量表-随机初始化
train_x, vocab_size = get_vocabulary(train_x)
test_x, vocab_size_test = get_vocabulary(test_x)
print("datas shape:",train_x.shape)
embeddings = tf.get_variable("embeddings", [vocab_size, embedding_size],
initializer=tf.truncated_normal_initializer)
# 将词索引号转换为词向量[None, max_document_length] => [None, max_document_length, embedding_size]
embedded = tf.nn.embedding_lookup(embeddings, datas_placeholder)
# 转换为LSTM的输入格式，要求是数组，数组的每个元素代表一个Batch下，一个时序的数据（即一个词）
rnn_input = tf.unstack(embedded, max_document_length, axis=1)
# 定义LSTM网络结构
lstm_cell = BasicLSTMCell(num_units=num_units, forget_bias=1.0) # cell
lstm_cell = DropoutWrapper(cell=lstm_cell, input_keep_prob=1.0, output_keep_prob=keep_prob)
rnn_outputs, rnn_states = static_rnn(cell=lstm_cell, inputs=rnn_input, dtype=tf.float32) # network
# 最后一层
logits = tf.layers.dense(units=num_classes,inputs=rnn_outputs[-1]) # fully-connected
pred_labels = tf.arg_max(input=logits,dimension=1) # 概率最大的类别为预测的类别
# 定义损失函数, logists为网络最后一层输出, labels为真实标签
losses = tf.nn.softmax_cross_entropy_with_logits(logits=logits,
labels=tf.one_hot(labels_placeholder, num_classes))
mean_losses = tf.reduce_mean(losses)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(mean_losses)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
# 初始化变量
def lstm_cell():
"""lstm核"""
return BasicLSTMCell(self.config.hidden_dim,
state_is_tuple=True, )
