def _get_mean(self, _input):
"""
"""
dirs = self.directives
num_layers = dirs['num_layers']
num_nodes = dirs['num_nodes']
activation = dirs['activation']
net_grow_rate = dirs['net_grow_rate']
output_dim = self._oslot_to_shape[0][-1]
with tf.variable_scope(self.name + '_mean', reuse=tf.AUTO_REUSE):
# Define the Means
hid_layer = fully_connected(
_input,
num_nodes,
activation_fn=activation,
biases_initializer=tf.random_normal_initializer(
stddev=1 / np.sqrt(num_nodes)))
for _ in range(num_layers - 1):
num_nodes = int(num_nodes * net_grow_rate)
hid_layer = fully_connected(
hid_layer,
num_nodes,
activation_fn=activation,
biases_initializer=tf.random_normal_initializer(
stddev=1 / np.sqrt(num_nodes)))
mean = fully_connected(hid_layer, output_dim, activation_fn=None)
return mean, hid_layer
def embedding_model(im_feats,
sent_feats,
train_phase,
im_labels,
fc_dim=2048,
embed_dim=512):
"""
Build two-branch embedding networks.
fc_dim: the output dimension of the first fc layer.
embed_dim: the output dimension of the second fc layer, i.e.
embedding space dimension.
"""
# Image branch.
im_fc1 = add_fc(im_feats, fc_dim, train_phase, 'im_embed_1')
im_fc2 = fully_connected(im_fc1,
embed_dim,
activation_fn=None,
scope='im_embed_2')
i_embed = tf.nn.l2_normalize(im_fc2, 1, epsilon=1e-10)
# Text branch.
sent_fc1 = add_fc(sent_feats, fc_dim, train_phase, 'sent_embed_1')
sent_fc2 = fully_connected(sent_fc1,
embed_dim,
activation_fn=None,
scope='sent_embed_2')
s_embed = tf.nn.l2_normalize(sent_fc2, 1, epsilon=1e-10)
return i_embed, s_embed
def get_network(img_emb, sent_emb): im_fc1 = add_fc(img_emb, 2048, True, 'im_embed_1') im_fc2 = fully_connected(im_fc1, 512, activation_fn=None, scope = 'im_embed_2') i_embed = tf.nn.l2_normalize(im_fc2, 1, epsilon=1e-10) sent_fc1 = add_fc(sent_emb, 2048, True,'sent_embed_1') sent_fc2 = fully_connected(sent_fc1, 512, activation_fn=None, scope = 'sent_embed_2') s_embed = tf.nn.l2_normalize(sent_fc2, 1, epsilon=1e-10) return i_embed, s_embed
def _build_out_default(self, Z):
"""
"""
ydim = self.ydim
# with tf.variable_scope('DetAE', reuse=tf.AUTO_REUSE):
full_out_1 = fully_connected(Z, 64)
full_out_2 = fully_connected(full_out_1, 128)
Yprime = fully_connected(full_out_2, ydim)
return Yprime
def _generate_out(self, Z): """ """ ydim = self.ydim full_out_1 = fully_connected(Z, 64) full_out_2 = fully_connected(full_out_1, 128) Yprime = fully_connected(full_out_2, ydim) return Yprime
def embedding_model(vd_feats,
sent_feats,
train_phase,
vd_labels,
fc_dim=2048,
embed_dim=512):
"""
Build two-branch embedding networks.
fc_dim: the output dimension of the first fc layer.
embed_dim: the output dimension of the second fc layer, i.e.
embedding space dimension.
"""
# video branch.
dim_hidden = 1000
dim_video = 4096
n_video_lstm_step = 80
batch_size = vd_feats.shape[0]
lstm1 = tf.contrib.rnn.BasicLSTMCell(dim_hidden, state_is_tuple=False)
lstm2 = tf.contrib.rnn.BasicLSTMCell(dim_hidden, state_is_tuple=False)
encode_video_W = tf.Variable(tf.random_uniform([dim_video, dim_hidden],
-0.1, 0.1),
name='encode_video_W')
encode_video_b = tf.Variable(tf.zeros([dim_hidden]), name='encode_video_b')
video_flat = tf.reshape(vd_feats, [-1, dim_video])
video_emb = tf.nn.xw_plus_b(video_flat, encode_video_W, encode_video_b)
video_emb = tf.reshape(video_emb,
[batch_size, n_video_lstm_step, dim_hidden])
state1 = tf.zeros([batch_size, lstm1.state_size])
state2 = tf.zeros([batch_size, lstm2.state_size])
padding = tf.zeros([batch_size, dim_hidden])
with tf.variable_scope(tf.get_variable_scope()) as scope:
for i in range(0, n_video_lstm_step):
if i > 0:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope("LSTM1"):
output1, state1 = lstm1(video_emb[:, i, :], state1)
with tf.variable_scope("LSTM2"):
output2, state2 = lstm2(tf.concat([padding, output1], 1),
state2)
im_fc1 = add_fc(tf.concat([state1, state2], 1), fc_dim, train_phase,
'vd_embed_1')
im_fc2 = fully_connected(im_fc1,
embed_dim,
activation_fn=None,
scope='vd_embed_2')
v_embed = tf.nn.l2_normalize(im_fc2, 1, epsilon=1e-10)
# Text branch.
sent_fc1 = add_fc(sent_feats, fc_dim, train_phase, 'sent_embed_1')
sent_fc2 = fully_connected(sent_fc1,
embed_dim,
activation_fn=None,
scope='sent_embed_2')
s_embed = tf.nn.l2_normalize(sent_fc2, 1, epsilon=1e-10)
return v_embed, s_embed
def _linear(self, input_tensor, output_nums, l2_reg, activation_fn=None):
if l2_reg <= 0:
return layers.fully_connected(input_tensor, output_nums, activation_fn=activation_fn,
weights_initializer=layers.xavier_initializer(),
biases_initializer=layers.xavier_initializer(),)
else:
return layers.fully_connected(input_tensor, output_nums, activation_fn=activation_fn,
weights_initializer=layers.xavier_initializer(),
biases_initializer=layers.xavier_initializer(),
weights_regularizer=layers.l2_regularizer(l2_reg), biases_regularizer=layers.l2_regularizer(l2_reg))
def encode_phrases(args, phrase_plh, train_phase_plh, num_phrases_plh, phrase_feature_dim, phrase_denom_plh, vecs):
final_embed = args.dim_embed
embed_dim = final_embed * 4
phrase_plh = tf.reshape(phrase_plh, [-1, num_phrases_plh, phrase_feature_dim])
# sometimes finetuning word embedding helps (with l2 reg), but often doesn't
# seem to make a big difference
word_embeddings = tf.get_variable('word_embeddings', vecs.shape, initializer=tf.constant_initializer(vecs), trainable = args.embedding_ft)
embedded_words = tf.nn.embedding_lookup(word_embeddings, phrase_plh)
# if you do finetune
embed_l2reg = tf.zeros(1)
if args.embedding_ft:
embed_l2reg = tf.nn.l2_loss(word_embeddings - vecs)
eps = 1e-10
if args.language_model == 'gru':
phrase_plh = tf.reshape(phrase_plh, [-1, phrase_feature_dim])
source_sequence_length = tf.reduce_sum(tf.cast(phrase_plh > 0, tf.int32), 1)
embedded_words = tf.reshape(embedded_words, [-1, phrase_feature_dim, vecs.shape[1]])
encoder_cell = tf.nn.rnn_cell.GRUCell(final_embed)
encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
encoder_cell, embedded_words, dtype=encoder_cell.dtype,
sequence_length=source_sequence_length)
final_outputs = extract_axis_1(encoder_outputs, source_sequence_length-1)
phrase_input = tf.reshape(final_outputs, [-1, num_phrases_plh, final_embed])
outputs = fully_connected(phrase_input, embed_dim, activation_fn = None,
weights_regularizer = tf.contrib.layers.l2_regularizer(0.005),
scope = 'phrase_encoder')
phrase_embed = tf.nn.l2_normalize(outputs, 2, epsilon=eps)
else:
num_words = tf.reduce_sum(tf.to_float(phrase_plh > 0), 2, keep_dims=True) + eps
phrase_input = tf.nn.l2_normalize(tf.reduce_sum(embedded_words, 2) / num_words, 2)
if args.language_model == 'attend':
context_vector = tf.tile(tf.expand_dims(phrase_input, 2), (1, 1, phrase_feature_dim, 1))
attention_inputs = tf.concat((context_vector, embedded_words), 3)
attention_weights = fully_connected(attention_inputs, 1,
weights_regularizer = l2_regularizer(0.0005),
scope = 'self_attend')
attention_weights = tf.nn.softmax(tf.squeeze(attention_weights))
phrase_input = tf.nn.l2_normalize(tf.reduce_sum(embedded_words * tf.expand_dims(attention_weights, 3), 2), 2)
phrase_input = tf.reshape(phrase_input, [-1, num_phrases_plh, vecs.shape[1]])
if args.cca_parameters:
parameters = pickle.load(open(args.cca_parameters, 'rb'))
phrase_embed = setup_initialize_fc_layers(args, phrase_input, parameters, 'lang', train_phase_plh, norm_axis=2)
else:
phrase_embed = embedding_branch(phrase_input, embed_dim, train_phase_plh, 'phrase', norm_axis=2)
concept_weights = embedding_branch(phrase_input, embed_dim, train_phase_plh, 'concept_weight',
do_l2norm = False, outdim = args.num_embeddings)
concept_loss = tf.reduce_sum(tf.norm(concept_weights, axis=2, ord=1)) / phrase_denom_plh
concept_weights = tf.nn.softmax(concept_weights)
return phrase_embed, concept_weights, concept_loss, embed_l2reg
def _build_default(self):
"""
"""
Y = self.Y
zdim = self.zdim
with tf.variable_scope('DetAE', reuse=tf.AUTO_REUSE):
full_in_1 = fully_connected(Y, 128)
full_in_2 = fully_connected(full_in_1, 64)
Z = fully_connected(full_in_2, zdim)
Yprime = self._build_out_default(Z)
return Yprime, Z
def _define_default(self):
"""
"""
Y = self.Y
zdim = self.zdim
full_in_1 = fully_connected(Y, 128)
full_in_2 = fully_connected(full_in_1, 64)
Z = fully_connected(full_in_2, zdim)
Yprime = self._generate_out(Z)
return Yprime, Z
def define_graph(input_image, batch_size):
print("string define the graph...")
# 卷积层1
conv2d_layer_1 = convolution2d(
input_image,
num_outputs = 32, # filter 个数
kernel_size = (5,5), # filter 的宽和高
activation_fn = tf.nn.relu,
weights_initializer = tf.random_normal_initializer,
stride = [2, 2],
trainable = True)
pool_layer_1 = tf.nn.max_pool(conv2d_layer_1, ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1],
padding = 'SAME')
# 卷积层2
conv2d_layer_2 = convolution2d(
pool_layer_1,
num_outputs = 64,
kernel_size = (5, 5),
activation_fn = tf.nn.relu,
weights_initializer = tf.random_normal_initializer,
stride = (1, 1),
trainable = True)
pool_layer_2 = tf.nn.max_pool(conv2d_layer_2, ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1],
padding = 'SAME')
flattened_layer_2 = tf.reshape(pool_layer_2, [ batch_size, -1 ])
# weight_init 参数也可以接收一个可调用参数，这里使用的了一个lambda 表达式返回了一个截断的正态分布，并指定了标准差
hidden_layer_3 = fully_connected(
flattened_layer_2,
512,
weights_initializer = lambda i, dtype, partition_info: tf.truncated_normal([38912, 512], stddev = 0.1),
activation_fn = tf.nn.relu
)
# 对一些神经元进行dropout，削减他们在模型中的重要性
hidden_layer_3 = tf.nn.dropout(hidden_layer_3, 0.1)
# 输出是前面的层与训练中可用的120个不同狗品种的全连接
final_fully_connected = fully_connected(
hidden_layer_3,
120, # 120 种狗
weights_initializer = lambda i, dtype, partition_info: tf.truncated_normal([512, 120], stddev = 0.1)
)
print("graph is ready")
return final_fully_connected
def generator(z, y):
'''
args:
z: random vector
returns:
net: generator network
'''
zf = layers.fully_connected(z, 1000, scope='fcz')
yf = layers.fully_connected(y, 200, scope='fcy')
net = tf.concat(1, [zf, yf])
net = layers.fully_connected(net, 1200, activation_fn=tf.nn.relu, scope='fc1')
net = layers.fully_connected(net, 1200, activation_fn=tf.nn.relu, scope='fc2')
net = layers.fully_connected(net, WIDTH*WIDTH, scope='fc3')
return net
def pose_net(tgt_image, src_image_stack, is_training=True):
inputs = tf.concat([tgt_image, src_image_stack], axis=3)
B, H, W, C = src_image_stack.get_shape().as_list()
num_source = int(C // 3)
with tf.variable_scope('pose_net') as sc:
end_points_collection = sc.original_name_scope + '_end_points'
with slim.arg_scope(
[slim.conv2d, slim.conv2d_transpose, fully_connected],
normalizer_fn=None,
weights_regularizer=slim.l2_regularizer(0.05),
activation_fn=tf.nn.relu,
outputs_collections=end_points_collection):
# cnv1 to cnv5b are shared between pose and explainability prediction
cnv1 = slim.conv2d(inputs, 16, [7, 7], stride=2, scope='cnv1')
cnv2 = slim.conv2d(cnv1, 32, [5, 5], stride=2, scope='cnv2')
cnv3 = slim.conv2d(cnv2, 64, [3, 3], stride=2, scope='cnv3')
cnv4 = slim.conv2d(cnv3, 128, [3, 3], stride=2, scope='cnv4')
cnv5 = slim.conv2d(cnv4, 256, [3, 3], stride=2, scope='cnv5')
# Pose specific layers
with tf.variable_scope('pose'):
cnv6 = slim.conv2d(cnv5, 256, [3, 3], stride=2, scope='cnv6')
cnv7 = slim.conv2d(cnv6, 512, [3, 3], stride=2, scope='cnv7')
flat_1 = tf.reshape(cnv7, (B, -1))
dense_1 = fully_connected(flat_1,
512,
activation_fn=tf.nn.relu)
pose_pred_xyz = fully_connected(dense_1,
3 * num_source,
activation_fn=None)
pose_pred_quat = fully_connected(dense_1,
3 * num_source,
activation_fn=None)
pose_avg = tf.concat((pose_pred_xyz, pose_pred_quat), 1)
pose_avg = 0.1 * pose_avg
pose_final = tf.reshape(pose_avg, [-1, num_source, 6])
end_points = utils.convert_collection_to_dict(
end_points_collection)
return pose_final, end_points
def setup_lstm(self, encoder_cell, embedded_word_ids, tokens,
source_sequence_length, reuse, suffix):
universal_embedding, avg_words = universal_embedding_layer(
embedded_word_ids, tokens, self.embed_dim, suffix)
if self.args.separate_lang_branch:
reuse = None
suffix = '_' + suffix
else:
suffix = ''
encoder_outputs, encoder_state = tf.nn.dynamic_rnn(
encoder_cell,
universal_embedding,
dtype=tf.float32,
sequence_length=source_sequence_length,
scope='rnn' + suffix)
final_outputs = extract_axis_1(encoder_outputs,
source_sequence_length - 1)
outputs = fully_connected(
final_outputs,
self.embed_dim,
activation_fn=None,
weights_regularizer=tf.contrib.layers.l2_regularizer(0.005),
scope='phrase_encoder' + suffix,
reuse=reuse)
sent_embed = tf.nn.l2_normalize(outputs, 1, epsilon=1e-10)
return sent_embed, avg_words
def __init__(self, lr, st_size, act_size):
# Current State Placeholder
self.current_state_in = tf.placeholder(shape=[1], dtype=tf.int32)
# Create a One hot vector
current_state_OneHot = tf.one_hot(self.current_state_in, st_size)
# Output of the Fully Connected Layer
output = fully_connected(current_state_OneHot, act_size,
activation_fn=tf.nn.sigmoid,
biases_initializer=None,
weights_initializer=tf.ones_initializer())
self.output = tf.reshape(output, [-1])
self.chosen_action = tf.argmax(self.output, 0)
# Placeholder to store rewards
self.reward_holder = tf.placeholder(shape=[1], dtype=tf.float32)
self.action_holder = tf.placeholder(shape=[1], dtype=tf.int32)
self.responsible_weight = tf.slice(self.output, self.action_holder, [1])
# Policy Gradient Calculation
self.loss = -(tf.log(self.responsible_weight) * self.reward_holder)
# Define and use Gradient Descent Optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
# Update the Neural Network Agen values
self.update = optimizer.minimize(self.loss)
# -------------------- EOC -----------------------
def add_fc(inputs, outdim, train_phase, scope_in):
fc = fully_connected(inputs, outdim, activation_fn=None, scope=scope_in + '/fc')
fc_bnorm = tf.layers.batch_normalization(fc, momentum=0.1, epsilon=1e-5,
training=train_phase, name=scope_in + '/bnorm')
fc_relu = tf.nn.relu(fc_bnorm, name=scope_in + '/relu')
fc_out = tf.layers.dropout(fc_relu, rate= 0.1, seed=0, training=train_phase, name=scope_in + '/dropout')
return fc_out
def setup_initialize_fc_layers(args,
feats,
parameters,
scope_in,
train_phase,
norm_axis=2):
for i, params in enumerate(parameters):
scaling = params['scaling']
outdim = len(scaling)
cca_mean, cca_proj = params[scope_in + '_mean'], params[scope_in +
'_proj']
weights_init = tf.constant_initializer(cca_proj, dtype=tf.float32)
weight_reg = weight_l2_regularizer(params[scope_in + '_proj'],
args.cca_weight_reg)
if (i + 1) < len(parameters):
activation_fn = tf.nn.relu
else:
activation_fn = None
feats = fully_connected(
feats - cca_mean,
outdim,
activation_fn=activation_fn,
weights_initializer=weights_init,
weights_regularizer=weight_reg,
#trainable=False,
scope=scope_in + '_embed_' + str(i)) * scaling
feats = tf.nn.l2_normalize(feats, norm_axis, epsilon=1e-10)
return feats
def embedding_branch(x,
embed_dim,
train_phase_plh,
scope_in,
do_l2norm=True,
outdim=None,
norm_axis=1):
"""Applies a pair of fully connected layers to the input tensor.
Arguments:
x -- input_tensor
embed_dim -- dimension of the input to the second fully connected layer
train_phase_plh -- indicator whether model is in training mode
scope_in -- scope prefix for the desired layers
do_l2norm -- indicates if the output should be l2 normalized
outdim -- dimension of the output embedding, if None outdim=embed_dim
"""
embed_fc1 = add_fc(x, embed_dim, train_phase_plh, scope_in + '_embed_1')
if outdim is None:
outdim = embed_dim
l2_reg = tf.contrib.layers.l2_regularizer(0.001)
embed_fc2 = fully_connected(embed_fc1,
outdim,
activation_fn=None,
weights_regularizer=l2_reg,
scope=scope_in + '_embed_2')
if do_l2norm:
embed_fc2 = tf.nn.l2_normalize(embed_fc2, norm_axis)
return embed_fc2
def embedding_model(im_feats,
sent_feats,
train_phase,
im_labels,
fc_dim=1024,
embed_dim=1024):
"""
Build two-branch embedding networks.
fc_dim: the output dimension of the first fc layer.
embed_dim: the output dimension of the second fc layer, i.e.
embedding space dimension.
"""
# Image branch.
# layer_1 = tf.add(tf.matmul(im_feats, wf['h1']), bf['b1'])
# layer_2 = tf.add(tf.matmul(layer_1, wf['h2']), bf['b2'])
# layer_3 = tf.add(tf.matmul(layer_2, wf['h3']), bf['b3'])
im_fc1 = add_fc(im_feats, fc_dim, train_phase, 'im_embed_1')
im_fc2 = fully_connected(im_fc1,
embed_dim,
activation_fn=None,
scope='im_embed_2')
fc_bnorm = tf.layers.batch_normalization(im_fc2,
momentum=0.1,
epsilon=1e-5,
training=train_phase)
# im_fc2 = tf.layers.dense(im_fc1, embed_dim, activation=tf.nn.tanh)
i_embed = tf.nn.l2_normalize(fc_bnorm, 1, epsilon=1e-10)
# Voice branch.
# layer_1 = tf.add(tf.matmul(sent_feats, wv['h1']), bv['b1'])
# layer_2 = tf.add(tf.matmul(layer_1, wv['h2']), bv['b2'])
# layer_3 = tf.add(tf.matmul(layer_2, wv['h3']), bv['b3'])
sent_fc1 = add_fc(sent_feats, fc_dim, train_phase, 'sent_embed_1')
sent_fc2 = fully_connected(sent_fc1,
embed_dim,
activation_fn=None,
scope='sent_embed_2')
fc_bnorm_sent = tf.layers.batch_normalization(sent_fc2,
momentum=0.1,
epsilon=1e-5,
training=train_phase)
# sent_fc2 = tf.layers.dense(sent_fc1, embed_dim, activation=None)
s_embed = tf.nn.l2_normalize(fc_bnorm_sent, 1, epsilon=1e-10)
return i_embed, s_embed
def _get_scale_tril(self, _input, hid_layer=None):
"""
"""
dirs = self.directives
num_layers = dirs['num_layers']
num_nodes = dirs['num_nodes']
activation = dirs['activation']
net_grow_rate = dirs['net_grow_rate']
output_dim = self._oslot_to_shape[0][-1]
with tf.variable_scope(self.name + '_scale', reuse=tf.AUTO_REUSE):
if dirs['share_params']:
output_chol = fully_connected(hid_layer,
output_dim**2,
activation_fn=None)
else:
print("_input:", _input)
hid_layer = fully_connected(
_input,
num_nodes,
activation_fn=activation,
biases_initializer=tf.random_normal_initializer(
stddev=1 / np.sqrt(num_nodes)))
for _ in range(num_layers - 1):
num_nodes = int(num_nodes * net_grow_rate)
hid_layer = fully_connected(
hid_layer,
num_nodes,
activation_fn=activation,
biases_initializer=tf.random_normal_initializer(
stddev=1 / np.sqrt(num_nodes)))
output_chol = fully_connected(
hid_layer,
output_dim**2,
activation_fn=None,
weights_initializer=tf.random_normal_initializer(
stddev=1e-4),
biases_initializer=tf.random_normal_initializer(
stddev=1 / np.sqrt(output_dim**2)))
# normalizer_fn=lambda x : x/tf.sqrt(x**2),
output_chol = tf.reshape(
output_chol,
# shape=[self.batch_size, output_dim, output_dim])
shape=[-1, output_dim, output_dim])
return output_chol
def setup_img_model(self, im_feats, train_phase):
im_fc1 = add_fc(im_feats, self.fc_dim, train_phase, 'im_embed_1')
im_fc2 = fully_connected(im_fc1,
self.embed_dim,
activation_fn=None,
scope='im_embed_2')
i_embed = tf.nn.l2_normalize(im_fc2, 1, epsilon=1e-10)
return i_embed
def discriminator(x,y):
'''
args:
x: data vector
returns:
net: discriminator network
'''
xf = layers.fully_connected(x, 1000, scope='fcx')
yf = layers.fully_connected(y, 200, scope='fcy')
net = tf.concat(1, [xf, yf])
net = layers.dropout(net, 0.2, scope='do1')
net = layers.fully_connected(net, 1200, activation_fn=tf.nn.sigmoid, scope='fc1')
net = layers.dropout(net, 0.5, scope='do2')
net = layers.fully_connected(net, 1200, activation_fn=tf.nn.sigmoid, scope='fc2')
net = compare_to_minibatch(net)
# no activation function because it's in the cost function used later.
net = layers.fully_connected(net, 1, scope='fc3', activation_fn=None)
return net
def embedding_model(im_feats, sent_feats, train_phase, im_labels,
fc_dim = 1024, embed_dim = 512):
"""
Build two-branch embedding networks.
fc_dim: the output dimension of the first fc layer.
embed_dim: the output dimension of the second fc layer, i.e.
embedding space dimension.
"""
# Image branch.
#is_training = tf.placeholder(dtype=tf.bool, shape=())
im_fc1 = add_fc(im_feats, fc_dim, train_phase, 'im_embed_1')
#im_fc2=add_fc(im_fc1,512,train_phase,'im_embed_2')
#im_fc3=add_fc(im_fc2,256,train_phase,'im_embed_3')
im_fc2 = fully_connected(im_fc1, embed_dim, activation_fn=None,
scope = 'im_embed_2')
i_embed = tf.nn.l2_normalize(im_fc2, 1, epsilon=1e-10)
#im_fc3 = fully_connected(i_embed, 30, activation_fn=None,
# scope = 'im_embed_3')
#attr1 = tf.nn.sigmoid(im_fc3,name = 'attr_rec1')
# Text branch.
#print("type",sent_feats)
sent_f=tf.to_int32(sent_feats,name='ToInt32')
sent_f=tf.one_hot(sent_f,2)
sent_fc0 = add_fc(sent_f, 4, train_phase,'sent_embed_0')
#print("sent_one_hot",sent_f)
#sent_fc0 = add_fc(sent_f,10,train_phase,'sent_embed_0')
sent_fc0 = tf.layers.flatten(sent_fc0)
#print("sent_fc_0",sent_fc0)
#sent_fc1 = add_fc(sent_fc0, 128, train_phase,'sent_embed_1')
#print("sent_fc1",sent_fc1)
sent_fc2 = add_fc(sent_fc0, 256, train_phase,'sent_embed_2')
#sent_fc3 = add_fc(sent_fc2,512 , train_phase,'sent_embed_3')
#sent_fc4 = add_fc(sent_fc2,1024 , train_phase,'sent_embed_4')
sent_fc3 = fully_connected(sent_fc2, embed_dim, activation_fn=None,
scope = 'sent_embed_3')
s_embed = tf.nn.l2_normalize(sent_fc3, 1, epsilon=1e-10)
#sent_fc3=im_fc3 = fully_connected(s_embed, 30, activation_fn=None,
# scope = 'sent_embed_3')
#attr2 = tf.nn.sigmoid(sent_fc3,name = 'attr_rec2')
#attr2 = fully_connected(sent_fc2,30,activation_fn=None,scope = 'attr_rec2')
return i_embed, s_embed
def decoder(self,x,keep_prob):
d_fc1 = fully_connected(x, 4096, None)
d_fc1 = tf.nn.dropout(d_fc1, keep_prob)
#print d_fc1
d_unflat1 = tf.reshape(d_fc1, [-1,16,16,16])
#print d_unflat1
d_conv1 = convolution2d_transpose(d_unflat1, 32, [5,5], [2,2], padding='SAME')
#print d_conv1
d_conv2 = convolution2d_transpose(d_conv1, 3, [5,5], [2,2], padding='SAME')
#print d_conv2
return d_conv2
def encoder(self,x, keep_prob):
e_conv1 = convolution2d(x, 32, [5,5], [2,2], padding='SAME')
#print e_conv1
e_conv2 = convolution2d(e_conv1, 16, [5,5], [2,2], padding='SAME')
#print e_conv2
e_flat1 = flatten(e_conv2)
#print e_flat1
#e_flat1 = tf.nn.dropout(e_flat1 keep_prob)
e_fc1 = fully_connected(e_flat1, self.params['final_layer'], None)
e_fc1 = tf.nn.dropout(e_fc1, keep_prob)
return e_fc1
def embedding_model(feats, train_phase, scope_name,
fc_dim = n_inputs, embed_dim = n_hidden):
"""
Build two-branch embedding networks.
fc_dim: the output dimension of the first fc layer.
embed_dim: the output dimension of the second fc layer, i.e.
embedding space dimension.
"""
# each branch.
fc1 = add_fc(feats, fc_dim, train_phase, scope_name)
fc2 = fully_connected(fc1, embed_dim, activation_fn=None,
scope = scope_name + '_2')
embed = tf.nn.l2_normalize(fc2, 1, epsilon=1e-10)
return embed
def setup_img_model(im_feats, train_phase, args, fc_dim=2048, embed_dim=512):
if args.init_filename:
parameters = pickle.load(open(args.init_filename, 'rb'))
i_embed = setup_initialize_fc_layers(im_feats, parameters, 'vis',
train_phase, args)
else:
im_fc1 = add_fc(im_feats, fc_dim, train_phase, 'im_embed_1')
im_fc2 = fully_connected(
im_fc1,
embed_dim,
activation_fn=None,
weights_regularizer=tf.contrib.layers.l2_regularizer(0.0005),
scope='im_embed_2')
i_embed = tf.nn.l2_normalize(im_fc2, 1, epsilon=1e-10)
return i_embed
def compare_to_minibatch(input, num_kernels=5, kernel_dim=3):
'''
take output of intermediate layer of the discriminator,
and compare individual samples within the minibatch
'''
#multiply discriminator layer by 3D tensor to produce matrix
x = layers.fully_connected(input, num_kernels * kernel_dim)
activation = tf.reshape(x, (-1, num_kernels, kernel_dim))
#compute L1-distance between rows of matrix
diffs = tf.expand_dims(activation, 3) - \
tf.expand_dims(tf.transpose(activation, [1, 2, 0]), 0)
abs_diffs = tf.reduce_sum(tf.abs(diffs), 2)
#apply negative exponential
minibatch_features = tf.reduce_sum(tf.exp(-abs_diffs), 2)
return tf.concat(1, [input, minibatch_features])
def universal_embedding_layer(embedded_word_ids,
tokens,
embed_dim,
suffix,
trainable=True):
universal_embedding = fully_connected(
embedded_word_ids,
embed_dim,
activation_fn=None,
weights_regularizer=tf.contrib.layers.l2_regularizer(0.005),
trainable=trainable,
scope='mule_' + suffix)
num_words = tf.reduce_sum(tf.to_float(tokens > 0), 1,
keep_dims=True) + 1e-10
avg_words = tf.nn.l2_normalize(
tf.reduce_sum(universal_embedding, 1) / num_words, 1)
return universal_embedding, avg_words
def get_phrase_scores(self, phrase_embed, region_embed, concept_weights):
elementwise_prod = tf.expand_dims(phrase_embed, 2) * tf.expand_dims(
region_embed, 1)
joint_embed_1 = add_fc(elementwise_prod, self.embed_dim,
self.train_phase, 'joint_embed_1')
joint_embed_2 = concept_layer(joint_embed_1, self.final_embed,
self.train_phase, 1, concept_weights)
for concept_id in range(2, self.args.num_embeddings + 1):
joint_embed_2 += concept_layer(joint_embed_1, self.final_embed,
self.train_phase, concept_id,
concept_weights)
joint_embed_3 = fully_connected(
joint_embed_2,
1,
activation_fn=None,
weights_regularizer=l2_regularizer(0.005),
scope='joint_embed_3')
joint_embed_3 = tf.squeeze(joint_embed_3, [3])
region_prob = 1. / (1. + tf.exp(-joint_embed_3))
return region_prob, joint_embed_3