n = trainX.shape[0]
for decay_count in decay:
tf.reset_default_graph()
with tf.Session() as sess:
# Create the model
x = tf.placeholder(tf.float32, [None, NUM_FEATURES])
y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES])
# Build the graph for the deep net
hidden_layer = layers.dense(
x,
25,
activation=tf.nn.relu,
kernel_initializer=init.orthogonal(np.sqrt(2)),
bias_initializer=init.zeros(),
kernel_regularizer=l2_regularizer(decay_count))
output_layer = layers.dense(
hidden_layer,
6,
kernel_initializer=init.orthogonal(),
bias_initializer=init.zeros(),
kernel_regularizer=l2_regularizer(decay_count))
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_, logits=output_layer)
loss = tf.reduce_mean(cross_entropy) + get_regularization_loss()
# Create the gradient descent optimizer with the given learning rate.
def build_net(self, scope):
init = tf.random_uniform_initializer(0., 1.)
if SHARED:
#Shared layer
with tf.variable_scope('shared'):
for i, layer in enumerate(SHARED_LAYER):
hidden = layers.dense(
self.s,
layer,
tf.nn.tanh,
kernel_initializer=init,
kernel_regularizer=tf.contrib.layers.l2_regularizer(
scale=L2_REG_LAMBDA),
name='layer_%s' % (i + 1))
with tf.variable_scope('Actor'):
a_output = layers.dense(hidden,
self.n_actions,
tf.nn.softmax,
kernel_initializer=init,
name='a_out')
with tf.variable_scope('Critic'):
c_output = layers.dense(hidden,
1,
kernel_initializer=init,
name='c_out')
params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=scope + '/shared')
return a_output, c_output, params
else:
# Separate Actor and Critic Network
with tf.variable_scope('Actor'):
#Create Actor Network
for layer in ACTOR_LAYER:
a_hidden = layers.dense(
self.s,
layer,
tf.nn.relu6,
kernel_initializer=init,
name='a_hidden',
kernel_regularizer=tf.contrib.layers.l2_regularizer(
scale=L2_REG_LAMBDA))
a_output = layers.dense(a_hidden,
self.n_actions,
tf.nn.softmax,
kernel_initializer=init,
name='a_out')
with tf.variable_scope('Critic'):
#Create Critic Network
for layer in CRITIC_LAYER:
c_hidden = layers.dense(
self.s,
128,
tf.nn.tanh,
kernel_initializer=init,
name='c_hidden',
kernel_regularizer=tf.contrib.layers.l2_regularizer(
scale=L2_REG_LAMBDA))
c_output = layers.dense(c_hidden,
1,
kernel_initializer=init,
name='c_out')
actor_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=scope + '/Actor')
critic_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=scope + '/Critic')
return a_output, c_output, actor_params, critic_params
def decoder(self, Z, verbose=True):
""" Draws the decoder fo the network
"""
dec_net = [Z]
prev_dec_shape = None
num_div = len([
stride for lay_num, (filters, kernel_size,
stride) in enumerate(self.decoder_dims)
if stride == 2
])
cur_shape = int(self.dims[1] / (2**(num_div - 1)))
for lay_num, (filters, kernel_size,
stride) in enumerate(self.decoder_dims):
#print( [i for i in tf.trainable_variables() if 'generator/' in i.name])
if kernel_size > 0: # if this is a convolutional layer
# this is the first layer and the first convolutional layer
if (lay_num == 0) or (self.decoder_dims[lay_num - 1][1] == 0):
dec_net.append(
tf.reshape(
layers.dense(dec_net[len(dec_net) - 1],
cur_shape * cur_shape * filters,
name='dec_' + str(lay_num),
activation=self.default_activation),
[self.batch_size, cur_shape, cur_shape, filters]))
elif stride == 2: # if the spatial size of the previous layer is greater than the image size of the current layer
# we need to resize the current network dims
cur_shape *= 2
dec_net.append(
tf.image.resize_nearest_neighbor(
dec_net[len(dec_net) - 1], (cur_shape, cur_shape)))
elif lay_num == len(
self.decoder_dims) - 1: # if this is the last layer
# append a normal layer
dec_net.append(
(layers.conv2d(dec_net[len(dec_net) - 1],
filters=filters,
kernel_size=kernel_size,
strides=1,
padding='same',
name='dec_' + str(lay_num),
activation=self.default_activation)))
# If the next layer is not convolutional but this one is
elif self.decoder_dims[lay_num + 1][1] == 0:
# flatten this layers
dec_net.append(
tf.contrib.layers.flatten(
layers.conv2d(dec_net[len(dec_net) - 1],
filters=filters,
kernel_size=kernel_size,
strides=1,
padding='same',
name='dec_' + str(lay_num),
activation=self.default_activation)))
else:
# append a normal layer
dec_net.append(
(layers.conv2d(dec_net[len(dec_net) - 1],
filters=filters,
kernel_size=kernel_size,
strides=1,
padding='same',
name='dec_' + str(lay_num),
activation=self.default_activation)))
else: # if this is a dense layer
# append the dense layer
dec_net.append(
layers.dense(dec_net[len(dec_net) - 1],
units=filters,
name='dec_' + str(lay_num),
activation=self.default_activation))
# append the output layer
if (self.dims[0] != shape(dec_net[-1])[1]) & (self.dims[1] != shape(
dec_net[-1])[2]):
print('warning: shape does not match image shape')
dec_net.append(
tf.image.resize_nearest_neighbor(dec_net[len(dec_net) - 1],
(self.dims[0], self.dims[1])))
dec_net.append(
layers.conv2d(dec_net[len(dec_net) - 1],
self.dims[2],
1,
strides=1,
activation=tf.sigmoid,
name='output_layer'))
dec_net.append(tf.contrib.layers.flatten(dec_net[len(dec_net) - 1]))
if verbose:
print('Decoder shapes: ', [shape(i) for i in dec_net])
return dec_net
def build_encoder(self):
"""
构建完整编码器
:return:
"""
with tf.variable_scope('encoder'):
encoder_cell = self.build_encoder_cell(self.hidden_size,
self.cell_type,
self.layer_size)
# self.encoder_embeddings可以理解为词向量词典，存储vocab_size个大小为embedding_size的词向量，随机初始化为正态分布的值；
# tf.nn.embedding_lookup（params, ids）函数的用法主要是选取一个张量里面索引对应的元素;params可以是张量也可以是数组等，id就是对应的索引。
# [self.encoder_inputs,self.encoder_vocab_size]*[self.encoder_vocab_size, self.embedding_dim]=[self.encoder_inputs,self.embedding_dim]
encoder_inputs_embedded = tf.nn.embedding_lookup(
self.encoder_embeddings, self.encoder_inputs)
encoder_inputs_embedded = layers.dense(
encoder_inputs_embedded, # 全连接层 相当于添加一个层
self.hidden_size,
use_bias=False,
name='encoder_residual_projection')
initial_state = encoder_cell.zero_state(self.batch_size,
dtype=tf.float32) # 初始化值
if self.bidirection: # 双向RNN
encoder_cell_bw = self.build_encoder_cell(
self.hidden_size, self.cell_type, self.layer_size)
'''
outputs为(output_fw, output_bw)，是一个包含前向cell输出tensor和后向cell输出tensor组成的二元组。
output_states为(output_state_fw, output_state_bw)，包含了前向和后向最后的隐藏状态的组成的二元组。
output_state_fw和output_state_bw的类型为LSTMStateTuple。
LSTMStateTuple由（c，h）组成，分别代表memory cell和hidden state。
'''
((encoder_fw_outputs, encoder_bw_outputs), (encoder_fw_state, encoder_bw_state)) \
= tf.nn.bidirectional_dynamic_rnn(
cell_bw=encoder_cell_bw, # 后向RNN
cell_fw=encoder_cell, # 前向RNN
inputs=encoder_inputs_embedded, # 输入
sequence_length=self.encoder_inputs_length, # 输入序列的实际长度
dtype=tf.float32,
swap_memory=True)
encoder_outputs = tf.concat(
(encoder_bw_outputs, encoder_fw_outputs), 2) # 在第二个维度拼接
encoder_final_state = []
# output_state_fw和output_state_bw的类型为LSTMStateTuple。
# LSTMStateTuple由（c，h）组成，分别代表memory cell和hidden state。
for i in range(self.layer_size):
c_fw, h_fw = encoder_fw_state[i]
c_bw, h_bw = encoder_bw_state[i]
c = tf.concat((c_fw, c_bw), axis=-1) # 在最高的维度进行拼接
h = tf.concat((h_fw, h_bw), axis=-1)
encoder_final_state.append(LSTMStateTuple(c=c, h=h))
encoder_final_state = tuple(encoder_final_state)
else:
encoder_outputs, encoder_final_state = tf.nn.dynamic_rnn(
cell=encoder_cell,
inputs=encoder_inputs_embedded,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
initial_state=initial_state,
swap_memory=True)
return encoder_outputs, encoder_final_state
def build_encoder(self):
"""构建编码器"""
# print("构建编码器")
with tf.variable_scope('encoder'):
# 构建 encoder_cell
encoder_cell = self.build_encoder_cell()
# 编码器的embedding运行在GPU/CPU上
with tf.device(_get_embed_device(self.input_vocab_size)):
# 加载预训练好的embedding
if self.pretrained_embedding:
self.encoder_embeddings = tf.Variable(tf.constant(
0.0,
shape=(self.input_vocab_size, self.embedding_size)),
trainable=True,
name='embeddings')
self.encoder_embeddings_placeholder = tf.placeholder(
tf.float32,
(self.input_vocab_size, self.embedding_size))
self.encoder_embeddings_init = self.encoder_embeddings.assign(
self.encoder_embeddings_placeholder)
else:
self.encoder_embeddings = tf.get_variable(
name='embedding',
shape=(self.input_vocab_size, self.embedding_size),
initializer=self.initializer,
dtype=tf.float32)
# embedding之后的输入 shape = (batch_size, time_step, embedding_size)
self.encoder_inputs_embedded = tf.nn.embedding_lookup(
params=self.encoder_embeddings, ids=self.encoder_inputs)
# 当时用残差网络时
if self.use_residual:
self.encoder_inputs_embedded = layers.dense(
self.encoder_inputs_embedded,
self.hidden_units,
use_bias=False,
name='encoder_residual_projection')
inputs = self.encoder_inputs_embedded
if self.time_major:
inputs = tf.transpose(inputs, (1, 0, 2))
if not self.bidirectional:
# 单向 RNN
(encoder_outputs, encoder_state) = tf.nn.dynamic_rnn(
cell=encoder_cell,
inputs=inputs,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
time_major=self.time_major,
parallel_iterations=self.parallel_iterations,
swap_memory=True)
else:
# 双向RNN
# encoder_cell_fw = self.build_encoder_cell()
encoder_cell_bw = self.build_encoder_cell() # backward cell
((encoder_fw_outputs, encoder_bw_outputs),
(encoder_fw_state,
encoder_bw_state)) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=encoder_cell,
cell_bw=encoder_cell_bw,
inputs=inputs,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
time_major=self.time_major,
parallel_iterations=self.parallel_iterations,
swap_memory=True)
# 首先合并两个方向 RNN 的输出
encoder_outputs = tf.concat(
(encoder_fw_outputs, encoder_bw_outputs), 2)
encoder_state = []
for i in range(self.depth):
encoder_state.append(encoder_fw_state[i])
encoder_state.append(encoder_bw_state[i])
encoder_state = tuple(encoder_state)
return encoder_outputs, encoder_state
def fc(self,
name,
x,
nb_nodes,
*,
norm_fn='none',
norm_params=None,
act_fn='none',
winit_fn='xavier',
binit_fn='zeros',
has_bias=True,
disp=True,
collect_end_points=True):
if callable(act_fn):
act_fn_str = 'func'
act_fn = act_fn
else:
act_fn_str = self.config.get(name + ' activation', act_fn)
act_fn = get_activation(act_fn_str)
if callable(norm_fn):
norm_fn_str = 'func'
norm_fn = norm_fn
else:
norm_fn_str = self.config.get(name + ' normalization', norm_fn)
norm_fn = get_normalization(norm_fn_str)
winit_fn_str = self.config.get(name + ' weightsinit', winit_fn)
if 'special' in winit_fn_str:
split = winit_fn_str.split()
winit_name = split[0]
if winit_name == 'glorot_uniform':
input_nb_nodes = int(x.get_shape()[-1])
filters_stdev = np.sqrt(2.0 / (input_nb_nodes + nb_nodes))
winit_fn = self.uniform_initializer(filters_stdev)
else:
raise Exception('Error weights initializer function name : ' +
winit_fn_str)
else:
winit_fn = get_weightsinit(winit_fn_str)
binit_fn_str = self.config.get(name + ' biasesinit', binit_fn)
binit_fn = get_weightsinit(binit_fn_str)
if self.using_tcl_library:
x = tcl.fully_connected(x,
nb_nodes,
activation_fn=act_fn,
normalizer_fn=norm_fn,
normalizer_params=norm_params,
weights_initializer=winit_fn,
scope=name)
else:
x = tl.dense(x,
nb_nodes,
use_bias=has_bias,
kernel_initializer=winit_fn,
bias_initializer=binit_fn,
trainable=True,
name=name)
with tf.variable_scope(name):
if norm_fn is not None:
norm_params = norm_params or {}
x = norm_fn(x, **norm_params)
if act_fn is not None:
x = act_fn(x)
if disp:
print('\t\tFC(' + str(name) + ') --> ', x.get_shape(), ' ',
(act_fn_str, norm_fn_str, winit_fn_str))
if collect_end_points:
self.end_points[name] = x
return x
def __init__(self,
state_size,
action_size,
hidden_layer_size,
learning_rate=0.00025,
model_name="model",
global_step=None):
self.state_size = state_size
self.action_size = action_size
self.hidden_layer_size = hidden_layer_size
self.input = tf.placeholder(shape=[
None, self.state_size[0], self.state_size[1], self.state_size[2]
],
dtype=tf.float32)
self.input_normalize = (self.input - (255.0 / 2)) / (255.0 / 2)
with tf.variable_scope(name_or_scope=model_name):
self.conv1 = layer.conv2d(inputs=self.input_normalize,
filters=32,
activation=tf.nn.relu,
kernel_size=[8, 8],
strides=[4, 4],
padding="SAME")
self.conv2 = layer.conv2d(inputs=self.conv1,
filters=64,
activation=tf.nn.relu,
kernel_size=[4, 4],
strides=[2, 2],
padding="SAME")
self.conv3 = layer.conv2d(inputs=self.conv2,
filters=64,
activation=tf.nn.relu,
kernel_size=[3, 3],
strides=[1, 1],
padding="SAME")
self.flat = layer.flatten(self.conv3)
self.L1 = layer.dense(self.flat,
self.hidden_layer_size,
activation=tf.nn.relu)
self.A1 = layer.dense(self.L1,
self.hidden_layer_size,
activation=tf.nn.relu)
self.Adventage = layer.dense(self.A1,
self.action_size,
activation=None)
self.V1 = layer.dense(self.L1,
self.hidden_layer_size,
activation=tf.nn.relu)
self.Value = layer.dense(self.V1, 1, activation=None)
self.Q_Out = self.Value + tf.subtract(
self.Adventage,
tf.reduce_mean(self.Adventage, axis=1, keep_dims=True))
self.predict = tf.arg_max(self.Q_Out, 1)
self.target_Q = tf.placeholder(shape=[None, self.action_size],
dtype=tf.float32)
self.loss = tf.losses.mean_squared_error(self.target_Q, self.Q_Out)
self.UpdateModel = tf.train.AdamOptimizer(
learning_rate=learning_rate).minimize(self.loss,
global_step=global_step)
self.trainable_var = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, model_name)
import numpy as np
import tensorflow as tf
from tensorflow import layers
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("../MNIST_data/", one_hot=True)
num_out = 10
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.int32)
h1 = layers.dense(x, 200, activation=tf.nn.relu)
h2 = layers.dense(x, 200, activation=tf.nn.relu)
out = layers.dense(x, num_out, activation=None)
loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(onehot_labels=y, logits=out))
optim = tf.train.AdamOptimizer(learning_rate=1e-4)
train_op = optim.minimize(loss)
correct_prediction = tf.equal(tf.argmax(out, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess = tf.InteractiveSession()
init = tf.global_variables_initializer()
init.run()
num_epoch = 20
batch_size = 100
num_iter_in_epoch = mnist.train.num_examples // batch_size
def discriminator(images, reuse=None, n_conv_layer=3):
if channel_first_disc:
channels_key = 'channels_first'
channels_code = "NCHW"
else:
channels_key = 'channels_last'
channels_code = "NHWC"
n_conv_layer = int(np.ceil(np.log2(resolution_image / size_init)))
n_filters = 1
print(' D: n-conv layer discriminator: ', n_conv_layer)
print(' D: n-filters discriminator: ', n_filters)
with tf.variable_scope(
'Discriminator',
reuse=tf.AUTO_REUSE): # Needed for later, in order to
# get variables of generator
print(' D: input')
print(images)
if channel_first_disc:
print('channel first disc ON')
output = tf.reshape(
images, [-1, channels, resolution_image, resolution_image])
else:
output = tf.reshape(
images, [-1, resolution_image, resolution_image, channels])
print(' D: channel reshape')
print(output)
# ----- LoopLayers, Conv, Leaky ----- #
for i in range(n_conv_layer):
print(' D: conv2d iter: ', i, ' - n_filters: ', n_filters)
# GETTING THE WEIGHTS THAT WILL BE NORMALIZED.
print(output)
print("Kernel print:" + "(" + str(i) + ")")
w = tf.get_variable("kernel" + str(i),
shape=[
kernel_size[0], kernel_size[1],
output.get_shape()[-1],
n_filters * const_filt
])
print(w)
print("constant print:" + "(" + str(i) + ")")
b = tf.get_variable("constant" + str(i), [n_filters * const_filt],
initializer=tf.constant_initializer(0.0))
print(b)
if sngan:
pesi = spectral_norm(w, i)
else:
pesi = w
output = tf.nn.conv2d(input=output,
filter=pesi,
strides=[1, strides, strides, 1],
padding='SAME',
data_format=channels_code) + b
print(output)
output = tf.maximum(leakage * output, output)
n_filters = int(n_filters * 2)
output = tf.reshape(
output,
[-1, size_init * size_init * (int(n_filters / 2) * const_filt)])
print(' D: reshape linear layer')
print(output)
# ----- Layer4, Dense, Linear ----- #
output = layers.dense(output, units=num_labels + 1)
print(' D: dense layer output')
print(output)
scores_out = tf.identity(output[:, :1], name='scores_out')
labels_out = tf.identity(output[:, 1:], name='labels_out')
print(' D: scores output')
print(scores_out)
print(' D: labels output')
print(labels_out)
return scores_out, labels_out
def _build_graph(self):
logging.debug("Building graph")
# rnn_inputs: [batch_size, max_len, input_size]
# rnn_inputs_len: [batch_size]
# target_outputs: [batch_size, max_len, output_size]
self.rnn_inputs, self.rnn_inputs_len, self.target_outputs = self.data_generator.inputs(
self.para.mode, self.para.batch_size)
#TODO recode for efficiency to allow parallelization
for graph_layer_index in range(self.para.num_graph_layers):
print(graph_layer_index)
if graph_layer_index == 0:
nodes_rnn_inputs = self.rnn_inputs
rnn_length = self.para.max_len
else:
rnn_length = self.para.num_units
nodes_rnn_outputs = []
for node_index in range(self.para.num_var):
# graph mask : [num_var]
graph_mask = self.graph_masks[node_index]
# embedding size
num_node_connections = np.sum(graph_mask)
# node_rnn_inputs : [batch_size, max_len, num_node_connections]
node_rnn_inputs = tf.reshape(
tf.boolean_mask(nodes_rnn_inputs, graph_mask, axis=2),
(-1, rnn_length, num_node_connections))
node_rnn_inputs_embed = tf.nn.relu(
dense(node_rnn_inputs, self.para.num_units))
node_rnn_inputs_embed = tf.unstack(node_rnn_inputs_embed,
axis=1)
_, node_rnn_states = tf.nn.static_rnn(
cell=self._build_rnn_cell(),
inputs=node_rnn_inputs_embed,
sequence_length=self.rnn_inputs_len,
dtype=self.dtype,
scope="graph" + str(graph_layer_index + 1) + "node" +
str(node_index + 1) + "lstm")
node_rnn_states = tf.concat(
[
node_rnn_states[i][1]
for i in range(self.para.num_layers)
],
1,
)
# rnn_output = dense(node_rnn_states, 1)
nodes_rnn_outputs.append(node_rnn_states)
# if not last layer reload rnn outputs as input
if graph_layer_index < self.para.num_graph_layers - 1:
nodes_rnn_inputs = tf.stack(nodes_rnn_outputs, axis=2)
else:
self.final_rnn_states = tf.stack(nodes_rnn_outputs, axis=2)
all_rnn_outputs = []
for node_index in range(self.para.num_var):
# graph mask : [num_var]
graph_mask = self.graph_masks[node_index]
# embedding size
num_node_connections = np.sum(graph_mask)
node_rnn_outputs = tf.reshape(
tf.boolean_mask(self.final_rnn_states, graph_mask, axis=2),
(-1, rnn_length * num_node_connections))
node_output = dense(node_rnn_outputs, 1)
all_rnn_outputs.append(node_output)
self.all_rnn_outputs = tf.concat(all_rnn_outputs, axis=1)
if self.para.highway > 0:
reg_outputs = tf.transpose(
self.rnn_inputs[:, -self.para.highway:, :], [0, 2, 1])
reg_outputs = dense(reg_outputs, 1)
self.all_rnn_outputs += tf.squeeze(reg_outputs)
if self.para.mode == "train" or self.para.mode == "validation":
self.labels = self.target_outputs[:, self.para.max_len - 1, :]
self.loss = self._compute_loss(outputs=self.all_rnn_outputs,
labels=self.labels)
elif self.para.mode == "test":
self.labels = self.target_outputs[:, self.para.max_len - 1, :]
if not self.para.mts:
self.all_rnn_outputs = tf.sigmoid(self.all_rnn_outputs)
def f_net(inputs, num_outputs, is_training):
inputs = inputs / 128 - 1.0
conv1 = layers.conv2d(inputs=inputs,
filters=16,
kernel_size=(8, 8),
strides=1,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='conv1')
pool1 = layers.max_pooling2d(inputs=conv1,
pool_size=3,
strides=4,
name='pool1')
conv2 = layers.conv2d(inputs=pool1,
filters=16,
kernel_size=(5, 5),
strides=1,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='conv2')
pool2 = layers.max_pooling2d(inputs=conv2,
pool_size=3,
strides=3,
name='pool2')
conv3 = layers.conv2d(inputs=pool2,
filters=64,
kernel_size=(3, 3),
strides=1,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='conv3')
pool3 = layers.max_pooling2d(
inputs=conv3,
pool_size=3,
strides=8,
name='pool3',
)
conv4 = layers.conv2d(inputs=pool3,
filters=64,
kernel_size=(3, 3),
strides=1,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='conv4')
pool4 = layers.max_pooling2d(inputs=conv4,
pool_size=3,
strides=8,
name='pool4')
depth = pool4.get_shape()[1:].num_elements()
inputs = tf.reshape(pool4, shape=[-1, depth])
hid1 = layers.dense(inputs=inputs,
units=256,
activation=tf.nn.relu,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='hid1')
hid2 = layers.dense(inputs=hid1,
units=256,
activation=tf.nn.relu,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='hid2')
q = layers.dense(inputs=hid2,
units=num_outputs,
activation=None,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='q')
q = tf.squeeze(q, name='out_sqz')
return q
def build_encoder(self):
"""
构建编码器
:return:
"""
with tf.variable_scope('encoder'):
encoder_cell = self.build_encoder_cell()
with tf.device(_get_embed_device(self.input_vocab_size)):
if self.pretrained_embedding:
self.encoder_embeddings = tf.Variable(tf.constant(
0.0,
shape=(self.input_vocab_size, self.embedding_size)),
trainable=True,
name='embeddings')
self.encoder_embeddings_placeholder = tf.placeholder(
tf.float32,
(self.input_vocab_size, self.embedding_size))
self.encoder_embeddings_init = self.encoder_embeddings.assign(
self.encoder_embeddings_placeholder)
else:
self.encoder_embeddings = tf.get_variable(
name='embeddings',
shape=(self.input_vocab_size, self.embedding_size),
initializer=self.initializer,
dtype=tf.float32)
self.encoder_inputs_embedded = tf.nn.embedding_lookup(
params=self.encoder_embeddings, ids=self.encoder_inputs)
if self.use_residual:
self.encoder_inputs_embedded = layers.dense(
self.encoder_inputs_embedded,
self.hidden_size,
use_bias=False,
name='encoder_residual_projection')
inputs = self.encoder_inputs_embedded
if self.time_major:
inputs = tf.transpose(inputs, (1, 0, 2))
if not self.bidirection:
(encoder_outputs, encoder_state) = tf.nn.dynamic_rnn(
cell=encoder_cell,
inputs=inputs,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
time_major=self.time_major,
parallel_iterations=self.parallel_iterations,
swap_memory=True)
else:
# 多了合并操作
encoder_cell_bw = self.build_encoder_cell()
((encoder_fw_outputs, encoder_bw_outputs),
(encoder_fw_state,
encoder_bw_state)) = tf.nn.bidirectional_dynamic_rnn(
cell_bw=encoder_cell_bw,
cell_fw=encoder_cell,
inputs=inputs,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
time_major=self.time_major,
parallel_iterations=self.parallel_iterations,
swap_memory=True)
encoder_outputs = tf.concat(
(encoder_bw_outputs, encoder_fw_outputs), 2)
encoder_state = []
for i in range(self.depth):
encoder_state.append(encoder_fw_state[i])
encoder_state.append(encoder_bw_state[i])
encoder_state = tuple(encoder_state)
return encoder_outputs, encoder_state
def dense_layer(self, x, n_node, name):
with tf.variable_scope(name, reuse=self.reuse):
x = layer.dense(x, n_node, activation=None, name='_d')
return x
def build_encoder(self):
"""
构建编码器
:return:encoder_outputs, 最后一层rnn的输出
encoder_state，每一层的final state
"""
with tf.variable_scope('encoder'):
encoder_cell = self.build_encoder_cell()
# 编码器的embedding
with tf.device(_get_embed_device(self.input_vocab_size)):
# 加载训练好的embedding
if self.pretrained_embedding:
# 预训练模式
self.encoder_embeddings = tf.Variable(tf.constant(
0.0,
shape=(self.input_vocab_size, self.embedding_size)),
trainable=True,
name='embeddings')
self.encoder_embeddings_placeholder = tf.placeholder(
tf.float32,
(self.input_vocab_size, self.embedding_size))
self.encoder_embeddings_init = \
self.encoder_embeddings.assign(
self.encoder_embeddings_placeholder)
else:
self.encoder_embeddings = tf.get_variable(
name='embedding',
shape=(self.input_vocab_size, self.embedding_size),
initializer=self.initializer,
dtype=tf.float32)
# embedded之后的输入 shape = (batch_size, time_step, embedding_size)
self.encoder_inputs_embedded = tf.nn.embedding_lookup(
params=self.encoder_embeddings, ids=self.encoder_inputs)
# 使用残差结构，先将输入的维度转换为隐藏层的维度
if self.use_residual:
self.encoder_inputs_embedded = \
layers.dense(self.encoder_inputs_embedded,
self.hidden_units,
use_bias=False,
name='encoder_residual_projection')
inputs = self.encoder_inputs_embedded
if self.time_major:
inputs = tf.transpose(inputs, (1, 0, 2))
if not self.bidirectional:
(encoder_outputs, encoder_state) = tf.nn.dynamic_rnn(
cell=encoder_cell,
inputs=inputs,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
time_major=self.time_major,
parallel_iterations=self.parallel_iterations,
swap_memory=True # 动态rnn，可以交换内存
)
else:
encoder_cell_bw = self.build_encoder_cell()
((encoder_fw_outputs, encoder_bw_outputs),
(encoder_fw_state,
encoder_bw_state)) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=encoder_cell,
cell_bw=encoder_cell_bw,
inputs=inputs,
sequence_length=self.encoder_inputs_length,
dtype=tf.float32,
time_major=self.time_major,
parallel_iterations=self.parallel_iterations,
swap_memory=True)
encoder_outputs = tf.concat(
(encoder_fw_outputs, encoder_bw_outputs), 2)
encoder_state = []
for i in range(self.depth):
c_fw, h_fw = encoder_fw_state[i]
c_bw, h_bw = encoder_bw_state[i]
c = tf.concat((c_fw, c_bw), axis=-1)
h = tf.concat((h_fw, h_bw), axis=-1)
encoder_state.append(LSTMStateTuple(c=c, h=h))
encoder_state = tuple(encoder_state)
return encoder_outputs, encoder_state
def discriminator(images, reuse=None, n_conv_layer=3):
"""
:param images: images that are input of the discriminator
:return: likeliness of the image
"""
if channel_first_disc == True:
channels_key = 'channels_first'
else:
channels_key = 'channels_last'
n_conv_layer = int(np.ceil(np.log2(resolution_image / size_init)))
n_filters = 1
print(' D: n-conv layer discriminator: ', n_conv_layer)
print(' D: n-filters discriminator: ', n_filters)
with tf.variable_scope('Discriminator', reuse=tf.AUTO_REUSE): # Needed for later, in order to
# get variables of generator
print(' D: input')
print(images)
if channel_first_disc:
print('channel first disc ON')
output = tf.reshape(images, [-1, channels, resolution_image, resolution_image])
else:
output = tf.reshape(images, [-1, resolution_image, resolution_image, channels])
print(' D: channel reshape')
print(output)
# ----- LoopLayers, Conv, Leaky ----- #
karras_disc_in = []
for i in range(n_conv_layer):
print(' D: conv2d iter: ', i, ' - n_filters: ', n_filters)
output = layers.conv2d(output,
filters=n_filters * const_filt,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=channels_key)
print(output)
output = tf.maximum(leakage * output, output)
n_filters = int(n_filters * 2)
karras_disc_in.append(output)
output = tf.reshape(output, [-1, size_init * size_init * (int(n_filters / 2) * const_filt)])
print(' D: reshape linear layer')
print(output)
# ----- Layer4, Dense, Linear ----- #
output = layers.dense(output, units=num_labels + 1)
print(' D: dense layer output')
print(output)
scores_out = tf.identity(output[:, :1], name='scores_out')
labels_out = tf.identity(output[:, 1:], name='labels_out')
print(' D: scores output')
print(scores_out)
print(' D: labels output')
print(labels_out)
return scores_out, labels_out
def generator(n_samples, noise_with_labels, reuse=None):
"""
:param n_samples: number of samples
:param noise_with_labels: latent noise + labels
:return: generated images
"""
with tf.variable_scope(
'Generator',
reuse=tf.AUTO_REUSE): # Needed for later, in order to get
# variables of discriminator
# ----- Layer1, Dense, Batch, Leaky ----- #
alpha = 0.01
output = layers.dense(inputs=noise_with_labels, units=4 * 4 * 4 * 64)
output = layers.batch_normalization(output)
output = tf.maximum(alpha * output, output)
if channel_first:
# size: 128 x 7 x 7
output = tf.reshape(output, (-1, 4 * 64, 4, 4))
bn_axis = 1 # [0, 2, 3] # first
else:
# size: 7 x 7 x 128
output = tf.reshape(output, (-1, 4, 4, 4 * 64))
bn_axis = -1 # [0, 1, 2] # last
print('channel first: FALSE')
print(output)
# ----- Layer2, deConv, Batch, Leaky ----- #
output = layers.conv2d_transpose(output,
filters=4 * DIM,
kernel_size=(5, 5),
strides=2,
padding='same')
print(output)
output = layers.batch_normalization(output, axis=bn_axis)
output = tf.maximum(alpha * output, output)
if channel_first:
output = output[:, :, :7, :7]
else:
print('cutted:')
print(output)
output = output[:, :7, :7, :]
# ----- Layer3, deConv, Batch, Leaky ----- #
output = layers.conv2d_transpose(output,
filters=2 * DIM,
kernel_size=(5, 5),
strides=2,
padding='same')
output = layers.batch_normalization(output, axis=bn_axis)
output = tf.maximum(alpha * output, output)
# ----- Layer4, deConv, Batch, Leaky ----- #
output = layers.conv2d_transpose(output,
filters=DIM,
kernel_size=(5, 5),
strides=2,
padding='same')
output = layers.batch_normalization(output, axis=bn_axis)
output = tf.maximum(alpha * output, output)
# ----- Layer5, deConv, Batch, Leaky ----- #
output = layers.conv2d_transpose(output,
filters=1,
kernel_size=(5, 5),
strides=1,
padding='same')
output = tf.nn.tanh(output)
output = tf.reshape(output, [-1, OUTPUT_DIM])
print('Generator output size:')
print(output)
return output
def rnn(self):
"""CNN模型"""
with tf.name_scope("rnn"):
"""构建编码器
"""
# print("多层rnn模型")
embedding_inputs = self.input_embedding()
print("embedding_inputs", embedding_inputs.shape)
rnn_cell = self.build_rnn_cell()
# 目前這部的具体用意还不知为什么，有待考察
if self.config.use_residual:
embedding_inputs = layers.dense(embedding_inputs,
self.config.hidden_dim,
activation=tf.nn.relu,
use_bias=False,
name='residual_projection')
inputs = embedding_inputs
if self.config.time_major:
inputs = tf.transpose(inputs, (1, 0, 2))
if not self.config.bidirectional:
# 单向 RNN
(encoder_outputs, encoder_state) = tf.nn.dynamic_rnn(
cell=rnn_cell,
inputs=inputs,
dtype=tf.float32,
time_major=self.config.time_major)
else:
# 双向 RNN
rnn_cell_bw = self.build_rnn_cell()
((encoder_fw_outputs, encoder_bw_outputs),
(encoder_fw_state, encoder_bw_state)) = \
tf.nn.bidirectional_dynamic_rnn(cell_fw=rnn_cell,
cell_bw=rnn_cell_bw,
inputs=inputs,
dtype=tf.float32,
time_major=self.config.time_major
)
# 首先合并两个方向 RNN 的输出
encoder_outputs = tf.concat(
(encoder_fw_outputs, encoder_bw_outputs), 2)
encoder_state = []
for i in range(self.config.num_layers):
encoder_state.append(encoder_fw_state[i])
encoder_state.append(encoder_bw_state[i])
encoder_state = tuple(encoder_state)
# _outputs, _ = self.build_rnn_model()
print("_outputs", encoder_outputs.shape)
last = encoder_outputs[:, -1, :]
print("last", last.shape)
with tf.name_scope("score"):
cur_layer = last
layer_dimension = [250, 100] # 神经网络层节点的个数
n_layers = len(layer_dimension) # 神经网络的层数
for i in range(0, n_layers):
out_dimension = layer_dimension[i]
fc = tf.layers.dense(inputs=cur_layer, \
units=out_dimension, \
activation=tf.nn.relu, \
activity_regularizer=tf.contrib.layers.l2_regularizer(0.001), \
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003), \
# name='fc1'
)
cur_layer = tf.contrib.layers.dropout(fc, self.keep_prob)
# 分类器，输出层
self.logits = tf.layers.dense(cur_layer,
self.config.num_classes,
name='fc2')
self.y_pred = tf.argmax(tf.nn.softmax(self.logits), 1) # 预测类别
with tf.name_scope("loss"):
# 使用优化方式，损失函数，交叉熵
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits=self.logits, labels=self.input_y)
self.loss = tf.reduce_mean(cross_entropy)
with tf.name_scope("optimize"):
# 优化器
self.optim = tf.train.AdamOptimizer(
learning_rate=self.config.learning_rate).minimize(self.loss)
with tf.name_scope("accuracy"):
# 准确率
correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred)
self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
def f_net(inputs):
inputs = inputs[0]
inputs = inputs / 128 - 1.0
# (640, 640, 3*n) -> ()
conv1 = layers.conv2d(inputs=inputs,
filters=16,
kernel_size=(8, 8),
strides=1,
kernel_regularizer=l2_regularizer(scale=1e-2),
activation=tf.nn.relu,
name='conv1')
print conv1.shape
pool1 = layers.max_pooling2d(inputs=conv1,
pool_size=3,
strides=4,
name='pool1')
print pool1.shape
conv2 = layers.conv2d(inputs=pool1,
filters=16,
kernel_size=(5, 5),
strides=1,
kernel_regularizer=l2_regularizer(scale=1e-2),
activation=tf.nn.relu,
name='conv2')
print conv2.shape
pool2 = layers.max_pooling2d(inputs=conv2,
pool_size=3,
strides=3,
name='pool2')
print pool2.shape
conv3 = layers.conv2d(inputs=pool2,
filters=64,
kernel_size=(3, 3),
strides=1,
kernel_regularizer=l2_regularizer(scale=1e-2),
activation=tf.nn.relu,
name='conv3')
print conv3.shape
pool3 = layers.max_pooling2d(
inputs=conv3,
pool_size=3,
strides=2,
name='pool3',
)
print pool3.shape
depth = pool3.get_shape()[1:].num_elements()
inputs = tf.reshape(pool3, shape=[-1, depth])
print inputs.shape
hid1 = layers.dense(inputs=inputs,
units=256,
activation=tf.nn.relu,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='hid1')
print hid1.shape
hid2 = layers.dense(inputs=hid1,
units=256,
activation=tf.nn.relu,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='hid2_adv')
print hid2.shape
adv = layers.dense(inputs=hid2,
units=NUM_PROXY_ACTION,
activation=None,
kernel_initializer=tf.random_uniform_initializer(
-3e-3, 3e-3),
kernel_regularizer=l2_regularizer(scale=1e-2),
name='adv')
print adv.shape
hid2 = layers.dense(inputs=hid1,
units=256,
activation=tf.nn.relu,
kernel_regularizer=l2_regularizer(scale=1e-2),
name='hid2_v')
print hid2.shape
v = layers.dense(inputs=hid2,
units=1,
activation=None,
kernel_initializer=tf.random_uniform_initializer(
-3e-3, 3e-3),
kernel_regularizer=l2_regularizer(scale=1e-2),
name='v')
print v.shape
q = tf.add(adv, v, name='q')
print q.shape
return {"q": q}
def main(params):
# load data, class data contains captions, images, image features (if avaliable)
if params.gen_val_captions < 0:
repartiton = False
else:
repartiton = True
data = Data(params,
True,
params.image_net_weights_path,
repartiton=repartiton,
gen_val_cap=params.gen_val_captions)
# load batch generator, repartiton to use more val set images in train
gen_batch_size = params.batch_size
if params.fine_tune:
gen_batch_size = params.batch_size
batch_gen = data.load_train_data_generator(gen_batch_size,
params.fine_tune)
# whether use presaved pretrained imagenet features (saved in pickle)
# feature extractor after fine_tune will be saved in tf checkpoint
# caption generation after fine_tune must be made with params.fine_tune=True
pretrained = not params.fine_tune
val_gen = data.get_valid_data(gen_batch_size,
val_tr_unused=batch_gen.unused_cap_in,
pretrained=pretrained)
test_gen = data.get_test_data(gen_batch_size, pretrained=pretrained)
# annotations vector of form <EOS>...<BOS><PAD>...
ann_inputs_enc = tf.placeholder(tf.int32, [None, None])
ann_inputs_dec = tf.placeholder(tf.int32, [None, None])
ann_lengths = tf.placeholder(tf.int32, [None])
if params.fine_tune:
# if fine_tune dont just load images_fv
image_f_inputs = tf.placeholder(tf.float32, [None, 224, 224, 3])
else:
# use prepared image features [batch_size, 4096] (fc2)
image_f_inputs = tf.placeholder(tf.float32, [None, 4096])
if params.use_c_v or (params.prior == 'GMM' or params.prior == 'AG'):
c_i = tf.placeholder(tf.float32, [None, 90])
else:
c_i = ann_lengths # dummy tensor
# because of past changes
image_batch, cap_enc, cap_dec, cap_len, cl_vectors = image_f_inputs,\
ann_inputs_enc, ann_inputs_dec, ann_lengths, c_i
# features, params.fine_tune stands for not using presaved imagenet weights
# here, used this dummy placeholder during fine_tune, will remove it in
# future releases, thats for saving image_net weights for futher usage
image_f_inputs2 = tf.placeholder_with_default(tf.ones([1, 224, 224, 3]),
shape=[None, 224, 224, 3],
name='dummy_ps')
if params.fine_tune:
image_f_inputs2 = image_batch
if params.mode == 'training' and params.fine_tune:
cnn_dropout = params.cnn_dropout
weights_regularizer = tf.contrib.layers.l2_regularizer(
params.weight_decay)
else:
cnn_dropout = 1.0
weights_regularizer = None
with tf.variable_scope("cnn", regularizer=weights_regularizer):
image_embeddings = vgg16(image_f_inputs2,
trainable_fe=params.fine_tune_fe,
trainable_top=params.fine_tune_top,
dropout_keep=cnn_dropout)
if params.fine_tune:
features = image_embeddings.fc2
else:
features = image_batch
# forward pass is expensive, so can use this method to reduce computation
if params.num_captions > 1 and params.mode == 'training': # [b_s, 4096]
features_tiled = tf.tile(tf.expand_dims(features, 1),
[1, params.num_captions, 1])
features = tf.reshape(features_tiled, [
tf.shape(features)[0] * params.num_captions,
params.cnn_feature_size
]) # [5 * b_s, 4096]
# dictionary
cap_dict = data.dictionary
params.vocab_size = cap_dict.vocab_size
# image features [b_size + f_size(4096)] -> [b_size + embed_size]
images_fv = layers.dense(features, params.embed_size, name='imf_emb')
# images_fv = tf.Print(images_fv, [tf.shape(features), features[0][0:10],
# image_embeddings.imgs[0][:10], images_fv])
# encoder, input fv and ...<BOS>,get z
if not params.no_encoder:
encoder = Encoder(images_fv, cap_enc, cap_len, params)
# decoder, input_fv, get x, x_logits (for generation)
decoder = Decoder(images_fv, cap_dec, cap_len, params, cap_dict)
if params.use_c_v or (params.prior == 'GMM' or params.prior == 'AG'):
# cluster vectors from "Diverse and Accurate Image Description.." paper.
# 80 is number of classes, for now hardcoded
# for GMM-CVAE must be specified
c_i_emb = layers.dense(cl_vectors, params.embed_size, name='cv_emb')
# map cluster vectors into embedding space
decoder.c_i = c_i_emb
decoder.c_i_ph = cl_vectors
if not params.no_encoder:
encoder.c_i = c_i_emb
encoder.c_i_ph = cl_vectors
if not params.no_encoder:
with tf.variable_scope("encoder"):
qz, tm_list, tv_list = encoder.q_net()
if params.prior == 'Normal':
# kld between normal distributions KL(q, p), see Kingma et.al
kld = -0.5 * tf.reduce_mean(
tf.reduce_sum(
1 + tf.log(tf.square(qz.distribution.std) + 0.00001) -
tf.square(qz.distribution.mean) -
tf.square(qz.distribution.std), 1))
elif params.prior == 'GMM':
# initialize sigma as constant, mu drawn randomly
# TODO: finish GMM loss implementation
c_means, c_sigma = init_clusters(params.num_clusters,
params.latent_size)
decoder.cap_clusters = c_means
kld = -0.5 * tf.reduce_mean(
tf.reduce_sum(
1 + tf.log(tf.square(qz.distribution.std) + 0.00001) -
tf.square(qz.distribution.mean) -
tf.square(qz.distribution.std), 1))
elif params.prior == 'AG':
c_means, c_sigma = init_clusters(params.num_clusters,
params.latent_size)
decoder.cap_clusters = c_means
kld_clusters = 0.5 + tf.log(qz.distribution.std+ 0.00001)\
- tf.log(c_sigma + 0.00001) - (
tf.square(qz.distribution.mean - tf.matmul(
tf.squeeze(c_i), c_means)) + tf.square(
qz.distribution.std))/(2*tf.square(c_sigma)+0.0000001)
kld = -0.5 * tf.reduce_sum(kld_clusters, 1)
with tf.variable_scope("decoder"):
if params.no_encoder:
dec_model, x_logits, shpe, _ = decoder.px_z_fi({})
else:
dec_model, x_logits, shpe, _ = decoder.px_z_fi({'z': qz})
# calculate rec. loss, mask padded part
labels_flat = tf.reshape(cap_enc, [-1])
ce_loss_padded = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=x_logits, labels=labels_flat)
loss_mask = tf.sign(tf.to_float(labels_flat))
batch_loss = tf.div(tf.reduce_sum(tf.multiply(ce_loss_padded, loss_mask)),
tf.reduce_sum(loss_mask),
name="batch_loss")
tf.losses.add_loss(batch_loss)
rec_loss = tf.losses.get_total_loss()
# kld weight annealing
anneal = tf.placeholder_with_default(0, [])
if params.fine_tune or params.restore:
annealing = tf.constant(1.0)
else:
if params.ann_param > 1:
annealing = (tf.tanh(
(tf.to_float(anneal) - 1000 * params.ann_param) / 1000) +
1) / 2
else:
annealing = tf.constant(1.0)
# overall loss reconstruction loss - kl_regularization
if not params.no_encoder:
lower_bound = rec_loss + tf.multiply(tf.to_float(annealing),
tf.to_float(kld)) / 10
else:
lower_bound = rec_loss
kld = tf.constant(0.0)
# optimization, can print global norm for debugging
optimize, global_step, global_norm = optimizers.non_cnn_optimizer(
lower_bound, params)
optimize_cnn = tf.constant(0.0)
if params.fine_tune and params.mode == 'training':
optimize_cnn, _ = optimizers.cnn_optimizer(lower_bound, params)
# cnn parameters update
# model restore
vars_to_save = tf.trainable_variables()
if not params.fine_tune_fe or not params.fine_tune_top:
cnn_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'cnn')
vars_to_save += cnn_vars
saver = tf.train.Saver(vars_to_save,
max_to_keep=params.max_checkpoints_to_keep)
# m_builder = tf.saved_model.builder.SavedModelBuilder('./saved_model')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer()
])
# train using batch generator, every iteration get
# f(I), [batch_size, max_seq_len], seq_lengths
if params.mode == "training":
if params.logging:
summary_writer = tf.summary.FileWriter(params.LOG_DIR,
sess.graph)
summary_writer.add_graph(sess.graph)
if not params.restore:
print("Loading imagenet weights for futher usage")
image_embeddings.load_weights(params.image_net_weights_path,
sess)
if params.restore:
print("Restoring from checkpoint")
saver.restore(
sess, "./checkpoints/{}.ckpt".format(params.checkpoint))
for e in range(params.num_epochs):
gs = tf.train.global_step(sess, global_step)
gs_epoch = 0
while True:
def stop_condition():
num_examples = gs_epoch * params.batch_size
if num_examples > params.num_ex_per_epoch:
return True
return False
for f_images_batch,\
captions_batch, cl_batch, c_v in batch_gen.next_batch(
use_obj_vectors=params.use_c_v,
num_captions=params.num_captions):
if params.num_captions > 1:
captions_batch, cl_batch, c_v = preprocess_captions(
captions_batch, cl_batch, c_v)
feed = {
image_f_inputs: f_images_batch,
ann_inputs_enc: captions_batch[1],
ann_inputs_dec: captions_batch[0],
ann_lengths: cl_batch,
anneal: gs
}
if params.use_c_v or (params.prior == 'GMM'
or params.prior == 'AG'):
feed.update({c_i: c_v[:, 1:]})
gs = tf.train.global_step(sess, global_step)
feed.update({anneal: gs})
# if gs_epoch == 0:
# print(sess.run(debug_print, feed))
kl, rl, lb, _, _, ann = sess.run([
kld, rec_loss, lower_bound, optimize, optimize_cnn,
annealing
], feed)
gs_epoch += 1
if gs % 500 == 0:
print("Epoch: {} Iteration: {} VLB: {} "
"Rec Loss: {}".format(
e, gs, np.mean(lb), rl))
if not params.no_encoder:
print("Annealing coefficient:"
"{} KLD: {}".format(ann, np.mean(kl)))
if stop_condition():
break
if stop_condition():
break
print("Epoch: {} Iteration: {} VLB: {} Rec Loss: {}".format(
e,
gs,
np.mean(lb),
rl,
))
def validate():
val_rec = []
for f_images_batch, captions_batch, cl_batch, c_v in val_gen.next_batch(
use_obj_vectors=params.use_c_v,
num_captions=params.num_captions):
gs = tf.train.global_step(sess, global_step)
if params.num_captions > 1:
captions_batch, cl_batch, c_v = preprocess_captions(
captions_batch, cl_batch, c_v)
feed = {
image_f_inputs: f_images_batch,
ann_inputs_enc: captions_batch[1],
ann_inputs_dec: captions_batch[0],
ann_lengths: cl_batch,
anneal: gs
}
if params.use_c_v or (params.prior == 'GMM'
or params.prior == 'AG'):
feed.update({c_i: c_v[:, 1:]})
rl = sess.run([rec_loss], feed_dict=feed)
val_rec.append(rl)
print("Validation reconstruction loss: {}".format(
np.mean(val_rec)))
print("-----------------------------------------------")
validate()
# save model
if not os.path.exists("./checkpoints"):
os.makedirs("./checkpoints")
save_path = saver.save(
sess, "./checkpoints/{}.ckpt".format(params.checkpoint))
print("Model saved in file: %s" % save_path)
# builder.add_meta_graph_and_variables(sess, ["main_model"])
if params.use_hdf5 and params.fine_tune:
batch_gen.h5f.close()
# run inference
if params.mode == "inference":
inference.inference(params, decoder, val_gen, test_gen,
image_f_inputs, saver, sess)
def __init__(self, name):
with tf.variable_scope(name):
self.GAMMA = 0.99
self.nActions = 3
self.state_shape = [80, 80, 1]
self.state = tf.placeholder(tf.float32, [None, *self.state_shape],
name='state')
net = layers.conv2d(self.state, 32, 8, 4, activation=tf.nn.relu)
net = layers.conv2d(net, 64, 4, 2, activation=tf.nn.relu)
net = layers.conv2d(net, 64, 3, 1, activation=tf.nn.relu)
net = tf.contrib.layers.flatten(net)
net = layers.dense(net, 512, activation=tf.nn.relu)
#Use Shared network to define actor logtis
actor_logits = layers.dense(net,
self.nActions,
activation=None,
name='actor_logits')
self.action_prob = tf.nn.softmax(actor_logits, name='action_prob')
#Use Shared network to define critic value
self.critic_value = tf.squeeze(
layers.dense(net, 1, activation=None, name='critic_value'))
self.actions = tf.placeholder(tf.int32, [None], name='actions')
self.advantage = tf.placeholder(tf.float32, [None],
name='advantage')
self.Return = tf.placeholder(tf.float32, [None], name='Return')
action_onehot = tf.one_hot(self.actions,
self.nActions,
name="action_onehot")
single_action_prob = tf.reduce_mean(self.action_prob *
action_onehot,
axis=1)
#Create actor Loss
entropy = tf.reduce_sum(-self.action_prob *
tf.log(self.action_prob + 1e-7),
axis=1)
log_action_prob = tf.log(single_action_prob + 1e-7)
actor_loss = -tf.reduce_mean(log_action_prob * self.advantage +
entropy * 0.005)
#Create Critic loss
critic_loss = tf.losses.mean_squared_error(
labels=self.Return, predictions=self.critic_value)
#define Total Loss
self.total_loss = actor_loss + critic_loss * 0.5
self.optimizer = tf.train.RMSPropOptimizer(learning_rate=1e-3,
decay=0.99)
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
scope=name)
self.gradients = self.optimizer.compute_gradients(
self.total_loss, var_list)
self.gradients_placeholders = []
for grad, var in self.gradients:
self.gradients_placeholders.append(
(tf.placeholder(var.dtype, shape=var.get_shape()), var))
self.apply_gradients = self.optimizer.apply_gradients(
self.gradients_placeholders)
self.graph_var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope=name)
def get_q_values_op(self, state, scope, reuse=False):
"""
Returns Q values for all actions
Args:
state: (tf tensor)
shape = (batch_size, img height, img width, nchannels)
scope: (string) scope name, that specifies if target network or not
reuse: (bool) reuse of variables in the scope
Returns:
out: (tf tensor) of shape = (batch_size, num_actions)
"""
# this information might be useful
num_actions = self.env.action_space.n
##############################################################
"""
TODO: implement the computation of Q values like in the paper
https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf
https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
you may find the section "model architecture" of the appendix of the
nature paper particulary useful.
store your result in out of shape = (batch_size, num_actions)
HINT:
- You may find the following functions useful:
- tf.layers.conv2d
- tf.layers.flatten
- tf.layers.dense
- Make sure to also specify the scope and reuse
"""
##############################################################
################ YOUR CODE HERE - 10-15 lines ################
with tf.variable_scope(scope, reuse=reuse) as _:
conv1 = layers.conv2d(state,
filters=32,
kernel_size=8,
strides=4,
activation=tf.nn.relu)
conv2 = layers.conv2d(conv1,
filters=64,
kernel_size=4,
strides=2,
activation=tf.nn.relu)
conv3 = layers.conv2d(conv2,
filters=64,
kernel_size=3,
strides=1,
activation=tf.nn.relu)
flatten = layers.flatten(conv3)
fc1 = layers.dense(flatten, units=512, activation=tf.nn.relu)
out = layers.dense(fc1, units=num_actions)
pass
##############################################################
######################## END YOUR CODE #######################
return out
def generator(n_samples, noise_with_labels, reuse=None):
"""
:param n_samples: number of samples
:param noise_with_labels: latent noise + labels
:return: generated images
"""
lod_in = tf.cast(
tf.get_variable('lod', initializer=np.float32(0.0), trainable=False),
'float32')
# (if image size is a power of 2 --> you can: n_filter = image_res/n_filter)
# get number of layers and filters
n_conv_layer = int(np.ceil(np.log2(resolution_image / size_init)))
n_filters = int(2**(n_conv_layer - 1))
print(' G: n-conv layer generator: ', n_conv_layer)
print(' G: n-filters generator: ', n_filters)
with tf.variable_scope(
'Generator', reuse=tf.AUTO_REUSE): # Needed for later, in order to
# get variables of discriminator
# ----- Layer1, Dense, Batch, Leaky ----- #
print(' G: units dense generator: ',
channels * (size_init * size_init) * (n_filters * const_filt))
output = layers.dense(inputs=noise_with_labels,
units=channels * (size_init * size_init) *
(n_filters * const_filt))
output = layers.batch_normalization(output)
output = tf.maximum(leakage * output, output)
print(' G: dense layer')
print(output)
if channel_first_gen:
# size: 128 x 7 x 7
output = tf.reshape(
output,
(-1, n_filters * const_filt * channels, size_init, size_init))
bn_axis = 1 # [0, 2, 3] # first
else:
# size: 7 x 7 x 128
output = tf.reshape(
output,
(-1, size_init, size_init, n_filters * const_filt * channels))
bn_axis = -1 # [0, 1, 2] # last
print(' G: channel reshape:')
print(output)
# ----- LoopLayers, deConv, Batch, Leaky ----- #
img_out_list = []
for i in range(n_conv_layer):
if resolution_image == 28 and size_init * (1 + i) == 8:
if channel_first_gen:
output = output[:, :, :7, :7]
else:
output = output[:, :7, :7, :]
print(' G: cut mnist, iteration: ', i)
print(output)
print(' G: conv2d_transpose iter', i, ' - tot filters: ',
n_filters * const_filt * channels, ' - n_filters: ',
n_filters)
output = layers.conv2d_transpose(output,
filters=n_filters * const_filt *
channels,
kernel_size=kernel_size,
strides=strides,
padding='same')
print(output)
output = layers.batch_normalization(output, axis=bn_axis)
output = tf.maximum(leakage * output, output) # relu
n_filters = int(n_filters / 2)
# -----------------------------------------------------------------------------
# KARRAS
img_new = toRGB(output)
if i == 0:
img_out = img_new
else:
img_out = upscale2d(img_out)
with tf.variable_scope('Layer%d' % i):
img_out = lerp_clip(img_new, img_out, lod_in - i)
img_out_list.append(img_out)
return img_out, img_out_list
# experiment with small datasets
trainX = trainX[:1000]
trainY = trainY[:1000]
for neuron_count in neurons:
tf.reset_default_graph()
with tf.Session() as sess:
# Create the model
lr = tf.placeholder(tf.float32, [])
x = tf.placeholder(tf.float32, [None, NUM_FEATURES])
y_ = tf.placeholder(tf.float32, [None, 1])
# Build the graph for the deep net
hidden_layer = layers.dense(x, neuron_count, activation=tf.nn.relu, kernel_initializer=init.orthogonal(np.sqrt(2)),
bias_initializer=init.zeros(), kernel_regularizer=l2_regularizer(1e-3))
output_layer = layers.dense(hidden_layer, 1, kernel_initializer=init.orthogonal(), bias_initializer=init.zeros(),
kernel_regularizer=l2_regularizer(1e-3))
loss = tf.reduce_mean(tf.square(y_ - output_layer)
) + get_regularization_loss()
# Create the gradient descent optimizer with the given learning rate.
optimizer = tf.train.GradientDescentOptimizer(lr)
train_op = optimizer.minimize(loss)
error = tf.reduce_mean(tf.square(y_ - output_layer))
fold_errors = []
for o in range(NUM_FOLDS):
sess.run(tf.global_variables_initializer())
def discriminator(images, lod_in=1, reuse=None):
"""
:param images: images that are input of the discriminator
:return: likeliness of the image
"""
lod_in = tf.cast(
tf.get_variable('lod', initializer=np.float32(0.0), trainable=False),
'float32')
if channel_first_disc == True:
channels_key = 'channels_first'
else:
channels_key = 'channels_last'
n_conv_layer = int(np.ceil(np.log2(resolution_image / size_init)))
n_filters = 1
print(' D: n-conv layer discriminator: ', n_conv_layer)
print(' D: n-filters discriminator: ', n_filters)
with tf.variable_scope(
'Discriminator',
reuse=tf.AUTO_REUSE): # Needed for later, in order to
# get variables of generator
print(' D: input')
print(images)
if channel_first_disc:
print('channel first disc ON')
output = tf.reshape(
images, [-1, channels, resolution_image, resolution_image])
else:
output = tf.reshape(
images, [-1, resolution_image, resolution_image, channels])
print(' D: channel reshape')
print(output)
# ----- LoopLayers, Conv, Leaky ----- #
img = output
output = fromRGB(output, n_filters)
for i in range(n_conv_layer - 1, -1, -1):
print(' D: conv2d iter: ', i, ' - n_filters: ', n_filters)
output = layers.conv2d(output,
filters=n_filters * const_filt,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=channels_key)
print(output)
output = tf.maximum(leakage * output, output)
n_filters = int(n_filters * 2)
x = output
img = downscale2d(img)
y = fromRGB(img, i - 1)
with tf.variable_scope('Layer%d' % i):
x = lerp_clip(x, y, lod_in - i)
output = x
# ----------------------------------------------------------
output = tf.reshape(
output,
[-1, size_init * size_init * (int(n_filters / 2) * const_filt)])
print(' D: reshape linear layer')
print(output)
# ----- Layer4, Dense, Linear ----- #
output = layers.dense(output, units=num_labels + 1)
print(' D: dense layer output')
print(output)
scores_out = tf.identity(output[:, :1], name='scores_out')
labels_out = tf.identity(output[:, 1:], name='labels_out')
print(' D: scores output')
print(scores_out)
print(' D: labels output')
print(labels_out)
return scores_out, labels_out
def ICM(self):
# Input
s_current = tf.placeholder(tf.float32, shape = [None,
self.img_size,
self.img_size,
self.Num_stacking * self.Num_colorChannel])
s_current = (s_current - (255.0/2)) / (255.0/2)
s_next = tf.placeholder(tf.float32, shape = [None,
self.img_size,
self.img_size,
self.Num_stacking * self.Num_colorChannel])
s_next = (s_next - (255.0/2)) / (255.0/2)
with tf.variable_scope('curiosity'):
# Convolution variables
w_conv1 = self.conv_weight_variable('_w_conv1', [3,3,self.Num_stacking * self.Num_colorChannel,32])
b_conv1 = self.bias_variable('_b_conv1',[32])
w_conv2 = self.conv_weight_variable('_w_conv2', [3,3,32,32])
b_conv2 = self.bias_variable('_b_conv2',[32])
w_conv3 = self.conv_weight_variable('_w_conv3', [3,3,32,32])
b_conv3 = self.bias_variable('_b_conv3',[32])
w_conv4 = self.conv_weight_variable('_w_conv4', [3,3,32,32])
b_conv4 = self.bias_variable('_b_conv4',[32])
# Feature Vector
s_conv1 = tf.nn.elu(self.conv2d(s_current, w_conv1, 2) + b_conv1)
s_conv2 = tf.nn.elu(self.conv2d(s_conv1, w_conv2, 2) + b_conv2)
s_conv3 = tf.nn.elu(self.conv2d(s_conv2, w_conv3, 2) + b_conv3)
s_conv4 = tf.nn.elu(self.conv2d(s_conv3, w_conv4, 2) + b_conv4)
s_conv4_flat_dim = s_conv4.shape[1]*s_conv4.shape[2]*s_conv4.shape[3]
s_conv4_flat = tf.reshape(s_conv4, [tf.shape(s_conv4)[0], s_conv4_flat_dim])
s_next_conv1 = tf.nn.elu(self.conv2d(s_next, w_conv1, 2) + b_conv1)
s_next_conv2 = tf.nn.elu(self.conv2d(s_next_conv1, w_conv2, 2) + b_conv2)
s_next_conv3 = tf.nn.elu(self.conv2d(s_next_conv2, w_conv3, 2) + b_conv3)
s_next_conv4 = tf.nn.elu(self.conv2d(s_next_conv3, w_conv4, 2) + b_conv4)
s_next_conv4_flat_dim = s_next_conv4.shape[1]*s_next_conv4.shape[2]*s_next_conv4.shape[3]
s_next_conv4_flat = tf.reshape(s_next_conv4, [tf.shape(s_next_conv4)[0], s_next_conv4_flat_dim])
# Forward Model
a_t = tf.placeholder(tf.float32, shape = [None, self.Num_action])
input_forward = tf.concat([s_conv4_flat, a_t], 1)
forward_fc1 = layer.dense(input_forward, 256, activation=tf.nn.relu)
forward_fc1 = tf.concat([forward_fc1, a_t], 1)
forward_fc2 = layer.dense(forward_fc1, s_next_conv4_flat.shape[1], activation=None)
r_i = (self.eta * 0.5) * tf.reduce_sum(tf.square(tf.subtract(forward_fc2, s_next_conv4_flat)), axis = 1)
Lf = tf.losses.mean_squared_error(forward_fc2, s_next_conv4_flat)
# Inverse Model
input_inverse = tf.concat([s_conv4_flat, s_next_conv4_flat], 1)
inverse_fc1 = layer.dense(input_inverse, 256, activation=tf.nn.relu)
inverse_fc2 = layer.dense(inverse_fc1, self.Num_action, activation=tf.nn.softmax)
Li = tf.losses.softmax_cross_entropy(a_t, inverse_fc2)
return s_current, s_next, a_t, r_i, Lf, Li
def make_network(self,
input_opr,
name,
train=True,
reuse=False,
batch_size=0):
w_reg = tf.contrib.layers.l2_regularizer(self.L2_REG)
with tf.variable_scope(name, reuse=reuse):
if self.greyscale:
input_opr = tf.image.rgb_to_grayscale(input_opr)
conv1 = tf.layers.conv2d(inputs=input_opr,
filters=8,
kernel_size=4,
strides=2,
activation=tf.nn.relu6,
trainable=train)
conv2 = tf.layers.conv2d(inputs=conv1,
filters=16,
kernel_size=3,
strides=2,
activation=tf.nn.relu6,
trainable=train)
conv3 = tf.layers.conv2d(inputs=conv2,
filters=32,
kernel_size=3,
strides=2,
activation=tf.nn.relu6,
trainable=train)
conv4 = tf.layers.conv2d(inputs=conv3,
filters=64,
kernel_size=3,
strides=2,
activation=tf.nn.relu6,
trainable=train)
conv5 = tf.layers.conv2d(inputs=conv4,
filters=128,
kernel_size=3,
strides=1,
activation=tf.nn.relu6,
trainable=train)
conv6 = tf.layers.conv2d(inputs=conv5,
filters=256,
kernel_size=3,
strides=1,
activation=tf.nn.relu6,
trainable=train)
flatten = layers.flatten(inputs=conv6)
fc1 = layers.dense(flatten,
units=self.num_unit,
activation=tf.nn.relu6,
trainable=train,
kernel_regularizer=w_reg)
fc2 = layers.dense(fc1,
units=self.num_unit,
activation=tf.nn.relu6,
trainable=train,
kernel_regularizer=w_reg)
value_out = self.value_output_layer(fc1, train, w_reg)
if self.is_continuous:
policy_out = self.continuous_policy_output_layer(
fc2, train, w_reg)
else:
policy_out = self.discrete_policy_output_layer(
fc2, train, w_reg)
params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=name)
return value_out, policy_out, params, None, None
def encoder(self, X, verbose=True):
""" Draws the encoder of the network
"""
enc_net = [
tf.reshape(
X, [self.batch_size, self.dims[0], self.dims[1], self.dims[2]])
]
for lay_num, (filters, kernel_size,
stride) in enumerate(self.encoder_dims):
if kernel_size > 0: # if this is a convolutional layer
if lay_num == len(
self.encoder_dims) - 1: # if this is the last layer
enc_net.append(
tf.contrib.layers.flatten(
layers.conv2d(enc_net[len(enc_net) - 1],
filters=filters,
kernel_size=kernel_size,
strides=stride,
padding='same',
name='enc_' + str(lay_num),
activation=self.default_activation)))
else:
if self.encoder_dims[lay_num + 1][1] == 0:
# flatten this layer
enc_net.append(
tf.contrib.layers.flatten(
layers.conv2d(
enc_net[len(enc_net) - 1],
filters=filters,
kernel_size=kernel_size,
strides=stride,
padding='same',
name='enc_' + str(lay_num),
activation=self.default_activation)))
else:
enc_net.append(
layers.conv2d(enc_net[len(enc_net) - 1],
filters=filters,
kernel_size=kernel_size,
strides=stride,
padding='same',
name='enc_' + str(lay_num),
activation=self.default_activation))
else:
enc_net.append(
layers.dense(enc_net[len(enc_net) - 1],
units=filters,
name='enc_' + str(lay_num),
activation=self.default_activation))
enc_shapes = [shape(i) for i in enc_net]
# append latent layer
if self.latent_loss == 'VAE':
z_log_sigma_sq = layers.dense(enc_net[len(enc_net) - 1],
units=self.hidden_size,
activation=None,
name='z_log_sigma_squared')
else:
z_log_sigma_sq = 0
enc_net.append(
layers.dense(enc_net[len(enc_net) - 1],
units=self.hidden_size,
activation=None,
name='latent_layer')) # 32, 2
if verbose:
print('Encoder shapes: ', enc_shapes)
return enc_net, enc_shapes, z_log_sigma_sq
def generator(n_samples, noise_with_labels, reuse=None):
"""
:param n_samples: number of samples
:param noise_with_labels: latent noise + labels
:return: generated images
"""
# (if image size is a power of 2 --> you can: n_filter = image_res/n_filter)
# get number of layers and filters
n_conv_layer = int(np.ceil(np.log2(resolution_image / size_init)))
n_filters = int(2 ** (n_conv_layer - 1))
print(' G: n-conv layer generator: ', n_conv_layer)
print(' G: n-filters generator: ', n_filters)
with tf.variable_scope('Generator', reuse=tf.AUTO_REUSE): # Needed for later, in order to
# get variables of discriminator
# ----- Layer1, Dense, Batch, Leaky ----- #
print(' G: units dense generator: ', channels * (size_init * size_init) * (n_filters * const_filt))
output = layers.dense(inputs=noise_with_labels,
units=channels * (size_init * size_init) * (n_filters * const_filt))
output = layers.batch_normalization(output)
output = tf.maximum(leakage * output, output)
print(' G: dense layer')
print(output)
if channel_first_gen:
# size: 128 x 7 x 7
output = tf.reshape(output, (-1, n_filters * const_filt * channels, size_init, size_init))
bn_axis = 1 # [0, 2, 3] # first
else:
# size: 7 x 7 x 128
output = tf.reshape(output, (-1, size_init, size_init, n_filters * const_filt * channels))
bn_axis = -1 # [0, 1, 2] # last
print(' G: channel reshape:')
print(output)
# ----- LoopLayers, deConv, Batch, Leaky ----- #
karras_gen_out = []
for i in range(n_conv_layer):
if resolution_image == 28 and size_init * (1 + i) == 8:
if channel_first_gen:
output = output[:, :, :7, :7]
else:
output = output[:, :7, :7, :]
print(' G: cut mnist, iteration: ', i)
print(output)
print(' G: conv2d_transpose iter', i, ' - tot filters: ',
n_filters * const_filt * channels, ' - n_filters: ', n_filters)
output = layers.conv2d_transpose(output,
filters=n_filters * const_filt * channels,
kernel_size=kernel_size,
strides=strides,
padding='same')
print(output)
output = layers.batch_normalization(output, axis=bn_axis)
output = tf.maximum(leakage * output, output) # relu
n_filters = int(n_filters / 2)
karras_gen_out.append(output)
# ----- LastLayer, deConv, Batch, Leaky ----- #
print(' G: last conv2d_transpose layer - n filters layer: ', channels)
output = layers.conv2d_transpose(output,
filters=1 * channels,
kernel_size=kernel_size,
strides=1,
padding='same')
print(output)
output = tf.nn.tanh(output)
output = tf.reshape(output, [-1, OUTPUT_DIM])
print(' G: output reshape')
print(output)
return output
def __init__(self, state_size, action_size, hidden_layer_size,
learning_rate, name):
if isinstance(state_size, list) and len(state_size) == 3:
self.mode = 'visual'
elif isinstance(state_size, int):
self.mode = 'vector'
self.state_size = state_size
self.action_size = action_size
self.hidden_layer_size = hidden_layer_size
self.name = name
with tf.variable_scope(name):
if self.mode == 'visual':
self.observation = tf.placeholder(
tf.float32,
shape=[None, *self.state_size],
name="critic_observation")
self.O1 = layer.flatten(
layer.conv2d(self.observation,
64,
3,
activation=tf.nn.leaky_relu))
self.action = tf.placeholder(tf.float32,
shape=[None, self.action_size],
name="critic_action")
self.A1 = layer.dense(self.action,
self.hidden_layer_size // 2,
activation=tf.nn.leaky_relu)
self.L1 = tf.concat([self.O1, self.A1], 1)
self.L1 = layer.dense(self.L1,
self.hidden_layer_size,
activation=tf.nn.leaky_relu)
self.L2 = layer.dense(self.L1,
self.hidden_layer_size,
activation=tf.nn.leaky_relu)
self.L3 = layer.dense(self.L2,
self.hidden_layer_size,
activation=tf.nn.leaky_relu)
self.value = layer.dense(self.L3, 1, activation=None)
elif self.mode == 'vector':
self.observation = tf.placeholder(
tf.float32,
shape=[None, self.state_size],
name="critic_observation")
self.O1 = layer.dense(self.observation,
self.hidden_layer_size // 2,
activation=tf.nn.leaky_relu)
self.action = tf.placeholder(tf.float32,
shape=[None, self.action_size],
name="critic_action")
self.A1 = layer.dense(self.action,
self.hidden_layer_size // 2,
activation=tf.nn.leaky_relu)
self.L1 = tf.concat([self.O1, self.A1], 1)
self.L1 = layer.dense(self.L1,
self.hidden_layer_size,
activation=tf.nn.leaky_relu)
self.L2 = layer.dense(self.L1,
self.hidden_layer_size,
activation=tf.nn.leaky_relu)
self.L3 = layer.dense(self.L2,
self.hidden_layer_size,
activation=tf.nn.leaky_relu)
self.value = layer.dense(self.L3, 1, activation=None)
self.true_value = tf.placeholder(tf.float32, name="true_value")
self.loss = tf.losses.huber_loss(self.true_value, self.value)
self.Adam = tf.train.AdamOptimizer(learning_rate)
self.Update = self.Adam.minimize(self.loss)
self.trainable_var = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, self.name)
def f_net(inputs):
"""
action_num is set 5.
:param inputs:
:return:
"""
inputs = inputs[0]
inputs = inputs / 128 - 1.0
action_num = 3
l2 = 1e-4
# (350, 350, 3*n) -> ()
conv1 = layers.conv2d(inputs=inputs,
filters=16,
kernel_size=(8, 8),
strides=1,
kernel_regularizer=l2_regularizer(scale=l2),
activation=tf.nn.relu,
name='conv1')
print conv1.shape
pool1 = layers.max_pooling2d(inputs=conv1,
pool_size=3,
strides=4,
name='pool1')
print pool1.shape
conv2 = layers.conv2d(inputs=pool1,
filters=16,
kernel_size=(5, 5),
strides=1,
kernel_regularizer=l2_regularizer(scale=l2),
activation=tf.nn.relu,
name='conv2')
print conv2.shape
pool2 = layers.max_pooling2d(inputs=conv2,
pool_size=3,
strides=3,
name='pool2')
print pool2.shape
conv3 = layers.conv2d(inputs=pool2,
filters=64,
kernel_size=(3, 3),
strides=1,
kernel_regularizer=l2_regularizer(scale=l2),
activation=tf.nn.relu,
name='conv3')
print conv3.shape
pool3 = layers.max_pooling2d(
inputs=conv3,
pool_size=3,
strides=2,
name='pool3',
)
print pool3.shape
depth = pool3.get_shape()[1:].num_elements()
inputs = tf.reshape(pool3, shape=[-1, depth])
inputs = tf.stop_gradient(inputs)
print inputs.shape
hid1 = layers.dense(inputs=inputs,
units=256,
activation=tf.nn.relu,
kernel_regularizer=l2_regularizer(scale=l2),
name='hid1')
print hid1.shape
hid2 = layers.dense(inputs=hid1,
units=256,
activation=tf.nn.relu,
kernel_regularizer=l2_regularizer(scale=l2),
name='hid2')
print hid2.shape
pi = layers.dense(inputs=hid2,
units=action_num,
activation=tf.nn.softmax,
kernel_regularizer=l2_regularizer(scale=l2),
name='pi')
pi = tf.stop_gradient(pi)
q = layers.dense(inputs=hid2,
units=action_num,
kernel_regularizer=l2_regularizer(scale=l2),
name='q')
return {"pi": pi, "q": q}
