斯坦福大学人工智能实验室李飞飞教授，实现人工智能3要素：语法(syntax)、语义(semantics)、推理(inference)。语言、视觉。通过语法(语言语法解析、视觉三维结构解析)和语义(语言语义、视觉特体动作含义)作模型输入训练数据，实现推理能力，训练学习能力应用到工作，从新数据推断结论。《The Syntax,Semantics and Inference Mechanism in Natureal Language》 。
看图说话模型。输入一张图片，根据图像像给出描述图像内容自然语言，讲故事。翻译图像信息和文本信息。 。
原理。编码器-解码器框架，图像编码成固定中间矢量，解码成自然语言描述。编码器Inception V3图像识别模型，解码器LSTM网络。{s0,s1,…,sn-1}字幕词，{wes0,wes1,…,wesn-1}对应词嵌入向量，LSTM输出{p1,p2,…,pn}句子下一词生成概率分布，{logp1(s1),logp2(s2),…,logpn(sn)}正确词每个步骤对数似然，总和取负数是模型最小化目标。
最佳实践。微软Microsoft COCO Caption数据集 。Miscrosoft Common Objects in Context(COCO)数据集。超过30万张图片，200万个标记实体。对原COCO数据集33万张图片，用亚马逊Mechanical Turk服务，人工为每张图片生成至少5句标注，标注语句超过150万句。2014版本、2015版本。2014版本82783张图片，验证集40504张图片，测试集40775张图片。
TensorFlow-Slim图像分类库 。
构建模型。show_and_tell_model.py。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from im2txt.ops import image_embedding
from im2txt.ops import image_processing
from im2txt.ops import inputs as input_ops
class ShowAndTellModel(object):
"""Image-to-text implementation based on
"Show and Tell: A Neural Image Caption Generator"
Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan
"""
def __init__(self, config, mode, train_inception=False):
"""Basic setup.
Args:
config: Object containing configuration parameters.
mode: "train", "eval" or "inference".
train_inception: Whether the inception submodel variables are trainable.
"""
assert mode in ["train", "eval", "inference"]
self.config = config
self.mode = mode
self.train_inception = train_inception
# Reader for the input data.
self.reader = tf.TFRecordReader()
# To match the "Show and Tell" paper we initialize all variables with a
# random uniform initializer.
self.initializer = tf.random_uniform_initializer(
minval=-self.config.initializer_scale,
maxval=self.config.initializer_scale)
# A float32 Tensor with shape [batch_size, height, width, channels].
self.images = None
# An int32 Tensor with shape [batch_size, padded_length].
self.input_seqs = None
# An int32 Tensor with shape [batch_size, padded_length].
self.target_seqs = None
# An int32 0/1 Tensor with shape [batch_size, padded_length].
self.input_mask = None
# A float32 Tensor with shape [batch_size, embedding_size].
self.image_embeddings = None
# A float32 Tensor with shape [batch_size, padded_length, embedding_size].
self.seq_embeddings = None
# A float32 scalar Tensor; the total loss for the trainer to optimize.
self.total_loss = None
# A float32 Tensor with shape [batch_size * padded_length].
self.target_cross_entropy_losses = None
# A float32 Tensor with shape [batch_size * padded_length].
self.target_cross_entropy_loss_weights = None
# Collection of variables from the inception submodel.
self.inception_variables = []
# Function to restore the inception submodel from checkpoint.
self.init_fn = None
# Global step Tensor.
self.global_step = None
def is_training(self):
"""Returns true if the model is built for training mode."""
return self.mode == "train"
def process_image(self, encoded_image, thread_id=0):
"""Decodes and processes an image string.
Args:
encoded_image: A scalar string Tensor; the encoded image.
thread_id: Preprocessing thread id used to select the ordering of color
distortions.
Returns:
A float32 Tensor of shape [height, width, 3]; the processed image.
"""
return image_processing.process_image(encoded_image,
is_training=self.is_training(),
height=self.config.image_height,
width=self.config.image_width,
thread_id=thread_id,
image_format=self.config.image_format)
def build_inputs(self):
"""Input prefetching, preprocessing and batching.
Outputs:
self.images
self.input_seqs
self.target_seqs (training and eval only)
self.input_mask (training and eval only)
"""
if self.mode == "inference":
# In inference mode, images and inputs are fed via placeholders.
image_feed = tf.placeholder(dtype=tf.string, shape=[], name="image_feed")
input_feed = tf.placeholder(dtype=tf.int64,
shape=[None], # batch_size
name="input_feed")
# Process image and insert batch dimensions.
images = tf.expand_dims(self.process_image(image_feed), 0)
input_seqs = tf.expand_dims(input_feed, 1)
# No target sequences or input mask in inference mode.
target_seqs = None
input_mask = None
else:
# Prefetch serialized SequenceExample protos.
input_queue = input_ops.prefetch_input_data(
self.reader,
self.config.input_file_pattern,
is_training=self.is_training(),
batch_size=self.config.batch_size,
values_per_shard=self.config.values_per_input_shard,
input_queue_capacity_factor=self.config.input_queue_capacity_factor,
num_reader_threads=self.config.num_input_reader_threads)
# Image processing and random distortion. Split across multiple threads
# with each thread applying a slightly different distortion.
assert self.config.num_preprocess_threads % 2 == 0
images_and_captions = []
for thread_id in range(self.config.num_preprocess_threads):
serialized_sequence_example = input_queue.dequeue()
encoded_image, caption = input_ops.parse_sequence_example(
serialized_sequence_example,
image_feature=self.config.image_feature_name,
caption_feature=self.config.caption_feature_name)
image = self.process_image(encoded_image, thread_id=thread_id)
images_and_captions.append([image, caption])
# Batch inputs.
queue_capacity = (2 * self.config.num_preprocess_threads *
self.config.batch_size)
images, input_seqs, target_seqs, input_mask = (
input_ops.batch_with_dynamic_pad(images_and_captions,
batch_size=self.config.batch_size,
queue_capacity=queue_capacity))
self.images = images
self.input_seqs = input_seqs
self.target_seqs = target_seqs
self.input_mask = input_mask
def build_image_embeddings(self):
"""Builds the image model subgraph and generates image embeddings.
Inputs:
self.images
Outputs:
self.image_embeddings
"""
inception_output = image_embedding.inception_v3(
self.images,
trainable=self.train_inception,
is_training=self.is_training())
self.inception_variables = tf.get_collection(
tf.GraphKeys.GLOBAL_VARIABLES, scope="InceptionV3")
# Map inception output into embedding space.
with tf.variable_scope("image_embedding") as scope:
image_embeddings = tf.contrib.layers.fully_connected(
inputs=inception_output,
num_outputs=self.config.embedding_size,
activation_fn=None,
weights_initializer=self.initializer,
biases_initializer=None,
scope=scope)
# Save the embedding size in the graph.
tf.constant(self.config.embedding_size, name="embedding_size")
self.image_embeddings = image_embeddings
def build_seq_embeddings(self):
"""Builds the input sequence embeddings.
Inputs:
self.input_seqs
Outputs:
self.seq_embeddings
"""
with tf.variable_scope("seq_embedding"), tf.device("/cpu:0"):
embedding_map = tf.get_variable(
name="map",
shape=[self.config.vocab_size, self.config.embedding_size],
initializer=self.initializer)
seq_embeddings = tf.nn.embedding_lookup(embedding_map, self.input_seqs)
self.seq_embeddings = seq_embeddings
def build_model(self):
"""Builds the model.
Inputs:
self.image_embeddings
self.seq_embeddings
self.target_seqs (training and eval only)
self.input_mask (training and eval only)
Outputs:
self.total_loss (training and eval only)
self.target_cross_entropy_losses (training and eval only)
self.target_cross_entropy_loss_weights (training and eval only)
"""
# This LSTM cell has biases and outputs tanh(new_c) * sigmoid(o), but the
# modified LSTM in the "Show and Tell" paper has no biases and outputs
# new_c * sigmoid(o).
lstm_cell = tf.contrib.rnn.BasicLSTMCell(
num_units=self.config.num_lstm_units, state_is_tuple=True)
if self.mode == "train":
lstm_cell = tf.contrib.rnn.DropoutWrapper(
lstm_cell,
input_keep_prob=self.config.lstm_dropout_keep_prob,
output_keep_prob=self.config.lstm_dropout_keep_prob)
with tf.variable_scope("lstm", initializer=self.initializer) as lstm_scope:
# Feed the image embeddings to set the initial LSTM state.
zero_state = lstm_cell.zero_state(
batch_size=self.image_embeddings.get_shape()[0], dtype=tf.float32)
_, initial_state = lstm_cell(self.image_embeddings, zero_state)
# Allow the LSTM variables to be reused.
lstm_scope.reuse_variables()
if self.mode == "inference":
# In inference mode, use concatenated states for convenient feeding and
# fetching.
tf.concat(axis=1, values=initial_state, name="initial_state")
# Placeholder for feeding a batch of concatenated states.
state_feed = tf.placeholder(dtype=tf.float32,
shape=[None, sum(lstm_cell.state_size)],
name="state_feed")
state_tuple = tf.split(value=state_feed, num_or_size_splits=2, axis=1)
# Run a single LSTM step.
lstm_outputs, state_tuple = lstm_cell(
inputs=tf.squeeze(self.seq_embeddings, axis=[1]),
state=state_tuple)
# Concatentate the resulting state.
tf.concat(axis=1, values=state_tuple, name="state")
else:
# Run the batch of sequence embeddings through the LSTM.
sequence_length = tf.reduce_sum(self.input_mask, 1)
lstm_outputs, _ = tf.nn.dynamic_rnn(cell=lstm_cell,
inputs=self.seq_embeddings,
sequence_length=sequence_length,
initial_state=initial_state,
dtype=tf.float32,
scope=lstm_scope)
# Stack batches vertically.
lstm_outputs = tf.reshape(lstm_outputs, [-1, lstm_cell.output_size])
with tf.variable_scope("logits") as logits_scope:
logits = tf.contrib.layers.fully_connected(
inputs=lstm_outputs,
num_outputs=self.config.vocab_size,
activation_fn=None,
weights_initializer=self.initializer,
scope=logits_scope)
if self.mode == "inference":
tf.nn.softmax(logits, name="softmax")
else:
targets = tf.reshape(self.target_seqs, [-1])
weights = tf.to_float(tf.reshape(self.input_mask, [-1]))
# Compute losses.
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=targets,
logits=logits)
batch_loss = tf.div(tf.reduce_sum(tf.multiply(losses, weights)),
tf.reduce_sum(weights),
name="batch_loss")
tf.losses.add_loss(batch_loss)
total_loss = tf.losses.get_total_loss()
# Add summaries.
tf.summary.scalar("losses/batch_loss", batch_loss)
tf.summary.scalar("losses/total_loss", total_loss)
for var in tf.trainable_variables():
tf.summary.histogram("parameters/" + var.op.name, var)
self.total_loss = total_loss
self.target_cross_entropy_losses = losses # Used in evaluation.
self.target_cross_entropy_loss_weights = weights # Used in evaluation.
def setup_inception_initializer(self):
"""Sets up the function to restore inception variables from checkpoint."""
if self.mode != "inference":
# Restore inception variables only.
saver = tf.train.Saver(self.inception_variables)
def restore_fn(sess):
tf.logging.info("Restoring Inception variables from checkpoint file %s",
self.config.inception_checkpoint_file)
saver.restore(sess, self.config.inception_checkpoint_file)
self.init_fn = restore_fn
def setup_global_step(self):
"""Sets up the global step Tensor."""
global_step = tf.Variable(
initial_value=0,
name="global_step",
trainable=False,
collections=[tf.GraphKeys.GLOBAL_STEP, tf.GraphKeys.GLOBAL_VARIABLES])
self.global_step = global_step
def build(self):
"""Creates all ops for training and evaluation."""
# 构建模型
self.build_inputs() # 构建输入数据
self.build_image_embeddings() # 采用Inception V3构建图像模型，输出图片嵌入向量
self.build_seq_embeddings() # 构建输入序列embeddings
self.build_model() # CNN、LSTM串联，构建完整模型
self.setup_inception_initializer() # 载入Inception V3预训练模型
self.setup_global_step() # 记录全局迭代次数
训练模型。train.py。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from im2txt import configuration
from im2txt import show_and_tell_model
FLAGS = tf.app.flags.FLAGS
tf.flags.DEFINE_string("input_file_pattern", "",
"File pattern of sharded TFRecord input files.")
tf.flags.DEFINE_string("inception_checkpoint_file", "",
"Path to a pretrained inception_v3 model.")
tf.flags.DEFINE_string("train_dir", "",
"Directory for saving and loading model checkpoints.")
tf.flags.DEFINE_boolean("train_inception", False,
"Whether to train inception submodel variables.")
tf.flags.DEFINE_integer("number_of_steps", 1000000, "Number of training steps.")
tf.flags.DEFINE_integer("log_every_n_steps", 1,
"Frequency at which loss and global step are logged.")
tf.logging.set_verbosity(tf.logging.INFO)
def main(unused_argv):
assert FLAGS.input_file_pattern, "--input_file_pattern is required"
assert FLAGS.train_dir, "--train_dir is required"
model_config = configuration.ModelConfig()
model_config.input_file_pattern = FLAGS.input_file_pattern
model_config.inception_checkpoint_file = FLAGS.inception_checkpoint_file
training_config = configuration.TrainingConfig()
# Create training directory.
# 创建训练结果存储路径
train_dir = FLAGS.train_dir
if not tf.gfile.IsDirectory(train_dir):
tf.logging.info("Creating training directory: %s", train_dir)
tf.gfile.MakeDirs(train_dir)
# Build the TensorFlow graph.
# 建立TensorFlow数据流图
g = tf.Graph()
with g.as_default():
# Build the model.
# 构建模型
model = show_and_tell_model.ShowAndTellModel(
model_config, mode="train", train_inception=FLAGS.train_inception)
model.build()
# Set up the learning rate.
# 定义学习率
learning_rate_decay_fn = None
if FLAGS.train_inception:
learning_rate = tf.constant(training_config.train_inception_learning_rate)
else:
learning_rate = tf.constant(training_config.initial_learning_rate)
if training_config.learning_rate_decay_factor > 0:
num_batches_per_epoch = (training_config.num_examples_per_epoch /
model_config.batch_size)
decay_steps = int(num_batches_per_epoch *
training_config.num_epochs_per_decay)
def _learning_rate_decay_fn(learning_rate, global_step):
return tf.train.exponential_decay(
learning_rate,
global_step,
decay_steps=decay_steps,
decay_rate=training_config.learning_rate_decay_factor,
staircase=True)
learning_rate_decay_fn = _learning_rate_decay_fn
# Set up the training ops.
# 定义训练操作
train_op = tf.contrib.layers.optimize_loss(
loss=model.total_loss,
global_step=model.global_step,
learning_rate=learning_rate,
optimizer=training_config.optimizer,
clip_gradients=training_config.clip_gradients,
learning_rate_decay_fn=learning_rate_decay_fn)
# Set up the Saver for saving and restoring model checkpoints.
saver = tf.train.Saver(max_to_keep=training_config.max_checkpoints_to_keep)
# Run training.
# 训练
tf.contrib.slim.learning.train(
train_op,
train_dir,
log_every_n_steps=FLAGS.log_every_n_steps,
graph=g,
global_step=model.global_step,
number_of_steps=FLAGS.number_of_steps,
init_fn=model.init_fn,
saver=saver)
if __name__ == "__main__":
tf.app.run()
预测生成模型。run_inference.py。
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import tensorflow as tf
from im2txt import configuration
from im2txt import inference_wrapper
from im2txt.inference_utils import caption_generator
from im2txt.inference_utils import vocabulary
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string("checkpoint_path", "",
"Model checkpoint file or directory containing a "
"model checkpoint file.")
tf.flags.DEFINE_string("vocab_file", "", "Text file containing the vocabulary.")
tf.flags.DEFINE_string("input_files", "",
"File pattern or comma-separated list of file patterns "
"of image files.")
tf.logging.set_verbosity(tf.logging.INFO)
def main(_):
# Build the inference graph.
g = tf.Graph()
with g.as_default():
model = inference_wrapper.InferenceWrapper()
restore_fn = model.build_graph_from_config(configuration.ModelConfig(),
FLAGS.checkpoint_path)
g.finalize()
# Create the vocabulary.
vocab = vocabulary.Vocabulary(FLAGS.vocab_file)
filenames = []
for file_pattern in FLAGS.input_files.split(","):
filenames.extend(tf.gfile.Glob(file_pattern))
tf.logging.info("Running caption generation on %d files matching %s",
len(filenames), FLAGS.input_files)
with tf.Session(graph=g) as sess:
# Load the model from checkpoint.
restore_fn(sess)
# Prepare the caption generator. Here we are implicitly using the default
# beam search parameters. See caption_generator.py for a description of the
# available beam search parameters.
generator = caption_generator.CaptionGenerator(model, vocab)
for filename in filenames:
with tf.gfile.GFile(filename, "r") as f:
image = f.read()
captions = generator.beam_search(sess, image)
print("Captions for image %s:" % os.path.basename(filename))
for i, caption in enumerate(captions):
# Ignore begin and end words.
sentence = [vocab.id_to_word(w) for w in caption.sentence[1:-1]]
sentence = " ".join(sentence)
print(" %d) %s (p=%f)" % (i, sentence, math.exp(caption.logprob)))
if __name__ == "__main__":
tf.app.run()
参考资料：
《TensorFlow技术解析与实战》
欢迎推荐上海机器学习工作机会，我的微信：qingxingfengzi
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