RNN
人每次思考時不會重頭開始，而是保留之前思考的一些結果爲現在的決策提供支持。例如我們對話時，我們會根據上下文的信息理解一句話的含義，而不是對每一句話重頭進行分析。傳統的神經網絡不能實現這個功能，這可能是其一大缺陷。例如卷積神經網絡雖然可以對圖像進行分類，但是可能無法對視頻中每一幀圖像發生的事情進行關聯分析，我們無法利用前一幀圖像的信息，而循環神經網絡則可以解決這個問題。
如上圖所示，x是RNN的輸入，s是RNN的一個節點，而o是輸出。我們對這個RNN輸入數據x，然後通過網絡計算並得到輸出結果o，再將某些信息（state，狀態）傳入到網絡的輸入。我們將o與label進行比較可以得到誤差，有了這個誤差之後，就能使用梯度下降（Gradient Descent）和Back-Propagation Through Time（BPTT）方法對網絡進行訓練，BPTT與訓練前饋神經網絡的傳統BP方法類似，也是使用反向傳播求梯度並更新網絡參數權重。另外，還有一種方法叫Real-Time Recurrent Learning(RTRL)，它可以正向求解梯度，不過其計算複雜度比較高。
RNN展開後，類似於有一系列輸入x和一系列輸出o的串聯的普通神經網絡，上一層的神經網絡會傳遞信息給下一層。這種串聯的結構天然就非常適合時間序列數據的處理和分析。需要注意的是，展開後的每一層級的神經網絡，其參數都是相同的，我們並不需要訓練成百上千層神經網絡的參數，只需要訓練一層RNN的參數。這就是它結構巧妙的地方，這裏共享參數的思想和卷積網絡中權值共享的方式也很類似。
LSTM
對於某些簡單的問題，可能只需要最後輸入的少量時序信息即可解決。但是對某些複雜問題，可能需要更早的一些信息，甚至是時間序列開頭的信息，但間隔太遠的輸入信息，RNN是難以記憶的，因此長程依賴（Long-term Dependencies）是傳統RNN的致命傷。
LSTM天生就是爲了解決長程依賴而設計的，不需要特別複雜地調試超參數，默認就可以記住長期的信息。
LSTM的內部結構相比RNN更復雜，其中包含了4層神經網絡，其中小圈圈是point-wise的操作，比如向量加法、點乘等，而小矩陣則代表一層可學習參數的神經網絡。
- LSTM單元上面的那條直線代表了LSTM的狀態state，它會貫穿所有串聯在一起的LSTM單元，從第一個LSTM單元一直流向最後一個LSTM單元，其中只有少量的線性干預和改變。
- 狀態state在這條隧道中傳遞時，LSTM單元可以對其添加或刪除信息，這些對信息流的修改操作由LSTM中的Gates控制。
- 這些Gates中包含了一個Sigmoid層和一個向量點乘的操作，這個Sigmoid層的輸出是0-1之間的值，它直接控制了信息傳遞的比例。
- 每個LSTM單元中包含了3個這樣的Gates，用來維護和控制單元的狀態信息。憑藉對狀態信息的存儲和修改，LSTM單元就可以實現長程記憶。
Tensorflow實現LSTM
下面我們就使用LSTM來實現一個語言模型，給定上文的語境，即歷史出現的單詞，語言模型可以預測下一個單詞出現的概率，使用的數據集：PTB
#%%
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import time
import numpy as np
import tensorflow as tf
import reader
#flags = tf.flags
#logging = tf.logging
#flags.DEFINE_string("save_path", None,
# "Model output directory.")
#flags.DEFINE_bool("use_fp16", False,
# "Train using 16-bit floats instead of 32bit floats")
#FLAGS = flags.FLAGS
#def data_type():
# return tf.float16 if FLAGS.use_fp16 else tf.float32
class PTBInput(object):
"""The input data."""
def __init__(self, config, data, name=None):
self.batch_size = batch_size = config.batch_size
self.num_steps = num_steps = config.num_steps
self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
self.input_data, self.targets = reader.ptb_producer(
data, batch_size, num_steps, name=name)
class PTBModel(object):
"""The PTB model."""
def __init__(self, is_training, config, input_):
self._input = input_
batch_size = input_.batch_size
num_steps = input_.num_steps
size = config.hidden_size
vocab_size = config.vocab_size
# Slightly better results can be obtained with forget gate biases
# initialized to 1 but the hyperparameters of the model would need to be
# different than reported in the paper.
def lstm_cell():
return tf.contrib.rnn.BasicLSTMCell(
size, forget_bias=0.0, state_is_tuple=True)
attn_cell = lstm_cell
if is_training and config.keep_prob < 1:
def attn_cell():
return tf.contrib.rnn.DropoutWrapper(
lstm_cell(), output_keep_prob=config.keep_prob)
cell = tf.contrib.rnn.MultiRNNCell(
[attn_cell() for _ in range(config.num_layers)], state_is_tuple=True)
self._initial_state = cell.zero_state(batch_size, tf.float32)
with tf.device("/cpu:0"):
embedding = tf.get_variable(
"embedding", [vocab_size, size], dtype=tf.float32)
inputs = tf.nn.embedding_lookup(embedding, input_.input_data)
if is_training and config.keep_prob < 1:
inputs = tf.nn.dropout(inputs, config.keep_prob)
# Simplified version of models/tutorials/rnn/rnn.py's rnn().
# This builds an unrolled LSTM for tutorial purposes only.
# In general, use the rnn() or state_saving_rnn() from rnn.py.
#
# The alternative version of the code below is:
#
# inputs = tf.unstack(inputs, num=num_steps, axis=1)
# outputs, state = tf.nn.rnn(cell, inputs,
# initial_state=self._initial_state)
outputs = []
state = self._initial_state
with tf.variable_scope("RNN"):
for time_step in range(num_steps):
if time_step > 0: tf.get_variable_scope().reuse_variables()
(cell_output, state) = cell(inputs[:, time_step, :], state)
outputs.append(cell_output)
output = tf.reshape(tf.concat(outputs, 1), [-1, size])
softmax_w = tf.get_variable(
"softmax_w", [size, vocab_size], dtype=tf.float32)
softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=tf.float32)
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(input_.targets, [-1])],
[tf.ones([batch_size * num_steps], dtype=tf.float32)])
self._cost = cost = tf.reduce_sum(loss) / batch_size
self._final_state = state
if not is_training:
return
self._lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),
config.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self._lr)
self._train_op = optimizer.apply_gradients(
zip(grads, tvars),
global_step=tf.contrib.framework.get_or_create_global_step())
self._new_lr = tf.placeholder(
tf.float32, shape=[], name="new_learning_rate")
self._lr_update = tf.assign(self._lr, self._new_lr)
def assign_lr(self, session, lr_value):
session.run(self._lr_update, feed_dict={self._new_lr: lr_value})
@property
def input(self):
return self._input
@property
def initial_state(self):
return self._initial_state
@property
def cost(self):
return self._cost
@property
def final_state(self):
return self._final_state
@property
def lr(self):
return self._lr
@property
def train_op(self):
return self._train_op
class SmallConfig(object):
"""Small config."""
init_scale = 0.1
learning_rate = 1.0
max_grad_norm = 5
num_layers = 2
num_steps = 20
hidden_size = 200
max_epoch = 4
max_max_epoch = 13
keep_prob = 1.0
lr_decay = 0.5
batch_size = 20
vocab_size = 10000
class MediumConfig(object):
"""Medium config."""
init_scale = 0.05
learning_rate = 1.0
max_grad_norm = 5
num_layers = 2
num_steps = 35
hidden_size = 650
max_epoch = 6
max_max_epoch = 39
keep_prob = 0.5
lr_decay = 0.8
batch_size = 20
vocab_size = 10000
class LargeConfig(object):
"""Large config."""
init_scale = 0.04
learning_rate = 1.0
max_grad_norm = 10
num_layers = 2
num_steps = 35
hidden_size = 1500
max_epoch = 14
max_max_epoch = 55
keep_prob = 0.35
lr_decay = 1 / 1.15
batch_size = 20
vocab_size = 10000
class TestConfig(object):
"""Tiny config, for testing."""
init_scale = 0.1
learning_rate = 1.0
max_grad_norm = 1
num_layers = 1
num_steps = 2
hidden_size = 2
max_epoch = 1
max_max_epoch = 1
keep_prob = 1.0
lr_decay = 0.5
batch_size = 20
vocab_size = 10000
def run_epoch(session, model, eval_op=None, verbose=False):
"""Runs the model on the given data."""
start_time = time.time()
costs = 0.0
iters = 0
state = session.run(model.initial_state)
fetches = {
"cost": model.cost,
"final_state": model.final_state,
}
if eval_op is not None:
fetches["eval_op"] = eval_op
for step in range(model.input.epoch_size):
feed_dict = {}
for i, (c, h) in enumerate(model.initial_state):
feed_dict[c] = state[i].c
feed_dict[h] = state[i].h
vals = session.run(fetches, feed_dict)
cost = vals["cost"]
state = vals["final_state"]
costs += cost
iters += model.input.num_steps
if verbose and step % (model.input.epoch_size // 10) == 10:
print("%.3f perplexity: %.3f speed: %.0f wps" %
(step * 1.0 / model.input.epoch_size, np.exp(costs / iters),
iters * model.input.batch_size / (time.time() - start_time)))
return np.exp(costs / iters)
raw_data = reader.ptb_raw_data('simple-examples/data/')
train_data, valid_data, test_data, _ = raw_data
config = SmallConfig()
eval_config = SmallConfig()
eval_config.batch_size = 1
eval_config.num_steps = 1
with tf.Graph().as_default():
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
with tf.name_scope("Train"):
train_input = PTBInput(config=config, data=train_data, name="TrainInput")
with tf.variable_scope("Model", reuse=None, initializer=initializer):
m = PTBModel(is_training=True, config=config, input_=train_input)
#tf.scalar_summary("Training Loss", m.cost)
#tf.scalar_summary("Learning Rate", m.lr)
with tf.name_scope("Valid"):
valid_input = PTBInput(config=config, data=valid_data, name="ValidInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mvalid = PTBModel(is_training=False, config=config, input_=valid_input)
#tf.scalar_summary("Validation Loss", mvalid.cost)
with tf.name_scope("Test"):
test_input = PTBInput(config=eval_config, data=test_data, name="TestInput")
with tf.variable_scope("Model", reuse=True, initializer=initializer):
mtest = PTBModel(is_training=False, config=eval_config,
input_=test_input)
sv = tf.train.Supervisor()
with sv.managed_session() as session:
for i in range(config.max_max_epoch):
lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0)
m.assign_lr(session, config.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
train_perplexity = run_epoch(session, m, eval_op=m.train_op,
verbose=True)
print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
valid_perplexity = run_epoch(session, mvalid)
print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))
test_perplexity = run_epoch(session, mtest)
print("Test Perplexity: %.3f" % test_perplexity)
# if FLAGS.save_path:
# print("Saving model to %s." % FLAGS.save_path)
# sv.saver.save(session, FLAGS.save_path, global_step=sv.global_step)
#if __name__ == "__main__":
# tf.app.run()