tensorflow2.0学习笔记： LSTM 文本数据生成

tensorflow2.0学习笔记： RNN 文本数据生成 详述了在莎士比亚数据集上，从数据处理到RNN实现的整个过程，但RNN的结果并不理想。本文通过LSTM，再次实现文本数据分类，结果比RNN较好。有关数据处理部分tensorflow2.0学习笔记： RNN 文本数据生成 中有详细注释，就整程序实现实现而言，RNN和LSTM仅在仅在建立模型中有稍微区别：

 keras.layers.SimpleRNN(units = rnn_units,return_sequences = True)
 替换为
 keras.layers.LSTM(units = rnn_units, stateful = True,
 								recurrent_initializer = 'glorot_uniform', 
 								return_sequences = True)

import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import tensorflow as tf

from tensorflow import keras

print(tf.__version__)

input_filepath = "./shakespeare.txt"
text = open(input_filepath,'r').read()
print(len(text))
print(text[0:100])

# 1.generate vocab
# 2.build mapping char -> id
# 3. data -> id_data
# 4. abcd -> bcd

vocab = sorted(set(text)) #排序(去重)
print(len(vocab))
print(vocab)

char2idx = {char:idx for idx,char in enumerate(vocab)}
print(char2idx)

idx2char = np.array(vocab)
print(idx2char)

text_as_int = np.array([char2idx[c] for c in text])
print(text_as_int[0:10])
print(text[0:10])

def split_input_target(id_text):
    '''abcde -> abcd,bcde'''
    return id_text[0:-1],id_text[1:]
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int) #转成dataset
seq_length = 100
seq_dataset = char_dataset.batch(seq_length + 1,drop_remainder = True)
for ch_id in char_dataset.take(2):
    print(ch_id,idx2char[ch_id.numpy()])

for seq_id in seq_dataset.take(2):
    print(seq_id)
    print(repr(''.join(idx2char[seq_id.numpy()])))

seq_dataset = seq_dataset.map(split_input_target)

for item_input, item_output in seq_dataset.take(2):
    print(item_input.numpy())
    print(item_output.numpy())

batch_size = 64
buffer_size = 10000
seq_dataset = seq_dataset.shuffle(buffer_size).batch(batch_size,drop_remainder=True)

vocab_size = len(vocab)
embedding_dim = 256
rnn_units = 1024

def build_model(vocab_size,embedding_dim,rnn_units,batch_size):
    model = keras.models.Sequential([
        keras.layers.Embedding(vocab_size,embedding_dim,
                               batch_input_shape = [batch_size,None]),
        keras.layers.LSTM(units = rnn_units,
                          stateful = True,
                          recurrent_initializer = 'glorot_uniform',
                          return_sequences = True),
        keras.layers.Dense(vocab_size)
    ])
    return model
model = build_model(vocab_size=vocab_size,embedding_dim=embedding_dim,rnn_units=rnn_units,batch_size=batch_size)

model.summary()

for input_example_batch,target_example_batch in seq_dataset.take(1):
    example_batch_predictions = model(input_example_batch)
    print(example_batch_predictions.shape)

# 1. random sampling. 65生成概率分布
# 2.greedy random
sample_indices = tf.random.categorical(logits = example_batch_predictions[0],num_samples = 1)
sample_indices = tf.squeeze(sample_indices,axis=-1)
print(sample_indices)

print("Input:",repr(''.join(idx2char[input_example_batch[0]])))
print()
print("Output",repr(''.join(idx2char[target_example_batch[0]])))
print()
print("Predictions",repr(''.join(idx2char[sample_indices])))

def loss(labels,logits):
    return keras.losses.sparse_categorical_crossentropy(
        labels,logits,from_logits=True)

model.compile(optimizer='adam',loss=loss)
example_loss = loss(target_example_batch,example_batch_predictions)
print(example_loss.shape)
print(example_loss.numpy().mean())

output_dir = "./text_generation_checkpoints"
if not os.path.exists(output_dir):
    os.mkdir(output_dir)
checkpoint_prefix = os.path.join(output_dir,'ckpt_{epoch}')
checkpoint_callback = keras.callbacks.ModelCheckpoint(
    filepath = checkpoint_prefix,
    save_weights_only=True)
epochs = 3
history = model.fit(seq_dataset,epochs=epochs,callbacks=[checkpoint_callback])

tf.train.latest_checkpoint(output_dir)

model2 = build_model(vocab_size,embedding_dim,rnn_units,batch_size = 1)
model2.load_weights(tf.train.latest_checkpoint(output_dir))
model2.build(tf.TensorShape([1,None]))
# start char sequence A ,
# A --> model -->b
# A.append(b) -> B
# B --> model -->c
# B.append(c) -> C
# ...
model2.summary()

def generate_text(model,start_string,num_generate = 1000):
    input_eval = [char2idx[ch] for ch in start_string]
    input_eval = tf.expand_dims(input_eval,0)
    
    text_generated = []
    model.reset_states()
        
    # temperature > 1, 概率分布更平滑，更接近 random 策略
    # temperature < 1, 概率分布更陡峭，更接近 greedy 策略
    temperature = 0.5
    
    
    for _ in range(num_generate):
        # model inference -> predictions
        # sample -> char -> text_generated
        # unpdate input_eval
        
        # predictions: [batch_size,input_eval_len,vocab_size]
        predictions = model(input_eval)
        # predictions:logits -> softmax -> prob
        # softmax:e^xi
        # eg:4,2 ,e^4/(e^4+e^2) = 0.88 ,e^2/(e^4+e^2) = 0.12
        # eg:2,1 ,e^2/(e^2+e^1) = 0.73 ,e^1/(e^2+e^1) = 0.27
        # 除以一个大于1的数，后进行sigmoid,概率分布变得更加平滑
        predictions = predictions / temperature
        # predictions: [input_eval_len,vocab_size]
        predictions = tf.squeeze(predictions,0)
        # predicted_ids:[input_eval_len,1]
        # a b c --> b c d 
        predicted_id = tf.random.categorical(predictions,num_samples=1)[-1,0].numpy()
        
        text_generated.append(idx2char[predicted_id])
        # s,x --> rnn s,y
        input_eval = tf.expand_dims([predicted_id],0)
    return start_string+''.join(text_generated)

new_text = generate_text(model2,"All:")
print(new_text)