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Keras（三十四）Transformer模型代码实现_transformer keras

作者：小小林熬夜学编程 | 2024-03-31 01:55:24

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transformer keras

一,加载数据

0,导入深度学习包

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
    

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

# 1. loads data
# 2. preprocesses data -> dataset
# 3. tools
# 3.1 generates position embedding
# 3.2 create mask. (a. padding, b. decoder)
# 3.3 scaled_dot_product_attention
# 4. builds model
# 4.1 MultiheadAttention
# 4.2 EncoderLayer
# 4.3 DecoderLayer
# 4.4 EncoderModel
# 4.5 DecoderModel
# 4.6 Transformer
# 5. optimizer & loss
# 6. train step -> train
# 7. Evaluate and Visualize
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1,下载数据

下载datasets数据，并将tensorflow_datasets文件夹放到home目录下

import tensorflow_datasets as tfds
examples, info = tfds.load('ted_hrlr_translate/pt_to_en',
                            with_info = True,
                            as_supervised = True
                           )
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2,将原始数据拆分为训练数据集和验证数据集

train_examples, val_examples = examples['train'], examples['validation']
print(info)
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3,查看验证数据中的葡萄牙语,英语数据example

for pt, en in train_examples.take(5):
    print(pt.numpy())
    print(en.numpy())
    print()
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二,数据预处理

1,定义,构建tokenizer

en_tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(
    (en.numpy() for pt, en in train_examples),
    target_vocab_size = 2 ** 13)
pt_tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(
    (pt.numpy() for pt, en in train_examples),
    target_vocab_size = 2 ** 13)
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2,测试tokenizer的encode,decode

sample_string = "Transformer is awesome."

tokenized_string = en_tokenizer.encode(sample_string)
print('Tokenized string is {}'.format(tokenized_string))

origin_string = en_tokenizer.decode(tokenized_string)
print('The original string is {}'.format(origin_string))

assert origin_string == sample_string

for token in tokenized_string:
    print('{} --> "{}"'.format(token, en_tokenizer.decode([token])))
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3,将原始数据转化为subword,并乱序,过滤掉大于40语句,根据batch数据做padding

buffer_size = 20000
batch_size = 64
max_length = 40

def encode_to_subword(pt_sentence, en_sentence):
    pt_sequence = [pt_tokenizer.vocab_size]     + pt_tokenizer.encode(pt_sentence.numpy())     + [pt_tokenizer.vocab_size + 1]
    en_sequence = [en_tokenizer.vocab_size]     + en_tokenizer.encode(en_sentence.numpy())     + [en_tokenizer.vocab_size + 1]
    return pt_sequence, en_sequence

def filter_by_max_length(pt, en):
    """过滤选择长度小于40的语句"""
    return tf.logical_and(tf.size(pt) <= max_length,
                          tf.size(en) <= max_length)

def tf_encode_to_subword(pt_sentence, en_sentence):
    """将[encode_to_subword]函数转化为tensor方法"""
    return tf.py_function(encode_to_subword,
                          [pt_sentence, en_sentence],
                          [tf.int64, tf.int64])
train_dataset = train_examples.map(tf_encode_to_subword)
train_dataset = train_dataset.filter(filter_by_max_length)
train_dataset = train_dataset.shuffle(
    buffer_size).padded_batch(
    batch_size, padded_shapes=([-1], [-1]))

valid_dataset = val_examples.map(tf_encode_to_subword)
valid_dataset = valid_dataset.filter(
    filter_by_max_length).padded_batch(
    batch_size, padded_shapes=([-1], [-1]))
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4,查看预处理后的batch数据

for pt_batch, en_batch in valid_dataset.take(1):
    print(pt_batch.shape, en_batch.shape)
    print(pt_batch)
    print()
    print(en_batch)
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三,位置编码 - 生成 position embedding

1,position对应embedding矩阵的计算公式

PE(pos, 2i)   = sin(pos / 10000^(2i/d_model))
PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))
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2,位置编码 - 生成 position embedding

# pos.shape: [sentence_length, 1]
# i.shape  : [1, d_model]
# result.shape: [sentence_length, d_model]
def get_angles(pos, i, d_model):
    """获取单词pos对应embeding的角度"""
    angle_rates = 1 / np.power(10000,
                               (2 * (i // 2)) / np.float32(d_model))
    return pos * angle_rates

def get_position_embedding(sentence_length, d_model):
    """返回position对应embedding矩阵"""

    
    angle_rads = get_angles(np.arange(sentence_length)[:, np.newaxis],
                            np.arange(d_model)[np.newaxis, :],
                            d_model)
    # sines.shape: [sentence_length, d_model / 2]
    # cosines.shape: [sentence_length, d_model / 2]
    sines = np.sin(angle_rads[:, 0::2])
    cosines = np.cos(angle_rads[:, 1::2])
    
    # position_embedding.shape: [sentence_length, d_model]
    position_embedding = np.concatenate([sines, cosines], axis = -1)
    
    # angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
    # angle_rads[:, 1::2] = cosines = np.cos(angle_rads[:, 1::2])
    # position_embedding = angle_rads
    
    # position_embedding.shape: [1, sentence_length, d_model]
    position_embedding = position_embedding[np.newaxis, ...]
    
    return tf.cast(position_embedding, dtype=tf.float32)

position_embedding = get_position_embedding(50, 512)
print(position_embedding.shape)
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3,打印矩阵图形

def plot_position_embedding(position_embedding):
    plt.pcolormesh(position_embedding[0], cmap = 'RdBu')
    plt.xlabel('Depth')
    plt.xlim((0, 512))
    plt.ylabel('Position')
    plt.colorbar()
    plt.show()
    
# plot_position_embedding(position_embedding)
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四,mask的构建

"""
1. padding mask, 
2. look ahead
"""
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1,padding mask

# batch_data.shape: [batch_size, seq_len]
def create_padding_mask(batch_data):
    padding_mask = tf.cast(tf.math.equal(batch_data, 0), tf.float32)
    # [batch_size, 1, 1, seq_len]
    return padding_mask[:, tf.newaxis, tf.newaxis, :]

x = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
create_padding_mask(x)
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2,decoder中的look ahead

# attention_weights.shape: [3,3]
# [[1, 0, 0],
#  [4, 5, 0],
#  [7, 8, 9]]
def create_look_ahead_mask(size):
    mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)
    return mask # (seq_len, seq_len)

create_look_ahead_mask(3)
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五,缩放点积注意力机制实现

1,定义缩放点积注意力机制函数

def scaled_dot_product_attention(q, k, v, mask):
    """
    Args:
    - q: shape == (..., seq_len_q, depth)
    - k: shape == (..., seq_len_k, depth)
    - v: shape == (..., seq_len_v, depth_v)
    - seq_len_k == seq_len_v
    - mask: shape == (..., seq_len_q, seq_len_k)
    Returns:
    - output: weighted sum
    - attention_weights: weights of attention
    """
    
    # matmul_qk.shape: (..., seq_len_q, seq_len_k)
    matmul_qk = tf.matmul(q, k, transpose_b = True)
    
    dk = tf.cast(tf.shape(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
    
    if mask is not None:
        # 使得在softmax后值趋近于0
        """在mask里，应该被忽略的我们会设成1，应该被保留的会设成0，而如果mask相应位置上为1，
        那么我们就给对应的logits 加上一个超级小的负数， -1000000000， 这样，对应的logits也就变成了一个超级小的数。
        然后在计算softmax的时候，一个超级小的数的指数会无限接近与0。也就是它对应的attention的权重就是0了。"""
        scaled_attention_logits += (mask * -1e9)
    
    # attention_weights.shape: (..., seq_len_q, seq_len_k)
    attention_weights = tf.nn.softmax(
        scaled_attention_logits, axis = -1)
    
    # output.shape: (..., seq_len_q, depth_v)
    output = tf.matmul(attention_weights, v)
    
    return output, attention_weights
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2,调用缩放点积注意力,返回权重和输出

def print_scaled_dot_product_attention(q, k, v):
    temp_out, temp_att = scaled_dot_product_attention(q, k, v, None)
    print("Attention weights are:")
    print(temp_att)
    print("Output is:")
    print(temp_out)
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3,测试

temp_k = tf.constant([[10, 0, 0],
                      [0, 10, 0],
                      [0, 0, 10],
                      [0, 0, 10]], dtype=tf.float32) # (4, 3)

temp_v = tf.constant([[1, 0],
                      [10, 0],
                      [100, 5],
                      [1000, 6]], dtype=tf.float32) # (4, 2)

temp_q1 = tf.constant([[0, 10, 0]], dtype=tf.float32) # (1, 3)
np.set_printoptions(suppress=True)
print_scaled_dot_product_attention(temp_q1, temp_k, temp_v)

temp_q2 = tf.constant([[0, 0, 10]], dtype=tf.float32) # (1, 3)
print_scaled_dot_product_attention(temp_q2, temp_k, temp_v)

temp_q3 = tf.constant([[10, 10, 0]], dtype=tf.float32) # (1, 3)
print_scaled_dot_product_attention(temp_q3, temp_k, temp_v)

temp_q4 = tf.constant([[0, 10, 0],
                       [0, 0, 10],
                       [10, 10, 0]], dtype=tf.float32) # (3, 3)
print_scaled_dot_product_attention(temp_q4, temp_k, temp_v)
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六,多头注意力机制实现

1,定义多头注意力函数

class MultiHeadAttention(keras.layers.Layer):
    """
    理论上:
    x -> Wq0 -> q0
    x -> Wk0 -> k0
    x -> Wv0 -> v0
    
    实战中:
    q -> Wq0 -> q0
    k -> Wk0 -> k0
    v -> Wv0 -> v0
    
    实战中技巧：
    q -> Wq -> Q -> split -> q0, q1, q2...
    """
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        assert self.d_model % self.num_heads == 0
        
        self.depth = self.d_model // self.num_heads
        
        self.WQ = keras.layers.Dense(self.d_model)
        self.WK = keras.layers.Dense(self.d_model)
        self.WV = keras.layers.Dense(self.d_model)
        
        self.dense = keras.layers.Dense(self.d_model)
    
    def split_heads(self, x, batch_size):
        # x.shape: (batch_size, seq_len, d_model)
        # d_model = num_heads * depth
        # x -> (batch_size, num_heads, seq_len, depth)
        
        x = tf.reshape(x,
                       (batch_size, -1, self.num_heads, self.depth))
        # print(x.shape)
        return tf.transpose(x, perm=[0, 2, 1, 3])
    
    def call(self, q, k, v, mask):
        batch_size = tf.shape(q)[0]
        
        q = self.WQ(q) # q.shape: (batch_size, seq_len_q, d_model)
        k = self.WK(k) # k.shape: (batch_size, seq_len_k, d_model)
        v = self.WV(v) # v.shape: (batch_size, seq_len_v, d_model)
        
        # q.shape: (batch_size, num_heads, seq_len_q, depth)
        q = self.split_heads(q, batch_size)
        # k.shape: (batch_size, num_heads, seq_len_k, depth)
        k = self.split_heads(k, batch_size)
        # v.shape: (batch_size, num_heads, seq_len_v, depth)
        v = self.split_heads(v, batch_size)
        
        # scaled_attention_outputs.shape: (batch_size, num_heads, seq_len_q, depth)
        # attention_weights.shape: (batch_size, num_heads, seq_len_q, seq_len_k)
        scaled_attention_outputs, attention_weights = \
        scaled_dot_product_attention(q, k, v, mask)
        
        # scaled_attention_outputs.shape: (batch_size, seq_len_q, num_heads, depth)
        scaled_attention_outputs = tf.transpose(
            scaled_attention_outputs, perm = [0, 2, 1, 3])
        # concat_attention.shape: (batch_size, seq_len_q, d_model)
        concat_attention = tf.reshape(scaled_attention_outputs,
                                      (batch_size, -1, self.d_model))
        
        # output.shape : (batch_size, seq_len_q, d_model)
        output = self.dense(concat_attention)
        
        return output, attention_weights
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2,测试

temp_mha = MultiHeadAttention(d_model=512, num_heads=8)
y = tf.random.uniform((1, 60, 256)) # (batch_size, seq_len_q, dim)
output, attn = temp_mha(y, y, y, mask = None)
print(output.shape)
print(attn.shape)
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七,FeedForward层次实现

1,定义FeedForward函数

def feed_forward_network(d_model, dff):
    # dff: dim of feed forward network.
    return keras.Sequential([
        keras.layers.Dense(dff, activation='relu'),
        keras.layers.Dense(d_model)
    ])
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2,测试

sample_ffn = feed_forward_network(512, 2048)
sample_ffn(tf.random.uniform((64, 50, 512))).shape
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八,定义EncoderLayer层

1,定义EncoderLayer层

class EncoderLayer(keras.layers.Layer):
    """
    x -> self attention -> add & normalize & dropout
      -> feed_forward -> add & normalize & dropout
    """
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(EncoderLayer, self).__init__()
        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = feed_forward_network(d_model, dff)
        
        self.layer_norm1 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        self.layer_norm2 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        
        self.dropout1 = keras.layers.Dropout(rate)
        self.dropout2 = keras.layers.Dropout(rate)
    
    def call(self, x, training, encoder_padding_mask):
        # x.shape          : (batch_size, seq_len, dim=d_model)
        # attn_output.shape: (batch_size, seq_len, d_model)
        # out1.shape       : (batch_size, seq_len, d_model)
        attn_output, _ = self.mha(x, x, x, encoder_padding_mask)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layer_norm1(x + attn_output)
        
        # ffn_output.shape: (batch_size, seq_len, d_model)
        # out2.shape      : (batch_size, seq_len, d_model)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layer_norm2(out1 + ffn_output)
        
        return out2
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2,测试

sample_encoder_layer = EncoderLayer(512, 8, 2048)
sample_input = tf.random.uniform((64, 50, 512))
sample_output = sample_encoder_layer(sample_input, False, None)
print(sample_output.shape)
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九,定义DecoderLayer层

1,定义DecoderLayer层

class DecoderLayer(keras.layers.Layer):
    """
    x -> self attention -> add & normalize & dropout -> out1
    out1 , encoding_outputs -> attention -> add & normalize & dropout -> out2
    out2 -> ffn -> add & normalize & dropout -> out3
    """
    def __init__(self, d_model, num_heads, dff, rate = 0.1):
        super(DecoderLayer, self).__init__()
        
        self.mha1 = MultiHeadAttention(d_model, num_heads)
        self.mha2 = MultiHeadAttention(d_model, num_heads)
        
        self.ffn = feed_forward_network(d_model, dff)
        
        self.layer_norm1 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        self.layer_norm2 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        self.layer_norm3 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        
        self.dropout1 = keras.layers.Dropout(rate)
        self.dropout2 = keras.layers.Dropout(rate)
        self.dropout3 = keras.layers.Dropout(rate)
    
    
    def call(self, x, encoding_outputs, training,
             decoder_mask, encoder_decoder_padding_mask):
        # decoder_mask: 由look_ahead_mask和decoder_padding_mask合并而来
        
        # x.shape: (batch_size, target_seq_len, d_model)
        # encoding_outputs.shape: (batch_size, input_seq_len, d_model)
        
        # attn1, out1.shape : (batch_size, target_seq_len, d_model)
        attn1, attn_weights1 = self.mha1(x, x, x, decoder_mask)
        attn1 = self.dropout1(attn1, training = training)
        out1 = self.layer_norm1(attn1 + x)
        
        # attn2, out2.shape : (batch_size, target_seq_len, d_model)
        attn2, attn_weights2 = self.mha2(
            out1, encoding_outputs, encoding_outputs,
            encoder_decoder_padding_mask)
        attn2 = self.dropout2(attn2, training = training)
        out2 = self.layer_norm2(attn2 + out1)
        
        # ffn_output, out3.shape: (batch_size, target_seq_len, d_model)
        ffn_output = self.ffn(out2)
        ffn_output = self.dropout3(ffn_output, training=training)
        out3 = self.layer_norm3(ffn_output + out2)
        
        return out3, attn_weights1, attn_weights2
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2,测试

sample_decoder_layer = DecoderLayer(512, 8, 2048) sample_decoder_input
= tf.random.uniform((64, 60, 512)) sample_decoder_output, sample_decoder_attn_weights1, sample_decoder_attn_weights2 = sample_decoder_layer(
    sample_decoder_input, sample_output, False, None, None)

print(sample_decoder_output.shape) print(sample_decoder_attn_weights1.shape) print(sample_decoder_attn_weights2.shape)
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十,EncoderModel的实现

1,EncoderModel的实现

class EncoderModel(keras.layers.Layer):
    def __init__(self, num_layers, input_vocab_size, max_length,
                 d_model, num_heads, dff, rate=0.1):
        super(EncoderModel, self).__init__()
        self.d_model = d_model
        self.num_layers = num_layers
        self.max_length = max_length
        
        self.embedding = keras.layers.Embedding(input_vocab_size,
                                                self.d_model)
        # position_embedding.shape: (1, max_length, d_model)
        self.position_embedding = get_position_embedding(max_length,
                                                         self.d_model)
        
        self.dropout = keras.layers.Dropout(rate)
        self.encoder_layers = [
            EncoderLayer(d_model, num_heads, dff, rate)
            for _ in range(self.num_layers)]
        
    def call(self, x, training, encoder_padding_mask):
        # x.shape: (batch_size, input_seq_len)
        input_seq_len = tf.shape(x)[1]
        tf.debugging.assert_less_equal(
            input_seq_len, self.max_length,
            "input_seq_len should be less or equal to self.max_length")
        
        # x.shape: (batch_size, input_seq_len, d_model)
        x = self.embedding(x)
        # x默认embeding初始化的时候是从0-1之间的均值分布中取到的，所以需要做一个缩放，使得他的值的范围为0·d_model。
        # 做了缩放之后，就使得x+position之后，x本身起的作用会比较大一些。
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.position_embedding[:, :input_seq_len, :]
        
        x = self.dropout(x, training = training)
        
        for i in range(self.num_layers):
            x = self.encoder_layers[i](x, training,
                                       encoder_padding_mask)
        
        # x.shape: (batch_size, input_seq_len, d_model)
        return x
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2,测试

sample_encoder_model = EncoderModel(2, 8500, max_length,
                                    512, 8, 2048)
sample_encoder_model_input = tf.random.uniform((64, 37))
sample_encoder_model_output = sample_encoder_model(
    sample_encoder_model_input, False, encoder_padding_mask = None)
print(sample_encoder_model_output.shape)
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十一,DecoderModel的实现

1,DecoderModel的实现

class DecoderModel(keras.layers.Layer):
    def __init__(self, num_layers, target_vocab_size, max_length,
                 d_model, num_heads, dff, rate=0.1):
        super(DecoderModel, self).__init__()
        self.num_layers = num_layers
        self.max_length = max_length
        self.d_model = d_model
        
        self.embedding = keras.layers.Embedding(target_vocab_size,
                                                d_model)
        self.position_embedding = get_position_embedding(max_length,
                                                         d_model)
        
        self.dropout = keras.layers.Dropout(rate)
        self.decoder_layers = [
            DecoderLayer(d_model, num_heads, dff, rate)
            for _ in range(self.num_layers)]
        
    
    def call(self, x, encoding_outputs, training,
             decoder_mask, encoder_decoder_padding_mask):
        # x.shape: (batch_size, output_seq_len)
        output_seq_len = tf.shape(x)[1]
        tf.debugging.assert_less_equal(
            output_seq_len, self.max_length,
            "output_seq_len should be less or equal to self.max_length")
        
        attention_weights = {}
        
        # x.shape: (batch_size, output_seq_len, d_model)
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.position_embedding[:, :output_seq_len, :]
        
        x = self.dropout(x, training = training)
        
        for i in range(self.num_layers):
            x, attn1, attn2 = self.decoder_layers[i](
                x, encoding_outputs, training,
                decoder_mask, encoder_decoder_padding_mask)
            attention_weights[
                'decoder_layer{}_att1'.format(i+1)] = attn1
            attention_weights[
                'decoder_layer{}_att2'.format(i+1)] = attn2
        # x.shape: (batch_size, output_seq_len, d_model)
        return x, attention_weights
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2,测试

sample_decoder_model = DecoderModel(2, 8000, max_length,
                                    512, 8, 2048)

sample_decoder_model_input = tf.random.uniform((64, 35))
sample_decoder_model_output, sample_decoder_model_att \
= sample_decoder_model(
    sample_decoder_model_input,
    sample_encoder_model_output,
    training = False, decoder_mask = None,
    encoder_decoder_padding_mask = None)

print(sample_decoder_model_output.shape)
for key in sample_decoder_model_att:
    print(sample_decoder_model_att[key].shape)
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十二,Transformer的实现

1,Transformer的实现

class Transformer(keras.Model):
    def __init__(self, num_layers, input_vocab_size, target_vocab_size,
                 max_length, d_model, num_heads, dff, rate=0.1):
        super(Transformer, self).__init__()
        
        self.encoder_model = EncoderModel(
            num_layers, input_vocab_size, max_length,
            d_model, num_heads, dff, rate)
        
        self.decoder_model = DecoderModel(
            num_layers, target_vocab_size, max_length,
            d_model, num_heads, dff, rate)
        
        self.final_layer = keras.layers.Dense(target_vocab_size)
    
    def call(self, inp, tar, training, encoder_padding_mask,
             decoder_mask, encoder_decoder_padding_mask):
        # encoding_outputs.shape: (batch_size, input_seq_len, d_model)
        encoding_outputs = self.encoder_model(
            inp, training, encoder_padding_mask)
        
        # decoding_outputs.shape: (batch_size, output_seq_len, d_model)
        decoding_outputs, attention_weights = self.decoder_model(
            tar, encoding_outputs, training,
            decoder_mask, encoder_decoder_padding_mask)
        
        # predictions.shape: (batch_size, output_seq_len, target_vocab_size)
        predictions = self.final_layer(decoding_outputs)
        
        return predictions, attention_weights
    
sample_transformer = Transformer(2, 8500, 8000, max_length,
                                 512, 8, 2048, rate = 0.1)
temp_input = tf.random.uniform((64, 26))
temp_target = tf.random.uniform((64, 31))

predictions, attention_weights = sample_transformer(
    temp_input, temp_target, training = False,
    encoder_padding_mask = None,
    decoder_mask = None,
    encoder_decoder_padding_mask = None)

print(predictions.shape)
for key in attention_weights:
    print(key, attention_weights[key].shape)
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十三,初始化参数,实例化Transformer

"""
# 1. initializes model.
# 2. define loss, optimizer, learning_rate schedule
# 3. train_step
# 4. train process
"""
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num_layers = 4
d_model = 128
dff = 512
num_heads = 8

input_vocab_size = pt_tokenizer.vocab_size + 2
target_vocab_size = en_tokenizer.vocab_size + 2

dropout_rate = 0.1

transformer = Transformer(num_layers,
                          input_vocab_size,
                          target_vocab_size,
                          max_length,
                          d_model, num_heads, dff, dropout_rate)
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十四,自定义学习率和优化器

1,学习率计算公式

lrate = (d_model ** -0.5) * min(step_num ** (-0.5),
                                step_num * warm_up_steps **(-1.5))
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2,自定义学习率

class CustomizedSchedule(
    keras.optimizers.schedules.LearningRateSchedule):
    def __init__(self, d_model, warmup_steps = 4000):
        super(CustomizedSchedule, self).__init__()
        
        self.d_model = tf.cast(d_model, tf.float32)
        self.warmup_steps = warmup_steps
    
    def __call__(self, step):
        arg1 = tf.math.rsqrt(step)
        arg2 = step * (self.warmup_steps ** (-1.5))
        
        arg3 = tf.math.rsqrt(self.d_model)
        
        return arg3 * tf.math.minimum(arg1, arg2)
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3,测试

learning_rate = CustomizedSchedule(d_model)
optimizer = keras.optimizers.Adam(learning_rate,
                                  beta_1 = 0.9,
                                  beta_2 = 0.98,
                                  epsilon = 1e-9)
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4,打印自定义学习率曲线

temp_learning_rate_schedule = CustomizedSchedule(d_model)

plt.plot(
    temp_learning_rate_schedule(
        tf.range(40000, dtype=tf.float32)))
plt.ylabel("Leraning rate")
plt.xlabel("Train step")
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十五,自定义损失函数

loss_object = keras.losses.SparseCategoricalCrossentropy(
    from_logits = True, reduction = 'none')

def loss_function(real, pred):
    mask = tf.math.logical_not(tf.math.equal(real, 0))
    loss_ = loss_object(real, pred)
    
    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask
    
    return tf.reduce_mean(loss_)
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十六,Mask的创建与使用

1,Mask的创建

def create_masks(inp, tar):
    """
    Encoder:
      - encoder_padding_mask (self attention of EncoderLayer)
    Decoder:
      - look_ahead_mask (self attention of DecoderLayer)
      - encoder_decoder_padding_mask (encoder-decoder attention of DecoderLayer)
      - decoder_padding_mask (self attention of DecoderLayer)
    """
    encoder_padding_mask = create_padding_mask(inp)
    encoder_decoder_padding_mask = create_padding_mask(inp)
    
    look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])
    decoder_padding_mask = create_padding_mask(tar)
    decoder_mask = tf.maximum(decoder_padding_mask,
                              look_ahead_mask)
    
    return encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask
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2,测试

temp_inp, temp_tar = iter(train_dataset.take(1)).next()
print(temp_inp.shape)
print(temp_tar.shape)
create_masks(temp_inp, temp_tar)
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十七,定义训练模型

1,定义单个训练step

train_loss = keras.metrics.Mean(name = 'train_loss')
train_accuracy = keras.metrics.SparseCategoricalAccuracy(
    name = 'train_accuracy')

@tf.function
def train_step(inp, tar):
    tar_inp  = tar[:, :-1]
    tar_real = tar[:, 1:]
    
    encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask \
    = create_masks(inp, tar_inp)
    
    with tf.GradientTape() as tape:
        predictions, _ = transformer(inp, tar_inp, True,
                                     encoder_padding_mask,
                                     decoder_mask,
                                     encoder_decoder_padding_mask)
        loss = loss_function(tar_real, predictions)
    
    gradients = tape.gradient(loss, transformer.trainable_variables)
    optimizer.apply_gradients(
        zip(gradients, transformer.trainable_variables))
    train_loss(loss)
    train_accuracy(tar_real, predictions)
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2,定义训练模型

EPOCHS = 20
def train_model(EPOCHS):
    
    for epoch in range(EPOCHS):
        try:
            start = time.time()
            train_loss.reset_states()
            train_accuracy.reset_states()

            for (batch, (inp, tar)) in enumerate(train_dataset):
                train_step(inp, tar)
                if batch % 100 == 0:
                    print('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(
                        epoch + 1, batch, train_loss.result(),
                        train_accuracy.result()))
                    checkpoint.save(file_prefix = checkpoint_prefix)

            print('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(
                epoch + 1, train_loss.result(), train_accuracy.result()))
            print('Time take for 1 epoch: {} secs\n'.format(
                time.time() - start))
        except Exception as e:
            print("报错啦!!! 报错信息:%s" % e)
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十八,checkpoints

checkpoint_dir = './checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                 model=transformer)
if not os.path.exists(checkpoint_dir):
    os.mkdir(checkpoint_dir)
    train_model(EPOCHS)
else:
    checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
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十九,模型预测实现

"""
eg: A B C D -> E F G H.
Train: A B C D, E F G -> F G H
Eval:  A B C D -> E
       A B C D, E -> F
       A B C D, E F -> G
       A B C D, E F G -> H
"""
def evaluate(inp_sentence):
    input_id_sentence = [pt_tokenizer.vocab_size] \
    + pt_tokenizer.encode(inp_sentence) + [pt_tokenizer.vocab_size + 1]
    # encoder_input.shape: (1, input_sentence_length)
    encoder_input = tf.expand_dims(input_id_sentence, 0)
    
    # decoder_input.shape: (1, 1)
    decoder_input = tf.expand_dims([en_tokenizer.vocab_size], 0)
    
    for i in range(max_length):
        encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask \
        = create_masks(encoder_input, decoder_input)
        # predictions.shape: (batch_size, output_target_len, target_vocab_size)
        predictions, attention_weights = transformer(
            encoder_input,
            decoder_input,
            False,
            encoder_padding_mask,
            decoder_mask,
            encoder_decoder_padding_mask)
        # predictions.shape: (batch_size, target_vocab_size)
        predictions = predictions[:, -1, :]
        
        predicted_id = tf.cast(tf.argmax(predictions, axis = -1),
                               tf.int32)
        
        if tf.equal(predicted_id, en_tokenizer.vocab_size + 1):
            return tf.squeeze(decoder_input, axis = 0), attention_weights
        
        decoder_input = tf.concat([decoder_input, [predicted_id]],
                                  axis = -1)
    return tf.squeeze(decoder_input, axis = 0), attention_weights
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二十,Attention可视化

def plot_encoder_decoder_attention(attention, input_sentence,
                                   result, layer_name):
    fig = plt.figure(figsize = (16, 8))
    
    input_id_sentence = pt_tokenizer.encode(input_sentence)
    
    # attention.shape: (num_heads, tar_len, input_len)
    attention = tf.squeeze(attention[layer_name], axis = 0)
    
    for head in range(attention.shape[0]):
        ax = fig.add_subplot(2, 4, head + 1)
        
        ax.matshow(attention[head][:-1, :])
        
        fontdict = {'fontsize': 10}
        
        ax.set_xticks(range(len(input_id_sentence) + 2))
        ax.set_yticks(range(len(result)))
        
        ax.set_ylim(len(result) - 1.5, -0.5)
        
        ax.set_xticklabels(
            ['<start>'] + [pt_tokenizer.decode([i]) for i in input_id_sentence] + ['<end>'],
            fontdict = fontdict, rotation = 90)
        ax.set_yticklabels(
            [en_tokenizer.decode([i]) for i in result if i < en_tokenizer.vocab_size],
            fontdict = fontdict)
        ax.set_xlabel('Head {}'.format(head + 1))
    plt.tight_layout() # 自适应调整间距等
    plt.show()    
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二十一,TransFormer模型示例展示

def translate(input_sentence, layer_name = ''):
    result, attention_weights = evaluate(input_sentence)
    
    predicted_sentence = en_tokenizer.decode(
        [i for i in result if i < en_tokenizer.vocab_size])
    
    print("Input: {}".format(input_sentence))
    print("Predicted translation: {}".format(predicted_sentence))
    
    if layer_name:
        plot_encoder_decoder_attention(attention_weights, input_sentence,
                                       result, layer_name)

translate('está muito frio aqui.')
print(translate('isto é minha vida'))
translate('você ainda está em casa?')
translate('este é o primeiro livro que eu já li')
translate('este é o primeiro livro que eu já li',
          layer_name = 'decoder_layer4_att2')
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二十二,总结代码

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 18 09:51:31 2021

@author: nijiahui
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
    

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

# 1. loads data
# 2. preprocesses data -> dataset
# 3. tools
# 3.1 generates position embedding
# 3.2 create mask. (a. padding, b. decoder)
# 3.3 scaled_dot_product_attention
# 4. builds model
# 4.1 MultiheadAttention
# 4.2 EncoderLayer
# 4.3 DecoderLayer
# 4.4 EncoderModel
# 4.5 DecoderModel
# 4.6 Transformer
# 5. optimizer & loss
# 6. train step -> train
# 7. Evaluate and Visualize

# 一,加载数据
# 1,下载数据
import tensorflow_datasets as tfds
examples, info = tfds.load('ted_hrlr_translate/pt_to_en',
                            with_info = True,
                            as_supervised = True
                           )
# 2,将原始数据拆分为训练数据集和验证数据集
train_examples, val_examples = examples['train'], examples['validation']
print(info)

# 3,查看验证数据中的葡萄牙语,英语数据example
for pt, en in train_examples.take(5):
    print(pt.numpy())
    print(en.numpy())
    print()

# 二,数据预处理
# 1,定义,构建tokenizer
en_tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(
    (en.numpy() for pt, en in train_examples),
    target_vocab_size = 2 ** 13)
pt_tokenizer = tfds.features.text.SubwordTextEncoder.build_from_corpus(
    (pt.numpy() for pt, en in train_examples),
    target_vocab_size = 2 ** 13)

# 2,测试tokenizer的encode,decode
sample_string = "Transformer is awesome."

tokenized_string = en_tokenizer.encode(sample_string)
print('Tokenized string is {}'.format(tokenized_string))

origin_string = en_tokenizer.decode(tokenized_string)
print('The original string is {}'.format(origin_string))

assert origin_string == sample_string

for token in tokenized_string:
    print('{} --> "{}"'.format(token, en_tokenizer.decode([token])))
    
# 3,将原始数据转化为subword,并乱序,过滤掉大于40语句,根据batch数据做padding
buffer_size = 20000
batch_size = 64
max_length = 40

def encode_to_subword(pt_sentence, en_sentence):
    pt_sequence = [pt_tokenizer.vocab_size]     + pt_tokenizer.encode(pt_sentence.numpy())     + [pt_tokenizer.vocab_size + 1]
    en_sequence = [en_tokenizer.vocab_size]     + en_tokenizer.encode(en_sentence.numpy())     + [en_tokenizer.vocab_size + 1]
    return pt_sequence, en_sequence

def filter_by_max_length(pt, en):
    """过滤选择长度小于40的语句"""
    return tf.logical_and(tf.size(pt) <= max_length,
                          tf.size(en) <= max_length)

def tf_encode_to_subword(pt_sentence, en_sentence):
    """将[encode_to_subword]函数转化为tensor方法"""
    return tf.py_function(encode_to_subword,
                          [pt_sentence, en_sentence],
                          [tf.int64, tf.int64])
train_dataset = train_examples.map(tf_encode_to_subword)
train_dataset = train_dataset.filter(filter_by_max_length)
train_dataset = train_dataset.shuffle(
    buffer_size).padded_batch(
    batch_size, padded_shapes=([-1], [-1]))

valid_dataset = val_examples.map(tf_encode_to_subword)
valid_dataset = valid_dataset.filter(
    filter_by_max_length).padded_batch(
    batch_size, padded_shapes=([-1], [-1]))
        
# 4,查看预处理后的batch数据
for pt_batch, en_batch in valid_dataset.take(1):
    print(pt_batch.shape, en_batch.shape)
    print(pt_batch)
    print()
    print(en_batch)
    
# 三,位置编码 - 生成 position embedding
# 1,生成position对应embedding矩阵
# PE(pos, 2i)   = sin(pos / 10000^(2i/d_model))
# PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))

# pos.shape: [sentence_length, 1]
# i.shape  : [1, d_model]
# result.shape: [sentence_length, d_model]
def get_angles(pos, i, d_model):
    """获取单词pos对应embeding的角度"""
    angle_rates = 1 / np.power(10000,
                               (2 * (i // 2)) / np.float32(d_model))
    return pos * angle_rates

def get_position_embedding(sentence_length, d_model):
    """返回position对应embedding矩阵"""

    
    angle_rads = get_angles(np.arange(sentence_length)[:, np.newaxis],
                            np.arange(d_model)[np.newaxis, :],
                            d_model)
    # sines.shape: [sentence_length, d_model / 2]
    # cosines.shape: [sentence_length, d_model / 2]
    sines = np.sin(angle_rads[:, 0::2])
    cosines = np.cos(angle_rads[:, 1::2])
    
    # position_embedding.shape: [sentence_length, d_model]
    position_embedding = np.concatenate([sines, cosines], axis = -1)
    
    # angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
    # angle_rads[:, 1::2] = cosines = np.cos(angle_rads[:, 1::2])
    # position_embedding = angle_rads
    
    # position_embedding.shape: [1, sentence_length, d_model]
    position_embedding = position_embedding[np.newaxis, ...]
    
    return tf.cast(position_embedding, dtype=tf.float32)

position_embedding = get_position_embedding(50, 512)
print(position_embedding.shape)

# 2,打印矩阵图形
def plot_position_embedding(position_embedding):
    plt.pcolormesh(position_embedding[0], cmap = 'RdBu')
    plt.xlabel('Depth')
    plt.xlim((0, 512))
    plt.ylabel('Position')
    plt.colorbar()
    plt.show()
    
# plot_position_embedding(position_embedding)

# 四,mask的构建
"""
1. padding mask, 
2. look ahead
"""

# 1,padding mask
# batch_data.shape: [batch_size, seq_len]
def create_padding_mask(batch_data):
    padding_mask = tf.cast(tf.math.equal(batch_data, 0), tf.float32)
    # [batch_size, 1, 1, seq_len]
    return padding_mask[:, tf.newaxis, tf.newaxis, :]

x = tf.constant([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]])
create_padding_mask(x)

# 2,decoder中的look ahead
# attention_weights.shape: [3,3]
# [[1, 0, 0],
#  [4, 5, 0],
#  [7, 8, 9]]
def create_look_ahead_mask(size):
    mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)
    return mask # (seq_len, seq_len)

create_look_ahead_mask(3)

# 五,缩放点积注意力机制实现
# 1,定义缩放点积注意力机制函数
def scaled_dot_product_attention(q, k, v, mask):
    """
    Args:
    - q: shape == (..., seq_len_q, depth)
    - k: shape == (..., seq_len_k, depth)
    - v: shape == (..., seq_len_v, depth_v)
    - seq_len_k == seq_len_v
    - mask: shape == (..., seq_len_q, seq_len_k)
    Returns:
    - output: weighted sum
    - attention_weights: weights of attention
    """
    
    # matmul_qk.shape: (..., seq_len_q, seq_len_k)
    matmul_qk = tf.matmul(q, k, transpose_b = True)
    
    dk = tf.cast(tf.shape(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
    
    if mask is not None:
        # 使得在softmax后值趋近于0
        """在mask里，应该被忽略的我们会设成1，应该被保留的会设成0，而如果mask相应位置上为1，
        那么我们就给对应的logits 加上一个超级小的负数， -1000000000， 这样，对应的logits也就变成了一个超级小的数。
        然后在计算softmax的时候，一个超级小的数的指数会无限接近与0。也就是它对应的attention的权重就是0了。"""
        scaled_attention_logits += (mask * -1e9)
    
    # attention_weights.shape: (..., seq_len_q, seq_len_k)
    attention_weights = tf.nn.softmax(
        scaled_attention_logits, axis = -1)
    
    # output.shape: (..., seq_len_q, depth_v)
    output = tf.matmul(attention_weights, v)
    
    return output, attention_weights

# 2,调用缩放点积注意力,返回权重和输出
def print_scaled_dot_product_attention(q, k, v):
    temp_out, temp_att = scaled_dot_product_attention(q, k, v, None)
    print("Attention weights are:")
    print(temp_att)
    print("Output is:")
    print(temp_out)

# 3,测试
temp_k = tf.constant([[10, 0, 0],
                      [0, 10, 0],
                      [0, 0, 10],
                      [0, 0, 10]], dtype=tf.float32) # (4, 3)

temp_v = tf.constant([[1, 0],
                      [10, 0],
                      [100, 5],
                      [1000, 6]], dtype=tf.float32) # (4, 2)

temp_q1 = tf.constant([[0, 10, 0]], dtype=tf.float32) # (1, 3)
np.set_printoptions(suppress=True)
print_scaled_dot_product_attention(temp_q1, temp_k, temp_v)

temp_q2 = tf.constant([[0, 0, 10]], dtype=tf.float32) # (1, 3)
print_scaled_dot_product_attention(temp_q2, temp_k, temp_v)

temp_q3 = tf.constant([[10, 10, 0]], dtype=tf.float32) # (1, 3)
print_scaled_dot_product_attention(temp_q3, temp_k, temp_v)

temp_q4 = tf.constant([[0, 10, 0],
                       [0, 0, 10],
                       [10, 10, 0]], dtype=tf.float32) # (3, 3)
print_scaled_dot_product_attention(temp_q4, temp_k, temp_v)

# 六,多头注意力机制实现
class MultiHeadAttention(keras.layers.Layer):
    """
    理论上:
    x -> Wq0 -> q0
    x -> Wk0 -> k0
    x -> Wv0 -> v0
    
    实战中:
    q -> Wq0 -> q0
    k -> Wk0 -> k0
    v -> Wv0 -> v0
    
    实战中技巧：
    q -> Wq -> Q -> split -> q0, q1, q2...
    """
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model
        assert self.d_model % self.num_heads == 0
        
        self.depth = self.d_model // self.num_heads
        
        self.WQ = keras.layers.Dense(self.d_model)
        self.WK = keras.layers.Dense(self.d_model)
        self.WV = keras.layers.Dense(self.d_model)
        
        self.dense = keras.layers.Dense(self.d_model)
    
    def split_heads(self, x, batch_size):
        # x.shape: (batch_size, seq_len, d_model)
        # d_model = num_heads * depth
        # x -> (batch_size, num_heads, seq_len, depth)
        
        x = tf.reshape(x,
                       (batch_size, -1, self.num_heads, self.depth))
        # print(x.shape)
        return tf.transpose(x, perm=[0, 2, 1, 3])
    
    def call(self, q, k, v, mask):
        batch_size = tf.shape(q)[0]
        
        q = self.WQ(q) # q.shape: (batch_size, seq_len_q, d_model)
        k = self.WK(k) # k.shape: (batch_size, seq_len_k, d_model)
        v = self.WV(v) # v.shape: (batch_size, seq_len_v, d_model)
        
        # q.shape: (batch_size, num_heads, seq_len_q, depth)
        q = self.split_heads(q, batch_size)
        # k.shape: (batch_size, num_heads, seq_len_k, depth)
        k = self.split_heads(k, batch_size)
        # v.shape: (batch_size, num_heads, seq_len_v, depth)
        v = self.split_heads(v, batch_size)
        
        # scaled_attention_outputs.shape: (batch_size, num_heads, seq_len_q, depth)
        # attention_weights.shape: (batch_size, num_heads, seq_len_q, seq_len_k)
        scaled_attention_outputs, attention_weights = \
        scaled_dot_product_attention(q, k, v, mask)
        
        # scaled_attention_outputs.shape: (batch_size, seq_len_q, num_heads, depth)
        scaled_attention_outputs = tf.transpose(
            scaled_attention_outputs, perm = [0, 2, 1, 3])
        # concat_attention.shape: (batch_size, seq_len_q, d_model)
        concat_attention = tf.reshape(scaled_attention_outputs,
                                      (batch_size, -1, self.d_model))
        
        # output.shape : (batch_size, seq_len_q, d_model)
        output = self.dense(concat_attention)
        
        return output, attention_weights
    
temp_mha = MultiHeadAttention(d_model=512, num_heads=8)
y = tf.random.uniform((1, 60, 256)) # (batch_size, seq_len_q, dim)
output, attn = temp_mha(y, y, y, mask = None)
print(output.shape)
print(attn.shape)

# 七,FeedForward层次实现
def feed_forward_network(d_model, dff):
    # dff: dim of feed forward network.
    return keras.Sequential([
        keras.layers.Dense(dff, activation='relu'),
        keras.layers.Dense(d_model)
    ])

sample_ffn = feed_forward_network(512, 2048)
sample_ffn(tf.random.uniform((64, 50, 512))).shape

# 八,定义EncoderLayer层
class EncoderLayer(keras.layers.Layer):
    """
    x -> self attention -> add & normalize & dropout
      -> feed_forward -> add & normalize & dropout
    """
    def __init__(self, d_model, num_heads, dff, rate=0.1):
        super(EncoderLayer, self).__init__()
        self.mha = MultiHeadAttention(d_model, num_heads)
        self.ffn = feed_forward_network(d_model, dff)
        
        self.layer_norm1 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        self.layer_norm2 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        
        self.dropout1 = keras.layers.Dropout(rate)
        self.dropout2 = keras.layers.Dropout(rate)
    
    def call(self, x, training, encoder_padding_mask):
        # x.shape          : (batch_size, seq_len, dim=d_model)
        # attn_output.shape: (batch_size, seq_len, d_model)
        # out1.shape       : (batch_size, seq_len, d_model)
        attn_output, _ = self.mha(x, x, x, encoder_padding_mask)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layer_norm1(x + attn_output)
        
        # ffn_output.shape: (batch_size, seq_len, d_model)
        # out2.shape      : (batch_size, seq_len, d_model)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layer_norm2(out1 + ffn_output)
        
        return out2

sample_encoder_layer = EncoderLayer(512, 8, 2048)
sample_input = tf.random.uniform((64, 50, 512))
sample_output = sample_encoder_layer(sample_input, False, None)
print(sample_output.shape)

# 九,定义DecoderLayer层
class DecoderLayer(keras.layers.Layer):
    """
    x -> self attention -> add & normalize & dropout -> out1
    out1 , encoding_outputs -> attention -> add & normalize & dropout -> out2
    out2 -> ffn -> add & normalize & dropout -> out3
    """
    def __init__(self, d_model, num_heads, dff, rate = 0.1):
        super(DecoderLayer, self).__init__()
        
        self.mha1 = MultiHeadAttention(d_model, num_heads)
        self.mha2 = MultiHeadAttention(d_model, num_heads)
        
        self.ffn = feed_forward_network(d_model, dff)
        
        self.layer_norm1 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        self.layer_norm2 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        self.layer_norm3 = keras.layers.LayerNormalization(
            epsilon = 1e-6)
        
        self.dropout1 = keras.layers.Dropout(rate)
        self.dropout2 = keras.layers.Dropout(rate)
        self.dropout3 = keras.layers.Dropout(rate)
    
    
    def call(self, x, encoding_outputs, training,
             decoder_mask, encoder_decoder_padding_mask):
        # decoder_mask: 由look_ahead_mask和decoder_padding_mask合并而来
        
        # x.shape: (batch_size, target_seq_len, d_model)
        # encoding_outputs.shape: (batch_size, input_seq_len, d_model)
        
        # attn1, out1.shape : (batch_size, target_seq_len, d_model)
        attn1, attn_weights1 = self.mha1(x, x, x, decoder_mask)
        attn1 = self.dropout1(attn1, training = training)
        out1 = self.layer_norm1(attn1 + x)
        
        # attn2, out2.shape : (batch_size, target_seq_len, d_model)
        attn2, attn_weights2 = self.mha2(
            out1, encoding_outputs, encoding_outputs,
            encoder_decoder_padding_mask)
        attn2 = self.dropout2(attn2, training = training)
        out2 = self.layer_norm2(attn2 + out1)
        
        # ffn_output, out3.shape: (batch_size, target_seq_len, d_model)
        ffn_output = self.ffn(out2)
        ffn_output = self.dropout3(ffn_output, training=training)
        out3 = self.layer_norm3(ffn_output + out2)
        
        return out3, attn_weights1, attn_weights2
    
sample_decoder_layer = DecoderLayer(512, 8, 2048)
sample_decoder_input = tf.random.uniform((64, 60, 512))
sample_decoder_output, sample_decoder_attn_weights1, sample_decoder_attn_weights2 = sample_decoder_layer(
    sample_decoder_input, sample_output, False, None, None)

print(sample_decoder_output.shape)
print(sample_decoder_attn_weights1.shape)
print(sample_decoder_attn_weights2.shape)

# 十,EncoderModel的实现
class EncoderModel(keras.layers.Layer):
    def __init__(self, num_layers, input_vocab_size, max_length,
                 d_model, num_heads, dff, rate=0.1):
        super(EncoderModel, self).__init__()
        self.d_model = d_model
        self.num_layers = num_layers
        self.max_length = max_length
        
        self.embedding = keras.layers.Embedding(input_vocab_size,
                                                self.d_model)
        # position_embedding.shape: (1, max_length, d_model)
        self.position_embedding = get_position_embedding(max_length,
                                                         self.d_model)
        
        self.dropout = keras.layers.Dropout(rate)
        self.encoder_layers = [
            EncoderLayer(d_model, num_heads, dff, rate)
            for _ in range(self.num_layers)]
        
    def call(self, x, training, encoder_padding_mask):
        # x.shape: (batch_size, input_seq_len)
        input_seq_len = tf.shape(x)[1]
        tf.debugging.assert_less_equal(
            input_seq_len, self.max_length,
            "input_seq_len should be less or equal to self.max_length")
        
        # x.shape: (batch_size, input_seq_len, d_model)
        x = self.embedding(x)
        # x默认embeding初始化的时候是从0-1之间的均值分布中取到的，所以需要做一个缩放，使得他的值的范围为0·d_model。
        # 做了缩放之后，就使得x+position之后，x本身起的作用会比较大一些。
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.position_embedding[:, :input_seq_len, :]
        
        x = self.dropout(x, training = training)
        
        for i in range(self.num_layers):
            x = self.encoder_layers[i](x, training,
                                       encoder_padding_mask)
        
        # x.shape: (batch_size, input_seq_len, d_model)
        return x
    
sample_encoder_model = EncoderModel(2, 8500, max_length,
                                    512, 8, 2048)
sample_encoder_model_input = tf.random.uniform((64, 37))
sample_encoder_model_output = sample_encoder_model(
    sample_encoder_model_input, False, encoder_padding_mask = None)
print(sample_encoder_model_output.shape)

# 十一,DecoderModel的实现
class DecoderModel(keras.layers.Layer):
    def __init__(self, num_layers, target_vocab_size, max_length,
                 d_model, num_heads, dff, rate=0.1):
        super(DecoderModel, self).__init__()
        self.num_layers = num_layers
        self.max_length = max_length
        self.d_model = d_model
        
        self.embedding = keras.layers.Embedding(target_vocab_size,
                                                d_model)
        self.position_embedding = get_position_embedding(max_length,
                                                         d_model)
        
        self.dropout = keras.layers.Dropout(rate)
        self.decoder_layers = [
            DecoderLayer(d_model, num_heads, dff, rate)
            for _ in range(self.num_layers)]
        
    
    def call(self, x, encoding_outputs, training,
             decoder_mask, encoder_decoder_padding_mask):
        # x.shape: (batch_size, output_seq_len)
        output_seq_len = tf.shape(x)[1]
        tf.debugging.assert_less_equal(
            output_seq_len, self.max_length,
            "output_seq_len should be less or equal to self.max_length")
        
        attention_weights = {}
        
        # x.shape: (batch_size, output_seq_len, d_model)
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.position_embedding[:, :output_seq_len, :]
        
        x = self.dropout(x, training = training)
        
        for i in range(self.num_layers):
            x, attn1, attn2 = self.decoder_layers[i](
                x, encoding_outputs, training,
                decoder_mask, encoder_decoder_padding_mask)
            attention_weights[
                'decoder_layer{}_att1'.format(i+1)] = attn1
            attention_weights[
                'decoder_layer{}_att2'.format(i+1)] = attn2
        # x.shape: (batch_size, output_seq_len, d_model)
        return x, attention_weights

sample_decoder_model = DecoderModel(2, 8000, max_length,
                                    512, 8, 2048)

sample_decoder_model_input = tf.random.uniform((64, 35))
sample_decoder_model_output, sample_decoder_model_att \
= sample_decoder_model(
    sample_decoder_model_input,
    sample_encoder_model_output,
    training = False, decoder_mask = None,
    encoder_decoder_padding_mask = None)

print(sample_decoder_model_output.shape)
for key in sample_decoder_model_att:
    print(sample_decoder_model_att[key].shape)
    
# 十二,Transformer的实现
class Transformer(keras.Model):
    def __init__(self, num_layers, input_vocab_size, target_vocab_size,
                 max_length, d_model, num_heads, dff, rate=0.1):
        super(Transformer, self).__init__()
        
        self.encoder_model = EncoderModel(
            num_layers, input_vocab_size, max_length,
            d_model, num_heads, dff, rate)
        
        self.decoder_model = DecoderModel(
            num_layers, target_vocab_size, max_length,
            d_model, num_heads, dff, rate)
        
        self.final_layer = keras.layers.Dense(target_vocab_size)
    
    def call(self, inp, tar, training, encoder_padding_mask,
             decoder_mask, encoder_decoder_padding_mask):
        # encoding_outputs.shape: (batch_size, input_seq_len, d_model)
        encoding_outputs = self.encoder_model(
            inp, training, encoder_padding_mask)
        
        # decoding_outputs.shape: (batch_size, output_seq_len, d_model)
        decoding_outputs, attention_weights = self.decoder_model(
            tar, encoding_outputs, training,
            decoder_mask, encoder_decoder_padding_mask)
        
        # predictions.shape: (batch_size, output_seq_len, target_vocab_size)
        predictions = self.final_layer(decoding_outputs)
        
        return predictions, attention_weights
    
sample_transformer = Transformer(2, 8500, 8000, max_length,
                                 512, 8, 2048, rate = 0.1)
temp_input = tf.random.uniform((64, 26))
temp_target = tf.random.uniform((64, 31))

predictions, attention_weights = sample_transformer(
    temp_input, temp_target, training = False,
    encoder_padding_mask = None,
    decoder_mask = None,
    encoder_decoder_padding_mask = None)

print(predictions.shape)
for key in attention_weights:
    print(key, attention_weights[key].shape)
        
"""
# 1. initializes model.
# 2. define loss, optimizer, learning_rate schedule
# 3. train_step
# 4. train process
"""
# 十三,初始化参数,实例化Transformer
num_layers = 4
d_model = 128
dff = 512
num_heads = 8

input_vocab_size = pt_tokenizer.vocab_size + 2
target_vocab_size = en_tokenizer.vocab_size + 2

dropout_rate = 0.1

transformer = Transformer(num_layers,
                          input_vocab_size,
                          target_vocab_size,
                          max_length,
                          d_model, num_heads, dff, dropout_rate)

# 十四,自定义学习率和优化器

# 1,学习率计算公式
# lrate = (d_model ** -0.5) * min(step_num ** (-0.5),
#                                 step_num * warm_up_steps **(-1.5))

# 2,自定义学习率
class CustomizedSchedule(
    keras.optimizers.schedules.LearningRateSchedule):
    def __init__(self, d_model, warmup_steps = 4000):
        super(CustomizedSchedule, self).__init__()
        
        self.d_model = tf.cast(d_model, tf.float32)
        self.warmup_steps = warmup_steps
    
    def __call__(self, step):
        arg1 = tf.math.rsqrt(step)
        arg2 = step * (self.warmup_steps ** (-1.5))
        
        arg3 = tf.math.rsqrt(self.d_model)
        
        return arg3 * tf.math.minimum(arg1, arg2)
    
# 3,测试
learning_rate = CustomizedSchedule(d_model)
optimizer = keras.optimizers.Adam(learning_rate,
                                  beta_1 = 0.9,
                                  beta_2 = 0.98,
                                  epsilon = 1e-9)

# 4,打印自定义学习率曲线
temp_learning_rate_schedule = CustomizedSchedule(d_model)

plt.plot(
    temp_learning_rate_schedule(
        tf.range(40000, dtype=tf.float32)))
plt.ylabel("Leraning rate")
plt.xlabel("Train step")

# 十五,自定义损失函数
loss_object = keras.losses.SparseCategoricalCrossentropy(
    from_logits = True, reduction = 'none')

def loss_function(real, pred):
    mask = tf.math.logical_not(tf.math.equal(real, 0))
    loss_ = loss_object(real, pred)
    
    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask
    
    return tf.reduce_mean(loss_)

# 十六,Mask的创建与使用
def create_masks(inp, tar):
    """
    Encoder:
      - encoder_padding_mask (self attention of EncoderLayer)
    Decoder:
      - look_ahead_mask (self attention of DecoderLayer)
      - encoder_decoder_padding_mask (encoder-decoder attention of DecoderLayer)
      - decoder_padding_mask (self attention of DecoderLayer)
    """
    encoder_padding_mask = create_padding_mask(inp)
    encoder_decoder_padding_mask = create_padding_mask(inp)
    
    look_ahead_mask = create_look_ahead_mask(tf.shape(tar)[1])
    decoder_padding_mask = create_padding_mask(tar)
    decoder_mask = tf.maximum(decoder_padding_mask,
                              look_ahead_mask)
    
    return encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask

temp_inp, temp_tar = iter(train_dataset.take(1)).next()
print(temp_inp.shape)
print(temp_tar.shape)
create_masks(temp_inp, temp_tar)

# 十七,定义训练模型
train_loss = keras.metrics.Mean(name = 'train_loss')
train_accuracy = keras.metrics.SparseCategoricalAccuracy(
    name = 'train_accuracy')

@tf.function
def train_step(inp, tar):
    tar_inp  = tar[:, :-1]
    tar_real = tar[:, 1:]
    
    encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask \
    = create_masks(inp, tar_inp)
    
    with tf.GradientTape() as tape:
        predictions, _ = transformer(inp, tar_inp, True,
                                     encoder_padding_mask,
                                     decoder_mask,
                                     encoder_decoder_padding_mask)
        loss = loss_function(tar_real, predictions)
    
    gradients = tape.gradient(loss, transformer.trainable_variables)
    optimizer.apply_gradients(
        zip(gradients, transformer.trainable_variables))
    train_loss(loss)
    train_accuracy(tar_real, predictions)

EPOCHS = 20
def train_model(EPOCHS):
    
    for epoch in range(EPOCHS):
        try:
            start = time.time()
            train_loss.reset_states()
            train_accuracy.reset_states()

            for (batch, (inp, tar)) in enumerate(train_dataset):
                train_step(inp, tar)
                if batch % 100 == 0:
                    print('Epoch {} Batch {} Loss {:.4f} Accuracy {:.4f}'.format(
                        epoch + 1, batch, train_loss.result(),
                        train_accuracy.result()))
                    checkpoint.save(file_prefix = checkpoint_prefix)

            print('Epoch {} Loss {:.4f} Accuracy {:.4f}'.format(
                epoch + 1, train_loss.result(), train_accuracy.result()))
            print('Time take for 1 epoch: {} secs\n'.format(
                time.time() - start))
        except Exception as e:
            print("报错啦!!! 报错信息:%s" % e)
    
# 十八,checkpoints
checkpoint_dir = './checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                 model=transformer)
if not os.path.exists(checkpoint_dir):
    os.mkdir(checkpoint_dir)
    train_model(EPOCHS)
else:
    checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))

# 十九,模型预测实现
"""
eg: A B C D -> E F G H.
Train: A B C D, E F G -> F G H
Eval:  A B C D -> E
       A B C D, E -> F
       A B C D, E F -> G
       A B C D, E F G -> H
"""
def evaluate(inp_sentence):
    input_id_sentence = [pt_tokenizer.vocab_size] \
    + pt_tokenizer.encode(inp_sentence) + [pt_tokenizer.vocab_size + 1]
    # encoder_input.shape: (1, input_sentence_length)
    encoder_input = tf.expand_dims(input_id_sentence, 0)
    
    # decoder_input.shape: (1, 1)
    decoder_input = tf.expand_dims([en_tokenizer.vocab_size], 0)
    
    for i in range(max_length):
        encoder_padding_mask, decoder_mask, encoder_decoder_padding_mask \
        = create_masks(encoder_input, decoder_input)
        # predictions.shape: (batch_size, output_target_len, target_vocab_size)
        predictions, attention_weights = transformer(
            encoder_input,
            decoder_input,
            False,
            encoder_padding_mask,
            decoder_mask,
            encoder_decoder_padding_mask)
        # predictions.shape: (batch_size, target_vocab_size)
        predictions = predictions[:, -1, :]
        
        predicted_id = tf.cast(tf.argmax(predictions, axis = -1),
                               tf.int32)
        
        if tf.equal(predicted_id, en_tokenizer.vocab_size + 1):
            return tf.squeeze(decoder_input, axis = 0), attention_weights
        
        decoder_input = tf.concat([decoder_input, [predicted_id]],
                                  axis = -1)
    return tf.squeeze(decoder_input, axis = 0), attention_weights

# 二十,Attention可视化
def plot_encoder_decoder_attention(attention, input_sentence,
                                   result, layer_name):
    fig = plt.figure(figsize = (16, 8))
    
    input_id_sentence = pt_tokenizer.encode(input_sentence)
    
    # attention.shape: (num_heads, tar_len, input_len)
    attention = tf.squeeze(attention[layer_name], axis = 0)
    
    for head in range(attention.shape[0]):
        ax = fig.add_subplot(2, 4, head + 1)
        
        ax.matshow(attention[head][:-1, :])
        
        fontdict = {'fontsize': 10}
        
        ax.set_xticks(range(len(input_id_sentence) + 2))
        ax.set_yticks(range(len(result)))
        
        ax.set_ylim(len(result) - 1.5, -0.5)
        
        ax.set_xticklabels(
            ['<start>'] + [pt_tokenizer.decode([i]) for i in input_id_sentence] + ['<end>'],
            fontdict = fontdict, rotation = 90)
        ax.set_yticklabels(
            [en_tokenizer.decode([i]) for i in result if i < en_tokenizer.vocab_size],
            fontdict = fontdict)
        ax.set_xlabel('Head {}'.format(head + 1))
    plt.tight_layout() # 自适应调整间距等
    plt.show()    
    
# 二十一,TransFormer模型示例展示
def translate(input_sentence, layer_name = ''):
    result, attention_weights = evaluate(input_sentence)
    
    predicted_sentence = en_tokenizer.decode(
        [i for i in result if i < en_tokenizer.vocab_size])
    
    print("Input: {}".format(input_sentence))
    print("Predicted translation: {}".format(predicted_sentence))
    
    if layer_name:
        plot_encoder_decoder_attention(attention_weights, input_sentence,
                                       result, layer_name)

translate('está muito frio aqui.')
print(translate('isto é minha vida'))
translate('você ainda está em casa?')
translate('este é o primeiro livro que eu já li')
translate('este é o primeiro livro que eu já li',
          layer_name = 'decoder_layer4_att2')

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