用人工智能取一个超酷的名字（二）

这篇文章代码有点多，不知道咱们有没有爱好看关于源码的深度解析的文章呢？

留言区和我说说~~

构建模型

接下来，咱们要运用 keras.Model 子类构建模型。 (For details see Making new Layers and Models via subclassing).

模型分为两大部分：

• Encoder
• Decoder

运用的首要结构为： LSTM

获取模型构建需要的参数

vocab_inp_size = len(inp_tokenizer.get_vocab()) + 1
vocab_tar_size = len(out_tokenizer.get_vocab()) + 1
max_length_input = example_input_batch.shape[1]
max_length_output = example_target_batch.shape[1]
embedding_dim = 256
units = 1024
steps_per_epoch = num_examples // BATCH_SIZE

print(
    "输入词典巨细 {}\n输出词典的巨细{}\n输入最大长度{}\n输出最大长度".format(
        vocab_inp_size, vocab_tar_size, max_length_input, max_length_output
    )
)

输入词典巨细 3910
输出词典的巨细2965
输入最大长度469
输出最大长度

example_input_batch.shape, example_target_batch.shape

(TensorShape([64, 469]), TensorShape([64, 25]))

print(example_input_batch)
print(example_target_batch)

tf.Tensor(
[[3907    7   59 ...    0    0    0]
 [3907    7  808 ...    0    0    0]
 [3907    7  124 ...    0    0    0]
 ...
 [3907    7 2498 ...    0    0    0]
 [3907    7  350 ...    0    0    0]
 [3907    7  209 ...    0    0    0]], shape=(64, 469), dtype=int64)
tf.Tensor(
[[2962    8    8 ...    0    0    0]
 [2962 1834  311 ...    0    0    0]
 [2962  759 1676 ...    0    0    0]
 ...
 [2962  706 1161 ...    0    0    0]
 [2962   99 1023 ...    0    0    0]
 [2962  128 1317 ...    0    0    0]], shape=(64, 25), dtype=int64)

Encoder 编码器

class Encoder(tf.keras.Model):
    def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
        super(Encoder, self).__init__()
        self.batch_sz = batch_sz
        self.enc_units = enc_units
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        ##-------- LSTM layer in Encoder ------- ##
        self.lstm_layer = tf.keras.layers.LSTM(
            self.enc_units,
            return_sequences=True,
            return_state=True,
            recurrent_initializer="glorot_uniform",
        )
    def call(self, x, hidden):
        x = self.embedding(x)
        output, h, c = self.lstm_layer(x, initial_state=hidden)
        return output, h, c
    def initialize_hidden_state(self):
        return [
            tf.zeros((self.batch_sz, self.enc_units)),
            tf.zeros((self.batch_sz, self.enc_units)),
        ]

编码器的验证

encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
# sample input
sample_hidden = encoder.initialize_hidden_state()
sample_output, sample_h, sample_c = encoder(example_input_batch, sample_hidden)
print(
    "Encoder output shape: (batch size, sequence length, units) {}".format(
        sample_output.shape
    )
)
print("编码器 h vecotr shape: (batch size, units) {}".format(sample_h.shape))
print("编码器 c vector shape: (batch size, units) {}".format(sample_c.shape))

Encoder output shape: (batch size, sequence length, units) (64, 469, 1024)
编码器 h vecotr shape: (batch size, units) (64, 1024)
编码器 c vector shape: (batch size, units) (64, 1024)

解码器

class Decoder(tf.keras.Model):
    def __init__(
        self, vocab_size, embedding_dim, dec_units, batch_sz, attention_type="luong"
    ):
        super(Decoder, self).__init__()
        self.batch_sz = batch_sz
        self.dec_units = dec_units
        self.attention_type = attention_type
        # Embedding Layer
        self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
        # Final Dense layer on which softmax will be applied
        self.fc = tf.keras.layers.Dense(vocab_size)
        # Define the fundamental cell for decoder recurrent structure
        self.decoder_rnn_cell = tf.keras.layers.LSTMCell(self.dec_units)
        # Sampler
        self.sampler = tfa.seq2seq.sampler.TrainingSampler()
        # Create attention mechanism with memory = None
        self.attention_mechanism = self.build_attention_mechanism(
            self.dec_units,
            None,
            self.batch_sz * [max_length_input],
            self.attention_type,
        )
        # Wrap attention mechanism with the fundamental rnn cell of decoder
        self.rnn_cell = self.build_rnn_cell(batch_sz)
        # Define the decoder with respect to fundamental rnn cell
        self.decoder = tfa.seq2seq.BasicDecoder(
            self.rnn_cell, sampler=self.sampler, output_layer=self.fc
        )
    def build_rnn_cell(self, batch_sz):
        rnn_cell = tfa.seq2seq.AttentionWrapper(
            self.decoder_rnn_cell,
            self.attention_mechanism,
            attention_layer_size=self.dec_units,
        )
        return rnn_cell
    def build_attention_mechanism(
        self, dec_units, memory, memory_sequence_length, attention_type="luong"
    ):
        # ------------- #
        # typ: Which sort of attention (Bahdanau, Luong)
        # dec_units: final dimension of attention outputs
        # memory: encoder hidden states of shape (batch_size, max_length_input, enc_units)
        # memory_sequence_length: 1d array of shape (batch_size) with every element set to max_length_input (for masking purpose)
        if attention_type == "bahdanau":
            return tfa.seq2seq.BahdanauAttention(
                units=dec_units,
                memory=memory,
                memory_sequence_length=memory_sequence_length,
            )
        else:
            return tfa.seq2seq.LuongAttention(
                units=dec_units,
                memory=memory,
                memory_sequence_length=memory_sequence_length,
            )
    def build_initial_state(self, batch_sz, encoder_state, Dtype):
        decoder_initial_state = self.rnn_cell.get_initial_state(
            batch_size=batch_sz, dtype=Dtype
        )
        decoder_initial_state = decoder_initial_state.clone(cell_state=encoder_state)
        return decoder_initial_state
    def call(self, inputs, initial_state):
        x = self.embedding(inputs)
        outputs, _, _ = self.decoder(
            x,
            initial_state=initial_state,
            sequence_length=self.batch_sz * [max_length_output - 1],
        )
        return outputs

解码器验证

import tensorflow_addons as tfa
decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE, "luong")
sample_x = tf.random.uniform((BATCH_SIZE, max_length_output))
decoder.attention_mechanism.setup_memory(sample_output)
initial_state = decoder.build_initial_state(
    BATCH_SIZE, [sample_h, sample_c], tf.float32
)
sample_decoder_outputs = decoder(sample_x, initial_state)
print("解码器输出 Shape: ", sample_decoder_outputs.rnn_output.shape)

解码器输出 Shape:  (64, 24, 2965)

界说优化器

optimizer = tf.keras.optimizers.Adam()
def loss_function(real, pred):
    # real shape = (BATCH_SIZE, max_length_output)
    # pred shape = (BATCH_SIZE, max_length_output, tar_vocab_size )
    cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
        from_logits=True, reduction="none"
    )
    loss = cross_entropy(y_true=real, y_pred=pred)
    mask = tf.logical_not(tf.math.equal(real, 0))  # output 0 for y=0 else output 1
    mask = tf.cast(mask, dtype=loss.dtype)
    loss = mask * loss
    loss = tf.reduce_mean(loss)
    return loss

检查点(Checkpoints)

import os
checkpoint_dir = "./training_checkpoints"
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer, encoder=encoder, decoder=decoder)

界说练习中一步的操作

@tf.function
def train_step(inp, targ, enc_hidden):
    loss = 0
    with tf.GradientTape() as tape:
        enc_output, enc_h, enc_c = encoder(inp, enc_hidden)
        dec_input = targ[:, :-1]  # Ignore <end> token
        real = targ[:, 1:]  # ignore <start> token
        # Set the AttentionMechanism object with encoder_outputs
        decoder.attention_mechanism.setup_memory(enc_output)
        # Create AttentionWrapperState as initial_state for decoder
        decoder_initial_state = decoder.build_initial_state(
            BATCH_SIZE, [enc_h, enc_c], tf.float32
        )
        pred = decoder(dec_input, decoder_initial_state)
        logits = pred.rnn_output
        loss = loss_function(real, logits)
    variables = encoder.trainable_variables + decoder.trainable_variables
    gradients = tape.gradient(loss, variables)
    optimizer.apply_gradients(zip(gradients, variables))
    return loss

练习模型

# 运用 GPU 进行练习
gpus = tf.config.list_physical_devices("GPU")
if gpus:
    # 强制运用第一块GPU
    try:
        tf.config.set_visible_devices(gpus[0], "GPU")
        logical_gpus = tf.config.list_logical_devices("GPU")
        print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
    except RuntimeError as e:
        # Visible devices must be set before GPUs have been initialized
        print(e)

1 Physical GPUs, 1 Logical GPU

import time
EPOCHS = 50
print("模型练习..")
for epoch in range(EPOCHS):
    start = time.time()
    enc_hidden = encoder.initialize_hidden_state()
    total_loss = 0
    # print(enc_hidden[0].shape, enc_hidden[1].shape)
    for (batch, (inp, targ)) in enumerate(train_dataset.take(steps_per_epoch)):
        batch_loss = train_step(inp, targ, enc_hidden)
        total_loss += batch_loss
        if batch % 100 == 0:
            print(
                "Epoch {} Batch {} Loss {:.4f}".format(
                    epoch + 1, batch, batch_loss.numpy()
                )
            )
    # saving (checkpoint) the model every 2 epochs
    if (epoch + 1) % 2 == 0:
        checkpoint.save(file_prefix=checkpoint_prefix)
    print("Epoch {} Loss {:.4f}".format(epoch + 1, total_loss / steps_per_epoch))
    print("每个 epoch 耗费时刻 {} sec\n".format(time.time() - start))
print("模型练习完毕")

模型练习..
Epoch 1 Batch 0 Loss 1.8893
Epoch 1 Batch 100 Loss 1.3055
Epoch 1 Loss 1.4054
每个 epoch 耗费时刻 107.30223488807678 sec
Epoch 2 Batch 0 Loss 1.2897
Epoch 2 Batch 100 Loss 1.2302
Epoch 2 Loss 1.2439
每个 epoch 耗费时刻 99.80383324623108 sec

运用模型起姓名

def generate_name(surname, gender, desc):
    # 输入文本
    gender = gender if gender else ""
    desc = desc if desc else ""
    text = "<bos>" + surname + "_" + gender + "_" + desc + "<eos>"
    inp_text = inp_tokenizer.encode(text)
    inp_tensor = tf.constant(inp_text.ids)
    inp_tensor = tf.expand_dims(inp_tensor, axis=0)
    # 恢复最近的 checkpoint
    checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
    # 前向计算进程
    inference_batch_size = inp_tensor.shape[0]
    enc_start_state = [
        tf.zeros((inference_batch_size, units)),
        tf.zeros((inference_batch_size, units)),
    ]
    enc_out, enc_h, enc_c = encoder(inp_tensor, enc_start_state)
    dec_h = enc_h
    dec_c = enc_c
    start_tokens = tf.fill([inference_batch_size], out_tokenizer.token_to_id("<bos>"))
    end_token = out_tokenizer.token_to_id("<eos>")
    greedy_sampler = tfa.seq2seq.GreedyEmbeddingSampler()
    beam_width = 3
    # 创建解码器实例
    decoder_instance = tfa.seq2seq.BasicDecoder(
        cell=decoder.rnn_cell,
        sampler=greedy_sampler,
        output_layer=decoder.fc,
        maximum_iterations=4,
    )
    # 设置注意力机制
    decoder.attention_mechanism.setup_memory(enc_out)
    decoder_initial_state = decoder.build_initial_state(
        inference_batch_size, [enc_h, enc_c], tf.float32
    )
    # 运用解码器生成文本
    decoder_embedding_matrix = decoder.embedding.variables[0]
    outputs, _, _ = decoder_instance(
        decoder_embedding_matrix,
        start_tokens=start_tokens,
        end_token=end_token,
        initial_state=decoder_initial_state,
    )
    print(outputs.sample_id.numpy().tolist()[0])
    return surname + "".join(
        out_tokenizer.decode(outputs.sample_id.numpy().tolist()[0])
    )

generate_name("刘", "女", "喜爱篮球运动")

通过模型，咱们能够看到生成了姓名是： 刘兰淇。怎么样，是不是还不错。

运用 beam 生成姓名

def beam_generate_name(surname, gender, desc, beam_width=3):
    # 输入文本
    gender = gender if gender else ""
    desc = desc if desc else ""
    text = "<bos>" + surname + "_" + gender + "_" + desc + "<eos>"
    inp_text = inp_tokenizer.encode(text)
    inp_tensor = tf.constant(inp_text.ids)
    inp_tensor = tf.expand_dims(inp_tensor, axis=0)
    inference_batch_size = inp_tensor.shape[0]
    result = ""
    enc_start_state = [
        tf.zeros((inference_batch_size, units)),
        tf.zeros((inference_batch_size, units)),
    ]
    enc_out, enc_h, enc_c = encoder(inp_tensor, enc_start_state)
    dec_h = enc_h
    dec_c = enc_c
    start_tokens = tf.fill([inference_batch_size], out_tokenizer.token_to_id("<bos>"))
    end_token = out_tokenizer.token_to_id("<eos>")
    enc_out = tfa.seq2seq.tile_batch(enc_out, multiplier=beam_width)
    decoder.attention_mechanism.setup_memory(enc_out)
    print(
        "beam_with * [batch_size, max_length_input, rnn_units] :  3 * [1, 16, 1024]] :",
        enc_out.shape,
    )
    hidden_state = tfa.seq2seq.tile_batch([enc_h, enc_c], multiplier=beam_width)
    decoder_initial_state = decoder.rnn_cell.get_initial_state(
        batch_size=beam_width * inference_batch_size, dtype=tf.float32
    )
    decoder_initial_state = decoder_initial_state.clone(cell_state=hidden_state)
    decoder_instance = tfa.seq2seq.BeamSearchDecoder(
        decoder.rnn_cell, beam_width=beam_width, output_layer=decoder.fc
    )
    decoder_embedding_matrix = decoder.embedding.variables[0]
    outputs, final_state, sequence_lengths = decoder_instance(
        decoder_embedding_matrix,
        start_tokens=start_tokens,
        end_token=end_token,
        initial_state=decoder_initial_state,
    )
    final_outputs = tf.transpose(outputs.predicted_ids, perm=(0, 2, 1))
    beam_scores = tf.transpose(
        outputs.beam_search_decoder_output.scores, perm=(0, 2, 1)
    )
    result, beam_scores = final_outputs.numpy(), beam_scores.numpy()
    for beam, score in zip(result, beam_scores):
        print(beam.shape, score.shape)
        print(beam.tolist())
        for i in range(beam_width):
            output = out_tokenizer.decode(beam.tolist()[i])
            print(surname + "".join(output))

beam_generate_name("白", "男", "喜爱跳舞")

这个模型生成的姓名是，白慕，是不是也还不错呢。

这篇文章代码有点多，不知道咱们有没有爱好看关于源码的深度解析的文章呢？

留言区和我说说~~

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