无法保存变压器模型 [英] Transformer model not able to be saved
问题描述
我正在尝试遵循以下惯例: NotImplementedError:在__init__中带有参数的图层必须覆盖get_config 我从答案中了解到,我需要将编码器和解码器作为类进行自定义(而不是像colab tutrial那样保留其功能),因此我在这里返回此模型的张量流文档:
I'm trying to follow this tutrial https://colab.research.google.com/github/tensorflow/examples/blob/master/community/en/transformer_chatbot.ipynb, However, when I tried to save the model in order to load it again without training I got an error mentioned here NotImplementedError: Layers with arguments in `__init__` must override `get_config` I understood from the answer that I need to make the encoder and decoder as classes and customise it(instead of leaving it as functions like the colab tutrial) so I went back to tensor flow documentation of this model here: https://www.tensorflow.org/tutorials/text/transformer#encoder_layer and tried to edit in it. I made the encoder layer as:
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, rate=0.1,**kwargs,):
#super(EncoderLayer, self).__init__()
super().__init__(**kwargs)
self.mha = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
def get_config(self):
config = super().get_config().copy()
config.update({
#'vocab_size': self.vocab_size,
#'num_layers': self.num_layers,
#'units': self.units,
'd_model': self.d_model,
'num_heads': self.num_heads,
'dropout': self.dropout,
})
return config
def call(self, x, training, mask):
attn_output, _ = self.mha(x, x, x, mask) # (batch_size, input_seq_len, d_model)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(x + attn_output) # (batch_size, input_seq_len, d_model)
ffn_output = self.ffn(out1) # (batch_size, input_seq_len, d_model)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = self.layernorm2(out1 + ffn_output) # (batch_size, input_seq_len, d_model)
return out2
,与解码器层类相同.然后在tf文档中使用相同的编码器
and same for the decoder layer class. Then the same encoder in the documentation of tf
class Encoder(tf.keras.layers.Layer):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Encoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
self.pos_encoding = positional_encoding(maximum_position_encoding,
self.d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask):
seq_len = tf.shape(x)[1]
# adding embedding and position encoding.
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x = self.enc_layers[i](x, training, mask)
return x # (batch_size, input_seq_len, d_model)
模型的功能为:
def transformer(vocab_size,
num_layers,
units,
d_model,
num_heads,
dropout,
name="transformer"):
inputs = tf.keras.Input(shape=(None,), name="inputs")
dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs")
enc_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='enc_padding_mask')(inputs)
# mask the future tokens for decoder inputs at the 1st attention block
look_ahead_mask = tf.keras.layers.Lambda(
create_look_ahead_mask,
output_shape=(1, None, None),
name='look_ahead_mask')(dec_inputs)
# mask the encoder outputs for the 2nd attention block
dec_padding_mask = tf.keras.layers.Lambda(
create_padding_mask, output_shape=(1, 1, None),
name='dec_padding_mask')(inputs)
enc_outputs = Encoder(
num_layers=num_layers, d_model=d_model, num_heads=num_heads,
input_vocab_size=vocab_size,
)(inputs=[inputs, enc_padding_mask])
dec_outputs = Decoder(
num_layers=num_layers, d_model=d_model, num_heads=num_heads,
target_vocab_size=vocab_size,
)(inputs=[dec_inputs, enc_outputs, look_ahead_mask, dec_padding_mask])
outputs = tf.keras.layers.Dense(units=vocab_size, name="outputs")(dec_outputs)
return tf.keras.Model(inputs=[inputs, dec_inputs], outputs=outputs, name=name)
并调用模型:
#the model itself with its paramters:
# Hyper-parameters
NUM_LAYERS = 3
D_MODEL = 256
#D_MODEL=tf.cast(D_MODEL, tf.float32)
NUM_HEADS = 8
UNITS = 512
DROPOUT = 0.1
model = transformer(
vocab_size=VOCAB_SIZE,
num_layers=NUM_LAYERS,
units=UNITS,
d_model=D_MODEL,
num_heads=NUM_HEADS,
dropout=DROPOUT)
但是,我得到了这个错误:
TypeError: __init__() missing 2 required positional arguments: 'dff' and 'maximum_position_encoding'
我真的很困惑,我不理解文档中的dff和最大位置编码是什么意思,当我从编码器和解码器类中删除它们时,出现了另一个错误,因为positional_encoding函数将最大位置作为输入,并且dff传递为在班级内部输入.我不确定应该做什么,因为我不确定我是否遵循正确的步骤
However, I got that error:
TypeError: __init__() missing 2 required positional arguments: 'dff' and 'maximum_position_encoding'
I am really confused and I don't understand what dff and maximum position encoding mean in the documentation and when I removed them from the encoder and decoder classes, I got anther error as positional_encoding function takes maximum position as input and also dff is passed as input inside the class. I am not so sure what I should do as I am not sure whether I am following the right steps or not
推荐答案
如果调用transformer
时收到此错误,则说明问题是创建模型,而不是保存模型.
If you get this error while calling transformer
then your problem is with creating the model, not saving it.
除此之外,我发现您的get_config
有几个问题:
Other than that, I see several issues with your get_config
:
- 您定义了
dropout
而不是rate
. - 在
__init__
上未定义或分配您要处理的属性(self.d_model
等). - 您的
Encoder
类不存在.
- You defined
dropout
instead ofrate
. - The attributes you address (
self.d_model
etc.) are not defined or assigned at__init__
. - It doesn't exist for your
Encoder
class.
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