ValueError：维度必须相等，但在 time2vec 示例中为 2 和 1 为 LSTM 和 T2V 创建生成器定义模型结构FIT T2V + LSTM

如何解决ValueError：维度必须相等，但在 time2vec 示例中为 2 和 1 为 LSTM 和 T2V 创建生成器定义模型结构FIT T2V + LSTM

我有 2 个输入和 4 个输出。我想使用 time2vec 来预测输出。我使用了 https://towardsdatascience.com/time2vec-for-time-series-features-encoding-a03a4f3f937e 中的代码，它适用于一个输入和一个输出。但是当我想使用 for（2 个输入和 4 个输出）时，它给了我以下错误：

import numpy as np
import tensorflow as tf
from keras.layers import Dense,Dropout,Activation,Flatten,LSTM,Embedding,Input,concatenate,Lambda
from sklearn.preprocessing import MinMaxScaler
from keras.callbacks import EarlyStopping
import keras
import random
import os
from sklearn.metrics import mean_absolute_error
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tensorflow.keras.callbacks import * 
from tensorflow.keras.optimizers import *
from tensorflow.keras import backend as K
from kerashypetune import KerasGridSearch
import matplotlib.pyplot as plt
w               = 5
ts              = 10              
nt              = 10           
ntest           = nt + int(percent*nt) 
X_train = np.random.rand(90,5,2)
X_test = np.random.rand(5,2)
y_train = np.random.rand(90,4)
y_test = np.random.rand(5,4)

""" ### 定义 T2V 层 ###

class T2V(Layer):
    def __init__(self,output_dim=None,**kwargs):
        self.output_dim = output_dim
        super(T2V,self).__init__(**kwargs)
    
    def build(self,input_shape):
        self.W = self.add_weight(name='W',shape=(1,self.output_dim),initializer='uniform',trainable=True)
        self.P = self.add_weight(name='P',trainable=True)
        self.w = self.add_weight(name='w',1),trainable=True)
        self.p = self.add_weight(name='p',trainable=True)
        super(T2V,self).build(input_shape)
    
    def call(self,x):
        original = self.w * x + self.p
        sin_trans = K.sin(K.dot(x,self.W) + self.P)
        return K.concatenate([sin_trans,original],-1)

为 LSTM 和 T2V 创建生成器

sequence_length = w
def gen_sequence(id_df,seq_length,seq_cols):
    data_matrix  = id_df[seq_cols].values
    num_elements = data_matrix.shape[0]
for start,stop in zip(range(0,num_elements-seq_length),range(seq_length,num_elements)):
    yield data_matrix[start:stop,:]

def gen_labels(id_df,label):
    data_matrix  = id_df[label].values
    num_elements = data_matrix.shape[0]
    return data_matrix[seq_length:num_elements,:]

定义模型结构

def set_seed_TF2(seed):
    tf.random.set_seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    random.seed(seed)

def T2V_NN(param,dim):
    inp = Input(shape=(dim,2))
    x = T2V(param['t2v_dim'])(inp)
    x = LSTM(param['unit'],activation=param['act'])(x)
    x = Dense(2)(x)
    m = Model(inp,x)
    m.compile(loss='mse',optimizer=Adam(lr=param['lr']))
    return m

def NN(param,2))
    x = LSTM(param['unit'],activation=param['act'])(inp)
    x = Dense(2)(x)
    m = Model(inp,optimizer=Adam(lr=param['lr']))
    return m

参数网格

param_grid = {'unit': [64,32],'t2v_dim': [128,64],'lr': [1e-2,1e-3],'act': ['elu','relu'],'epochs': 1,'batch_size': [512,1024]}

FIT T2V + LSTM

es = EarlyStopping(patience=5,verbose=0,min_delta=0.001,monitor='val_loss',mode='auto',restore_best_weights=True)

hypermodel = lambda x: T2V_NN(param=x,dim=sequence_length)

kgs_t2v = KerasGridSearch(hypermodel,param_grid,greater_is_better=False,tuner_verbose=1)
kgs_t2v.set_seed(set_seed_TF2,seed=33)
kgs_t2v.search(X_train,y_train,validation_split=0.2,callbacks=[es],shuffle=False)

但是当我运行模型时，出现了这个错误：

ValueError: Dimensions must be equal,but are 2 and 1 for '{{node t2v_2/MatMul}} = MatMul[T=DT_FLOAT,transpose_a=false,transpose_b=false](t2v_2/Reshape,t2v_2/Reshape_1)' with input shapes: [?,2],[1,128].

你能帮我解决这个问题吗？

解决方法

您必须更改 T2V 层内部和网络内部的参数才能正确匹配形状

class T2V(Layer):
    
    def __init__(self,output_dim=None,**kwargs):
        self.output_dim = output_dim
        super(T2V,self).__init__(**kwargs)
    
    def build(self,input_shape):
        self.W = self.add_weight(name='W',shape=(input_shape[-1],self.output_dim),initializer='uniform',trainable=True)
        self.P = self.add_weight(name='P',shape=(input_shape[1],trainable=True)
        self.w = self.add_weight(name='w',1),trainable=True)
        self.p = self.add_weight(name='p',trainable=True)
        super(T2V,self).build(input_shape)
    
    def call(self,x):
        original = self.w * x + self.p
        sin_trans = K.sin(K.dot(x,self.W) + self.P)
        return K.concatenate([sin_trans,original],-1)

创建一个虚拟示例

n_sample = 90
timesteps = 5
feat_inp = 2
feat_out = 4

X = np.random.uniform(0,1,(n_sample,timesteps,feat_inp))
y = np.random.uniform(0,feat_out))

def T2V_NN():
    inp = Input(shape=(timesteps,feat_inp))
    x = T2V(32)(inp)
    x = LSTM(8)(x)
    x = Dense(feat_out)(x)
    m = Model(inp,x)
    m.compile(loss='mse',optimizer='adam')
    return m

model = T2V_NN()
model.fit(X,y,epochs=3)