串联嵌入层后，在Keras中拟合模型时出现断言错误

2024-06-02 • 问答

我是嵌入式层的新手，对此有问题。我试图拟合神经网络模型，但首先我使用嵌入层对数据集的分类特征进行了编码。这是我的代码：

import numpy as np 
def preproc(X_train,categorical_vars,other_cols) : 

    input_list_train = []
    for c in categorical_vars :

        jjj = np.asarray(X_train[c].tolist())
        jjj = pd.factorize( jjj )[0]
        input_list_train.append( np.asarray(jjj)  )
        """
        raw_vals = np.unique(X_train[c])
        val_map = {}
        for i in range(len(raw_vals)):
            val_map[raw_vals[i]] = i       
        input_list_train.append(X_train[c].map(val_map).values)
        """
    #the rest of the columns
    input_list_train.append(X_train[other_cols].values)
    return input_list_train 

X_train = preproc(X_train,categorical_columns,numeric_columns)
X_validation = preproc(X_validation,numeric_columns)
X_test = preproc(X_test,numeric_columns)


from keras.layers import *
from keras.models import *

models = []

for categorical_var in categorical_columns:
    model = Sequential()
    model.reset_states( )
    no_of_unique_cat  = train_df[categorical_var].nunique()
    embedding_size = min(np.ceil((no_of_unique_cat)/2),50 )
    embedding_size = int(embedding_size)
    model.add(  Embedding( no_of_unique_cat+1,embedding_size,input_length = 1 ) )
    model.add(Reshape(target_shape=(embedding_size,)))
    models.append( model )


model_rest = Sequential()
model_rest.add(Dense(  64,input_dim = (train_df.shape[1] - len(categorical_columns)) ))
model_rest.reset_states( )
models.append(model_rest)

layer_nodes = len(X_train) + 1
dropout_hidden_layers = 0.5
output_nodes = 1

full_model = Sequential()
full_model.add(concatenate(models))


full_model.add(Dense(units = layer_nodes,kernel_initializer = "uniform"))
full_model.add(activation('relu'))
full_model.add(Dropout(dropout_hidden_layers))


full_model.add(Dense(units = layer_nodes,kernel_initializer = "uniform"))
full_model.add(activation('relu'))
full_model.add(Dropout(dropout_hidden_layers))



full_model.add(Dense(units = output_nodes,kernel_initializer = "uniform",activation = "sigmoid"))


full_model.compile(optimizer = "adam",loss = "binary_crossentropy",metrics = ["accuracy"])


history  =  full_model.fit(X_train,y_train,epochs =  200,batch_size = 20)

我遇到下一个错误：


Traceback (most recent call last):

  File "<ipython-input-28-d5c2b04c2cc3>",line 51,in <module>
    history  =  full_model.fit(X_train,batch_size = 20)

  File "/home/javier/anaconda3/lib/python3.7/site-packages/keras/engine/training.py",line 1154,in fit
    batch_size=batch_size)

  File "/home/javier/anaconda3/lib/python3.7/site-packages/keras/engine/training.py",line 504,in _standardize_user_data
    self._set_inputs(x)

  File "/home/javier/anaconda3/lib/python3.7/site-packages/keras/engine/training.py",line 414,in _set_inputs
    assert len(inputs) == 1

AssertionError

可能是哪个问题？

models = [] inps = [] for categorical_var in categorical_columns: inp = Input((1,)) no_of_unique_cat = df[categorical_var].max() embedding_size = int(min(np.ceil((no_of_unique_cat)/2),50 )) x = Embedding( no_of_unique_cat+1,embedding_size )(inp) x = Flatten()(x) inps.append( inp ) models.append( x ) inp = Input(((df.shape[1] - len(categorical_columns)),)) x = Dense( 64 )(inp) inps.append( inp ) models.append( x ) dropout_hidden_layers = 0.5 output_nodes = 1 x = Concatenate()(models) x = Dense(128,kernel_initializer = "uniform")(x) x = Activation('relu')(x) x = Dropout(dropout_hidden_layers)(x) x = Dense(64,kernel_initializer = "uniform")(x) x = Activation('relu')(x) x = Dropout(dropout_hidden_layers)(x) out = Dense(units = output_nodes,kernel_initializer = "uniform",activation = "sigmoid")(x) full_model = Model(inps,out) full_model.compile(optimizer = "adam",loss = "binary_crossentropy",metrics = ["accuracy"]) full_model.summary()

串联嵌入层后，在Keras中拟合模型时出现断言错误

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