形状の問題を出力するカテゴリ変数

Question

をしようとしているときに、私は3つのカテゴリーを有していてもよく、出力変数を予測するには、次の、かなり単純なコードを使用しています：

n_factors = 20 
np.random.seed = 42 

def embedding_input(name, n_in, n_out, reg): 
    inp = Input(shape=(1,), dtype='int64', name=name) 
    return inp, Embedding(n_in, n_out, input_length=1, W_regularizer=l2(reg))(inp) 

user_in, u = embedding_input('user_in', n_users, n_factors, 1e-4) 
artifact_in, a = embedding_input('artifact_in', n_artifacts, n_factors, 1e-4) 

mt = Input(shape=(31,)) 
mr = Input(shape=(1,)) 
sub = Input(shape=(24,)) 

def onehot(featurename): 
    onehot_encoder = OneHotEncoder(sparse=False) 
    onehot_encoded = onehot_encoder.fit_transform(Modality_Durations[featurename].reshape(-1, 1)) 
    trn_onehot_encoded = onehot_encoded[msk] 
    val_onehot_encoded = onehot_encoded[~msk] 
    return trn_onehot_encoded, val_onehot_encoded 

trn_onehot_encoded_mt, val_onehot_encoded_mt = onehot('modality_type') 
trn_onehot_encoded_mr, val_onehot_encoded_mr = onehot('roleid') 
trn_onehot_encoded_sub, val_onehot_encoded_sub = onehot('subject') 
trn_onehot_encoded_quartile, val_onehot_encoded_quartile = onehot('quartile') 

# Model 
x = merge([u, a], mode='concat') 
x = Flatten()(x) 
x = merge([x, mt], mode='concat') 
x = merge([x, mr], mode='concat') 
x = merge([x, sub], mode='concat') 
x = Dense(10, activation='relu')(x) 
BatchNormalization() 
x = Dense(3, activation='softmax')(x) 
nn = Model([user_in, artifact_in, mt, mr, sub], x) 
nn.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) 

nn.optimizer.lr = 0.001 
nn.fit([trn.member_id, trn.artifact_id, trn_onehot_encoded_mt, trn_onehot_encoded_mr, trn_onehot_encoded_sub], trn_onehot_encoded_quartile, 
     batch_size=256, 
     epochs=2, 
     validation_data=([val.member_id, val.artifact_id, val_onehot_encoded_mt, val_onehot_encoded_mr, val_onehot_encoded_sub], val_onehot_encoded_quartile) 
    ) 

はここでモデルの概要です：

____________________________________________________________________________________________________ 
Layer (type)      Output Shape   Param #  Connected to      
==================================================================================================== 
user_in (InputLayer)    (None, 1)    0            
____________________________________________________________________________________________________ 
artifact_in (InputLayer)   (None, 1)    0            
____________________________________________________________________________________________________ 
embedding_9 (Embedding)   (None, 1, 20)   5902380  user_in[0][0]      
____________________________________________________________________________________________________ 
embedding_10 (Embedding)   (None, 1, 20)   594200  artifact_in[0][0]     
____________________________________________________________________________________________________ 
merge_25 (Merge)     (None, 1, 40)   0   embedding_9[0][0]     
                    embedding_10[0][0]    
____________________________________________________________________________________________________ 
flatten_7 (Flatten)    (None, 40)   0   merge_25[0][0]     
____________________________________________________________________________________________________ 
input_13 (InputLayer)   (None, 31)   0            
____________________________________________________________________________________________________ 
merge_26 (Merge)     (None, 71)   0   flatten_7[0][0]     
                    input_13[0][0]     
____________________________________________________________________________________________________ 
input_14 (InputLayer)   (None, 1)    0            
____________________________________________________________________________________________________ 
merge_27 (Merge)     (None, 72)   0   merge_26[0][0]     
                    input_14[0][0]     
____________________________________________________________________________________________________ 
input_15 (InputLayer)   (None, 24)   0            
____________________________________________________________________________________________________ 
merge_28 (Merge)     (None, 96)   0   merge_27[0][0]     
                    input_15[0][0]     
____________________________________________________________________________________________________ 
dense_13 (Dense)     (None, 10)   970   merge_28[0][0]     
____________________________________________________________________________________________________ 
dense_14 (Dense)     (None, 3)    33   dense_13[0][0]     
==================================================================================================== 
Total params: 6,497,583 
Trainable params: 6,497,583 
Non-trainable params: 0 
_____________________________ 

しかしfit文で、私は次のエラーを取得する：

--------------------------------------------------------------------------- 
ValueError        Traceback (most recent call last) 
<ipython-input-71-7de0782d7d5d> in <module>() 
     5  batch_size=256, 
     6  epochs=2, 
----> 7  validation_data=([val.member_id, val.artifact_id, val_onehot_encoded_mt, val_onehot_encoded_mr, val_onehot_encoded_sub], val_onehot_encoded_quartile) 
     8  ) 
     9 # nn.fit([trn.member_id, trn.artifact_id, trn_onehot_encoded_mt, trn_onehot_encoded_mr, trn_onehot_encoded_sub], trn.duration_new, 

/home/prateek_dl/anaconda3/lib/python3.5/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs) 
    1520    class_weight=class_weight, 
    1521    check_batch_axis=False, 
-> 1522    batch_size=batch_size) 
    1523   # Prepare validation data. 
    1524   do_validation = False 

/home/prateek_dl/anaconda3/lib/python3.5/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size) 
    1380          output_shapes, 
    1381          check_batch_axis=False, 
-> 1382          exception_prefix='target') 
    1383   sample_weights = _standardize_sample_weights(sample_weight, 
    1384              self._feed_output_names) 

/home/prateek_dl/anaconda3/lib/python3.5/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix) 
    142        ' to have shape ' + str(shapes[i]) + 
    143        ' but got array with shape ' + 
--> 144        str(array.shape)) 
    145  return arrays 
    146 

ValueError: Error when checking target: expected dense_14 to have shape (None, 1) but got array with shape (1956554, 3) 

このエラーを解決するにはどうすればよいですか？最終層がに応じて(None,3)を出力する必要がある場合、(None,1)を期待しているのはなぜですか？

ご協力いただければ幸いです。

Answer 1

sparse_categorical_entropyの代わりにcategorical_entropyを使用してエラーを修正しました。