Form Probleme keras in während die Ausgabe kategorische Variable versucht

Question

ich folgendes verwende, ziemlich einfachen Code, um eine Ausgangsgröße vorherzusagen, die drei Kategorien haben:

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) 
    ) 

Hier ist die Zusammenfassung des Modells:

____________________________________________________________________________________________________ 
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 
_____________________________ 

Aber auf der fit Aussage, erhalte ich folgende Fehlermeldung:

--------------------------------------------------------------------------- 
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) 

Wie behebe ich diesen Fehler? Warum erwartet die letzte Schicht (None,1), wenn sie gemäß summary()(None,3) ausgeben muss?

Jede Hilfe würde sehr geschätzt werden.

Answer 1

Ich habe den Fehler mit categorical_entropy statt sparse_categorical_entropy behoben.