とValueError：エラーモデル標的をチェック：期待dense_4

Question

私はエラーがあります：私はこのスクリプトを実行すると

ValueError: Error when checking model target: expected dense_4 to have shape (None, 2) but got array with shape (12956, 1) 

を。

def image_text_model(image_features, text_features, n_classes): 
    # fine-tune the last layer 
    image_features = Input(shape=image_features.shape[1:], dtype='float32') 

    n_text_features = text_features.shape[1] 
    text_features = Input(shape=text_features.shape[1:], dtype='float32') 

    # text model 
    x_text = Dense(256, activation='elu', kernel_regularizer=l2(1e-5))(text_features) 
    x_text = Dropout(0.5)(x_text) 

    # image model 
    x_img = Dense(256, activation='elu')(image_features) 
    x_img = Dropout(0.5)(x_img) 
    x_img = Dense(256, activation='elu')(x_img) 
    x_img = Dropout(0.5)(x_img) 

    merged = concatenate([x_img, x_text]) 
    predictions = Dense(n_classes, activation='softmax')(merged) 

    model = Model(inputs=[image_features, text_features], outputs=[predictions]) 
    model.compile(optimizer='adam', 
        loss='categorical_crossentropy', 
        metrics=['accuracy']) 

    return model 



# dev 
df = pd.read_csv(os.path.join(data_dir, 'amazon_products_dev.csv')) 
dev_image_list = df['image_file'].values 
dev_text = df['title'].values.tolist() 
dev_categories = df['product_category'].values 

# encode labels (binary labels) 
encoder = LabelBinarizer() 
train_labels = encoder.fit_transform(train_categories) 
dev_labels = encoder.transform(dev_categories) 

# get features from a pre-trained resnet model 
vec = ResNetVectorizer(batch_size=500, 
         image_dir=image_dir, 
         use_cache=True, 
         cache_dir=cache_dir) 
train_image_features = vec.transform(train_image_list) 
dev_image_features = vec.transform(dev_image_list) 


# get text features 
tfidf = TfidfVectorizer(ngram_range=(1,1), stop_words='english', max_features=5000) 
train_text_features = tfidf.fit_transform(train_text) 
dev_text_features = tfidf.transform(dev_text).toarray() 

# fine-tune the last layer 
n_classes = encoder.classes_.shape[0] 
model = image_text_model(train_image_features, train_text_features, n_classes) 


data_gen = sparse_batch_generator(train_image_features, train_text_features, train_labels, shuffle=True) 
steps_per_epoch = int(np.ceil(train_image_features.shape[0]/32.)) 
model.fit_generator(data_gen, 
        steps_per_epoch=steps_per_epoch, 
        epochs=50, 
validation_data=[[dev_image_features, dev_text_features], dev_labels]) 

私はこのトピックを参照してください：ValueError: Error when checking model target: expected dense_4 to have shape (None, 4) but got array with shape (13252, 1) しかし、私は私のスクリプトにそれを使用する方法を知りません。

ご回答いただきありがとうございます。

Answer 1

あなたの出力は(None, 2)を期待して、現在あなただけの2つのクラスを持っている必要があります。二つのクラスを扱う場合しかし、あなたはマトリックス構造は

[[0,1], 
[1,0], 
[1,0]] 

または

[[0], 
[1], 
[1]] 

のいずれかとすることができるSklearns LabelBinarizerゼロと1の者の列に二つのクラスの行列に変換します。クラス1の場合は0、クラス2の場合は1です。出力層はちょうど

predictions = Dense(1, activation='sigmoid')(merged)