ValueError:检查模型目标时出错:应为dense_4

Question

我有一个错误：

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

当我运行这个脚本时。

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])

我看到这个主题：ValueError: Error when checking model target: expected dense_4 to have shape (None, 4) but got array with shape (13252, 1)，但我不知道如何在我的脚本中使用它。

提前感谢您的答复。

Answer 1

目前你必须只有两个类，因为你的输出是期望的(None, 2)。但是，当使用两个类时，矩阵结构可以是

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

或

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

Sklearns LabelBinarizer将具有两个类的矩阵转换为一列0和1的列，其中0表示类1，1表示类2。所以你的输出层应该是

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