python-3.x - 如何预处理数据并将其输入keras模型？

Question

我有一个包含两列的数据集:路径和类。我想用它来微调 VGGface。

dataset.head(5):


    path            class
0   /f3_224x224.jpg red
1   /bc_224x224.jpg orange
2   /1c_224x224.jpg brown
3   /4b_224x224.jpg red
4   /0c_224x224.jpg yellow

我想使用这些路径来预处理图像并提供给 keras。我的预处理函数如下:

from keras.preprocessing.image import img_to_array, load_img

def prep_image(photo):
    img = image.load_img(path + photo, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = utils.preprocess_input(x, version=1)
    return x

我使用以下代码准备数据集:

from sklearn.model_selection import train_test_split

path = list(dataset.columns.values)
path.remove('class')
X = dataset[path]
y = dataset['class']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

我使用以下代码训练我的模型:

nb_class = 4
hidden_dim = 512

vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vgg_model.get_layer('pool5').output
x = Flatten(name='flatten')(last_layer)
x = Dense(hidden_dim, activation='relu', name='fc6')(x)
x = Dense(hidden_dim, activation='relu', name='fc7')(x)
out = Dense(nb_class, activation='softmax', name='fc8')(x)
custom_vgg_model = Model(vgg_model.input, out)
custom_vgg_model.compile(

        optimizer="adam",
        loss="categorical_crossentropy"
    )

custom_vgg_model.fit(X_train, y_train, epochs=50, batch_size=16)
test_loss, test_acc = model.evaluate(X_test, y_test)

但是我得到了值错误，因为我无法弄清楚如何预处理图像并提供数组。我如何转换 X_train/test 数据帧的路径并将其替换为 prep_image 函数的输出？

ValueError: Error when checking input: expected input_2 to have 4 dimensions, but got array with shape (50297, 1)

所以形状应该是 (50297, 224, 224, 3)。

Answer 1

X_train、X_test 看起来基本上只是路径名。在数据准备步骤中，您只需修改代码，例如添加最后两行。

from sklearn.model_selection import train_test_split

path = list(dataset.columns.values)
path.remove('class')
X = dataset[path]
y = dataset['class']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
X_train = np.array([prep_image(path)[0] for path in X_train])
X_test = np.array([prep_image(path)[0] for path in X_test])