Question

在尝试拟合我的模型时，我得到了不兼容的形状。我正在使用具有 CNN 架构 (Vgg16) 的一类分类。通过函数 wrap_generator() 生成假标签以创建 2 个输出。 Keras 版本 = 2.2.5 Tensorflow 版本 = 2.1.0

这是我的脚本

:show

当我尝试拟合模型时，出现以下错误：

tf.compat.v1.disable_eager_execution()
SHAPE = (224,224,3)
batch_size = 2
def get_model(train=True):
    
    pre_process = Lambda(preprocess_input)
    vgg = VGG16(weights = 'imagenet', include_top = True, input_shape = SHAPE)
    vgg = Model(vgg.input, vgg.layers[-3].output)
    vgg.trainable = False
    inp = Input(SHAPE)
    vgg_16_process = pre_process(GaussianNoise(0.1)(inp))
    vgg_out = vgg(vgg_16_process)
    noise = Lambda(tf.zeros_like)(vgg_out)
    noise = GaussianNoise(0.1)(noise)
    if train:
        x = Lambda(lambda z: tf.concat(z, axis=0))([vgg_out,noise])
        x = Activation('relu')(x)
    else:
        x = vgg_out  
    x = Dense(512, activation='relu')(x)
    x = Dense(128, activation='relu')(x)
    out = Dense(2, activation='softmax')(x)
    model = Model(inp, out)
    model.compile(Adam(lr=1e-4), loss='binary_crossentropy')
    return model

train_datagen = ImageDataGenerator()
test_datagen = ImageDataGenerator()


def wrap_generator(generator):
    while True:
        x,y = next(generator)
        zeros = tf.zeros_like(y) + tf.constant([1.,0.])
        y=tf.constant([1.,0.])
        x= np.array(x)
        an_array = y.eval(session=tf.compat.v1.Session())
        from keras.utils import to_categorical
        an_array = to_categorical(an_array)
        yield (x, an_array)


train_generator = train_datagen.flow_from_directory(
            '..\\dataset\\train\\',
            target_size = (SHAPE[0], SHAPE[1]),
            batch_size = batch_size,
            class_mode = 'categorical',
            shuffle = True,
            seed = 3,
            classes = ['0_ok']
    )

test_generator = test_datagen.flow_from_directory(
            '..\\dataset\\test\\',
            target_size = (SHAPE[0], SHAPE[1]),
            batch_size = batch_size,
            class_mode = 'categorical',
            shuffle = True,
            seed = 3,
            classes = ['0_ok', '1_nok']
)

tf.random.set_seed(3)
os.environ['PYTHONHASHSEED'] = str(2)
np.random.seed(3)
random.seed(3)

session_conf = tf.compat.v1.ConfigProto(
    intra_op_parallelism_threads=1, 
    inter_op_parallelism_threads=1
)
sess = tf.compat.v1.Session(
    graph=tf.compat.v1.get_default_graph(), 
    config=session_conf
)
tf.compat.v1.keras.backend.set_session(sess)


model = get_model()
model.fit(wrap_generator(train_generator), steps_per_epoch=train_generator.samples/train_generator.batch_size, epochs=2)

我确认我的训练样本数量是批量大小的倍数。我验证了在 train 目录中创建的类与脚本中的相同。当我显示我的输入形状时，我的输出形状得到了 (2,224,224,3) 和 (2,2)。 len（输入）= len（输出）= 2 我试图将 Flatten 添加到我的输入中，但我仍然有同样的错误。如何修复此错误？

InvalidArgumentError：不兼容的形状：[2,2] 与 [4,2]

0 个答案: