keras模型类中的输入层以numpy数组或张量作为输入给出类型错误。那么正确的类型是什么？

Question

如果不是numpy数组，如何给模型输入？

def createmodel():
    myInput = Input(shape=(96, 96, 3))

x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D(pool_size=3, strides=2)(x)
x = Lambda(LRN2D, name='lrn_1')(x)
x = Conv2D(64, (1, 1), name='conv2')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)
x = Activation('relu')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = Conv2D(192, (3, 3), name='conv3')(x)
x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)
x = Activation('relu')(x)
x = Lambda(LRN2D, name='lrn_2')(x)
x = ZeroPadding2D(padding=(1, 1))(x)
x = MaxPooling2D(pool_size=3, strides=2)(x)

# Inception3a
inception_3a_3x3 = Conv2D(96, (1, 1), name='inception_3a_3x3_conv1')(x)
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn1')(inception_3a_3x3)
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
inception_3a_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(inception_3a_3x3)
inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn2')(inception_3a_3x3)
inception_3a_3x3 = Activation('relu')(inception_3a_3x3)

inception_3a_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(x)
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn1')(inception_3a_5x5)
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
inception_3a_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(inception_3a_5x5)
inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn2')(inception_3a_5x5)
inception_3a_5x5 = Activation('relu')(inception_3a_5x5)

inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
inception_3a_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(inception_3a_pool)
inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_pool_bn')(inception_3a_pool)
inception_3a_pool = Activation('relu')(inception_3a_pool)
inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)

inception_3a_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(x)
inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_1x1_bn')(inception_3a_1x1)
inception_3a_1x1 = Activation('relu')(inception_3a_1x1)

inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3)

# Inception3b
inception_3b_3x3 = Conv2D(96, (1, 1), name='inception_3b_3x3_conv1')(inception_3a)
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn1')(inception_3b_3x3)
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
inception_3b_3x3 = Conv2D(128, (3, 3), name='inception_3b_3x3_conv2')(inception_3b_3x3)
inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn2')(inception_3b_3x3)
inception_3b_3x3 = Activation('relu')(inception_3b_3x3)

inception_3b_5x5 = Conv2D(32, (1, 1), name='inception_3b_5x5_conv1')(inception_3a)
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn1')(inception_3b_5x5)
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
inception_3b_5x5 = Conv2D(64, (5, 5), name='inception_3b_5x5_conv2')(inception_3b_5x5)
inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn2')(inception_3b_5x5)
inception_3b_5x5 = Activation('relu')(inception_3b_5x5)

inception_3b_pool = Lambda(lambda x: x**2, name='power2_3b')(inception_3a)
inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
inception_3b_pool = Lambda(lambda x: x*9, name='mult9_3b')(inception_3b_pool)
inception_3b_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_3b')(inception_3b_pool)
inception_3b_pool = Conv2D(64, (1, 1), name='inception_3b_pool_conv')(inception_3b_pool)
inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_pool_bn')(inception_3b_pool)
inception_3b_pool = Activation('relu')(inception_3b_pool)
inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)

inception_3b_1x1 = Conv2D(64, (1, 1), name='inception_3b_1x1_conv')(inception_3a)
inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_1x1_bn')(inception_3b_1x1)
inception_3b_1x1 = Activation('relu')(inception_3b_1x1)

inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3)

# Inception3c
inception_3c_3x3 = utils.conv2d_bn(inception_3b,
                                   layer='inception_3c_3x3',
                                   cv1_out=128,
                                   cv1_filter=(1, 1),
                                   cv2_out=256,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(2, 2),
                                   padding=(1, 1))

inception_3c_5x5 = utils.conv2d_bn(inception_3b,
                                   layer='inception_3c_5x5',
                                   cv1_out=32,
                                   cv1_filter=(1, 1),
                                   cv2_out=64,
                                   cv2_filter=(5, 5),
                                   cv2_strides=(2, 2),
                                   padding=(2, 2))

inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)

inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)

#inception 4a
inception_4a_3x3 = utils.conv2d_bn(inception_3c,
                                   layer='inception_4a_3x3',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   cv2_out=192,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(1, 1),
                                   padding=(1, 1))
inception_4a_5x5 = utils.conv2d_bn(inception_3c,
                                   layer='inception_4a_5x5',
                                   cv1_out=32,
                                   cv1_filter=(1, 1),
                                   cv2_out=64,
                                   cv2_filter=(5, 5),
                                   cv2_strides=(1, 1),
                                   padding=(2, 2))

inception_4a_pool = Lambda(lambda x: x**2, name='power2_4a')(inception_3c)
inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
inception_4a_pool = Lambda(lambda x: x*9, name='mult9_4a')(inception_4a_pool)
inception_4a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_4a')(inception_4a_pool)
inception_4a_pool = utils.conv2d_bn(inception_4a_pool,
                                   layer='inception_4a_pool',
                                   cv1_out=128,
                                   cv1_filter=(1, 1),
                                   padding=(2, 2))
inception_4a_1x1 = utils.conv2d_bn(inception_3c,
                                   layer='inception_4a_1x1',
                                   cv1_out=256,
                                   cv1_filter=(1, 1))
inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3)

#inception4e
inception_4e_3x3 = utils.conv2d_bn(inception_4a,
                                   layer='inception_4e_3x3',
                                   cv1_out=160,
                                   cv1_filter=(1, 1),
                                   cv2_out=256,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(2, 2),
                                   padding=(1, 1))
inception_4e_5x5 = utils.conv2d_bn(inception_4a,
                                   layer='inception_4e_5x5',
                                   cv1_out=64,
                                   cv1_filter=(1, 1),
                                   cv2_out=128,
                                   cv2_filter=(5, 5),
                                   cv2_strides=(2, 2),
                                   padding=(2, 2))
inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)

inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)

#inception5a
inception_5a_3x3 = utils.conv2d_bn(inception_4e,
                                   layer='inception_5a_3x3',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   cv2_out=384,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(1, 1),
                                   padding=(1, 1))

inception_5a_pool = Lambda(lambda x: x**2, name='power2_5a')(inception_4e)
inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
inception_5a_pool = Lambda(lambda x: x*9, name='mult9_5a')(inception_5a_pool)
inception_5a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_5a')(inception_5a_pool)
inception_5a_pool = utils.conv2d_bn(inception_5a_pool,
                                   layer='inception_5a_pool',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   padding=(1, 1))
inception_5a_1x1 = utils.conv2d_bn(inception_4e,
                                   layer='inception_5a_1x1',
                                   cv1_out=256,
                                   cv1_filter=(1, 1))

inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)

#inception_5b
inception_5b_3x3 = utils.conv2d_bn(inception_5a,
                                   layer='inception_5b_3x3',
                                   cv1_out=96,
                                   cv1_filter=(1, 1),
                                   cv2_out=384,
                                   cv2_filter=(3, 3),
                                   cv2_strides=(1, 1),
                                   padding=(1, 1))
inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
inception_5b_pool = utils.conv2d_bn(inception_5b_pool,
                                   layer='inception_5b_pool',
                                   cv1_out=96,
                                   cv1_filter=(1, 1))
inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)

inception_5b_1x1 = utils.conv2d_bn(inception_5a,
                                   layer='inception_5b_1x1',
                                   cv1_out=256,
                                   cv1_filter=(1, 1))
inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)

av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
reshape_layer = Flatten()(av_pool)
dense_layer = Dense(128, name='dense_layer')(reshape_layer)
norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)


# Final Model
return Model(inputs=[myInput], outputs=norm_layer)

将上述模型称为：

from keras import backend as K
from keras.models import Model
from keras.layers import Input, Layer


# Input for anchor, positive and negative images
in_a =np.random.randint(10,100,(1,96,96,3)).astype(float)
in_p =np.random.randint(10,100,(1,96,96,3)).astype(float)
in_n =np.random.randint(10,100,(1,96,96,3)).astype(float)

in_a_a =K.variable(value=in_a)
in_p_p =K.variable(value=in_p)
in_n_n =K.variable(value=in_n)

# # Output for anchor, positive and negative embedding vectors
# # The nn4_small model instance is shared (Siamese network)

emb_a = nn4_small2(in_a_a)
emb_p = nn4_small2(in_p_p)
emb_n = nn4_small2(in_n_n)

class TripletLossLayer(Layer):
    def __init__(self, alpha, **kwargs):
        self.alpha = alpha
        super(TripletLossLayer, self).__init__(**kwargs)

    def triplet_loss(self, inputs):
        a, p, n = inputs
        p_dist = K.sum(K.square(a-p), axis=-1)
        n_dist = K.sum(K.square(a-n), axis=-1)
        return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)

    def call(self, inputs):
        loss = self.triplet_loss(inputs)
        self.add_loss(loss)
        return loss

# # # Layer that computes the triplet loss from anchor, positive and negative embedding vectors
triplet_loss_layer = TripletLossLayer(alpha=0.2, name='triplet_loss_layer')([emb_a, emb_p, emb_n])

# # # Model that can be trained with anchor, positive negative images
nn4_small2_train = Model([in_a, in_p, in_n], triplet_loss_layer)

在此行中出现类型错误：

  

nn4_small2_train =模型（[in_a，in_p，in_n]，triplet_loss_layer）

包装器中的

c：\ users \ amark \ anaconda3 \ envs \ python3.5 \ lib \ site-packages \ keras \ _ legacy \ interfaces.py（* args，** kwargs）
     89 warnings.warn（＆＃39;更新您对Keras 2 API的' + object_name + 90 '电话：＆＃39; +签名，stacklevel = 2）
---＆GT; 91 return func（* args，** kwargs）
     92 wrapper._original_function = func
     93返回包装器

init 中的

c：\ users \ amark \ anaconda3 \ envs \ python3.5 \ lib \ site-packages \ keras \ engine \ topology.py（自我，输入，输出，名称）<登记/>    1526
   1527＃检查输入冗余。
- ＆GT; 1528 if len（set（self.inputs））！= len（self.inputs）：
   1529引发ValueError（＆＃39;传递给模型的输入列表＆＃39;
   1530＆＃39;是多余的。 ＆＃39;

TypeError：不可用类型：＆＃39; numpy.ndarray＆＃39;

如果我尝试使用以下内容：

nn4_small2_train = Model([in_a_a, in_p_p, in_n_n], triplet_loss_layer)

然后引发的错误是：

  

TypeError：模型的输入张量必须是Keras张量。发现:(缺少Keras元数据）

Answer 1

您正在传递numpy数组作为构建模型的输入，这是不对的，您应该传递Input的实例。

在您的特定情况下，您正在传递in_a, in_p, in_n，但是要构建模型，您应该给出Input的实例，而不是K.variables（您的in_a_a, in_p_p, in_n_n）或numpy数组。为varibles赋值也没有意义。首先，您可以在没有任何特定输入值的情况下以符号方式构建模型，然后您可以使用实际输入值对其进行训练或预测。