私はノイズテンソルを生成するとき、私はこのエラーを取得：はAttributeError：「テンソル」オブジェクトは、この行を何属性「_keras_history」

Question

を持っていない：

z = random_normal(shape = (-1, 8, 8, 256), 
        mean = 0.0, stddev = 1.0, dtype = None, seed = None) 

はエラーを与える：

AttributeError: 'Tensor' object has no attribute '_keras_history'. 

は誰いずれかを持っていますアイデアをどのように解決できますか？

Answer 1

しかし、私は実際問題を回避することができました。私はzに関数の入力のように与えました。私はそれを作成するために入力層を使用しました。

image = Input(shape = (128, 128, 3)) 
noise = random_normal(shape = (-1, 8, 8, 100), mean = 0.0, stddev = 1.0, dtype = None, seed = None) 
noise = Input(tensor = noise) 

gen_out = generator_network(image, noise) 
gen_model = Model(inputs = [image, noise], outputs = gen_out) 

def generator_network(input_tensor, noise): 
    """ 
    The generator network, G has two pathways, with one global network Gg processing 
    the global structure of the face and a local one for the main face features: eyes, 
    mouth, nose. Each path has an encoder - decoder structure with skip connections. 
    :INPUT : Input tensor corresponding to an image, a face profile. 
    :OUTPUT: Output tensor that corresponds to the frontal face. 
    """ 
    # Global pathway encoder 
    conv0 = Conv2D(filters = 64, kernel_size = (7, 7), padding = 'same', strides = (1, 1))(input_tensor) 
    conv0 = BatchNormalization()(conv0) 
    conv0 = LeakyReLU(0.2)(conv0) 
    conv0 = resnet_block(conv0, 64) 

    conv1 = Conv2D(filters = 64, kernel_size = (5, 5), padding = 'same', strides = (2, 2))(conv0) 
    conv1 = BatchNormalization()(conv1) 
    conv1 = LeakyReLU(0.2)(conv1) 
    conv1 = resnet_block(conv1, 64) 

    conv2 = Conv2D(filters = 128, kernel_size = (3, 3), padding = 'same', strides = (2, 2))(conv1) 
    conv2 = BatchNormalization()(conv2) 
    conv2 = LeakyReLU(0.2)(conv2) 
    conv2 = resnet_block(conv2, 128) 

    conv3 = Conv2D(filters = 256, kernel_size = (3, 3), padding = 'same', strides = (2, 2))(conv2) 
    conv3 = BatchNormalization()(conv3) 
    conv3 = LeakyReLU(0.2)(conv3) 
    conv3 = resnet_block(conv3, 256) 

    conv4 = Conv2D(filters = 512, kernel_size = (3, 3), padding = 'same', strides = (2, 2))(conv3) 
    conv4 = BatchNormalization()(conv4) 
    conv4 = LeakyReLU(0.2)(conv4) 
    for i in range(4): 
     conv4 = resnet_block(conv4, 512) 

    fc1 = Dense(512)(conv4) 
    f1 = Lambda(lambda x: x[:, : , :, 0:256])(fc1) 
    f2 = Lambda(lambda x: x[:, : , :, 256:512])(fc1) 
    fc2 = maximum([f1, f2]) 

    # Concatenation with noise vector 
    v = concatenate([fc2, noise])