Python 矩阵大小不兼容：在[0]：[161024]中，在[1]：[16384,1]中，在DCGAN中

我正在尝试建立一个DCGANN

我收到：

InvalidArgumentError: Matrix size-incompatible: In[0]: [16,1024], In[1]: [16384,1]
     [[{{node model_69/dense_50/BiasAdd}}]]

我试图在鉴别器中添加一个重塑，但没有成功

我的图像有维度：

（64,64,3）

鉴别器：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_59 (InputLayer)        (None, 64, 64, 3)         0         
_________________________________________________________________
conv2d_201 (Conv2D)          (None, 32, 32, 128)       9728      
_________________________________________________________________
leaky_re_lu_99 (LeakyReLU)   (None, 32, 32, 128)       0         
_________________________________________________________________
batch_normalization_200 (Bat (None, 32, 32, 128)       512       
_________________________________________________________________
conv2d_202 (Conv2D)          (None, 16, 16, 256)       819456    
_________________________________________________________________
leaky_re_lu_100 (LeakyReLU)  (None, 16, 16, 256)       0         
_________________________________________________________________
batch_normalization_201 (Bat (None, 16, 16, 256)       1024      
_________________________________________________________________
conv2d_203 (Conv2D)          (None, 8, 8, 512)         3277312   
_________________________________________________________________
leaky_re_lu_101 (LeakyReLU)  (None, 8, 8, 512)         0         
_________________________________________________________________
batch_normalization_202 (Bat (None, 8, 8, 512)         2048      
_________________________________________________________________
conv2d_204 (Conv2D)          (None, 4, 4, 1024)        13108224  
_________________________________________________________________
leaky_re_lu_102 (LeakyReLU)  (None, 4, 4, 1024)        0         
_________________________________________________________________
batch_normalization_203 (Bat (None, 4, 4, 1024)        4096      
_________________________________________________________________
flatten_25 (Flatten)         (None, 16384)             0         
_________________________________________________________________
dense_50 (Dense)             (None, 1)                 16385     
=================================================================
Total params: 17,238,785
Trainable params: 17,234,945
Non-trainable params: 3,840


discriminator_out: Tensor("model_69/dense_50/Sigmoid:0", shape=(?, 1), dtype=float32)

gan模型：

Layer (type)                 Output Shape              Param #   
=================================================================
input_61 (InputLayer)        (None, 100)               0         
_________________________________________________________________
model_70 (Model)             (None, 4, 4, 3)           18876163  
_________________________________________________________________
model_69 (Model)             (None, 1)                 17238785  
=================================================================
Total params: 36,114,948
Trainable params: 18,872,323
Non-trainable params: 17,242,625

---

生成器的输出张量具有形状

（无，4，4，3）

，这与预期的形状

（无，64，64，3）

不同。这是由于使用了跨步卷积

以下生成器生成尺寸为64x64x3的图像：

def generator(gen_inputs):
    # 4x4x1024
    inputs = Input(shape=(gen_inputs,))
    x = Dense(4 * 4 * 1024, activation='relu')(inputs)
    x = Reshape((4, 4, 1024))(x)
    x = BatchNormalization()(x)

    # 8x8x512
    x = UpSampling2D()(x)
    x = Conv2D(512, (5, 5), activation='relu', padding='same')(x)
    x = BatchNormalization()(x)

    # 16x16x256
    x = UpSampling2D()(x)
    x = Conv2D(256, (5, 5), activation='relu', padding='same')(x)
    x = BatchNormalization()(x)

    # 32x32x128
    x = UpSampling2D()(x)
    x = Conv2D(128, (5, 5), activation='relu', padding='same')(x)
    x = BatchNormalization()(x)

    # 64x64x3
    x = UpSampling2D()(x)
    out = Conv2D(3, (5, 5), activation='tanh', padding='same')(x)

    return Model(inputs, out)

---

X_列车的形状是什么？哪一行抛出了错误？@rvinas:gen\u loss=gan.train\u on\u batch（noise，np.ones（（batch\u size，1））调用时出错。。X_序列形状：

（22125,64,64,3）

根据GAN总结，为什么发电机的输出形状是

（无，4,4,3）

！64而不是4！谢谢请回答。

def generator(gen_inputs):
    # 4x4x1024
    inputs = Input(shape=(gen_inputs,))
    x = Dense(4 * 4 * 1024, activation='relu')(inputs)
    x = Reshape((4, 4, 1024))(x)
    x = BatchNormalization()(x)

    # 8x8x512
    x = UpSampling2D()(x)
    x = Conv2D(512, (5, 5), activation='relu', padding='same')(x)
    x = BatchNormalization()(x)

    # 16x16x256
    x = UpSampling2D()(x)
    x = Conv2D(256, (5, 5), activation='relu', padding='same')(x)
    x = BatchNormalization()(x)

    # 32x32x128
    x = UpSampling2D()(x)
    x = Conv2D(128, (5, 5), activation='relu', padding='same')(x)
    x = BatchNormalization()(x)

    # 64x64x3
    x = UpSampling2D()(x)
    out = Conv2D(3, (5, 5), activation='tanh', padding='same')(x)

    return Model(inputs, out)