运行时错误:由于无法获取输入，导致GAN的图形断开连接 [英] Runtime Error: Disconnected graph for GANs because input can't be obtained
问题描述
这是我的鉴别器架构:
def build_discriminator(img_shape,embedding_shape):
model1 = Sequential()
model1.add(Conv2D(32, kernel_size=5, strides=2, input_shape=img_shape, padding="same"))
model1.add(LeakyReLU(alpha=0.2))
model1.add(Dropout(0.25))
model1.add(Conv2D(48, kernel_size=5, strides=2, padding="same"))
#model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model1.add(BatchNormalization(momentum=0.8))
model1.add(LeakyReLU(alpha=0.2))
model1.add(Dropout(0.25))
model1.add(Conv2D(64, kernel_size=5, strides=2, padding="same"))
model1.add(BatchNormalization(momentum=0.8))
model1.add(LeakyReLU(alpha=0.2))
model1.add(Dropout(0.25))
model1.add(Conv2D(128, kernel_size=5, strides=2, padding="same"))
model1.add(BatchNormalization(momentum=0.8))
model1.add(LeakyReLU(alpha=0.2))
model1.add(Dropout(0.25))
model1.add(Conv2D(256, kernel_size=5, strides=2, padding="same"))
model1.add(BatchNormalization(momentum=0.8))
model1.add(LeakyReLU(alpha=0.2))
model1.add(Dropout(0.25))
model1.add(Flatten())
model1.add(Dense(200))
model2=Sequential()
model2.add(Dense(50, input_shape=embedding_shape))
model2.add(Dense(100))
model2.add(Dense(200))
model2.add(Flatten())
merged_model = Sequential()
merged_model.add(Merge([model1, model2], mode='concat'))
merged_model.add(Dense(1, activation='sigmoid', name='output_layer'))
#merged_model.compile(loss='binary_crossentropy', optimizer='adam',
#metrics=['accuracy'])
#model1.add(Dense(1, activation='sigmoid'))
merged_model.summary()
merged_model.input_shape
img = Input(shape=img_shape)
emb = Input(shape=embedding_shape)
validity = merged_model([img,emb])
return Model([img,emb],validity)
这是生成器体系结构:
def build_generator(latent_dim=484):
model = Sequential()
model.add(Dense(624 * 2 * 2, activation="relu", input_dim=latent_dim))
model.add(Reshape((2, 2, 624)))
model.add(UpSampling2D())
model.add(Conv2D(512, kernel_size=5, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
#4x4x512
model.add(Conv2D(256, kernel_size=5, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
#8x8x256
model.add(Conv2D(128, kernel_size=5, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
#16x16x128
model.add(Conv2D(64, kernel_size=5, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
#32x32x64
model.add(Conv2D(32, kernel_size=5, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(Activation("relu"))
model.add(UpSampling2D())
#64x64x32
model.add(Conv2D(3, kernel_size=5, padding="same"))
model.add(Activation("tanh"))
#128x128x3
noise = Input(shape=(latent_dim,))
img = model(noise)
return Model(noise, img)
这就是我建立GAN网络的方式:
and here is how I am making the GAN network:
optimizer = Adam(0.0004, 0.5)
discriminator=build_discriminator((128,128,3),(1,128,3))
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
# Build the generator
generator = build_generator()
# The generator takes noise as input and generates imgs
z = Input(shape=(100+384,))
img = generator(z)
# For the combined model we will only train the generator
discriminator.trainable = False
temp=Input(shape=(1,128,3))
# The discriminator takes generated images as input and determines validity
valid = discriminator([img,temp])
# The combined model (stacked generator and discriminator)
# Trains the generator to fool the discriminator
combined = Model(z, valid)
combined.compile(loss='binary_crossentropy', optimizer=optimizer)
鉴别器有2个模型，将输入形状为128x128x3的图像和形状为1x128x3的嵌入图像，然后将这两个模型合并.生成器模型只是获取噪声并生成128x128x3图像.因此，在combined = Model(z, valid)行，我收到以下错误:
The discriminator have 2 models, and will get as input an image of shape 128x128x3 and an embedding of shape 1x128x3 and both models are merged then. The generator model just gets noise and generates a 128x128x3 image. So at the line combined = Model(z, valid) I am getting the followiing error:
RuntimeError: Graph disconnected: cannot obtain value for tensor Tensor("input_5:0", shape=(?, 1, 128, 3), dtype=float32) at layer "input_5". The following previous layers were accessed without issue: ['input_4', 'model_2']
我认为这是因为判别器找不到嵌入的输入，但我正在将其输入形状为(1,128,3)的张量，就像将噪声输入到生成器模型一样.谁能帮我哪里做错了吗?
which I think is because of the fact that discriminator can't find embedding input but I am feeding it a tensor of shape (1,128,3), just like noise is being fed to the generator model. Can anyone please help me where I am doing wrong?
在设置完所有内容后，我将如何根据噪声生成图像并将嵌入的矢量合并在一起，鉴别器将使用图像和矢量来识别伪造品:
And after everything is set here is how I will generate images from noise and embedding vector merged together and discriminator will take image and vector to identify fakes:
#texts has embedding vectors
pics=np.array(pics) . #images
noise = np.random.normal(0, 1, (batch_size, 100))
j=0
latent_code=[]
for j in range(len(texts)): #appending embedding at the end of noise
n=np.append(noise[j],texts[j])
n=n.tolist()
latent_code.append(n)
latent_code=np.array(latent_code)
gen_imgs = generator.predict(latent_code) #gen making fakes
j=0
vects=[]
for im in gen_imgs:
t=np.array(texts[j])
t=np.reshape(t,[128,3])
t=np.expand_dims(t, axis=0)
vects.append(t)
j+=1
vects=np.array(vects) #vector of ?,1,128,3
#disc marking fakes and reals
d_loss_real = discriminator.train_on_batch([pics,vects], valid)
d_loss_fake = discriminator.train_on_batch([gen_pics,vects], fake)
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
g_loss = combined.train_on_batch(latent_code, valid)
推荐答案
您已经忘记将temp添加为GAN的输入之一(这就是错误原因，因为它不能提供相应的张量基本上已断开连接):
You have forgotten to add the temp as one of the inputs of the GAN (that's why the error says it can't feed the corresponding tensor since it is essentially disconnected):
combined = Model([z, temp], valid)
作为旁注，我强烈建议使用 Keras Functional API 用于构建复杂的多分支模型，例如您的鉴别器.它更易于使用，更灵活，更不易出错.
As a side note, I highly recommend to use Keras Functional API for building complicated and multi branch models like your discriminator. It is much easier to use, being more flexible and less error-prone.
例如，这是您编写的描述符，但我已使用Functional API对其进行了重写.我个人认为，遵循起来要容易得多
For example, this is the descriminator you have written but I have rewritten it using Functional API. I personally think it is much easier to follow:
def build_discriminator(img_shape,embedding_shape):
input_img = Input(shape=img_shape)
x = Conv2D(32, kernel_size=5, strides=2, padding="same")(input_img)
x = LeakyReLU(alpha=0.2)(x)
x = Dropout(0.25)(x)
x = Conv2D(48, kernel_size=5, strides=2, padding="same")(x)
x = BatchNormalization(momentum=0.8)(x)
x = LeakyReLU(alpha=0.2)(x)
x = Dropout(0.25)(x)
x = Conv2D(64, kernel_size=5, strides=2, padding="same")(x)
x = BatchNormalization(momentum=0.8)(x)
x = LeakyReLU(alpha=0.2)(x)
x = Dropout(0.25)(x)
x = Conv2D(128, kernel_size=5, strides=2, padding="same")(x)
x = BatchNormalization(momentum=0.8)(x)
x = LeakyReLU(alpha=0.2)(x)
x = Dropout(0.25)(x)
x = Conv2D(256, kernel_size=5, strides=2, padding="same")(x)
x = BatchNormalization(momentum=0.8)(x)
x = LeakyReLU(alpha=0.2)(x)
x = Dropout(0.25)(x)
x = Flatten()(x)
output_img = Dense(200)(x)
input_emb = Input(shape=embedding_shape)
y = Dense(50)(input_emb)
y = Dense(100)(y)
y = Dense(200)(y)
output_emb = Flatten()(y)
merged = concatenate([output_img, output_emb])
output_merge = Dense(1, activation='sigmoid', name='output_layer')(merged)
return Model([input_img, input_emb], output_merge)
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