如何绘制 CNN 输出的特定层的轮廓？

Question

我正在尝试从 CNN 中的特定层获取输出并使用 matplotlib.pyplot.contour 将其可视化。这是我的 CNN 架构

input_img = Input(shape=(384, 192, 2))
## Encoder
x = Conv2D(16, (3, 3), activation='tanh', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='tanh', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='tanh', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='tanh', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(4, (3, 3), activation='tanh', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(4, (3, 3), activation='tanh', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Reshape([6*3*4])(x)
encoded = Dense(2,activation='tanh')(x)
## Two variables
val1= Lambda(lambda x: x[:,0:1])(encoded)
val2= Lambda(lambda x: x[:,1:2])(encoded)
## Decoder 1
x1 = Dense(6*3*4,activation='tanh')(val1)
x1 = Reshape([6,3,4])(x1)
x1 = UpSampling2D((2,2))(x1)
x1 = Conv2D(4,(3,3),activation='tanh',padding='same')(x1)
x1 = UpSampling2D((2,2))(x1)
x1 = Conv2D(8,(3,3),activation='tanh',padding='same')(x1)
x1 = UpSampling2D((2,2))(x1)
x1 = Conv2D(8,(3,3),activation='tanh',padding='same')(x1)
x1 = UpSampling2D((2,2))(x1)
x1 = Conv2D(8,(3,3),activation='tanh',padding='same')(x1)
x1 = UpSampling2D((2,2))(x1)
x1 = Conv2D(16,(3,3),activation='tanh',padding='same')(x1)
x1 = UpSampling2D((2,2))(x1)
x1d = Conv2D(2,(3,3),activation='linear',padding='same')(x1)
## Decoder 2
x2 = Dense(6*3*4,activation='tanh')(val2)
x2 = Reshape([6,3,4])(x2)
x2 = UpSampling2D((2,2))(x2)
x2 = Conv2D(4,(3,3),activation='tanh',padding='same')(x2)
x2 = UpSampling2D((2,2))(x2)
x2 = Conv2D(8,(3,3),activation='tanh',padding='same')(x2)
x2 = UpSampling2D((2,2))(x2)
x2 = Conv2D(8,(3,3),activation='tanh',padding='same')(x2)
x2 = UpSampling2D((2,2))(x2)
x2 = Conv2D(8,(3,3),activation='tanh',padding='same')(x2)
x2 = UpSampling2D((2,2))(x2)
x2 = Conv2D(16,(3,3),activation='tanh',padding='same')(x2)
x2 = UpSampling2D((2,2))(x2)
x2d = Conv2D(2,(3,3),activation='linear',padding='same')(x2)

decoded = Add()([x1d,x2d])

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='mse')

# Check the network structure
autoencoder.summary()

我得到 x1d 层的输出作为下面的代码，输出形状为 TensorShape([None, 72])

r1 = autoencoder.layers[13].output
r1.shape

如何使用 matplotlib 轮廓可视化此 r1 输出，输出应该像这张照片。

我感谢任何帮助或建议。非常感谢！

Answer 1

这个 pcolormesh 适合我。

plt.pcolormesh(X_train[0][:,:,0].T, cmap = cm.jet)

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