cgan的网络结构中判别器 生成器的网络结构和参数量怎么查看

要查看CGAN的网络结构和参数量，需要先定义CGAN的生成器和判别器模型。一般来说，CGAN的网络结构包括两个部分：生成器和判别器。生成器和判别器都是一个神经网络模型，需要定义对应的网络层和参数。

在使用TensorFlow或PyTorch等深度学习框架时，可以使用summary或print等方法来查看模型的网络结构和参数量。例如，在TensorFlow中，可以使用以下代码来打印生成器和判别器的网络结构和参数量：

# 导入 TensorFlow 和 keras
import tensorflow as tf
from tensorflow import keras

# 定义生成器和判别器
def make_generator_model():
    model = keras.Sequential()
    model.add(keras.layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.LeakyReLU())

    model.add(keras.layers.Reshape((7, 7, 256)))
    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size

    model.add(keras.layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.LeakyReLU())

    model.add(keras.layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.LeakyReLU())

    model.add(keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 28, 28, 1)

    return model

def make_discriminator_model():
    model = keras.Sequential()
    model.add(keras.layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',
                                     input_shape=[28, 28, 1]))
    model.add(keras.layers.LeakyReLU())
    model.add(keras.layers.Dropout(0.3))

    model.add(keras.layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(keras.layers.LeakyReLU())
    model.add(keras.layers.Dropout(0.3))

    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(1))

    return model

# 打印生成器和判别器的网络结构和参数量
generator = make_generator_model()
discriminator = make_discriminator_model()

generator.summary()
discriminator.summary()

这段代码使用Keras框架实现了一个简单的CGAN模型。通过调用summary方法，可以打印出生成器和判别器的网络结构和参数量。其中，生成器和判别器的网络结构分别如下所示：

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 12544)             1254400   
_________________________________________________________________
batch_normalization_1 (Batch (None, 12544)             50176     
_________________________________________________________________
leaky_re_lu_2 (LeakyReLU)    (None, 12544)             0         
_________________________________________________________________
reshape_1 (Reshape)          (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, 7, 7, 128)         819200    
_________________________________________________________________
batch_normalization_2 (Batch (None, 7, 7, 128)         512       
_________________________________________________________________
leaky_re_lu_3 (LeakyReLU)    (None, 7, 7, 128)         0         
_________________________________________________________________
conv2d_transpose_4 (Conv2DTr (None, 14, 14, 64)        204800    
_________________________________________________________________
batch_normalization_3 (Batch (None, 14, 14, 64)        256       
_________________________________________________________________
leaky_re_lu_4 (LeakyReLU)    (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_transpose_5 (Conv2DTr (None, 28, 28, 1)         1600      
=================================================================
Total params: 2,329,944
Trainable params: 2,304,056
Non-trainable params: 25,888
_________________________________________________________________
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 14, 14, 64)        1664      
_________________________________________________________________
leaky_re_lu_5 (LeakyReLU)    (None, 14, 14, 64)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 7, 7, 128)         204928    
_________________________________________________________________
leaky_re_lu_6 (LeakyReLU)    (None, 7, 7, 128)         0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 7, 7, 128)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 6272)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 6273      
=================================================================
Total params: 212,865
Trainable params: 212,865
Non-trainable params: 0
_________________________________________________________________

可以看到，生成器和判别器的网络结构非常清晰，参数量也很容易计算。例如，生成器的参数量为2,329,944，判别器的参数量为212,865。