剪枝训练好的GAN超分辨率模型 代码

下面是一个简单的剪枝训练GAN超分辨率模型的示例代码，仅供参考：

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
import numpy as np

# 定义GAN超分辨率模型
def build_model():
    input_shape = (None, None, 3)
    inputs = layers.Input(shape=input_shape)
    x = layers.Conv2D(64, 3, padding='same', activation='relu')(inputs)
    x = layers.Conv2D(64, 3, padding='same', activation='relu')(x)
    x = layers.Conv2D(64, 3, padding='same', activation='relu')(x)
    x = layers.Conv2D(64, 3, padding='same', activation='relu')(x)
    x = layers.Conv2DTranspose(32, 3, strides=2, padding='same')(x)
    x = layers.Conv2DTranspose(3, 3, strides=2, padding='same')(x)
    outputs = layers.Activation('sigmoid')(x)

    model = models.Model(inputs=inputs, outputs=outputs)
    return model

# 定义剪枝策略
def prune(model, pruned_fraction):
    # 按权重大小剪枝
    weights = []
    for layer in model.layers:
        if isinstance(layer, layers.Conv2D):
            weights.append(layer.weights[0].numpy().flatten())
    all_weights = np.concatenate(weights)
    threshold_index = int(pruned_fraction * len(all_weights))
    threshold = np.partition(np.abs(all_weights), threshold_index)[threshold_index]
    for layer in model.layers:
        if isinstance(layer, layers.Conv2D):
            weights = layer.weights[0].numpy()
            mask = np.abs(weights) > threshold
            layer.set_weights([weights * mask, layer.weights[1].numpy()])

# 训练原始模型
def train():
    model = build_model()
    loss_fn = tf.keras.losses.MeanSquaredError()
    optimizer = optimizers.Adam(learning_rate=0.001)
    model.compile(optimizer=optimizer, loss=loss_fn)

    x_train = np.random.randn(100, 64, 64, 3)
    y_train = np.random.randn(100, 128, 128, 3)
    model.fit(x_train, y_train, epochs=10)

    return model

# 剪枝训练模型
def prune_train(model, pruned_fraction):
    prune(model, pruned_fraction)
    loss_fn = tf.keras.losses.MeanSquaredError()
    optimizer = optimizers.Adam(learning_rate=0.001)
    model.compile(optimizer=optimizer, loss=loss_fn)

    x_train = np.random.randn(100, 64, 64, 3)
    y_train = np.random.randn(100, 128, 128, 3)
    model.fit(x_train, y_train, epochs=5)

    return model

# 测试模型
def test(model):
    x_test = np.random.randn(10, 64, 64, 3)
    y_test = np.random.randn(10, 128, 128, 3)
    loss = model.evaluate(x_test, y_test)
    print('Test loss:', loss)

# 训练和测试模型
model = train()
test(model)

pruned_fraction = 0.5
model = prune_train(model, pruned_fraction)
test(model)

这个示例代码中，我们定义了一个简单的GAN超分辨率模型，然后使用随机数据训练原始模型，再按权重大小剪枝50%，最后使用微调训练剪枝后的模型并测试性能。在实际使用中，需要根据具体的任务和数据集调整模型和剪枝策略，以达到最佳的性能和效果。