DL之DCGAN：基于keras框架利用深度卷積對抗網絡DCGAN算法對MNIST數據集實現圖像生成

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目錄

基于keras框架利用深度卷積對抗網絡DCGAN算法對MNIST數據集實現圖像生成

設計思路

輸出結果

核心代碼

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相關文章
DL之DCGAN：基于keras框架利用深度卷積對抗網絡DCGAN算法對MNIST數據集實現圖像生成
DL之DCGAN：基于keras框架利用深度卷積對抗網絡DCGAN算法對MNIST數據集實現圖像生成實現

基于keras框架利用深度卷積對抗網絡DCGAN算法對MNIST數據集實現圖像生成

設計思路

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輸出結果

X像素取值范圍是[-1.0, 1.0] _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_1 (Dense) (None, 1024) 103424 _________________________________________________________________ activation_1 (Activation) (None, 1024) 0 _________________________________________________________________ dense_2 (Dense) (None, 6272) 6428800 _________________________________________________________________ batch_normalization_1 (Batch (None, 6272) 25088 _________________________________________________________________ activation_2 (Activation) (None, 6272) 0 _________________________________________________________________ reshape_1 (Reshape) (None, 7, 7, 128) 0 _________________________________________________________________ up_sampling2d_1 (UpSampling2 (None, 14, 14, 128) 0 _________________________________________________________________ conv2d_1 (Conv2D) (None, 14, 14, 64) 204864 _________________________________________________________________ activation_3 (Activation) (None, 14, 14, 64) 0 _________________________________________________________________ up_sampling2d_2 (UpSampling2 (None, 28, 28, 64) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 28, 28, 1) 1601 _________________________________________________________________ activation_4 (Activation) (None, 28, 28, 1) 0 ================================================================= Total params: 6,763,777 Trainable params: 6,751,233 Non-trainable params: 12,544 _________________________________________________________________ _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_3 (Conv2D) (None, 28, 28, 64) 1664 _________________________________________________________________ activation_5 (Activation) (None, 28, 28, 64) 0 _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64) 0 _________________________________________________________________ conv2d_4 (Conv2D) (None, 10, 10, 128) 204928 _________________________________________________________________ activation_6 (Activation) (None, 10, 10, 128) 0 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 5, 5, 128) 0 _________________________________________________________________ flatten_1 (Flatten) (None, 3200) 0 _________________________________________________________________ dense_3 (Dense) (None, 1024) 3277824 _________________________________________________________________ activation_7 (Activation) (None, 1024) 0 _________________________________________________________________ dense_4 (Dense) (None, 1) 1025 _________________________________________________________________ activation_8 (Activation) (None, 1) 0 ================================================================= Total params: 3,485,441 Trainable params: 3,485,441 Non-trainable params: 0 _________________________________________________________________ 2020-11-24 21:53:56.659897: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 (25, 28, 28, 1)

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核心代碼

def generator_model():model = Sequential()model.add(Dense(input_dim=100, units=1024)) # 1034 1024model.add(Activation('tanh'))model.add(Dense(128*7*7))model.add(BatchNormalization())model.add(Activation('tanh'))model.add(Reshape((7, 7, 128), input_shape=(128*7*7,)))model.add(UpSampling2D(size=(2, 2)))model.add(Conv2D(64, (5, 5), padding='same'))model.add(Activation('tanh'))model.add(UpSampling2D(size=(2, 2)))model.add(Conv2D(1, (5, 5), padding='same'))model.add(Activation('tanh'))return model def discriminator_model(): # 定義鑒別網絡：輸入一張圖像，輸出0(偽造)/1(真實)model = Sequential()model.add(Conv2D(64, (5, 5),padding='same',input_shape=(28, 28, 1)))model.add(Activation('tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Conv2D(128, (5, 5)))model.add(Activation('tanh'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Flatten())model.add(Dense(1024))model.add(Activation('tanh'))model.add(Dense(1))model.add(Activation('sigmoid'))return modelg = generator_model() g.summary()d = discriminator_model() d.summary()

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總結

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