用Keras进行高级深度学习：使用Keras进行高级深度学习，由Packt发布
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用Keras进行高级深度学习：使用Keras进行高级深度学习，由Packt发布 （189个子文件） 
checkpoint 101B checkpoint 91B checkpoint 89B checkpoint 89B cvae_cnn_mnist.tf.data-00000-of-00002 7KB beta-cvae_cnn_mnist.tf.data-00000-of-00002 7KB vae_cnn_mnist.tf.data-00000-of-00002 6KB vae_mlp_mnist.tf.data-00000-of-00002 5KB vae_mlp_mnist.tf.data-00001-of-00002 9.24MB cvae_cnn_mnist.tf.data-00001-of-00002 1.33MB beta-cvae_cnn_mnist.tf.data-00001-of-00002 1.33MB vae_cnn_mnist.tf.data-00001-of-00002 1.02MB
cifar10_colorization.gif 27.46MB stackedgan_mnist.gif 656KB infogan_mnist.gif 611KB cgan_mnist.gif 600KB acgan_mnist.gif 568KB wgan_mnist.gif 552KB dcgan_mnist.gif 524KB lsgan_mnist.gif 512KB car.gif 112KB cartpole.gif 86KB ResNet56v2-3layer-drinks-best-iou.h5 42.7MB stackedgan_mnist-gen0.h5 9.94MB ResNet56v2-4layer-norm-improved_loss-extra_anchors-drinks-200.h5 9.41MB ResNet56v2-4layer-norm-extra_anchors-drinks-200.h5 9.41MB ResNet56v2-4layer-norm-smooth_l1-extra_anchors-drinks-200.h5 9.41MB ResNet56v2-4layer-smooth_l1-extra_anchors-drinks-200.h5 9.41MB ResNet56v2-4layer-extra_anchors-drinks-200.h5 9.41MB cyclegan_mnist_svhn-g_target.h5 9.28MB cyclegan_mnist_svhn-g_source.h5 9.27MB head5-best.h5 6.84MB head4-best.h5 6.66MB head3-best.h5 6.49MB head2-best.h5 6.31MB head1-best.h5 6.13MB cgan_mnist.h5 5.25MB acgan_mnist.h5 5.25MB dcgan_mnist.h5 5.01MB wgan_mnist.h5 5.01MB lsgan_mnist.h5 5.01MB infogan_mnist.h5 4.4MB cyclegan_cifar10-g_target.h5 3.39MB cyclegan_cifar10-g_source.h5 3.39MB stackedgan_mnist-gen1.h5 1.66MB actor_weights.h5 169KB actor_weights-a2c.h5 169KB actor_weights-reinforce-baseline.h5 169KB value_weights.h5 164KB value_weights-reinforce-baseline.h5 164KB value_weights-a2c.h5 164KB encoder_weights.h5 163KB encoder_weights-reinforce-baseline.h5 163KB cvae_cnn_mnist.tf.index 3KB beta-cvae_cnn_mnist.tf.index 3KB vae_cnn_mnist.tf.index 3KB vae_mlp_mnist.tf.index 2KB LICENSE 1KB README.md 12KB README.md 1KB README.md 898B README.md 805B README.md 763B README.md 440B
car.mp4 53KB cartpole.mp4 20KB q-frozenlake.npy 640B q-frozenlake-slippery.npy 640B
cifar10_colorized.png 6.91MB SVHNMNIST_cycle.png 3.44MB MNISTSVHN_cycle.png 2.1MB predicted_color_vs_gt.png 1.93MB SVHN2MNIST.png 1.66MB MNIST_SVHN_samples.png 1.57MB gray_to_color.png 1.53MB gray_color_mayon.png 1.37MB colorized_images.png 1.05MB sample_pixel_class.png 820KB Figure11.13.5.detection_normalized_smooth_l1_focal.png 784KB Figure11.13.3.detection_normalized.png 780KB Figure11.13.4.detection_normalized_smooth_l1.png 779KB Figure11.13.1.detection_unnormalized_offsets.png 778KB Figure11.13.2.detection_smooth_l1.png 776KB MNIST2SVHN.png 623KB digits_over_latent_mlp.png 615KB digits_over_latent.png 592KB pixelwise_classifier.png 574KB network_architecture.png 499KB sample_input.png 437KB 0010019-input.png 341KB test_color.png 271KB vae_mean_cnn.png 258KB colorized.png 254KB cvae_cnn.png 232KB vae_mean.png 218KB 0010019-instance.png 217KB vae_mean_mlp.png 204KB digit_latent_codes.png 203KB latent_codes.png 203KB test_gray.png 176KB共 189 条
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