文件列表:

LICENSE (1350, 2019-03-22)
activation (0, 2019-08-26)
activation\activate_z.m (472, 2019-03-22)
activation\delta_activation_function.m (559, 2019-03-22)
activation\delta_leaky_relu.m (157, 2019-03-22)
activation\delta_relu.m (144, 2019-03-22)
activation\delta_sigmoid.m (213, 2019-03-22)
activation\delta_tanh.m (67, 2019-03-22)
activation\leaky_relu.m (169, 2019-03-22)
activation\relu.m (83, 2019-03-22)
activation\sigmoid.m (60, 2019-03-22)
error_term (0, 2019-08-26)
error_term\delta_sigmoid_cross_entropy.m (694, 2019-03-22)
error_term\get_error_term_from_atrous_conv2d_layer.m (553, 2019-03-22)
error_term\get_error_term_from_batch_norm_layer.m (1441, 2019-03-22)
error_term\get_error_term_from_conv2d_layer.m (546, 2019-08-26)
error_term\get_error_term_from_conv2d_transpose_layer.m (1936, 2019-03-22)
error_term\get_error_term_from_fully_connect_layer.m (121, 2019-03-22)
error_term\get_error_term_from_reshape_layer.m (137, 2019-03-22)
error_term\get_error_term_from_sub_sampling_layer.m (374, 2019-03-22)
error_term\sigmoid_cross_entropy.m (284, 2019-03-22)
example_1.m (1603, 2019-08-26)
example_2.m (1144, 2019-03-22)
example_3.m (1277, 2019-03-22)
example_4.m (1372, 2019-08-26)
gan_train.m (3196, 2019-03-22)
gradient (0, 2019-08-26)
gradient\calculate_gradient_for_atrous_conv2d_layer.m (772, 2019-03-22)
gradient\calculate_gradient_for_batch_norm_layer.m (731, 2019-03-22)
gradient\calculate_gradient_for_conv2d_layer.m (739, 2019-03-22)
gradient\calculate_gradient_for_conv2d_transpose_layer.m (1260, 2019-03-22)
gradient\calculate_gradient_for_fully_connect_layer.m (179, 2019-03-22)
layer (0, 2019-08-26)
layer\atrous_conv2d.m (893, 2019-08-26)
layer\batch_norm.m (1074, 2019-03-22)
layer\check_layer_field_names.m (579, 2019-03-22)
layer\conv2d.m (1062, 2019-08-26)
... ...

# GAN-Base-on-Matlab ## data ### mnist_uint8 [download link](https://github.com/rasmusbergpalm/DeepLearnToolbox/blob/master/data/mnist_uint8.mat) ## Example ### example_1 * network structure: ``` generator.layers = { struct('type', 'input', 'output_shape', [100, batch_size]) struct('type', 'fully_connect', 'output_shape', [3136, batch_size], 'activation', 'leaky_relu') struct('type', 'reshape', 'output_shape', [7,7,***, batch_size]) struct('type', 'conv2d_transpose', 'output_shape', [14, 14, 32, batch_size], 'kernel_size', 5, 'stride', 2, 'padding', 'same', 'activation', 'leaky_relu') struct('type', 'conv2d_transpose', 'output_shape', [28, 28, 1, batch_size], 'kernel_size', 5, 'stride', 2, 'padding', 'same', 'activation', 'sigmoid') }; discriminator.layers = { struct('type', 'input', 'output_shape', [28, 28, 1, batch_size]) struct('type', 'conv2d', 'output_maps', 32, 'kernel_size', 5, 'padding', 'same', 'activation', 'leaky_relu') struct('type', 'sub_sampling', 'scale', 2) struct('type', 'conv2d', 'output_maps', ***, 'kernel_size', 5, 'padding', 'same', 'activation', 'leaky_relu') struct('type', 'sub_sampling', 'scale', 2) struct('type', 'reshape', 'output_shape', [3136, batch_size]) struct('type', 'fully_connect', 'output_shape', [1, batch_size], 'activation', 'sigmoid') }; ``` * result:

### example_2 * network structure: ``` generator.layers = { struct('type', 'input', 'output_shape', [100, batch_size]) struct('type', 'fully_connect', 'output_shape', [1024, batch_size], 'activation', 'relu') struct('type', 'fully_connect', 'output_shape', [28*28, batch_size], 'activation', 'sigmoid') struct('type', 'reshape', 'output_shape', [28, 28, 1, batch_size]) }; discriminator.layers = { struct('type', 'input', 'output_shape', [28,28,1, batch_size]) struct('type', 'reshape', 'output_shape', [28*28, batch_size]) struct('type', 'fully_connect', 'output_shape', [1024, batch_size], 'activation', 'relu') struct('type', 'fully_connect', 'output_shape', [1, batch_size], 'activation', 'sigmoid') }; ``` * result:

## Reference 1. `https://grzegorzgwardys.wordpress.com/2016/04/22/8/` 2. `Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning[J]. 2016.` 3. `https://github.com/rasmusbergpalm/DeepLearnToolbox/tree/master/CNN` 4. `http://neuralnetworksanddeeplearning.com/index.html`

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