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github.com/bamos/dcgan-completion.tensorflow
/ functions
Functions
31 in github.com/bamos/dcgan-completion.tensorflow
⨍
Functions
31
◇
Types & classes
2
↓ 9 callers
Function
save_images
(images, size, image_path)
utils.py:24
↓ 4 callers
Function
conv2d
(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="conv2d")
ops.py:50
↓ 4 callers
Function
lrelu
(x, leak=0.2, name="lrelu")
ops.py:90
↓ 4 callers
Function
make_gif
(images, fname, duration=2, true_image=False)
utils.py:131
↓ 3 callers
Function
get_image
(image_path, image_size, is_crop=True)
utils.py:21
↓ 2 callers
Function
conv2d_transpose
(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name
ops.py:64
↓ 2 callers
Function
dataset_files
Returns a list of all image files in the given directory
model.py:21
↓ 2 callers
Method
discriminator
(self, image, reuse=False)
model.py:404
↓ 2 callers
Function
inverse_transform
(images)
utils.py:64
↓ 2 callers
Function
linear
(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False)
ops.py:96
↓ 2 callers
Method
load
(self, checkpoint_dir)
model.py:457
↓ 2 callers
Function
merge
(images, size)
utils.py:33
↓ 1 callers
Method
build_model
(self)
model.py:85
↓ 1 callers
Function
center_crop
(x, crop_h, crop_w=None, resize_w=64)
utils.py:47
↓ 1 callers
Method
complete
(self, config)
model.py:246
↓ 1 callers
Method
generator
(self, z)
model.py:418
↓ 1 callers
Function
imread
(path)
utils.py:27
↓ 1 callers
Function
imsave
(images, size, path)
utils.py:43
↓ 1 callers
Method
save
(self, checkpoint_dir, step)
model.py:449
↓ 1 callers
Method
train
(self, config)
model.py:145
↓ 1 callers
Function
transform
(image, npx=64, is_crop=True)
utils.py:56
Method
__call__
(self, x, train)
ops.py:22
Method
__init__
(self, epsilon=1e-5, momentum = 0.9, name="batch_norm")
ops.py:15
Method
__init__
Args: sess: TensorFlow session batch_size: The size of batch. Should be specified before training. lowre
model.py:28
Function
binary_cross_entropy
Computes binary cross entropy given `preds`. For brevity, let `x = `, `z = targets`. The logistic loss is loss(x, z) = - sum_i (x[i] *
ops.py:26
Function
conv_cond_concat
Concatenate conditioning vector on feature map axis.
ops.py:44
Method
make_dir
(name)
model.py:247
Function
make_frame
(t)
utils.py:134
Function
merge_images
(images, size)
utils.py:30
Function
to_json
(output_path, *layers)
utils.py:68
Function
visualize
(sess, dcgan, config, option)
utils.py:148