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fix loss history

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iperov 2019-01-11 19:19:29 +04:00
parent e6fa021c3d
commit 2473dabf37
4 changed files with 53 additions and 36 deletions
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2
README.md
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@ -153,7 +153,7 @@ Only H64 model reasonable to train on home CPU.
### Mac/linux/docker script support.
This repo supports only windows build of scripts. If you want to support mac/linux/docker - create such fork, it will be referenced here.
This repo supports only windows build of scripts. If you want to support mac/linux/docker - create fork, it will be referenced here.
### Prebuilt windows app:

34
mainscripts/Trainer.py
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@ -196,29 +196,28 @@ def previewThread (input_queue, output_queue):
lh_len = len(loss_history)
l_per_col = lh_len / w
plist_max = [ [ max (0.0, 0.0, *[ loss_history[i_ab][p]
for i_ab in range( int(col*l_per_col), int((col+1)*l_per_col) )
]
)
for p in range(0,loss_count)
]
for col in range(0, w)
]
plist_min = [ [ min (plist_max[col][p],
plist_max[col][p],
plist_max = [ [ max (0.0, loss_history[int(col*l_per_col)][p],
*[ loss_history[i_ab][p]
for i_ab in range( int(col*l_per_col), int((col+1)*l_per_col) )
]
)
for p in range(0,loss_count)
for p in range(loss_count)
]
for col in range(0, w)
for col in range(w)
]
plist_min = [ [ min (plist_max[col][p], loss_history[int(col*l_per_col)][p],
*[ loss_history[i_ab][p]
for i_ab in range( int(col*l_per_col), int((col+1)*l_per_col) )
]
)
for p in range(loss_count)
]
for col in range(w)
]
plist_abs_max = np.mean(loss_history[ len(loss_history) // 5 : ]) * 2
if l_per_col >= 1.0:
for col in range(0, w):
for p in range(0,loss_count):
point_color = [1.0]*c
@ -241,10 +240,7 @@ def previewThread (input_queue, output_queue):
last_line_t = int((lh_lines-1)*lh_line_height)
last_line_b = int(lh_lines*lh_line_height)
if epoch != 0:
lh_text = 'Loss history. Epoch: %d' % (epoch)
else:
lh_text = 'Loss history.'
lh_text = 'Epoch: %d' % (epoch) if epoch != 0 else ''
lh_img[last_line_t:last_line_b, 0:w] += image_utils.get_text_image ( (w,last_line_b-last_line_t,c), lh_text, color=head_text_color )

2
models/ModelBase.py
View file

@ -49,7 +49,7 @@ class ModelBase(object):
self.loss_history = model_data['loss_history'] if 'loss_history' in model_data.keys() else []
self.sample_for_preview = model_data['sample_for_preview'] if 'sample_for_preview' in model_data.keys() else None
ask_override = self.is_training_mode and self.epoch != 0 and input_in_time ("Press enter during 2 seconds to override some model settings.", 2)
ask_override = self.is_training_mode and self.epoch != 0 and input_in_time ("Press enter in 2 seconds to override some model settings.", 2)
if self.epoch == 0:
print ("\nModel first run. Enter model options as default for each run.")

21
nnlib/nnlib.py
View file

@ -40,6 +40,7 @@ class nnlib(object):
DSSIMMaskLoss = None
PixelShuffler = None
SubpixelUpscaler = None
AddUniformNoise = None
ResNet = None
UNet = None
@ -101,6 +102,7 @@ DSSIMLoss = nnlib.DSSIMLoss
DSSIMMaskLoss = nnlib.DSSIMMaskLoss
PixelShuffler = nnlib.PixelShuffler
SubpixelUpscaler = nnlib.SubpixelUpscaler
AddUniformNoise = nnlib.AddUniformNoise
"""
code_import_keras_contrib_string = \
"""
@ -541,6 +543,25 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
nnlib.PixelShuffler = PixelShuffler
nnlib.SubpixelUpscaler = PixelShuffler
class AddUniformNoise(keras.layers.Layer):
def __init__(self, power=1.0, minval=-1.0, maxval=1.0, **kwargs):
super(AddUniformNoise, self).__init__(**kwargs)
self.power = power
self.supports_masking = True
self.minval = minval
self.maxval = maxval
def call(self, inputs, training=None):
def noised():
return inputs + self.power*K.random_uniform(shape=K.shape(inputs), minval=self.minval, maxval=self.maxval)
return K.in_train_phase(noised, inputs, training=training)
def get_config(self):
config = {'power': self.power, 'minval': self.minval, 'maxval': self.maxval}
base_config = super(AddUniformNoise, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
nnlib.AddUniformNoise = AddUniformNoise
@staticmethod
def import_keras_contrib(device_config = None):
if nnlib.keras_contrib is not None: