Automatically format python files

[?]

Jul 11, 2021, 8:56 PM

3QGP6RXLMNWYWMACHKKYEVECRXP7ESGOSWTEY72O46VLXQR5O6IAC

Dependencies

[2] RT3FMMOB Fix total crust mass calculation (ie don't add age)
[3] TCRWQZ2C Model trainer: use less preferred activation function because tfjs can deserialize it
[4] ZLSXVDET Model trainer: export model in tfjs format once trained
[5] XYPKZANT Model trainer: Make the shore focused loss function even more focused
[6] TZVTKOJE Use named variables for planetary radius
[7] IGYI5RVV Figure out how to draw on the sdl2 window
[8] FL3C6ERZ Assume python is being run with -m switch
[9] EB3DTD43 Model trainer: use ground altitude instead of crust mass distribution
[10] ALMG52BO model trainer: speed up by waiting 15 seconds between monitor renders
[11] CWOSQTC4 Trust tensorflow's thread safety (it wasn't the cause of an earlier bug)
[12] 6W7MFV2F D20-based neural network architecture
[13] QMNTEX7P Try a loss function that has higher values for errors near sea level
[14] 7ML3OFE7 Model trainer: initial train and visualize thread pair (total crust mass; todo: altitude instead)
[15] 4IXWCEHM Gamma correction
[16] SJHJS463 Model trainer: main function to open sdl window and launch second thread for actual training
[17] ABITXOUU Model trainer: Add provisional colouration
[18] F5KOGCLK Expand model capacity
[19] 3AJMKZ7C Model trainer: include land mass calculation in click handler
[20] X5U7KRUI Parse some of the data into tensors
[21] 6AXPZL5P Try to offload tight loop to rust (for now it segfaults)
[22] E742MTJA Fix segfault (don't pass pointers between functions in different SDL versions), minor refactor
[23] 2ABZP2KN Model trainer: save map.png after training
[24] ROQCAPZJ Begin function for showing the map (for now just opens SDL window)
[25] J6HV4MNJ Model trainer: mouse click handler (prints normalized vector from planet's center)
[26] TCMUFA6E Try some different hyperparameters
[27] ZM2EMAZO Start doing python multi-module stuff properly
[28] PG7KSKJL Add surface distance measurement to mouse click handler
[29] 5AMZXFS5 model trainer: function to generate inputs to pass neural network for rendering equirectangular projection

Change contents

edit in trainmodel/src/simsave.py at line 5

[7.85]

[7.85]
replacement in trainmodel/src/simsave.py at line 8

[7.103]→[7.103:1084](∅→∅),[7.1084]→[2.0:234](∅→∅)

def __init__(self, stream):
def check(item):
if type(item) == dict:
return {k: check(v) for k, v in item.items()}
elif type(item) == list:
return list(map(check, item))
elif type(item) == str and item.startswith('buffer:'):
return base64.b64decode(item[len('buffer:'):])
else:
return item
self.raw = check(json.load(stream))
def vertex(obj):
return [obj['x'], obj['y'], obj['z']]
self.vertices = tf.convert_to_tensor(list(map(vertex, self.raw['model']['bodies']['world']['grid']['vertices'])), dtype=tf.float32)
crust = tf.convert_to_tensor(list(map(lambda x: x[0], struct.iter_unpack('f', self.raw['model']['bodies']['world']['lithosphere']['total_crust']))))
crust = tf.reshape(crust, (8, self.vertices.shape[0]))
self.crust_layers = {SimResult.crust_columns()[i]: crust[i] for i in range(0,8)}
total_crust = tf.zeros((self.vertices.shape[0],), dtype=tf.float32)
for k, v in self.crust_layers.items():
if k != 'age':
total_crust += v
self.crust_layers['total'] = total_crust

[7.103]

[7.1131]

def __init__(self, stream):
def check(item):
if type(item) == dict:
return {k: check(v) for k, v in item.items()}
elif type(item) == list:
return list(map(check, item))
elif type(item) == str and item.startswith('buffer:'):
return base64.b64decode(item[len('buffer:'):])
else:
return item

self.raw = check(json.load(stream))

def vertex(obj):
return [obj['x'], obj['y'], obj['z']]
replacement in trainmodel/src/simsave.py at line 24

[7.1132]→[7.1132:1184](∅→∅)

def seed(self):
return self.raw['seed']

[7.1132]

[7.1277]

self.vertices = tf.convert_to_tensor(list(
map(vertex, self.raw['model']['bodies']['world']['grid']['vertices'])),
dtype=tf.float32)
crust = tf.convert_to_tensor(
list(
map(
lambda x: x[0],
struct.iter_unpack(
'f', self.raw['model']['bodies']['world']['lithosphere']
['total_crust']))))
crust = tf.reshape(crust, (8, self.vertices.shape[0]))
self.crust_layers = {
SimResult.crust_columns()[i]: crust[i]
for i in range(0, 8)
}
total_crust = tf.zeros((self.vertices.shape[0], ), dtype=tf.float32)
for k, v in self.crust_layers.items():
if k != 'age':
total_crust += v
self.crust_layers['total'] = total_crust
replacement in trainmodel/src/simsave.py at line 45

[7.1278]→[7.1278:1435](∅→∅)

def crust_columns():
return ['sediment','sedimentary','metamorphic','felsic_plutonic','felsic_volcanic','mafic_volcanic','mafic_plutonic','age']

[7.1278]

[7.1435]

def seed(self):
return self.raw['seed']
edit in trainmodel/src/simsave.py at line 48

[7.1436]

def crust_columns():
return [
'sediment', 'sedimentary', 'metamorphic', 'felsic_plutonic',
'felsic_volcanic', 'mafic_volcanic', 'mafic_plutonic', 'age'
]
replacement in trainmodel/src/nativehelpers.py at line 6

[7.212]→[7.212:359](∅→∅)

native = ctypes.cdll.LoadLibrary(os.path.dirname(os.path.realpath(__file__)) + '/helpers.' + ('dylib' if platform.system() == 'Darwin' else 'so'))

[7.212]

[7.393]

native = ctypes.cdll.LoadLibrary(
os.path.dirname(os.path.realpath(__file__)) + '/helpers.' +
('dylib' if platform.system() == 'Darwin' else 'so'))
replacement in trainmodel/src/nativehelpers.py at line 10

[7.394]→[7.359:451](∅→∅),[7.359]→[7.359:451](∅→∅)

native.render_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, ctypes.c_int]

[7.394]

[7.451]

native.render_tensor.argtypes = [
ctypes.c_void_p, ctypes.c_int, ctypes.c_int, ctypes.c_int
]
replacement in trainmodel/src/nativehelpers.py at line 16

[7.562]→[7.395:491](∅→∅)

native.equirectangular.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int]

[7.562]

[7.491]

native.equirectangular.argtypes = [
ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int
]
replacement in trainmodel/src/nativehelpers.py at line 21

[7.1]→[7.1:134](∅→∅)

native.colourize_heightmap.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int]

[7.1]

[7.134]

native.colourize_heightmap.argtypes = [
ctypes.POINTER(ctypes.c_double),
ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int
]
replacement in trainmodel/src/nativehelpers.py at line 27

[7.1]→[7.1:129](∅→∅)

native.landmass_steradians.argtypes = [ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int]

[7.1]

[7.129]

native.landmass_steradians.argtypes = [
ctypes.POINTER(ctypes.c_double), ctypes.c_int, ctypes.c_int, ctypes.c_int,
ctypes.c_int
]
edit in trainmodel/src/model.py at line 5

[7.22]

[7.0]
replacement in trainmodel/src/model.py at line 8

[7.26]→[3.0:402](∅→∅),[3.402]→[7.0:349](∅→∅),[7.376]→[7.0:349](∅→∅),[7.349]→[3.403:1055](∅→∅),[3.1055]→[7.1312:1367](∅→∅),[7.1312]→[7.1312:1367](∅→∅)

layer1 = keras.layers.Dense(20, activation=keras.activations.softplus)(inputs)
layer2 = keras.layers.Dense(20, activation=keras.activations.softplus)(layer1)
layer3 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer2]))
layer4 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer2]))
layer5 = keras.layers.Dense(88, activation=keras.activations.relu)(keras.layers.concatenate([layer2,layer3]))
layer6 = keras.layers.Dense(88, activation=keras.activations.relu)(keras.layers.concatenate([layer2,layer4,layer5]))
layer7 = keras.layers.Dense(88, activation=keras.activations.relu)(keras.layers.concatenate([layer5,layer6]))
layer8 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer3,layer5,layer7]))
layer9 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer4,layer6,layer7]))
layer10 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer7,layer8,layer9]))
layer11 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer3,layer8,layer10]))
layer12 = keras.layers.Dense(20, activation=keras.activations.softplus)(keras.layers.concatenate([layer1,layer4,layer9,layer10,layer11]))
return keras.layers.concatenate([layer11,layer12])

[7.26]

[7.1194]

layer1 = keras.layers.Dense(20,
activation=keras.activations.softplus)(inputs)
layer2 = keras.layers.Dense(20,
activation=keras.activations.softplus)(layer1)
layer3 = keras.layers.Dense(20, activation=keras.activations.softplus)(
keras.layers.concatenate([layer1, layer2]))
layer4 = keras.layers.Dense(20, activation=keras.activations.softplus)(
keras.layers.concatenate([layer1, layer2]))
layer5 = keras.layers.Dense(88, activation=keras.activations.relu)(
keras.layers.concatenate([layer2, layer3]))
layer6 = keras.layers.Dense(88, activation=keras.activations.relu)(
keras.layers.concatenate([layer2, layer4, layer5]))
layer7 = keras.layers.Dense(88, activation=keras.activations.relu)(
keras.layers.concatenate([layer5, layer6]))
layer8 = keras.layers.Dense(20, activation=keras.activations.softplus)(
keras.layers.concatenate([layer3, layer5, layer7]))
layer9 = keras.layers.Dense(20, activation=keras.activations.softplus)(
keras.layers.concatenate([layer4, layer6, layer7]))
layer10 = keras.layers.Dense(20, activation=keras.activations.softplus)(
keras.layers.concatenate([layer7, layer8, layer9]))
layer11 = keras.layers.Dense(20, activation=keras.activations.softplus)(
keras.layers.concatenate([layer1, layer3, layer8, layer10]))
layer12 = keras.layers.Dense(20, activation=keras.activations.softplus)(
keras.layers.concatenate([layer1, layer4, layer9, layer10, layer11]))
return keras.layers.concatenate([layer11, layer12])
edit in trainmodel/src/model.py at line 34

[7.1195]

[7.1195]
replacement in trainmodel/src/model.py at line 36

[7.1208]→[7.1208:1245](∅→∅),[7.1245]→[7.1368:1535](∅→∅),[7.1535]→[7.1668:1745](∅→∅),[7.1668]→[7.1668:1745](∅→∅)

inputs = keras.Input(shape=(3,))
d1 = icosahedral(inputs)
d2 = icosahedral(inputs)
outputs = keras.layers.Dense(1, activation=keras.activations.sigmoid)(keras.layers.concatenate([d1,d2]))
return keras.Model(inputs=inputs, outputs=outputs, name="geology_model")

[7.1208]

[7.1745]

inputs = keras.Input(shape=(3, ))
d1 = icosahedral(inputs)
d2 = icosahedral(inputs)
outputs = keras.layers.Dense(1, activation=keras.activations.sigmoid)(
keras.layers.concatenate([d1, d2]))
return keras.Model(inputs=inputs, outputs=outputs, name="geology_model")
replacement in trainmodel/src/model.py at line 45

[7.40]→[5.0:106](∅→∅),[5.106]→[7.130:185](∅→∅),[7.266]→[7.130:185](∅→∅),[7.130]→[7.130:185](∅→∅)

y_true = tf.math.asinh((y_true - 0.1) * 5.) / 5.
y_pred = tf.math.asinh((y_pred - 0.1) * 5.) / 5.
return tf.math.reduce_mean((y_true - y_pred) ** 2)

[7.40]

[7.185]

y_true = tf.math.asinh((y_true - 0.1) * 5.) / 5.
y_pred = tf.math.asinh((y_pred - 0.1) * 5.) / 5.
return tf.math.reduce_mean((y_true - y_pred)**2)
replacement in trainmodel/src/model.py at line 51

[7.1776]→[7.23:114](∅→∅)

return (simresult.vertices, (geology.isostatic_displacement(simresult) + 1.) / 25000.)

[7.1776]

[7.1872]

return (simresult.vertices,
(geology.isostatic_displacement(simresult) + 1.) / 25000.)
replacement in trainmodel/src/model.py at line 56

[7.1901]→[7.1901:2054](∅→∅)

custom_objects = {"asinh": tf.math.asinh}
with keras.utils.custom_object_scope(custom_objects):
return keras.models.clone_model(source)

[7.1901]

custom_objects = {"asinh": tf.math.asinh}
with keras.utils.custom_object_scope(custom_objects):
return keras.models.clone_model(source)
replacement in trainmodel/src/geology.py at line 11

[7.406]→[7.406:828](∅→∅)

'fine_sediment': 1500.,
'voarse_sediment': 1500.,
'sediment': 1500.,
'sedimentary': 2600.,
'metamorphic': 2800.,
'felsic_plutonic': 2600.,
'felsic_volcanic': 2600.,
'mafic_volcanic_min': 2890., # Carlson & Raskin 1984
'mafic_volcanic_max': 3300.,
'mantle': 3075., # derived empirically using isostatic model
'ocean': 1026.,
}

[7.406]

[7.828]

'fine_sediment': 1500.,
'voarse_sediment': 1500.,
'sediment': 1500.,
'sedimentary': 2600.,
'metamorphic': 2800.,
'felsic_plutonic': 2600.,
'felsic_volcanic': 2600.,
'mafic_volcanic_min': 2890., # Carlson & Raskin 1984
'mafic_volcanic_max': 3300.,
'mantle': 3075., # derived empirically using isostatic model
'ocean': 1026.,
}
replacement in trainmodel/src/geology.py at line 26

[7.855]→[7.855:1406](∅→∅)

fraction_of_lifetime = saved.crust_layers['age'] / 7889537440886400.0
mafic_density = material_density['mafic_volcanic_min'] + (material_density['mafic_volcanic_max'] - material_density['mafic_volcanic_min']) * fraction_of_lifetime
total_thickness = (saved.crust_layers['mafic_volcanic'] + saved.crust_layers['mafic_plutonic']) / mafic_density
for rocktype, density in material_density.items():
if rocktype in saved.crust_layers:
total_thickness += saved.crust_layers[rocktype] / density
return total_thickness

[7.855]

[7.1406]

fraction_of_lifetime = saved.crust_layers['age'] / 7889537440886400.0
mafic_density = material_density['mafic_volcanic_min'] + (
material_density['mafic_volcanic_max'] -
material_density['mafic_volcanic_min']) * fraction_of_lifetime
total_thickness = (saved.crust_layers['mafic_volcanic'] +
saved.crust_layers['mafic_plutonic']) / mafic_density
for rocktype, density in material_density.items():
if rocktype in saved.crust_layers:
total_thickness += saved.crust_layers[rocktype] / density
return total_thickness
replacement in trainmodel/src/geology.py at line 39

[7.1442]→[7.1442:1559](∅→∅)

thickness = get_thickness(saved)
return thickness - saved.crust_layers['total'] / material_density['mantle']

[7.1442]

thickness = get_thickness(saved)
return thickness - saved.crust_layers['total'] / material_density['mantle']
replacement in trainmodel/src/drawmap.py at line 16

[7.12]→[7.98:201](∅→∅),[7.553]→[7.98:201](∅→∅),[7.98]→[7.98:201](∅→∅)

window = sdl2.ext.Window("Training neural network: memorizing fictional world map", size=(1024,512))

[7.12]

[7.202]

window = sdl2.ext.Window(
"Training neural network: memorizing fictional world map",
size=(1024, 512))
replacement in trainmodel/src/drawmap.py at line 27

[7.252]→[7.34:82](∅→∅),[7.82]→[7.13:41](∅→∅),[7.41]→[7.1723:1853](∅→∅),[7.1853]→[7.262:402](∅→∅),[7.262]→[7.262:402](∅→∅),[7.402]→[7.252:408](∅→∅),[7.864]→[7.252:408](∅→∅),[7.252]→[7.252:408](∅→∅),[7.408]→[7.0:53](∅→∅),[7.53]→[7.23:106](∅→∅),[7.106]→[7.1854:1969](∅→∅),[7.1969]→[6.77:226](∅→∅),[6.226]→[7.106:320](∅→∅),[7.2093]→[7.106:320](∅→∅),[7.106]→[7.106:320](∅→∅),[7.320]→[6.227:325](∅→∅),[6.325]→[7.397:459](∅→∅),[7.397]→[7.397:459](∅→∅),[7.135]→[7.408:431](∅→∅),[7.459]→[7.408:431](∅→∅),[7.408]→[7.408:431](∅→∅)

if time.monotonic() - last_rendered > 15:
outputs = m(inputs)
image = tensor_to_surface(tf.math.multiply(255, colourize_heightmap(tf.reshape(outputs, (512, 1024, outputs.shape[1])))))
sdl2.SDL_BlitSurface(image, None, windowsurface, None)
last_rendered = time.monotonic()
sdl2.SDL_FreeSurface(image)
events = sdl2.ext.get_events()
for event in events:
if event.type == sdl2.SDL_QUIT:
running = False
break
elif event.type == sdl2.SDL_MOUSEBUTTONUP:
point = list(inputs[event.button.y * 1024 + event.button.x].numpy())
solid_angle = landmass_steradians(tf.reshape(outputs, (512,1024,1)), event.button.x, event.button.y)
print('Clicked land mass: %s square Km | %s square miles' % (solid_angle * planet_radius_km**2, solid_angle * planet_radius_miles**2))
if last_clicked != None:
dug = math.sqrt(sum(map((lambda p: (p[0]-p[1])**2), zip(last_clicked, point))))
arc = math.acos(1 - dug**2 / 2) # cosine rule, a == b == 1
print('%s Km | %s miles' % (arc * planet_radius_km, arc * planet_radius_miles))
print(point)
last_clicked = point
window.refresh()

[7.252]

[7.431]

if time.monotonic() - last_rendered > 15:
outputs = m(inputs)
image = tensor_to_surface(
tf.math.multiply(
255,
colourize_heightmap(
tf.reshape(outputs, (512, 1024, outputs.shape[1])))))
sdl2.SDL_BlitSurface(image, None, windowsurface, None)
last_rendered = time.monotonic()
sdl2.SDL_FreeSurface(image)
events = sdl2.ext.get_events()
for event in events:
if event.type == sdl2.SDL_QUIT:
running = False
break
elif event.type == sdl2.SDL_MOUSEBUTTONUP:
point = list(inputs[event.button.y * 1024 + event.button.x].numpy())
solid_angle = landmass_steradians(tf.reshape(outputs, (512, 1024, 1)),
event.button.x, event.button.y)
print('Clicked land mass: %s square Km | %s square miles' %
(solid_angle * planet_radius_km**2,
solid_angle * planet_radius_miles**2))
if last_clicked != None:
dug = math.sqrt(
sum(map((lambda p: (p[0] - p[1])**2), zip(last_clicked, point))))
arc = math.acos(1 - dug**2 / 2) # cosine rule, a == b == 1
print('%s Km | %s miles' %
(arc * planet_radius_km, arc * planet_radius_miles))
print(point)
last_clicked = point
window.refresh()
edit in trainmodel/src/drawmap.py at line 60

[7.278]

[7.120]
replacement in trainmodel/src/drawmap.py at line 62

[7.163]→[7.163:392](∅→∅)

n = numpy.zeros((height, width, 3), numpy.float64)
helpers.equirectangular(ctypes.cast(ctypes.c_voidp(n.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.c_int(width), ctypes.c_int(height))
return tf.constant(n)

[7.163]

[7.90]

n = numpy.zeros((height, width, 3), numpy.float64)
helpers.equirectangular(
ctypes.cast(ctypes.c_voidp(n.ctypes.data),
ctypes.POINTER(ctypes.c_double)), ctypes.c_int(width),
ctypes.c_int(height))
return tf.constant(n)
edit in trainmodel/src/drawmap.py at line 69

[7.91]

[7.460]
replacement in trainmodel/src/drawmap.py at line 71

[7.491]→[7.491:516](∅→∅),[7.516]→[7.900:1089](∅→∅)

shape = source.shape
source = tf.cast(source, tf.uint8).numpy()
return helpers.render_tensor(ctypes.c_void_p(source.ctypes.data), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]), ctypes.c_int(shape[2]))

[7.491]

[7.913]

shape = source.shape
source = tf.cast(source, tf.uint8).numpy()
return helpers.render_tensor(ctypes.c_void_p(source.ctypes.data),
ctypes.c_int(shape[1]), ctypes.c_int(shape[0]),
ctypes.c_int(shape[2]))
edit in trainmodel/src/drawmap.py at line 77

[7.914]

[7.1478]
replacement in trainmodel/src/drawmap.py at line 79

[7.1511]→[7.1511:1908](∅→∅)

shape = source.shape
n = numpy.zeros((shape[0],shape[1],3), numpy.float64)
source = tf.cast(source, tf.float64).numpy()
helpers.colourize_heightmap(ctypes.cast(ctypes.c_voidp(n.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.cast(ctypes.c_voidp(source.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]))
return tf.constant(n)

[7.1511]

[7.2094]

shape = source.shape
n = numpy.zeros((shape[0], shape[1], 3), numpy.float64)
source = tf.cast(source, tf.float64).numpy()
helpers.colourize_heightmap(
ctypes.cast(ctypes.c_voidp(n.ctypes.data),
ctypes.POINTER(ctypes.c_double)),
ctypes.cast(ctypes.c_voidp(source.ctypes.data),
ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]),
ctypes.c_int(shape[0]))
return tf.constant(n)
replacement in trainmodel/src/drawmap.py at line 92

[7.2134]→[7.2134:2411](∅→∅)

shape = source.shape
source = tf.cast(source, tf.float64).numpy()
return helpers.landmass_steradians(ctypes.cast(ctypes.c_voidp(source.ctypes.data), ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]), ctypes.c_int(shape[0]), ctypes.c_int(x), ctypes.c_int(y))

[7.2134]

shape = source.shape
source = tf.cast(source, tf.float64).numpy()
return helpers.landmass_steradians(
ctypes.cast(ctypes.c_voidp(source.ctypes.data),
ctypes.POINTER(ctypes.c_double)), ctypes.c_int(shape[1]),
ctypes.c_int(shape[0]), ctypes.c_int(x), ctypes.c_int(y))
edit in trainmodel/src/__main__.py at line 9

[7.44]

[7.29]
replacement in trainmodel/src/__main__.py at line 12

[7.66]→[7.51:103](∅→∅),[7.80]→[7.51:103](∅→∅),[7.51]→[7.51:103](∅→∅),[7.103]→[7.81:129](∅→∅),[7.129]→[7.67:146](∅→∅),[7.146]→[4.29:83](∅→∅)

source = simsave.SimResult(open(filename, 'r'))
training_data = model.training_data(source)
m.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000)
tfjs.converters.save_keras_model(m, 'tfjs_model')

[7.66]

[7.128]

source = simsave.SimResult(open(filename, 'r'))
training_data = model.training_data(source)
m.fit(x=training_data[0], y=training_data[1], batch_size=100, epochs=5000)
tfjs.converters.save_keras_model(m, 'tfjs_model')
replacement in trainmodel/src/__main__.py at line 17

[7.129]→[7.25:110](∅→∅),[7.110]→[7.147:199](∅→∅),[7.199]→[7.1909:1985](∅→∅),[7.156]→[7.222:335](∅→∅),[7.1985]→[7.222:335](∅→∅),[7.222]→[7.222:335](∅→∅)

inputs = tf.reshape(drawmap.inputs_equirectangular(2048,1024), (2048 * 1024, 3))
outputs = tf.reshape(m(inputs), (1024,2048, 1))
outputs = tf.cast(drawmap.colourize_heightmap(outputs) * 255, tf.uint8)
outputs = tf.io.encode_png(outputs).numpy()
f = open("map.png", 'wb')
f.write(outputs)
f.close()

[7.129]

[7.335]

inputs = tf.reshape(drawmap.inputs_equirectangular(2048, 1024),
(2048 * 1024, 3))
outputs = tf.reshape(m(inputs), (1024, 2048, 1))
outputs = tf.cast(drawmap.colourize_heightmap(outputs) * 255, tf.uint8)
outputs = tf.io.encode_png(outputs).numpy()
f = open("map.png", 'wb')
f.write(outputs)
f.close()
replacement in trainmodel/src/__main__.py at line 28

[7.151]→[7.240:327](∅→∅),[7.327]→[7.187:229](∅→∅),[7.229]→[7.376:390](∅→∅),[7.376]→[7.376:390](∅→∅),[7.390]→[7.200:281](∅→∅),[7.281]→[7.230:252](∅→∅),[7.508]→[7.230:252](∅→∅),[7.230]→[7.230:252](∅→∅),[7.252]→[7.282:301](∅→∅),[7.301]→[7.270:291](∅→∅),[7.534]→[7.270:291](∅→∅),[7.270]→[7.270:291](∅→∅)

m = model.model()
m.compile(
optimizer=keras.optimizers.RMSprop(),
loss=model.shore_focused_loss
)
bg_thread = threading.Thread(target=train, args=(m, sys.argv[1]), kwargs={})
bg_thread.start()
drawmap.run(m)
bg_thread.join()

[7.151]

[7.291]

m = model.model()
m.compile(optimizer=keras.optimizers.RMSprop(),
loss=model.shore_focused_loss)
bg_thread = threading.Thread(target=train, args=(m, sys.argv[1]), kwargs={})
bg_thread.start()
drawmap.run(m)
bg_thread.join()
replacement in trainmodel/src/__main__.py at line 36

[7.297]→[7.297:366](∅→∅),[7.44]→[7.45:46](∅→∅),[7.366]→[7.45:46](∅→∅),[7.45]→[7.45:46](∅→∅)

print("Expecting filename of tectonic.js save", file=sys.stderr)

[7.297]

print("Expecting filename of tectonic.js save", file=sys.stderr)