fix(model): checkpoints in h5 format
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b377c6dd5f
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@ -99,8 +99,8 @@ class Model():
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def save(self, path):
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def save(self, path):
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logger.debug('Saving model weights to path: %s', path)
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logger.debug('Saving model weights to path: %s', path)
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self.model.save_weights(path + '/checkpoint.hd5')
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self.model.save_weights(path + '/checkpoint.h5', save_format='h5')
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return path + '/checkpoint'
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return path + '/checkpoint.h5'
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def evaluate(self):
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def evaluate(self):
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return self.model.evaluate(
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return self.model.evaluate(
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@ -10,7 +10,7 @@ from model import Model
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B_params = {
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B_params = {
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'batch_size': tune.grid_search([256]),
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'batch_size': tune.grid_search([256]),
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'layers': tune.grid_search([[512, 512]]),
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'layers': tune.grid_search([[512, 512]]),
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'lr': tune.grid_search([1e-4]),
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'lr': tune.grid_search([3e-4]),
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'optimizer': tune.grid_search([tf.keras.optimizers.Adam]),
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'optimizer': tune.grid_search([tf.keras.optimizers.Adam]),
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}
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}
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@ -46,8 +46,8 @@ class TuneB(tune.Trainable):
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return self.model.restore(path)
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return self.model.restore(path)
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A_params = {
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A_params = {
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'batch_size': tune.grid_search([32]),
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'batch_size': tune.grid_search([128]),
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'layers': tune.grid_search([[32, 32]]),
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'layers': tune.grid_search([[64, 64]]),
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'lr': tune.grid_search([1e-4]),
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'lr': tune.grid_search([1e-4]),
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'optimizer': tune.grid_search([tf.keras.optimizers.Adam]),
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'optimizer': tune.grid_search([tf.keras.optimizers.Adam]),
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}
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}
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@ -163,8 +163,8 @@ def continent_agent(ground, position, size):
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while True:
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while True:
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# if trials > CONTINENT_MAX_TRIALS:
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# if trials > CONTINENT_MAX_TRIALS:
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# print('couldnt proceed')
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# print('couldnt proceed')
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# if size <= 0 or trials > CONTINENT_MAX_TRIALS: break
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if size <= 0 or trials > CONTINENT_MAX_TRIALS: break
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if size <= 0: break
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# if size <= 0: break
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dx = np.random.randint(2) or -1
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dx = np.random.randint(2) or -1
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dy = np.random.randint(2) or -1
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dy = np.random.randint(2) or -1
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@ -184,8 +184,8 @@ def continent_agent(ground, position, size):
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trials = 0
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trials = 0
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size -= 1
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size -= 1
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ground[x, y] = np.random.randint(1, p['ground_noise'])
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ground[x, y] = np.random.randint(1, p['ground_noise'])
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# else:
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else:
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# trials += 1
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trials += 1
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def neighbours(ground, position, radius):
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def neighbours(ground, position, radius):
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x, y = position
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x, y = position
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@ -271,7 +271,7 @@ def generate_map(**kwargs):
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ym = ym * -1
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ym = ym * -1
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random_size = ground_size / continents
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random_size = ground_size / continents
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continent_agent(ground, position, size=random_size)
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continent_agent(ground, position, size=random_size)
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ground = ndimage.gaussian_filter(ground, sigma=(1 - p['sharpness']) * 20)
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ground = ndimage.gaussian_filter(ground, sigma=(1 - p['sharpness']) * 20)
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@ -12,7 +12,7 @@ function generate() {
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const queryString = new URLSearchParams(formData).toString()
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const queryString = new URLSearchParams(formData).toString()
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map.src = '/map?' + queryString;
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map.src = '/map?' + queryString;
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map.classList.add('d-none');
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map.classList.add('d-none');
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map.width = formData.get('width');
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}
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}
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mapSettings.addEventListener('submit', (e) => {
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mapSettings.addEventListener('submit', (e) => {
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@ -21,7 +21,7 @@
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<aside class='col-3 px-4 bg-dark text-light text-center py-3'>
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<aside class='col-3 px-4 bg-dark text-light text-center py-3'>
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<h3>World Map Generator</h3>
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<h3>World Map Generator</h3>
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<div class='panel px-4'>
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<div class='panel px-4 pb-3'>
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<form class='mt-5' id='map-settings'>
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<form class='mt-5' id='map-settings'>
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{% for k, v in parameters.items() %}
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{% for k, v in parameters.items() %}
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<div class='form-group'>
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<div class='form-group'>
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