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https://github.com/wassname/keras-language-modeling.git
synced 2026-09-09 11:25:29 +08:00
prints loss and validation loss
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+21
-21
@@ -136,7 +136,7 @@ class Evaluator:
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if hist.history['val_loss'][0] < val_loss['loss']:
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val_loss = {'loss': hist.history['val_loss'][0], 'epoch': i}
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log('%s -- Epoch %d ' % (self.get_time(), i) +
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'Loss = %.4f ' % hist.history['val_loss'][0] +
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'Loss = %.4f, Validation Loss = %.4f ' % (hist.history['loss'][0], hist.history['val_loss'][0]) +
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'(Best: Loss = %.4f, Epoch = %d)' % (val_loss['loss'], val_loss['epoch']))
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self.save_epoch(i)
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@@ -207,6 +207,26 @@ class Evaluator:
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if __name__ == '__main__':
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if len(sys.argv) >= 2 and sys.argv[1] == 'serve':
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from flask import Flask
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app = Flask(__name__)
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port = 5000
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lines = list()
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def log(x):
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lines.append(x)
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@app.route('/')
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def home():
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return ('<html><body><h1>Training Log</h1>' +
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''.join(['<code>{}</code><br/>'.format(line) for line in lines]) +
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'</body></html>')
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def start_server():
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app.run(debug=False, use_evalex=False, port=port)
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thread.start_new_thread(start_server, tuple())
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print('Serving to port %d' % port, file=sys.stderr)
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import numpy as np
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conf = {
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@@ -230,26 +250,6 @@ if __name__ == '__main__':
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}
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}
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if len(sys.argv) >= 2 and sys.argv[1] == 'serve':
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from flask import Flask
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app = Flask(__name__)
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port = 5000
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lines = list()
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def log(x):
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lines.append(x)
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@app.route('/')
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def home():
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return ('<html><body><h1>Training Log</h1>' +
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''.join(['<code>{}</code><br/>'.format(line) for line in lines]) +
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'</body></html>')
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def start_server():
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app.run(debug=False, use_evalex=False, port=port)
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thread.start_new_thread(start_server, tuple())
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print('Serving to port %d' % port, file=sys.stderr)
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from keras_models import AttentionModel
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evaluator = Evaluator(conf, model=AttentionModel, optimizer='rmsprop')
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