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## hello.ipynb

      
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              November 3, 2018 18:24
            
              
                test_hello
              
          
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## Keras_random_seed.ipynb

      
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              November 1, 2018 14:36
            
              
                Keras random seed fix
              
          
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## Titanic-Copy3.ipynb

      
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              Created
              November 1, 2018 10:48
            
          
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## Titanic-ES.ipynb

      
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              November 1, 2018 06:37
            
          
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## colab-gym-keras-rl.ipynb

      
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              April 5, 2019 20:44
            
          
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## Kaggle-mnist-CNN-ES.ipynb

      
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              November 1, 2018 06:39
            
          
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## Keras_EarlyStopping.py
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.000001,verbose=1)
checkpointer = ModelCheckpoint(filepath='checkpoint.hdf5', verbose=1, save_best_only=True)
early_stopping = EarlyStopping(patience=10, verbose=1)

history = model.fit(x_train, y_train,
                    epochs=300,
                    batch_size=32,
                    verbose=1,

## Keras_random_seed.py
import numpy as np
import tensorflow as tf
import random as rn
import os

os.environ['PYTHONHASHSEED'] = '0'
random_n = 123
np.random.seed(random_n)
rn.seed(random_n)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)

## dqn-atari-keras-rl.ipynb

      
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                / dqn-atari-keras-rl.ipynb
            
            
              Created
              September 29, 2018 17:17
            
          
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## Kagge-Titanic-Keras.ipynb

      
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              January 9, 2019 04:39
            
          
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	from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

	reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.000001,verbose=1)
	checkpointer = ModelCheckpoint(filepath='checkpoint.hdf5', verbose=1, save_best_only=True)
	early_stopping = EarlyStopping(patience=10, verbose=1)

	history = model.fit(x_train, y_train,
	epochs=300,
	batch_size=32,
	verbose=1,
	import numpy as np
	import tensorflow as tf
	import random as rn
	import os

	os.environ['PYTHONHASHSEED'] = '0'
	random_n = 123
	np.random.seed(random_n)
	rn.seed(random_n)
	session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)