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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)

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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,

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