import keras
import numpy as np 
import scipy
import h5py
from keras.layers import Embedding, recurrent, Activation, Dense, Flatten
import os
from keras import backend as K
from keras import metrics
from keras.callbacks import *
from keras.preprocessing import sequence
from keras.models import Sequential,load_model
from keras.layers import LSTM
from keras.layers.wrappers import Bidirectional
from keras.layers.core import Dropout
from keras.backend.tensorflow_backend import set_session
import preprocess
import visual
from util import *

from keras.preprocessing.text import one_hot


import tensorflow as tf
import matplotlib.pyplot as plt

# X_train,Y_train,X_test,Y_test = preprocess.dataz5ss()
X_train,Y_train,X_test,Y_test = preprocess.dataz3ss()


from keras import backend as K
from keras.engine.topology import Layer
from keras import constraints, regularizers, initializers, activations



batch_size = 512
model = Sequential()
model.add(Embedding(4, 100, input_length=len(X_train[0])))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(Attention(len(X_train[0])))
# model.add(Flatten())
model.add(Dense(512))
# model.add(Dropout(0.5))
model.add(Dense(5, activation='softmax'))
# try using different optimizers and different optimizer configs
print(model.summary())
model.compile(loss='mse',
              optimizer='adam',
              metrics=[det_coeff])
  

print('Train...')
history= model.fit(X_train, Y_train,
          batch_size=batch_size,
          epochs=5,
          validation_split=0.2
          )

visual.graph_epoch(history)

scores = model.evaluate(X_test, Y_test,verbose=1,batch_size=batch_size)

Y_pred=model.predict(X_test)

visual.pred_test_visual(Y_pred, Y_test, scores)

5086d8f574d752d407cdba46325f0b0403016988
<mask token>

<mask token>
model.add(Embedding(4, 100, input_length=len(X_train[0])))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(Attention(len(X_train[0])))
model.add(Dense(512))
model.add(Dense(5, activation='softmax'))
print(model.summary())
model.compile(loss='mse', optimizer='adam', metrics=[det_coeff])
print('Train...')
<mask token>
visual.graph_epoch(history)
<mask token>
visual.pred_test_visual(Y_pred, Y_test, scores)

<mask token>
X_train, Y_train, X_test, Y_test = preprocess.dataz3ss()
<mask token>
batch_size = 512
model = Sequential()
model.add(Embedding(4, 100, input_length=len(X_train[0])))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(Attention(len(X_train[0])))
model.add(Dense(512))
model.add(Dense(5, activation='softmax'))
print(model.summary())
model.compile(loss='mse', optimizer='adam', metrics=[det_coeff])
print('Train...')
history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=5,
    validation_split=0.2)
visual.graph_epoch(history)
scores = model.evaluate(X_test, Y_test, verbose=1, batch_size=batch_size)
Y_pred = model.predict(X_test)
visual.pred_test_visual(Y_pred, Y_test, scores)

import keras
import numpy as np
import scipy
import h5py
from keras.layers import Embedding, recurrent, Activation, Dense, Flatten
import os
from keras import backend as K
from keras import metrics
from keras.callbacks import *
from keras.preprocessing import sequence
from keras.models import Sequential, load_model
from keras.layers import LSTM
from keras.layers.wrappers import Bidirectional
from keras.layers.core import Dropout
from keras.backend.tensorflow_backend import set_session
import preprocess
import visual
from util import *
from keras.preprocessing.text import one_hot
import tensorflow as tf
import matplotlib.pyplot as plt
X_train, Y_train, X_test, Y_test = preprocess.dataz3ss()
from keras import backend as K
from keras.engine.topology import Layer
from keras import constraints, regularizers, initializers, activations
batch_size = 512
model = Sequential()
model.add(Embedding(4, 100, input_length=len(X_train[0])))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(Attention(len(X_train[0])))
model.add(Dense(512))
model.add(Dense(5, activation='softmax'))
print(model.summary())
model.compile(loss='mse', optimizer='adam', metrics=[det_coeff])
print('Train...')
history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=5,
    validation_split=0.2)
visual.graph_epoch(history)
scores = model.evaluate(X_test, Y_test, verbose=1, batch_size=batch_size)
Y_pred = model.predict(X_test)
visual.pred_test_visual(Y_pred, Y_test, scores)

import keras
import numpy as np 
import scipy
import h5py
from keras.layers import Embedding, recurrent, Activation, Dense, Flatten
import os
from keras import backend as K
from keras import metrics
from keras.callbacks import *
from keras.preprocessing import sequence
from keras.models import Sequential,load_model
from keras.layers import LSTM
from keras.layers.wrappers import Bidirectional
from keras.layers.core import Dropout
from keras.backend.tensorflow_backend import set_session
import preprocess
import visual
from util import *

from keras.preprocessing.text import one_hot


import tensorflow as tf
import matplotlib.pyplot as plt

# X_train,Y_train,X_test,Y_test = preprocess.dataz5ss()
X_train,Y_train,X_test,Y_test = preprocess.dataz3ss()


from keras import backend as K
from keras.engine.topology import Layer
from keras import constraints, regularizers, initializers, activations



batch_size = 512
model = Sequential()
model.add(Embedding(4, 100, input_length=len(X_train[0])))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(LSTM(100, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))
model.add(Attention(len(X_train[0])))
# model.add(Flatten())
model.add(Dense(512))
# model.add(Dropout(0.5))
model.add(Dense(5, activation='softmax'))
# try using different optimizers and different optimizer configs
print(model.summary())
model.compile(loss='mse',
              optimizer='adam',
              metrics=[det_coeff])
  

print('Train...')
history= model.fit(X_train, Y_train,
          batch_size=batch_size,
          epochs=5,
          validation_split=0.2
          )

visual.graph_epoch(history)

scores = model.evaluate(X_test, Y_test,verbose=1,batch_size=batch_size)

Y_pred=model.predict(X_test)

visual.pred_test_visual(Y_pred, Y_test, scores)
