Value error: Input 0 is incompatible with layer lstm_1: expected ndim=3 found ndim=2

from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Model
from keras.layers import Input, Embedding
from keras.layers.recurrent import LSTM
X_train=[[2.78600000e-01 2.82757400e-01 2.76869970e-01 2.80388000e-01
  3.60376079e+04 1.90218336e+04 1.70157743e+04]
 [2.83000000e-01 2.84000000e-01 2.81890000e-01 2.82000000e-01
  3.53780466e+04 2.82764990e+04 7.10154764e+03]
 [2.77187580e-01 2.80199220e-01 2.77187580e-01 2.79080000e-01
  2.35652761e+04 1.24994396e+04 1.10658366e+04]
 [2.59700000e-01 2.61424560e-01 2.59000000e-01 2.59407990e-01
  2.36833303e+04 7.34975170e+03 1.63335786e+04]
 [2.79896000e-01 2.81313000e-01 2.79190000e-01 2.81313000e-01
  2.51838229e+04 1.31842398e+04 1.19995830e+04]
 [2.78465000e-01 2.79681450e-01 2.76725810e-01 2.77500000e-01
  2.76347957e+04 1.42297337e+04 1.34050620e+04]
 [2.82397000e-01 2.84000000e-01 2.80766550e-01 2.80774000e-01
  2.87671254e+04 1.99310413e+04 8.83608415e+03]
 [2.81602530e-01 2.82200000e-01 2.79641610e-01 2.81703000e-01
  2.39929677e+04 1.15329457e+04 1.24600220e+04]
 [2.81098400e-01 2.82113640e-01 2.79680000e-01 2.81834000e-01
  1.96216530e+04 1.05283765e+04 9.09327653e+03]
 [2.72602000e-01 2.72975000e-01 2.70201690e-01 2.71607000e-01
  3.55454105e+04 1.30756085e+04 2.24698020e+04]
 [2.61429500e-01 2.62359500e-01 2.59688080e-01 2.61501000e-01
  3.56094304e+04 1.74528080e+04 1.81566224e+04]
 [2.74529400e-01 2.75488470e-01 2.71126270e-01 2.71999900e-01
  3.21329360e+04 2.02489339e+04 1.18840021e+04]
 [2.78880000e-01 2.82636430e-01 2.78880000e-01 2.82190000e-01
  3.10086357e+04 2.16044104e+04 9.40422533e+03]
 [2.80404000e-01 2.83151620e-01 2.80404000e-01 2.82485000e-01
  3.03894170e+04 2.11720382e+04 9.21737884e+03]
 [2.82424000e-01 2.83755760e-01 2.81501490e-01 2.81962000e-01
  3.06450959e+04 1.65164240e+04 1.41286719e+04]
 [2.82520000e-01 2.83995670e-01 2.76903070e-01 2.78560000e-01
  4.77410935e+04 2.83238304e+04 1.94172631e+04]
 [2.81370000e-01 2.81676760e-01 2.79700000e-01 2.80640000e-01
  2.46504873e+04 1.38296071e+04 1.08208802e+04]
 [2.75670000e-01 2.75990870e-01 2.73017640e-01 2.74820000e-01
  2.91194357e+04 1.36947841e+04 1.54246517e+04]
 [2.80888000e-01 2.80888000e-01 2.78666280e-01 2.79820000e-01
  2.41666058e+04 1.24623000e+04 1.17043058e+04]
 [2.79790140e-01 2.80610000e-01 2.78300500e-01 2.79180000e-01
  2.35998209e+04 1.07440209e+04 1.28558000e+04]
 [2.69872390e-01 2.74908110e-01 2.68363160e-01 2.69638970e-01
  6.41172506e+04 2.17278523e+04 4.23893983e+04]
 [2.78900000e-01 2.80210980e-01 2.77700000e-01 2.77700000e-01
  2.19803111e+04 6.97927479e+03 1.50010364e+04]
 [2.71326000e-01 2.75511700e-01 2.70788000e-01 2.75000000e-01
  4.28620722e+04 2.01289447e+04 2.27331275e+04]
 [2.77534000e-01 2.80142000e-01 2.77534000e-01 2.80141990e-01
  2.70091556e+04 1.74026111e+04 9.60654448e+03]
 [2.81930000e-01 2.83299130e-01 2.80201400e-01 2.81510010e-01
  2.98520547e+04 1.49594137e+04 1.48926410e+04]
 [2.65388000e-01 2.67260000e-01 2.64567730e-01 2.66420000e-01
  3.10870979e+04 1.41024655e+04 1.69846323e+04]
 [2.71999900e-01 2.76300000e-01 2.70180720e-01 2.73324800e-01
  5.12261852e+04 3.16549469e+04 1.95712383e+04]
 [2.84481700e-01 2.86284200e-01 2.82002840e-01 2.82238000e-01
  3.75707296e+04 1.88603052e+04 1.87104245e+04]
 [2.77140000e-01 2.79000000e-01 2.74391620e-01 2.75560000e-01
  3.61027684e+04 1.28104341e+04 2.32923343e+04]
 [2.83300000e-01 2.83500000e-01 2.82000000e-01 2.82790000e-01
  2.63724235e+04 7.94261098e+03 1.84298125e+04]
 [2.79630000e-01 2.83438950e-01 2.78001400e-01 2.83008990e-01
  3.56796906e+04 2.45191182e+04 1.11605723e+04]
 [2.77000000e-01 2.80000000e-01 2.76938670e-01 2.77100010e-01
  4.08886039e+04 1.90994871e+04 2.17891168e+04]
 [2.80650000e-01 2.83299130e-01 2.80650000e-01 2.83297710e-01
  2.56485489e+04 1.09265443e+04 1.47220047e+04]
 [2.76370000e-01 2.78791310e-01 2.74943380e-01 2.77040000e-01
  4.14867065e+04 2.65096934e+04 1.49770131e+04]
 [2.79700000e-01 2.82139380e-01 2.78400000e-01 2.78400000e-01
  2.33800196e+04 6.82057226e+03 1.65594474e+04]
 [2.75000000e-01 2.77566600e-01 2.73963840e-01 2.77010000e-01
  3.19495548e+04 1.51222792e+04 1.68272756e+04]
 [2.82956000e-01 2.84198580e-01 2.81350000e-01 2.81767030e-01
  2.38226591e+04 9.45715914e+03 1.43655000e+04]
 [2.83200000e-01 2.84290680e-01 2.80380810e-01 2.82342000e-01
  2.70031998e+04 1.44732040e+04 1.25299958e+04]
 [2.80550000e-01 2.83962500e-01 2.80550000e-01 2.82190000e-01
  2.32394500e+04 1.00332635e+04 1.32061865e+04]
 [2.61345000e-01 2.63677600e-01 2.60125130e-01 2.63676340e-01
  2.33067193e+04 1.02968559e+04 1.30098634e+04]
 [2.60532580e-01 2.61444440e-01 2.59229580e-01 2.60420000e-01
  3.74683580e+04 1.51105777e+04 2.23577803e+04]
 [2.80786000e-01 2.81389900e-01 2.78691910e-01 2.81098550e-01
  2.10760043e+04 1.54530869e+04 5.62291742e+03]
 [2.81262410e-01 2.81324540e-01 2.77839020e-01 2.77868360e-01
  3.10954846e+04 1.26860737e+04 1.84094109e+04]
 [2.76000000e-01 2.78245220e-01 2.75700000e-01 2.76300000e-01
  8.08472729e+03 3.74783545e+03 4.33689183e+03]
 [2.70000000e-01 2.77995780e-01 2.69367370e-01 2.77058270e-01
  3.84301681e+04 2.95685773e+04 8.86159080e+03]
 [2.77710000e-01 2.79221940e-01 2.75700000e-01 2.76998500e-01
  1.47668621e+04 8.66472312e+03 6.10213894e+03]
 [2.75000000e-01 2.75280000e-01 2.73050150e-01 2.73685000e-01
  2.33177410e+04 5.90505176e+03 1.74126892e+04]
 [2.75346810e-01 2.78010830e-01 2.74460000e-01 2.77000000e-01
  2.69416974e+04 9.92176561e+03 1.70199317e+04]
 [2.73660000e-01 2.78414720e-01 2.73660000e-01 2.78001000e-01
  5.79762548e+04 3.03296966e+04 2.76465581e+04]
 [2.81749230e-01 2.81749510e-01 2.76725810e-01 2.76752880e-01
  3.99833338e+04 1.75818136e+04 2.24015202e+04]
 [2.76000000e-01 2.77103610e-01 2.73000000e-01 2.73000000e-01
  3.63982284e+04 2.49075280e+04 1.14907004e+04]
 [2.81847030e-01 2.84000000e-01 2.81603940e-01 2.82760000e-01
  3.99809882e+04 1.86956282e+04 2.12853600e+04]
 [2.82000000e-01 2.85283820e-01 2.80900000e-01 2.80900000e-01
  3.39773848e+04 2.25675899e+04 1.14097949e+04]
 [2.77500000e-01 2.86284200e-01 2.77500000e-01 2.83131760e-01
  3.85133240e+04 2.56409113e+04 1.28724127e+04]
 [2.73170000e-01 2.73282280e-01 2.71147390e-01 2.72000000e-01
  3.64682647e+04 2.23353736e+04 1.41328911e+04]
 [2.82480000e-01 2.83816930e-01 2.80945290e-01 2.82260000e-01
  2.51942191e+04 1.23841003e+04 1.28101187e+04]
 [2.82000000e-01 2.85500000e-01 2.81510020e-01 2.84481700e-01
  5.59107165e+04 3.86464413e+04 1.72642752e+04]
 [2.78414720e-01 2.78998330e-01 2.74202890e-01 2.75350000e-01
  2.43068703e+04 1.34113944e+04 1.08954759e+04]
 [2.80180000e-01 2.82311050e-01 2.79502000e-01 2.81948000e-01
  2.75609765e+04 2.15873095e+04 5.97366700e+03]
 [2.83700000e-01 2.83800000e-01 2.81602530e-01 2.83000000e-01
  3.34594939e+04 1.00549563e+04 2.34045375e+04]
 [2.79360000e-01 2.79820000e-01 2.77800000e-01 2.79660000e-01
  2.35498474e+04 1.00087174e+04 1.35411300e+04]
 [2.83137510e-01 2.86917130e-01 2.81526990e-01 2.82469000e-01
  6.11975185e+04 2.33256881e+04 3.78718304e+04]
 [2.83000000e-01 2.83610000e-01 2.82201440e-01 2.83001530e-01
  3.10099492e+04 2.31742723e+04 7.83567688e+03]
 [2.79953000e-01 2.81319100e-01 2.77700010e-01 2.80167120e-01
  2.49682794e+04 1.42239213e+04 1.07443581e+04]
 [2.76853010e-01 2.77781300e-01 2.75000000e-01 2.75830000e-01
  3.02674270e+04 1.54482632e+04 1.48191638e+04]
 [2.65478420e-01 2.66983030e-01 2.63673460e-01 2.65620000e-01
  3.37218154e+04 2.31362797e+04 1.05855357e+04]
 [2.83798550e-01 2.83800000e-01 2.82000000e-01 2.83430000e-01
  2.59344106e+04 1.68113540e+04 9.12305666e+03]
 [2.80999990e-01 2.82747090e-01 2.79821480e-01 2.80195000e-01
  3.08992135e+04 1.80184319e+04 1.28807816e+04]
 [2.66622000e-01 2.69873740e-01 2.65690540e-01 2.69873740e-01
  2.50400942e+04 1.54247714e+04 9.61532278e+03]
 [2.83480000e-01 2.84679420e-01 2.81749240e-01 2.84289260e-01
  2.59109261e+04 2.03125721e+04 5.59835404e+03]
 [2.75330000e-01 2.77392810e-01 2.75000000e-01 2.76540000e-01
  2.46925384e+04 1.80902964e+04 6.60224207e+03]
 [2.63000000e-01 2.65990100e-01 2.61837450e-01 2.65230000e-01
  2.04726206e+04 1.15174026e+04 8.95521793e+03]
 [2.74980000e-01 2.75976990e-01 2.73048750e-01 2.75630000e-01
  2.15891089e+04 5.52461693e+03 1.60644920e+04]
 [2.75147000e-01 2.76823420e-01 2.75147000e-01 2.76819360e-01
  2.43841704e+04 1.63767031e+04 8.00746732e+03]
 [2.78860000e-01 2.79730000e-01 2.77298090e-01 2.78000000e-01
  2.34632973e+04 8.06449057e+03 1.53988068e+04]
 [2.80299000e-01 2.80299000e-01 2.77200000e-01 2.77898000e-01
  2.77825076e+04 1.41312352e+04 1.36512724e+04]
 [2.76304290e-01 2.80191650e-01 2.76304290e-01 2.79410000e-01
  2.89037927e+04 1.73078564e+04 1.15959363e+04]
 [2.76493060e-01 2.76493070e-01 2.71148750e-01 2.73871040e-01
  5.85867465e+04 4.56133058e+04 1.29734407e+04]
 [2.78975700e-01 2.81179920e-01 2.78460000e-01 2.79850000e-01
  2.28744184e+04 1.28689184e+04 1.00055000e+04]
 [2.81310000e-01 2.81319100e-01 2.79644420e-01 2.80999990e-01
  2.39608975e+04 8.67736805e+03 1.52835295e+04]
 [2.61598660e-01 2.62180680e-01 2.59000000e-01 2.60034010e-01
  2.93108777e+04 1.42164484e+04 1.50944293e+04]
 [2.76420000e-01 2.77405100e-01 2.74796030e-01 2.77000000e-01
  3.51117439e+04 1.23945439e+04 2.27172000e+04]
 [2.77850000e-01 2.80624810e-01 2.77850000e-01 2.79920000e-01
  3.31952252e+04 1.53457988e+04 1.78494264e+04]
 [2.83710000e-01 2.85200000e-01 2.81749230e-01 2.83000000e-01
  3.10185390e+04 1.44219818e+04 1.65965572e+04]
 [2.78118000e-01 2.79260000e-01 2.78001410e-01 2.78618000e-01
  2.18256000e+04 1.36779000e+04 8.14770000e+03]
 [2.80528000e-01 2.81570000e-01 2.79700000e-01 2.79700000e-01
  2.21553518e+04 7.48280000e+03 1.46725518e+04]]
Y_train=[[1 0 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [0 0 1]
 [0 0 1]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [0 0 1]
 [1 0 0]
 [0 0 1]]
vis= Input(shape=(7,))
hidden1 = LSTM(10)(vis)
drop1=Dropout(0.1)(hidden1)
hidden2 = Dense(64, activation='relu')(drop1)
drop2=Dropout(0.2)(hidden2)
hidden3 = Dense(32, activation='relu')(drop2)
drop3=Dropout(0.1)(hidden3)
output = Dense(3, activation='softmax')(drop3)
model = Model(inputs=vis, outputs=output)
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X_train, Y_train, 
          nb_epoch = 100, 
          batch_size = 128, 
          verbose=1, 
          shuffle=True)

from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Model
from keras.layers import Input, Embedding
from keras.layers.recurrent import LSTM
import numpy as np
X=[[0.2597, 0.26142456, 0.259, 0.25940799, 23683.33027302, 7349.7516953, 16333.578577720002], [0.26053258, 0.26144444, 0.25922958, 0.26042, 37468.35797686, 15110.577669050004, 22357.78030781], [0.2614295, 0.2623595, 0.25968808, 0.261501, 35609.43035447999, 17452.80799378, 18156.6223607], [0.26159866, 0.26218068, 0.259, 0.26003401, 29310.877699889996, 14216.448366519999, 15094.429333369999], [0.261345, 0.2636776, 0.26012513, 0.26367634, 23306.719302679998, 10296.85594638, 13009.863356300004], [0.263, 0.2659901, 0.26183745, 0.26523, 20472.620550369993, 11517.402618899998, 8955.21793147], [0.26547842, 0.26698303, 0.26367346, 0.26562, 33721.81542282, 23136.27974657, 10585.53567625], [0.265388, 0.26726, 0.26456773, 0.26642, 31087.09787857, 14102.465529410001, 16984.632349159998], [0.266622, 0.26987374, 0.26569054, 0.26987374, 25040.094169600005, 15424.771387949992, 9615.322781650004], [0.26987239, 0.27490811, 0.26836316, 0.26963897, 64117.25057878001, 21727.852268989995, 42389.39830979], [0.27, 0.27799578, 0.26936737, 0.27705827, 38430.16810217998, 29568.57730271999, 8861.590799460004], [0.27649306, 0.27649307, 0.27114875, 0.27387104, 58586.746477270004, 45613.30578957, 12973.440687700002], [0.27317, 0.27328228, 0.27114739, 0.272, 36468.26466711001, 22335.37356814, 14132.891098969998], [0.272602, 0.272975, 0.27020169, 0.271607, 35545.41054462999, 13075.608538260001, 22469.802006369995], [0.271326, 0.2755117, 0.270788, 0.275, 42862.07223425001, 20128.944692099998, 22733.127542149992], [0.275, 0.27528, 0.27305015, 0.273685, 23317.74097547, 5905.05176192, 17412.689213550002], [0.27366, 0.27841472, 0.27366, 0.278001, 57976.254754509995, 30329.696638399997, 27646.55811611], [0.27841472, 0.27899833, 0.27420289, 0.27535, 24306.87031516, 13411.3943727, 10895.47594246], [0.27498, 0.27597699, 0.27304875, 0.27563, 21589.10892766, 5524.616931, 16064.49199666], [0.27533, 0.27739281, 0.275, 0.27654, 24692.53844407, 18090.29637307, 6602.242071], [0.27685301, 0.2777813, 0.275, 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10744.02091308, 12855.800008], [0.28018, 0.28231105, 0.279502, 0.281948, 27560.976517299998, 21587.3095173, 5973.6669999999995], [0.28065, 0.28329913, 0.28065, 0.28329771, 25648.548908520006, 10926.54425621, 14722.004652309999], [0.2832, 0.28429068, 0.28038081, 0.282342, 27003.19980656, 14473.20399943, 12529.995807130003], [0.28055, 0.2839625, 0.28055, 0.28219, 23239.45000078, 10033.263502779999, 13206.186498000003], [0.282, 0.28528382, 0.2809, 0.2809, 33977.38481012, 22567.58987056, 11409.79493956], [0.282956, 0.28419858, 0.28135, 0.28176703, 23822.65914036, 9457.15914036, 14365.5], [0.28371, 0.2852, 0.28174923, 0.283, 31018.538976599993, 14421.981788269999, 16596.55718833], [0.28174923, 0.28174951, 0.27672581, 0.27675288, 39983.33376085002, 17581.813597790006, 22401.52016306], [0.27771, 0.27922194, 0.2757, 0.2769985, 14766.862060359997, 8664.72311597, 6102.138944390001], [0.276, 0.27824522, 0.2757, 0.2763, 8084.727286100002, 3747.83545222, 4336.89183388], [0.27630429, 0.28019165, 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11999.58304813], [0.28131, 0.2813191, 0.27964442, 0.28099999, 23960.897537210007, 8677.368053920001, 15283.529483290002], [0.28099999, 0.28274709, 0.27982148, 0.280195, 30899.213540260003, 18018.43194296, 12880.7815973], [0.280786, 0.2813899, 0.27869191, 0.28109855, 21076.004285770003, 15453.08686551, 5622.91742026], [0.2810984, 0.28211364, 0.27968, 0.281834, 19621.65303179, 10528.37649789, 9093.2765339], [0.280404, 0.28315162, 0.280404, 0.282485, 30389.417046650004, 21172.03820529, 9217.37884136], [0.283, 0.284, 0.28189, 0.282, 35378.04660218, 28276.498958120006, 7101.54764406], [0.28379855, 0.2838, 0.282, 0.28343, 25934.410614080003, 16811.35395502, 9123.056659060001], [0.2833, 0.2835, 0.282, 0.28279, 26372.423464009997, 7942.61098105, 18429.81248296], [0.283, 0.28361, 0.28220144, 0.28300153, 31009.94916985, 23174.27229463, 7835.676875219999], [0.2837, 0.2838, 0.28160253, 0.283, 33459.49385401, 10054.95631047, 23404.53754354], [0.282424, 0.28375576, 0.28150149, 0.281962, 30645.095918060004, 16516.42399394, 14128.67192412], [0.28184703, 0.284, 0.28160394, 0.28276, 39980.98818710999, 18695.62820111, 21285.359986], [0.28248, 0.28381693, 0.28094529, 0.28226, 25194.219057890004, 12384.10034424, 12810.11871365], [0.28136, 0.28315162, 0.28065895, 0.282558, 28568.39880644, 13366.801236439998, 15201.597569999998], [0.28315009, 0.28315009, 0.28133073, 0.28186, 26078.04937989, 8396.76730989, 17681.28207], [0.282292, 0.28247, 0.28112, 0.28228, 23389.6, 9858.4, 13531.2], [0.280934, 0.28314947, 0.28000002, 0.28027, 28000.19882476, 19211.48449476, 8788.71433], [0.280494, 0.28247089, 0.2791, 0.279851, 25297.18122269, 10622.46398321, 14674.71723948], [0.279342, 0.28117992, 0.2791, 0.280447, 22961.361987509998, 15178.72007481, 7782.6419127], [0.279488, 0.279488, 0.27672582, 0.278479, 48094.78673676, 15348.71315201, 32746.07358475], [0.278306, 0.27922195, 0.27691, 0.27855, 33922.53809246, 25327.632554, 8594.90553846], [0.27809, 0.27922195, 0.27711602, 0.27884, 22336.11480516, 10303.83483116, 12032.279974000001], [0.27894, 0.27999857, 0.27769435, 0.279, 26666.522157130003, 14478.494383510002, 12188.027773620002]]
Y=[[1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [1, 0, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 0, 1], [1, 0, 0], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 0, 1], [1, 0, 0], [1, 0, 0], [0, 0, 1]]
X_train=np.array(X)
Y_train=np.array(Y)
vis= Input(shape=(7,))
hidden1 = LSTM(10)(vis)
drop1=Dropout(0.1)(hidden1)
hidden2 = Dense(64, activation='relu')(drop1)
drop2=Dropout(0.2)(hidden2)
hidden3 = Dense(32, activation='relu')(drop2)
drop3=Dropout(0.1)(hidden3)
output = Dense(3, activation='softmax')(drop3)
model = Model(inputs=vis, outputs=output)
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X_train, Y_train, 
          nb_epoch = 100, 
          batch_size = 128, 
          verbose=1, 
          shuffle=True)