have dataset with 41 features and one output which has 23 class
i modified the imdb sentimental classification example lstm for my dataset and it is showing error. please have a look on the attached image.

I am trying to implement

1. two memory blocks and two cells each
2. four memory blocks and two cells each
3. two memory blocks and four cells each
4. eight memory blocks and four cells each

from future import print_function
import numpy as np
np.random.seed(1337)

from keras.preprocessing import sequence
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM

from sklearn.cross_validation import train_test_split
import pandas as pd

max_features = 494021
maxlen = 100 # cut texts after this number of words (among top max_features most common words)
batch_size = 32

print('Loading data...')
data = pd.read_csv('kddtrain.csv', header=None)

X = data.iloc[:,1:42]
y = data.iloc[:,0]

X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2,random_state=42)

print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
print(X_train.shape)

print('Pad sequences (samples x time)')

X_train = sequence.pad_sequences(X_train, maxlen=maxlen)

X_test = sequence.pad_sequences(X_test, maxlen=maxlen)

print('X_train shape:', X_train.shape)

print('X_test shape:', X_test.shape)

print('Build model...')
model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen, dropout=0.5))
model.add(LSTM(128, dropout_W=0.5, dropout_U=0.1)) # try using a GRU instead, for fun
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam')

print('Train...')
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=15,
validation_data=(X_test, y_test), show_accuracy=True)
score, acc = model.evaluate(X_test, y_test,
batch_size=batch_size,
show_accuracy=True)
print('Test score:', score)
print('Test accuracy:', acc)