I know how a RNN, LSTM, neural nets,activation function works but from various available LSTM models I dont know what should I use for which data and when. I created these 5 models as a sample of different varites of LSTM models I have seen but I dont know which optimal sequence dataset should use. I have most of my confussion in the second/third lines of these models. Are model1 and model4 are same? Why is model1.add(LSTM(10, input_shape=(max_len, 1), return_sequences=False)) different from model4.add(Embedding(X_train.shape[1], 128, input_length=max_len)) . I would much appreciate If some one can explain these five models in simple english.
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional
from keras.models import Sequential
from keras.layers.wrappers import TimeDistributed
#model1
model1 = Sequential()
model1.add(LSTM(10, input_shape=(max_len, 1), return_sequences=False))
model1.add(Dense(1, activation='sigmoid'))
model1.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print model1.summary()
#model2
model2 = Sequential()
model2.add(LSTM(10, batch_input_shape=(1, 1, 1), return_sequences=False, stateful=True))
model2.add(Dense(1, activation='sigmoid'))
model2.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print model2.summary()
#model3
model3 = Sequential()
model3.add(TimeDistributed(Dense(X_train.shape[1]), input_shape=(X_train.shape[1],1)))
model3.add(LSTM(10, return_sequences=False))
model3.add(Dense(1, activation='sigmoid'))
model3.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print model3.summary()
#model4
model4 = Sequential()
model4.add(Embedding(X_train.shape[1], 128, input_length=max_len))
model4.add(LSTM(10))
model4.add(Dense(1, activation='sigmoid'))
model4.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print model4.summary()
#model5
model5 = Sequential()
model5.add(Embedding(X_train.shape[1], 128, input_length=max_len))
model5.add(Bidirectional(LSTM(10)))
model5.add(Dense(1, activation='sigmoid'))
model5.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print model5.summary()