X_train.shape = (243*100*4)  # Samples * Time steps * Features
Y_train.shape = (243,)       # either 0 or 1 for each samples

X_validate.shape : (31, 100, 4) # Samples * Time steps * Features
Y_validate.shape : (31,)        # either 0 or 1 for each samples

X_test.shape : (28, 100, 4) # Samples * Time steps * Features
Y_test.shape : (28,)        # either 0 or 1 for each samples

1. Train the model with random time length batches
2. Predict the class, if random time length batches provided as input to the model

input_ = Input(shape=(None,4))
x = LSTM(16, return_sequences=True)(input_)
x = LSTM(8, return_sequences=True)(x)
output = TimeDistributed(Dense(2, activation='sigmoid'))(x)

# Model
model = Model(inputs=input_, outputs=output)

print(model.summary())

# Compile
model.compile(
  loss='binary_crossentropy',
  optimizer=Adam(lr=1e-4),
  metrics=['accuracy']
)


def common_generator(X, Y):
  while True:
    sequence_length = random.randrange(60,100,5)
    # I want my model to be trained with random time length b/w 50 to 100 with multiples of 5

    x_train = X[:, :sequence_length, :]

    y = to_categorical(Y)
    y_train = np.repeat(y[:, np.newaxis], sequence_length, axis=1)

   # For my convenience i changed my Y_train shape from (243,) to (243, sequence_length, 2)
    Refer picture below for better understanding

    yield (x_train, y_train)

trainGen = common_generator(X_train,Y_train)
ValGen  = common_generator(X_validate, Y_validate)

H = model.fit_generator(trainGen, steps_per_epoch=25, validation_data=ValGen, validation_steps=3, epochs=150)

Epoch 150/150
25/25 [==============================] - 5s 187ms/step - loss: 0.3706 - acc: 0.8574 - val_loss: 0.3254 - val_acc: 0.8733