I have been trying to train a model for tf.keras.datasets.imdb using LSTM in tensorflow. After some processing i have input -> x_train shape: (25000, 100). Since i cannot feed it in a LSTM layer directly, i used a lambda function to change dimension of input:
model = Sequential([
Lambda(lambda x: tf.expand_dims(x,axis=-1)),
LSTM(62, dropout=0.2, recurrent_dropout=0.2, return_sequences=True),
LSTM(32),
Dense(1, activation='sigmoid')
])
But i am getting a horrible accuracy (around 55%).
But upon using embedding layer the accuracy increases to 90% using same hyper parameters:
model = Sequential([
Embedding(5000, 32),
LSTM(62, dropout=0.2, recurrent_dropout=0.2, return_sequences=True),
LSTM(32),
Dense(1, activation='sigmoid')
])
Am i doing something wrong in the first case?
