I have an input sequence with 2-dimensions train_seq with shape (100000, 200, 2) i.e. 100000 training examples, sequence length of 200, and 2 features.
The sequences are text, so each element is one word with a vocabulary of 5000 words. Hence, I want to use an embedding layer prior to my LSTM.
MAX_SEQUENCE_LENGTH = 200
EMBEDDING_SIZE = 64
MAX_FEATURES = 5000
NUM_CATEGORIES = 5
model_input = Input(shape=(MAX_SEQUENCE_LENGTH,2))
x = Embedding(output_dim=EMBEDDING_SIZE, input_dim=MAX_FEATURES, input_length=(MAX_SEQUENCE_LENGTH,2))(model_input)
x_lstm = LSTM(64)(x)
x = Dense(128, activation='relu', name = 'lstm')(x_lstm)
output = Dense(NUM_CATEGORIES, activation='sigmoid')(x)
model = Model(inputs=[model_input], outputs=[output])
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
However, I'm not able to build the model and get the following error:
ValueError: Input 0 is incompatible with layer lstm: expected ndim=3, found ndim=4. By looking at the model summary without the LSTM, I can see that the output shape of my embedding is (None, 200, 2, 64)
Layer (type) Output Shape Param #
=================================================================
merged_input (InputLayer) (None, 200, 2) 0
_________________________________________________________________
embedding (Embedding) (None, 200, 2, 64) 196096
_________________________________________________________________
Note that this architecture works when the input sequence is 1-dimensional. Can a LSTM receive a 2-dimensional sequence? How do I tell the LSTM layer that the input shape is (None, 200, 2, 64)?
Any help would be appreciated
The solution is to add the input shape to the LSTM layer:
x_lstm = LSTM(64, input_shape=(MAX_SEQUENCE_LENGTH,2))(x)
Followed by a Flatten layer
x = Flatten()(x_lstm)