Keras 功能 API 嵌入层 output 到 LSTM

Question

When passing the output of my embedding layer to the LSTM layer I'm running into a ValueError that I cannot figure out. My model is:

def lstm_mod(self, n_cells,batch_size):
        input = tf.keras.Input((self.n_seq, self.n_features))
        embedding = tf.keras.layers.Embedding(batch_size,self.n_seq,input_length=self.n_clusters)(input)
        x= tf.keras.layers.LSTM(n_cells)(embedding) 
        out = tf.keras.layers.Dense(1)(x)
        model = tf.keras.Model(input, out,name="LSTM")
        model.compile(loss='mse', optimizer='Adam')
        return model 

The error is:

ValueError: Input 0 of layer lstm is incompatible with the layer: expected ndim=3, found ndim=4. Full shape received: [None, 128, 7, 128]

Given that the dimensions passed to the model input and the embedding layer are consistent through the arguments of the model I'm puzzled by this. Any guidance is appreciated.

Answer 1

Keras adds an additional dimension ( None ) when you feed your data through your model because it processes your data in batches.

In this line:

input = tf.keras.Input((self.n_seq, self.n_features))

You've defined a 2-dimensional input, and Keras adds a 3rd dimension (the batch), hence expected ndim=3 .

However, the data that is being passed to the input layer is 4-dimensional, which means that your actual input data shape is 3-dimensional + the batch dimension, not 2-dimensional + batch.

To fix this you need to either re-shape your 3-D input to 2-D, or add an additional dimension to the input shape.

Print out the values for self.n_seq and self.n_features and find out what is missing from the shape 128, 7, 128 and that should guide you as to what you need to add.