I want to use matrix multiplication inside TF model. My model is a NN with input shape = (1,9). And I want to get a product of this vectors by themself (i.e. I want to get a matrix-product equals multiplication of transposed input vector by itself, so its shape equals (9,9)).
Code example:
inputs = tf.keras.layers.Input(shape=(1,9))
outputs = tf.keras.layers.Dense(1, activation='linear')(tf.transpose(inputs) @ inputs)
model = tf.keras.Model(inputs, outputs)
adam = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(optimizer=adam, loss='mse', metrics=['mae'])
But I have problem with shape of such result. In the case of the above code I get a next architecture:
If I understand correctly, first dimension (None) in the input layer corresponds to size of batch of input data. And when I use transpose operation, it applies to all dimensions in this shape. So I get result with shape (9,1,9) after transpose and multiplication. But I think, that it is not correctly. Because I want to get product of transposed input vector by itself for all vectors in batch (i.e. correct shape for result which I want to get is (None, 9, 9)).
Getting this product as input for the model (compute this multiplication outside this model) is not suitable. Because I want to have in my model original input vector and the result of multiplication to do some operations after (above architecture is not full and using as example).
How can I get correct result? What is correct way to multiply matrices and vectors in TF, if we want to apply this operation to all vectors (matrices) in batch?
>Solution :
Try tf.linalg.matmul, since it will respect the batch dimension:
import tensorflow as tf
inputs = tf.keras.layers.Input(shape=(1,9))
outputs = tf.keras.layers.Dense(1, activation='linear')(tf.linalg.matmul(inputs, inputs, transpose_a=True))
model = tf.keras.Model(inputs, outputs)
adam = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(optimizer=adam, loss='mse', metrics=['mae'])
print(model.summary())
Model: "model_3"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) [(None, 1, 9)] 0 []
tf.linalg.matmul_3 (TFOpLambda (None, 9, 9) 0 ['input_5[0][0]',
) 'input_5[0][0]']
dense_4 (Dense) (None, 9, 1) 10 ['tf.linalg.matmul_3[0][0]']
==================================================================================================
Total params: 10
Trainable params: 10
Non-trainable params: 0
__________________________________________________________________________________________________
None