attention-model

How to replace this naive code with scaled_dot_product_attention() in Pytorch? Question: Consider a code fragment from Crossformer: def forward(self, queries, keys, values): B, L, H, E = queries.shape _, S, _, D = values.shape scale = self.scale or 1./sqrt(E) scores = torch.einsum("blhe,bshe->bhls", queries, keys) A = self.dropout(torch.softmax(scale * scores, dim=-1)) V = torch.einsum("bhls,bshd->blhd", A, values) …

Tensorflow Multi Head Attention on Inputs: 4 x 5 x 20 x 64 with attention_axes=2 throwing mask dimension error (tf 2.11.0) Question: The expectation here is that the attention is applied on the 2nd dimension (4, 5, 20, 64). I am trying to apply self attention using the following code (issue reproducible with this code): …

Adding Attention on top of simple LSTM layer in Tensorflow 2.0 Question: I have a simple network of one LSTM and two Dense layers as such: model = tf.keras.Sequential() model.add(layers.LSTM(20, input_shape=(train_X.shape[1], train_X.shape[2]))) model.add(layers.Dense(20, activation=’sigmoid’)) model.add(layers.Dense(1, activation=’sigmoid’)) model.compile(loss=’mean_squared_error’) It is training on data with 3 inputs (normalized 0 to 1.0) and 1 output (binary) for the …