def run_transducer(current_block, transducer_width):
# apply softmax on the correct outputs
transducer_out = softmax(
split_logits[current_block][0:transducer_width], axis=2)
return transducer_out
def forward(X, W1, b1, W2, b2):
Z = np.tanh(X.dot(W1) + b1)
return softmax(Z.dot(W2) + b2), Z
def forward(self, X):
# Z = relu(X.dot(self.W1) + self.b1)
Z = np.tanh(X.dot(self.W1) + self.b1)
return softmax(Z.dot(self.W2) + self.b2), Z
def forward(X, W, b):
return softmax(X.dot(W) + b)
def forward(self, X):
return softmax(X.dot(self.W) + self.b)