def forward(self, x): """Performs the forward pass. Args: x: 2-d array of size batch_size x image_size. Returns: A 2-d array of size batch_size x num_classes. """ def sigmoid(x): return 1.0 / (1.0 + np.exp(-x)) for w, b in zip(self.weights, self.biases): x = sigmoid(np.dot(w, x.T).T + b) return x
def foo(tree_arg): x, (y, z) = tree_arg return tf_np.dot(x, tf_np.dot(y, z))
def foo2(tree_arg): x, dct = tree_arg y, z = dct['a'], dct['b'] return tf_np.dot(x, tf_np.dot(y, z))