def _predicted_mean_op(self, activations): activation, activation_size = activations[-1] predicted_mean = model_utils.fully_connected( activation, activation_size, self.output_window_size * self.num_features, name="predicted_mean", activation=None) return array_ops.reshape(predicted_mean, [-1, self.output_window_size, self.num_features])
def _predicted_mean_op(self, activations): activation, activation_size = activations[-1] predicted_mean = model_utils.fully_connected( activation, activation_size, self.output_window_size * self.num_features, name="predicted_mean", activation=None) return array_ops.reshape(predicted_mean, [-1, self.output_window_size, self.num_features])
def _create_hidden_stack(self, activation, activation_size): activations = [] for layer_number, layer_size in enumerate(self.hidden_layer_sizes): # TODO(agarwal): Migrate to fully_connected in tf slim activation = model_utils.fully_connected( activation, activation_size, layer_size, name="layer_{}".format(layer_number)) activation_size = layer_size activations.append((activation, activation_size)) return activations
def _create_hidden_stack(self, activation, activation_size): activations = [] for layer_number, layer_size in enumerate(self.hidden_layer_sizes): # TODO(agarwal): Migrate to fully_connected in tf slim activation = model_utils.fully_connected( activation, activation_size, layer_size, name="layer_{}".format(layer_number)) activation_size = layer_size activations.append((activation, activation_size)) return activations
def _predicted_covariance_op(self, activations, num_values): activation, activation_size = activations[-1] if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS: log_sigma_square = model_utils.fully_connected( activation, activation_size, self.output_window_size * num_values, name="log_sigma_square", activation=None) predicted_covariance = gen_math_ops.exp(log_sigma_square) predicted_covariance = array_ops.reshape( predicted_covariance, [-1, self.output_window_size, num_values]) else: shape = array_ops.stack([ array_ops.shape(activation)[0], constant_op.constant(self.output_window_size), constant_op.constant(num_values) ]) predicted_covariance = array_ops.ones(shape=shape, dtype=activation.dtype) return predicted_covariance
def _predicted_covariance_op(self, activations, num_values): activation, activation_size = activations[-1] if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS: log_sigma_square = model_utils.fully_connected( activation, activation_size, self.output_window_size * num_values, name="log_sigma_square", activation=None) predicted_covariance = gen_math_ops.exp(log_sigma_square) predicted_covariance = array_ops.reshape( predicted_covariance, [-1, self.output_window_size, num_values]) else: shape = array_ops.stack([ array_ops.shape(activation)[0], constant_op.constant(self.output_window_size), constant_op.constant(num_values) ]) predicted_covariance = array_ops.ones(shape=shape, dtype=activation.dtype) return predicted_covariance