def actor_optimizer(self): action = K.placeholder(shape=[None, self.action_size]) advantages = K.placeholder(shape=[ None, ]) #advatages -> *multi-step* policy = self.actor.output action_prob = K.sum(action * policy, axis=1) cross_entropy = K.log(action_prob + 1e-10) * advantages cross_entropy = -K.mean(cross_entropy) # add (-entropy) to loss function, for enthusiastic search minus_entropy = K.sum(policy * K.log(policy + 1e-10), axis=1) minus_entropy = K.mean(minus_entropy) # optimizing loss minimizes cross_entropy, maximizes entropy loss = cross_entropy #+ 0.01 * minus_entropy optimizer = Adam(lr=self.actor_lr) updates = optimizer.get_updates(loss, self.actor.trainable_weights) train = K.function([self.actor.input, action, advantages], [loss], updates=updates) return train
def __call__(self, y_true, y_pred): # There are additional parameters for this function # Note: some of the 'modes' for edge behavior do not yet have a gradient definition in the Theano tree # and cannot be used for learning kernel = [self.kernel_size, self.kernel_size] y_true = KC.reshape(y_true, [-1] + list(self.__int_shape(y_pred)[1:])) y_pred = KC.reshape(y_pred, [-1] + list(self.__int_shape(y_pred)[1:])) patches_pred = KC.extract_image_patches(y_pred, kernel, kernel, 'valid', self.dim_ordering) patches_true = KC.extract_image_patches(y_true, kernel, kernel, 'valid', self.dim_ordering) # Reshape to get the var in the cells bs, w, h, c1, c2, c3 = self.__int_shape(patches_pred) patches_pred = KC.reshape(patches_pred, [-1, w, h, c1 * c2 * c3]) patches_true = KC.reshape(patches_true, [-1, w, h, c1 * c2 * c3]) # Get mean u_true = KC.mean(patches_true, axis=-1) u_pred = KC.mean(patches_pred, axis=-1) # Get variance var_true = K.var(patches_true, axis=-1) var_pred = K.var(patches_pred, axis=-1) # Get std dev covar_true_pred = K.mean(patches_true * patches_pred, axis=-1) - u_true * u_pred ssim = (2 * u_true * u_pred + self.c1) * (2 * covar_true_pred + self.c2) denom = (K.square(u_true) + K.square(u_pred) + self.c1) * (var_pred + var_true + self.c2) ssim /= denom # no need for clipping, c1 and c2 make the denom non-zero return K.mean((1.0 - ssim) / 2.0)
def SCD(y_true, y_pred): s_t = y_true - K.mean(y_true, axis=1, keepdims=True) s_p = y_pred - K.mean(y_true, axis=1, keepdims=True) return 1 - K.mean( K.l2_normalize(s_t + K.epsilon(), axis=-1) * K.l2_normalize(s_p + K.epsilon(), axis=-1))
def normSAD2(y_true, y_pred): y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1) y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1) mse = K.mean(K.square(y_true - y_pred), axis=-1) # sad = -K.log(1.0-K.mean(y_true2 * y_pred2/np.pi, axis=-1)) sad = K.mean(y_true2 * y_pred2, axis=-1) # sid = SID(y_true,y_pred) return 0.005 * mse - 0.75 * sad
def qnet(observation_space, action_space, net_name, net_size): num_actions = action_space.n net_size = int(net_size) net_name = net_name.lower() state, feature, net = _atari_state_feature_net(observation_space, net_name) # dueling or regular dqn/drqn if 'dueling' in net_name: value1 = net(net_size, activation='relu')(feature) adv1 = net(net_size, activation='relu')(feature) value2 = Dense(1)(value1) adv2 = Dense(num_actions)(adv1) mean_adv2 = Lambda(lambda x: K.mean(x, axis=1))(adv2) ones = K.ones([1, num_actions]) lambda_exp = lambda x: K.dot(K.expand_dims(x, axis=1), -ones) exp_mean_adv2 = Lambda(lambda_exp)(mean_adv2) sum_adv = layers.add([exp_mean_adv2, adv2]) exp_value2 = Lambda(lambda x: K.dot(x, ones))(value2) q_value = layers.add([exp_value2, sum_adv]) else: hid = net(net_size, activation='relu')(feature) q_value = Dense(num_actions)(hid) # build model return models.Model(inputs=state, outputs=q_value)
def __call__(self, x): regularization = 0. if self.l1: # X=K.eval(x) diff = x[1:] - x[:-1] regularization += K.mean(K.sqrt(diff**2 + 0.000001)) return regularization * self.l1
def _get_avg_bone_len(arg): bone_list = tf.unstack(arg[:, :, 0, :], axis=1) bones = [ bone_list[j] - bone_list[i] for i, j in zip(members_from, members_to) ] bones = K.expand_dims(K.stack(bones, axis=1), axis=2) bone_len = K.sqrt( K.sum(K.square(bones), axis=-1, keepdims=True) + K.epsilon()) return K.mean(bone_len, axis=1, keepdims=True)
def normSAD2(y_true, y_pred): # y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1) # y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1) mse = K.mean(K.square(y_true - y_pred)) sad = SAD(y_true, y_pred) # sad = -K.log(1.0-SAD(y_true, y_pred)/np.pi) # sid = SID(y_true,y_pred) # return 0.005 * mse + 0.75 * sad return 0.005 * mse + 10.0 * sad
def normSAD(y_true, y_pred): # y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1) # y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1) mse = K.mean(K.square(y_true - y_pred)) # sad = -K.log(1.0-K.mean(y_true2 * y_pred2/np.pi, axis=-1)) sad = SAD(y_true, y_pred) # sid = SID(y_true,y_pred) # return 0.008*mse-1.0*sad return 0.008 * mse + 1.0 * sad
def critic_optimizer(self): discounted_prediction = K.placeholder(shape=(None, )) value = self.critic.output # loss = MSE(discounted_prediction, value) loss = K.mean(K.square(discounted_prediction - value)) optimizer = Adam(lr=self.critic_lr) updates = optimizer.get_updates(loss, self.critic.trainable_weights) train = K.function([self.critic.input, discounted_prediction], [loss], updates=updates) return train
def MSE_KL(y_true, y_pred): # y_true=y_true[:,-162:] y_true = K.switch( K.min(y_true) < 0, y_true - K.min(y_true) + K.epsilon(), y_true + K.epsilon()) y_pred = K.switch( K.min(y_pred) < 0, y_pred - K.min(y_pred) + K.epsilon(), y_pred + K.epsilon()) p_n = y_true / K.max(y_true, axis=1, keepdims=True) q_n = y_pred / K.max(y_pred, axis=1, keepdims=True) return K.mean(K.square(y_true - y_pred), axis=-1) + 0.5 * (K.sum(p_n * K.log(p_n / q_n)) + K.sum( (1.001 - p_n) * K.log((1.01 - p_n) / (1.001 - q_n))))
def add_loss(model, W): inputs = model.inputs[0] abnormal = model.inputs[1] # abnormal = K.print_tensor(abnormal, message='abnormal = ') outputs = model.outputs[0] z_mean = model.get_layer('z_mean').output z_log_var = model.get_layer('z_log_var').output beta = K.sum(1.0 - abnormal, axis=-1, keepdims=True) / W # beta = K.print_tensor(beta, message='beta = ') reconstruction_loss = mean_squared_error(inputs, outputs) reconstruction_loss *= W kl_loss = 1 + z_log_var - beta * K.square(z_mean) - K.exp(z_log_var) kl_loss = K.sum(kl_loss, axis=-1) kl_loss *= -0.5 vae_loss = K.mean(reconstruction_loss + kl_loss) model.add_loss(vae_loss)
def classifier(self, x): scope = Scoping.get_global_scope() with scope.name_scope('classifier'): if self.data_set == 'NTURGBD': blocks = [{'size': 128, 'bneck': 32, 'groups': 16, 'strides': 1}, {'size': 256, 'bneck': 64, 'groups': 16, 'strides': 2}, {'size': 512, 'bneck': 128, 'groups': 16, 'strides': 2}] n_reps = 3 else: blocks = [{'size': 64, 'bneck': 32, 'groups': 8, 'strides': 3}, {'size': 128, 'bneck': 64, 'groups': 8, 'strides': 3}] n_reps = 3 def _data_augmentation(x): return K.in_train_phase(_sim_occlusions(_jitter_height(x)), x) x = Lambda(_data_augmentation, name=scope+"data_augmentation")(x) x = CombMatrix(self.njoints, name=scope+'comb_matrix')(x) x = EDM(name=scope+'edms')(x) x = Reshape((self.njoints * self.njoints, self.seq_len, 1), name=scope+'resh_in')(x) x = BatchNormalization(axis=-1, name=scope+'bn_in')(x) x = Conv2D(blocks[0]['bneck'], 1, 1, name=scope+'conv_in', **CONV2D_ARGS)(x) for i in range(len(blocks)): for j in range(n_reps): with scope.name_scope('block_%d_%d' % (i, j)): x = _conv_block(x, blocks[i]['size'], blocks[i]['bneck'], blocks[i]['groups'], 3, blocks[i]['strides'] if j == 0 else 1) x = Lambda(lambda args: K.mean(args, axis=(1, 2)), name=scope+'mean_pool')(x) x = BatchNormalization(axis=-1, name=scope + 'bn_out')(x) x = Activation('relu', name=scope + 'relu_out')(x) x = Dropout(self.dropout, name=scope+'dropout')(x) x = Dense(self.num_actions, activation='softmax', name=scope+'label')(x) return x
def call(self, inputs, training=None): input_shape = K.int_shape(inputs) reduction_axes = list(range(0, len(input_shape))) if (self.axis is not None): del reduction_axes[self.axis] del reduction_axes[0] mean = K.mean(inputs, reduction_axes, keepdims=True) stddev = K.std(inputs, reduction_axes, keepdims=True) + self.epsilon normed = (inputs - mean) / stddev broadcast_shape = [1] * len(input_shape) if self.axis is not None: broadcast_shape[self.axis] = input_shape[self.axis] if self.scale: broadcast_gamma = K.reshape(self.gamma, broadcast_shape) normed = normed * broadcast_gamma if self.center: broadcast_beta = K.reshape(self.beta, broadcast_shape) normed = normed + broadcast_beta return normed
def call(self, x, mask=None): if self.mode == 0 or self.mode == 2: assert self.built, 'Layer must be built before being called' input_shape = K.int_shape(x) reduction_axes = list(range(len(input_shape))) del reduction_axes[self.axis] broadcast_shape = [1] * len(input_shape) broadcast_shape[self.axis] = input_shape[self.axis] mean_batch, var_batch = _moments(x, reduction_axes, shift=None, keep_dims=False) std_batch = (K.sqrt(var_batch + self.epsilon)) r_max_value = K.get_value(self.r_max) r = std_batch / (K.sqrt(self.running_std + self.epsilon)) r = K.stop_gradient(K.clip(r, 1 / r_max_value, r_max_value)) d_max_value = K.get_value(self.d_max) d = (mean_batch - self.running_mean) / K.sqrt(self.running_std + self.epsilon) d = K.stop_gradient(K.clip(d, -d_max_value, d_max_value)) if sorted(reduction_axes) == range(K.ndim(x))[:-1]: x_normed_batch = (x - mean_batch) / std_batch x_normed = (x_normed_batch * r + d) * self.gamma + self.beta else: # need broadcasting broadcast_mean = K.reshape(mean_batch, broadcast_shape) broadcast_std = K.reshape(std_batch, broadcast_shape) broadcast_r = K.reshape(r, broadcast_shape) broadcast_d = K.reshape(d, broadcast_shape) broadcast_beta = K.reshape(self.beta, broadcast_shape) broadcast_gamma = K.reshape(self.gamma, broadcast_shape) x_normed_batch = (x - broadcast_mean) / broadcast_std x_normed = (x_normed_batch * broadcast_r + broadcast_d) * broadcast_gamma + broadcast_beta # explicit update to moving mean and standard deviation self.add_update([ K.moving_average_update(self.running_mean, mean_batch, self.momentum), K.moving_average_update(self.running_std, std_batch**2, self.momentum) ], x) # update r_max and d_max r_val = self.r_max_value / ( 1 + (self.r_max_value - 1) * K.exp(-self.t)) d_val = self.d_max_value / (1 + ( (self.d_max_value / 1e-3) - 1) * K.exp(-(2 * self.t))) self.add_update([ K.update(self.r_max, r_val), K.update(self.d_max, d_val), K.update_add(self.t, K.variable(np.array([self.t_delta]))) ], x) if self.mode == 0: if sorted(reduction_axes) == range(K.ndim(x))[:-1]: x_normed_running = K.batch_normalization( x, self.running_mean, self.running_std, self.beta, self.gamma, epsilon=self.epsilon) else: # need broadcasting broadcast_running_mean = K.reshape(self.running_mean, broadcast_shape) broadcast_running_std = K.reshape(self.running_std, broadcast_shape) broadcast_beta = K.reshape(self.beta, broadcast_shape) broadcast_gamma = K.reshape(self.gamma, broadcast_shape) x_normed_running = K.batch_normalization( x, broadcast_running_mean, broadcast_running_std, broadcast_beta, broadcast_gamma, epsilon=self.epsilon) # pick the normalized form of x corresponding to the training phase # for batch renormalization, inference time remains same as batchnorm x_normed = K.in_train_phase(x_normed, x_normed_running) elif self.mode == 1: # sample-wise normalization m = K.mean(x, axis=self.axis, keepdims=True) std = K.sqrt( K.var(x, axis=self.axis, keepdims=True) + self.epsilon) x_normed_batch = (x - m) / (std + self.epsilon) r_max_value = K.get_value(self.r_max) r = std / (self.running_std + self.epsilon) r = K.stop_gradient(K.clip(r, 1 / r_max_value, r_max_value)) d_max_value = K.get_value(self.d_max) d = (m - self.running_mean) / (self.running_std + self.epsilon) d = K.stop_gradient(K.clip(d, -d_max_value, d_max_value)) x_normed = ((x_normed_batch * r) + d) * self.gamma + self.beta # update r_max and d_max t_val = K.get_value(self.t) r_val = self.r_max_value / ( 1 + (self.r_max_value - 1) * np.exp(-t_val)) d_val = self.d_max_value / (1 + ( (self.d_max_value / 1e-3) - 1) * np.exp(-(2 * t_val))) t_val += float(self.t_delta) self.add_update([ K.update(self.r_max, r_val), K.update(self.d_max, d_val), K.update(self.t, t_val) ], x) return x_normed
def edm_loss(y_true, y_pred): return K.mean(K.sum(K.square(edm(y_true) - edm(y_pred)), axis=[1, 2]))
def rmse(y_true, y_pred): return backend.sqrt(backend.mean(backend.square(y_pred - y_true), axis=-1))
def normalized_mse_percentage_error(y_true, y_pred): y_true = y_true / y_true.max() y_pred = y_pred / y_pred.max() diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), None)) return 100. * K.mean(diff, axis=-1)
def SAD(y_true, y_pred): y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1) y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1) sad = -K.mean(y_true2 * y_pred2, axis=-1) return sad
def normMSE(y_true, y_pred): y_true2 = K.l2_normalize(y_true + K.epsilon(), axis=-1) y_pred2 = K.l2_normalize(y_pred + K.epsilon(), axis=-1) mse = K.mean(K.square(y_true - y_pred)) return mse