def basic_accuracy(self, y_true, y_pred, go_backwards=False):
"""训练过程中显示逐帧准确率的函数，排除了mask的影响
此处y_true需要是整数形式（非one hot）
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2)
mask = K.cast(mask, K.floatx())
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 反转相关
if self.hidden_dim is None:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
trans = K.transpose(self.trans)
else:
trans = self.trans
histoty = K.gather(trans, y_true)
else:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
r_trans, l_trans = self.l_trans, self.r_trans
else:
l_trans, r_trans = self.l_trans, self.r_trans
histoty = K.gather(l_trans, y_true)
histoty = tf.einsum('bnd,kd->bnk', histoty, r_trans)
# 计算逐标签accuracy
histoty = K.concatenate([y_pred[:, :1], histoty[:, :-1]], 1)
y_pred = (y_pred + histoty) / 2
y_pred = K.cast(K.argmax(y_pred, 2), 'int32')
isequal = K.cast(K.equal(y_true, y_pred), K.floatx())
return K.sum(isequal * mask) / K.sum(mask)
def basic_loss(self, y_true, y_pred, go_backwards=False):
"""y_true需要是整数形式（非one hot）
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2)
mask = K.cast(mask, K.floatx())
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 反转相关
if self.hidden_dim is None:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
trans = K.transpose(self.trans)
else:
trans = self.trans
histoty = K.gather(trans, y_true)
else:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
r_trans, l_trans = self.l_trans, self.r_trans
else:
l_trans, r_trans = self.l_trans, self.r_trans
histoty = K.gather(l_trans, y_true)
histoty = tf.einsum('bnd,kd->bnk', histoty, r_trans)
# 计算loss
histoty = K.concatenate([y_pred[:, :1], histoty[:, :-1]], 1)
y_pred = (y_pred + histoty) / 2
loss = K.sparse_categorical_crossentropy(
y_true, y_pred, from_logits=True
)
return K.sum(loss * mask) / K.sum(mask)
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
self.weights = [self.iterations]
lr = self.learning_rate
for i, (p, g) in enumerate(zip(params, grads)):
g2 = K.square(g) + self.epsilon1
shape, dtype = K.int_shape(p), K.dtype(p)
factored_shape = self.factored_shape(shape)
if factored_shape is None:
# 定义参数
v = K.zeros(shape, dtype=dtype, name='v_' + str(i))
self.weights.append(v)
# 定义更新
v_t = self.beta2 * v + (1.0 - self.beta2) * g2
self.updates.append(K.update(v, v_t))
else:
# 定义参数
shape1, axis1, shape2, axis2 = factored_shape
vr = K.zeros(shape1, dtype=dtype, name='vr_' + str(i))
vc = K.zeros(shape2, dtype=dtype, name='vc_' + str(i))
self.weights.extend([vr, vc])
# 定义更新
vr_t = self.beta2 * vr + K.mean(g2, axis=axis1, keepdims=True)
vc_t = self.beta2 * vc + K.mean(g2, axis=axis2, keepdims=True)
self.updates.extend([K.update(vr, vr_t), K.update(vc, vc_t)])
# 合成矩阵
v_t = vr_t * vc_t / K.mean(vr_t, axis=axis2, keepdims=True)
# 增量主体
u = g / K.sqrt(v_t)
# 增量裁剪
if self.clipping_threshold is not None:
u_rms = K.mean(K.sum(K.square(u)))
d = self.clipping_threshold
u = u / K.maximum(1.0, u_rms / d)
# 增量滑动
if self.beta1 > 0.0:
# 定义参数
m = K.zeros(shape, dtype=dtype, name='m_' + str(i))
self.weights.append(m)
# 定义更新
m_t = self.beta1 * m + (1.0 - self.beta1) * u
self.updates.append(K.update(m, m_t))
u = m_t
# 增量调整
if self.multiply_by_parameter_scale:
u = u * K.maximum(K.mean(K.sum(K.square(p))), self.epsilon2)
# 更新参数
self.updates.append(K.update(p, p - lr * u))
return self.updates
def sparse_accuracy(self, y_true, y_pred):
"""训练过程中显示逐帧准确率的函数，排除了mask的影响
此处y_true需要是整数形式（非one hot）
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2)
mask = K.cast(mask, K.floatx())
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 逐标签取最大来粗略评测训练效果
y_pred = K.cast(K.argmax(y_pred, 2), 'int32')
isequal = K.cast(K.equal(y_true, y_pred), K.floatx())
return K.sum(isequal * mask) / K.sum(mask)
def _resource_apply(self, grad, var, indices=None):
lr = self.learning_rate
g2 = K.square(grad) + self.epsilon1
shape = K.int_shape(var)
factored_shape = self.factored_shape(shape)
if factored_shape is None:
v = self.get_slot(var, 'v')
# 定义更新
v_t = self.beta2 * v + (1.0 - self.beta2) * g2
v_t = K.update(v, v_t)
else:
shape1, axis1, shape2, axis2 = factored_shape
vr = self.get_slot(var, 'vr')
vc = self.get_slot(var, 'vc')
# 定义更新
vr_t = self.beta2 * vr + K.mean(g2, axis=axis1, keepdims=True)
vc_t = self.beta2 * vc + K.mean(g2, axis=axis2, keepdims=True)
vr_t, vc_t = K.update(vr, vr_t), K.update(vc, vc_t)
# 合成矩阵
v_t = vr_t * vc_t / K.mean(vr_t, axis=axis2, keepdims=True)
# 增量主体
u = grad / K.sqrt(v_t)
# 增量裁剪
if self.clipping_threshold is not None:
u_rms = K.mean(K.sum(K.square(u)))
d = self.clipping_threshold
u = u / K.maximum(1.0, u_rms / d)
# 增量滑动
if self.beta1 > 0.0:
m = self.get_slot(var, 'm')
# 定义更新
m_t = self.beta1 * m + (1.0 - self.beta1) * u
u = K.update(m, m_t)
# 增量调整
if self.multiply_by_parameter_scale:
u = u * K.maximum(K.mean(K.sum(K.square(var))), self.epsilon2)
# 更新参数
return K.update(var, var - lr * u)
def reverse_sequence(self, inputs, mask=None):
if mask is None:
return [x[:, ::-1] for x in inputs]
else:
length = K.cast(K.sum(mask, 1), 'int32')
return [tf.reverse_sequence(x, length, seq_axis=1) for x in inputs]
