TensorFlow 2.0 GradientTape 返回 None 作为手动模型的梯度
TensorFlow 2.0 GradientTape returne None as gradients for Manual Models
我正在尝试手动创建逻辑回归模型,但是 GradientTape returns None键入梯度
class LogisticRegressionTF:
def __init__(self,dim):
#dim = X_train.shape[0]
tf.random.set_seed(1)
weight_init = tf.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform", seed=1)
zeros_init = tf.zeros_initializer()
self.W = tf.Variable(zeros_init([dim,1]), trainable=True, name="W")
self.b = tf.Variable(zeros_init([1]), trainable=True, name="b")
def sigmoid(self,z):
x = tf.Variable(z, trainable=True,dtype=tf.float32, name='x')
sigmoid = tf.sigmoid(x)
result = sigmoid
return result
def predict(self, x):
x = tf.cast(x, dtype=tf.float32)
h = tf.sigmoid(tf.add(tf.matmul(tf.transpose(self.W), x), self.b))
return h
def loss(self,logits, labels):
z = tf.Variable(logits, trainable=False,dtype=tf.float32, name='z')
y = tf.Variable(labels, trainable=False,dtype=tf.float32, name='y')
m = tf.cast(tf.size(z), dtype=tf.float32)
cost = tf.divide(tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1-z)),-m)
return cost
def fit(self,X_train, Y_train, lr_rate = 0.01, epochs = 1000):
costs=[]
optimizer = tf.optimizers.SGD(learning_rate=lr_rate)
for i in range(epochs):
current_loss = self.loss(self.predict(X_train), Y_train)
print(current_loss)
with tf.GradientTape() as t:
t.watch([self.W, self.b])
currt_loss = self.loss(self.predict(X_train), Y_train)
print(currt_loss)
grads = t.gradient(currt_loss, [self.W, self.b])
print(grads)
#optimizer.apply_gradients(zip(grads,[self.W, self.b]))
self.W.assign_sub(lr_rate * grads[0])
self.b.assign_sub(lr_rate * grads[1])
if(i %100 == 0):
print('Epoch %2d: , loss=%2.5f' %(i, current_loss))
costs.append(current_loss)
plt.plot(costs)
plt.ylim(0,50)
plt.ylabel('Cost J')
plt.xlabel('Iterations')
log_reg = LogisticRegressionTF(train_set_x.shape[0])
log_reg.fit(train_set_x, train_set_y)
这给出了一个类型错误,这是由于梯度返回 None
tf.Tensor(0.6931474, shape=(), dtype=float32)
tf.Tensor(0.6931474, shape=(), dtype=float32)
[None, None]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-192-024668d532b0> in <module>()
1 log_reg = LogisticRegressionTF(train_set_x.shape[0])
----> 2 log_reg.fit(train_set_x, train_set_y)
<ipython-input-191-4fef932eb231> in fit(self, X_train, Y_train, lr_rate, epochs)
40 print(grads)
41 #optimizer.apply_gradients(zip(grads,[self.W, self.b]))
---> 42 self.W.assign_sub(lr_rate * grads[0])
43 self.b.assign_sub(lr_rate * grads[1])
44 if(i %100 == 0):
TypeError: unsupported operand type(s) for *: 'float' and 'NoneType'
我的假设函数是 tf.sigmoid(tf.add(tf.matmul(tf.transpose(self.W), x), self.b))
我已经手动将成本函数定义为 tf.divide(tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1- z)),-m), 其中m为训练样例数
验证它返回的损失为 tf.Tensor(0.6931474, shape=(), dtype=float32)
我也做了一个 t.watch()
但什么也没发生它仍然返回 [None, None]
train_set_y.dtype is dtype('int64')
train_set_x.dtype is dtype('float64')
train_set_x.shape is (12288, 209)
train_set_y.shape is (1, 209)
type(train_set_x) is numpy.ndarray
我哪里错了??
谢谢
在我的环境中,Tensorflow 是 运行 Eagerly
即它在 Eager Execution 上。我们可以使用 tf.executing_eagerly()
检查它是否启用了急切执行 returns True
问题出在 loss(self,logits, labels):
函数
Logits 不应该是`tf.Variable(...)'
应该改为 z = logits
,并且 logits 应该被视为 Tensor 对象而不是 tf.Variable 对象。
我也将 tf.divide 更改为 Eager 模式(虽然不是必需的)
之前:
def loss(self,logits, labels):
z = tf.Variable(logits, trainable=False,dtype=tf.float32, name='z')
y = tf.Variable(labels, trainable=False,dtype=tf.float32, name='y')
m = tf.cast(tf.size(z), dtype=tf.float32)
cost = tf.divide(tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1-z)),-m)
return cost
之后:
def loss(self,logits, labels):
z = logits
y = tf.constant(labels,dtype=tf.float32, name='y')
m = tf.cast(tf.size(z), dtype=tf.float32)
cost = (-1/m)*tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1-z))
return cost
我正在尝试手动创建逻辑回归模型,但是 GradientTape returns None键入梯度
class LogisticRegressionTF:
def __init__(self,dim):
#dim = X_train.shape[0]
tf.random.set_seed(1)
weight_init = tf.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform", seed=1)
zeros_init = tf.zeros_initializer()
self.W = tf.Variable(zeros_init([dim,1]), trainable=True, name="W")
self.b = tf.Variable(zeros_init([1]), trainable=True, name="b")
def sigmoid(self,z):
x = tf.Variable(z, trainable=True,dtype=tf.float32, name='x')
sigmoid = tf.sigmoid(x)
result = sigmoid
return result
def predict(self, x):
x = tf.cast(x, dtype=tf.float32)
h = tf.sigmoid(tf.add(tf.matmul(tf.transpose(self.W), x), self.b))
return h
def loss(self,logits, labels):
z = tf.Variable(logits, trainable=False,dtype=tf.float32, name='z')
y = tf.Variable(labels, trainable=False,dtype=tf.float32, name='y')
m = tf.cast(tf.size(z), dtype=tf.float32)
cost = tf.divide(tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1-z)),-m)
return cost
def fit(self,X_train, Y_train, lr_rate = 0.01, epochs = 1000):
costs=[]
optimizer = tf.optimizers.SGD(learning_rate=lr_rate)
for i in range(epochs):
current_loss = self.loss(self.predict(X_train), Y_train)
print(current_loss)
with tf.GradientTape() as t:
t.watch([self.W, self.b])
currt_loss = self.loss(self.predict(X_train), Y_train)
print(currt_loss)
grads = t.gradient(currt_loss, [self.W, self.b])
print(grads)
#optimizer.apply_gradients(zip(grads,[self.W, self.b]))
self.W.assign_sub(lr_rate * grads[0])
self.b.assign_sub(lr_rate * grads[1])
if(i %100 == 0):
print('Epoch %2d: , loss=%2.5f' %(i, current_loss))
costs.append(current_loss)
plt.plot(costs)
plt.ylim(0,50)
plt.ylabel('Cost J')
plt.xlabel('Iterations')
log_reg = LogisticRegressionTF(train_set_x.shape[0])
log_reg.fit(train_set_x, train_set_y)
这给出了一个类型错误,这是由于梯度返回 None
tf.Tensor(0.6931474, shape=(), dtype=float32)
tf.Tensor(0.6931474, shape=(), dtype=float32)
[None, None]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-192-024668d532b0> in <module>()
1 log_reg = LogisticRegressionTF(train_set_x.shape[0])
----> 2 log_reg.fit(train_set_x, train_set_y)
<ipython-input-191-4fef932eb231> in fit(self, X_train, Y_train, lr_rate, epochs)
40 print(grads)
41 #optimizer.apply_gradients(zip(grads,[self.W, self.b]))
---> 42 self.W.assign_sub(lr_rate * grads[0])
43 self.b.assign_sub(lr_rate * grads[1])
44 if(i %100 == 0):
TypeError: unsupported operand type(s) for *: 'float' and 'NoneType'
我的假设函数是 tf.sigmoid(tf.add(tf.matmul(tf.transpose(self.W), x), self.b))
我已经手动将成本函数定义为 tf.divide(tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1- z)),-m), 其中m为训练样例数
验证它返回的损失为 tf.Tensor(0.6931474, shape=(), dtype=float32)
我也做了一个 t.watch()
但什么也没发生它仍然返回 [None, None]
train_set_y.dtype is dtype('int64')
train_set_x.dtype is dtype('float64')
train_set_x.shape is (12288, 209)
train_set_y.shape is (1, 209)
type(train_set_x) is numpy.ndarray
我哪里错了??
谢谢
在我的环境中,Tensorflow 是 运行 Eagerly
即它在 Eager Execution 上。我们可以使用 tf.executing_eagerly()
检查它是否启用了急切执行 returns True
问题出在 loss(self,logits, labels):
函数
Logits 不应该是`tf.Variable(...)'
应该改为 z = logits
,并且 logits 应该被视为 Tensor 对象而不是 tf.Variable 对象。
我也将 tf.divide 更改为 Eager 模式(虽然不是必需的)
之前:
def loss(self,logits, labels):
z = tf.Variable(logits, trainable=False,dtype=tf.float32, name='z')
y = tf.Variable(labels, trainable=False,dtype=tf.float32, name='y')
m = tf.cast(tf.size(z), dtype=tf.float32)
cost = tf.divide(tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1-z)),-m)
return cost
之后:
def loss(self,logits, labels):
z = logits
y = tf.constant(labels,dtype=tf.float32, name='y')
m = tf.cast(tf.size(z), dtype=tf.float32)
cost = (-1/m)*tf.reduce_sum(y*tf.math.log(z) + (1-y)*tf.math.log(1-z))
return cost