tensorflow 2 api 回归 tensorflow.python.framework.ops.EagerTensor' 对象不可调用
tensorflow 2 api regression tensorflow.python.framework.ops.EagerTensor' object is not callable
我正在尝试使用 tensorflow 2 实现多元回归 api。
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': np.array([100, 105.4, 108.3, 111.1, 113, 114.7]),
'B': np.array([11, 11.8, 12.3, 12.8, 13.1,13.6]),
'C': np.array([55, 56.3, 57, 58, 59.5, 60.4]),
'Target': np.array([4000, 4200.34, 4700, 5300, 5800, 6400])})
X = df.iloc[:, :3].values
Y = df.iloc[:, 3].values
plt.scatter(X[:, 0], Y)
plt.show()
X = tf.convert_to_tensor(X, dtype=tf.float32)
Y = tf.convert_to_tensor(Y, dtype=tf.float32)
def poly_model(X, w, b):
mult = tf.matmul(X, w)
pred = tf.add(tf.matmul(X, w), b)
return pred
w = tf.cast(tf.Variable(np.random.randn(3, 1), name='weight'), tf.float32)
b = tf.Variable(np.random.randn(), name='bias')
model = poly_model(X, w, b)
cost = tf.reduce_sum(tf.square(Y - model))
train_op = tf.optimizers.SGD(0.001)
train_op.minimize(cost, var_list=[w])
在最后一行它抛出我:
tensorflow.python.framework.ops.EagerTensor' object is not callable
还有,我有点懵:
1) 如何在不使用 Session.
只需执行以下操作:output = train_op(X)
?
2) 我需要使用 tf.GradientTape() as tape
还是仅用于图表?
-- 错误跟踪--
TypeError Traceback (most recent call last)
<ipython-input-1-ffbbbe1a3709> in <module>()
32 train_op = tf.optimizers.SGD(0.001)
33
---> 34 train_op.minimize(cost, var_list=[w])
~/anaconda3/envs/dpl/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py in minimize(self, loss, var_list, grad_loss, name)
294 """
295 grads_and_vars = self._compute_gradients(
--> 296 loss, var_list=var_list, grad_loss=grad_loss)
297
298 return self.apply_gradients(grads_and_vars, name=name)
~/anaconda3/envs/dpl/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py in _compute_gradients(self, loss, var_list, grad_loss)
326 with backprop.GradientTape() as tape:
327 tape.watch(var_list)
--> 328 loss_value = loss()
329 grads = tape.gradient(loss_value, var_list, grad_loss)
330
TypeError: 'tensorflow.python.framework.ops.EagerTensor' object is not callable
2) 你肯定需要用到GradientTape
1)像这样:
import tensorflow as tf
import numpy as np
print("TensorFlow version: {}".format(tf.__version__))
print("Eager execution: {}".format(tf.executing_eagerly()))
x = np.array([
[100, 105.4, 108.3, 111.1, 113, 114.7],
[11, 11.8, 12.3, 12.8, 13.1, 13.6],
[55, 56.3, 57, 58, 59.5, 60.4]
])
y = np.array([4000, 4200.34, 4700, 5300, 5800, 6400])
class Model(object):
def __init__(self, x, y):
# Initialize variable to (5.0, 0.0)
# In practice, these should be initialized to random values.
self.W = tf.Variable(tf.random.normal((len(x), len(x[0]))))
self.b = tf.Variable(tf.random.normal((len(y),)))
def __call__(self, x):
return self.W * x + self.b
def loss(predicted_y, desired_y):
return tf.reduce_sum(tf.square(predicted_y - desired_y))
optimizer = tf.optimizers.Adam(0.1)
# noinspection PyPep8Naming
def train(model, inputs, outputs):
with tf.GradientTape() as t:
current_loss = loss(model(inputs), outputs)
grads = t.gradient(current_loss, [model.W, model.b])
optimizer.apply_gradients(zip(grads,[model.W, model.b]))
print(current_loss)
model = Model(x, y)
for i in range(10000):
# print(model.b.numpy())
train(model,x,y)
我正在尝试使用 tensorflow 2 实现多元回归 api。
import tensorflow as tf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': np.array([100, 105.4, 108.3, 111.1, 113, 114.7]),
'B': np.array([11, 11.8, 12.3, 12.8, 13.1,13.6]),
'C': np.array([55, 56.3, 57, 58, 59.5, 60.4]),
'Target': np.array([4000, 4200.34, 4700, 5300, 5800, 6400])})
X = df.iloc[:, :3].values
Y = df.iloc[:, 3].values
plt.scatter(X[:, 0], Y)
plt.show()
X = tf.convert_to_tensor(X, dtype=tf.float32)
Y = tf.convert_to_tensor(Y, dtype=tf.float32)
def poly_model(X, w, b):
mult = tf.matmul(X, w)
pred = tf.add(tf.matmul(X, w), b)
return pred
w = tf.cast(tf.Variable(np.random.randn(3, 1), name='weight'), tf.float32)
b = tf.Variable(np.random.randn(), name='bias')
model = poly_model(X, w, b)
cost = tf.reduce_sum(tf.square(Y - model))
train_op = tf.optimizers.SGD(0.001)
train_op.minimize(cost, var_list=[w])
在最后一行它抛出我:
tensorflow.python.framework.ops.EagerTensor' object is not callable
还有,我有点懵:
1) 如何在不使用 Session.
只需执行以下操作:output = train_op(X)
?
2) 我需要使用 tf.GradientTape() as tape
还是仅用于图表?
-- 错误跟踪--
TypeError Traceback (most recent call last)
<ipython-input-1-ffbbbe1a3709> in <module>()
32 train_op = tf.optimizers.SGD(0.001)
33
---> 34 train_op.minimize(cost, var_list=[w])
~/anaconda3/envs/dpl/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py in minimize(self, loss, var_list, grad_loss, name)
294 """
295 grads_and_vars = self._compute_gradients(
--> 296 loss, var_list=var_list, grad_loss=grad_loss)
297
298 return self.apply_gradients(grads_and_vars, name=name)
~/anaconda3/envs/dpl/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py in _compute_gradients(self, loss, var_list, grad_loss)
326 with backprop.GradientTape() as tape:
327 tape.watch(var_list)
--> 328 loss_value = loss()
329 grads = tape.gradient(loss_value, var_list, grad_loss)
330
TypeError: 'tensorflow.python.framework.ops.EagerTensor' object is not callable
2) 你肯定需要用到GradientTape
1)像这样:
import tensorflow as tf
import numpy as np
print("TensorFlow version: {}".format(tf.__version__))
print("Eager execution: {}".format(tf.executing_eagerly()))
x = np.array([
[100, 105.4, 108.3, 111.1, 113, 114.7],
[11, 11.8, 12.3, 12.8, 13.1, 13.6],
[55, 56.3, 57, 58, 59.5, 60.4]
])
y = np.array([4000, 4200.34, 4700, 5300, 5800, 6400])
class Model(object):
def __init__(self, x, y):
# Initialize variable to (5.0, 0.0)
# In practice, these should be initialized to random values.
self.W = tf.Variable(tf.random.normal((len(x), len(x[0]))))
self.b = tf.Variable(tf.random.normal((len(y),)))
def __call__(self, x):
return self.W * x + self.b
def loss(predicted_y, desired_y):
return tf.reduce_sum(tf.square(predicted_y - desired_y))
optimizer = tf.optimizers.Adam(0.1)
# noinspection PyPep8Naming
def train(model, inputs, outputs):
with tf.GradientTape() as t:
current_loss = loss(model(inputs), outputs)
grads = t.gradient(current_loss, [model.W, model.b])
optimizer.apply_gradients(zip(grads,[model.W, model.b]))
print(current_loss)
model = Model(x, y)
for i in range(10000):
# print(model.b.numpy())
train(model,x,y)