[英]keras layers has no attribute Dense
I'm currently involving Coursera-Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning course.我目前正在参与 Coursera-Introduction to TensorFlow for人工智能、机器学习和深度学习课程。 I got an error in the following code.
我在以下代码中遇到错误。
Here is my python code,这是我的python代码,
# y = 2x - 1
import tensorflow as tf
# helps us to represent our data as lists easily and quickly
import numpy as np
# framework for defining a neural network as a set of Sequential layers
from tensorflow import keras
# The LOSS function measures the guessed answers against the known correct
# answers and measures how well or how badly it did
# then uses the OPTIMIZER function to make another guess. Based on how the
# loss function went, it will try to minimize the loss.
model = tf.keras.Sequential([keras.layers.Dence(units=1, input_shape=
[1])])
model.compile(optimizer='sgd', loss='mean_squared_error')
# providing data
xs = np.array([-1.0,0.0,1.0,2.0,3.0,4.0],dtype=float)
ys = np.array([-3.0,-1.0,1.0,3.0,5.0,7.0],dtype=float)
# training neural network
model.fit(xs,ys,epochs=500)
# figure out value for unknown x
print(model.predict([10.0]))
I got this error message in terminal.我在终端中收到此错误消息。
C:\anaconda\envs\tfp\pythonw.exe C:/Users/USER/PycharmProjects/couseraTensorflow/helloWorld.py
Traceback (most recent call last):
File "C:/Users/USER/PycharmProjects/couseraTensorflow/helloWorld.py", line 11, in <module>
model = tf.keras.Sequential([keras.layers.Dence(units=1, input_shape=[1])])
AttributeError: module 'tensorflow._api.v1.keras.layers' has no attribute 'Dence'
Process finished with exit code 1
图层名称是 Den s e,而不是 Den c e。
try this in TF 2.x在 TF 2.x 中试试这个
import tensorflow as tf
# helps us to represent our data as lists easily and quickly
import numpy as np
# framework for defining a neural network as a set of Sequential layers
from tensorflow import keras
# The LOSS function measures the guessed answers against the known correct
# answers and measures how well or how badly it did
# then uses the OPTIMIZER function to make another guess. Based on how the
# loss function went, it will try to minimize the loss.
model = tf.keras.models.Sequential([keras.layers.Dense(units=1, input_shape=
[1])])
model.compile(optimizer='sgd', loss='mean_squared_error')
# providing data
xs = np.array([-1.0,0.0,1.0,2.0,3.0,4.0],dtype=float)
ys = np.array([-3.0,-1.0,1.0,3.0,5.0,7.0],dtype=float)
# training neural network
model.fit(xs,ys,epochs=500)
# figure out value for unknown x
print(model.predict([10.0]))
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