我的第一个深度网络
my first deep network
这是我的第一个深度神经网络，我在这段代码中遇到了问题
在实施的同时。
除了错误之外，代码很慢，这是另一回事。
代码如下：
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD, Adam
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
x, y = make_moons(n_samples=1000, noise=0.1, random_state=0)
plt.plot(x[y == 0, 0], x[y == 0, 1], 'ob', alpha=0.5)
plt.plot(x[y == 1, 0], x[y == 1, 1], 'xr', alpha=0.5)
plt.legend(['0', '1'])
plt.show()
print(x.shape)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.3, random_state=42)
model = Sequential
model.add(Dense(1, input_shape=(2,), activation='sigmoid'))
model.compile(Adam(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train,epochs=200, verbose=0)
results = model.evaluate(x_test, y_test)
print('The Accuracy score on the Train set is:\t{:0.3f}'.format(results[1]))
这里是错误
> Using TensorFlow backend. 2017-11-24 17:03:32.402292: W
> C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but
> these are available on your machine and could speed up CPU
> computations. 2017-11-24 17:03:32.402768: W
> C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but
> these are available on your machine and could speed up CPU
> computations. (1000, 2) Traceback (most recent call last): File
> "E:/Tutorial/Deep Learning.py", line 18, in <module>
> model.add(Dense(1, input_shape=(2,), activation='sigmoid')) TypeError: add() missing 1 required positional argument: 'layer'
您在 Sequential:
后缺少括号
model = Sequential()
如果您想编译 tensorflow 以使用特殊的 x86 指令（例如 AVX），请参阅 How to compile Tensorflow with SSE4.2 and AVX instructions? 但这可能比它值得的更多努力。
这是我的第一个深度神经网络，我在这段代码中遇到了问题 在实施的同时。 除了错误之外，代码很慢，这是另一回事。
代码如下：
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD, Adam
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
x, y = make_moons(n_samples=1000, noise=0.1, random_state=0)
plt.plot(x[y == 0, 0], x[y == 0, 1], 'ob', alpha=0.5)
plt.plot(x[y == 1, 0], x[y == 1, 1], 'xr', alpha=0.5)
plt.legend(['0', '1'])
plt.show()
print(x.shape)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.3, random_state=42)
model = Sequential
model.add(Dense(1, input_shape=(2,), activation='sigmoid'))
model.compile(Adam(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train,epochs=200, verbose=0)
results = model.evaluate(x_test, y_test)
print('The Accuracy score on the Train set is:\t{:0.3f}'.format(results[1]))
这里是错误
> Using TensorFlow backend. 2017-11-24 17:03:32.402292: W
> C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but
> these are available on your machine and could speed up CPU
> computations. 2017-11-24 17:03:32.402768: W
> C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but
> these are available on your machine and could speed up CPU
> computations. (1000, 2) Traceback (most recent call last): File
> "E:/Tutorial/Deep Learning.py", line 18, in <module>
> model.add(Dense(1, input_shape=(2,), activation='sigmoid')) TypeError: add() missing 1 required positional argument: 'layer'
您在 Sequential:
model = Sequential()
如果您想编译 tensorflow 以使用特殊的 x86 指令（例如 AVX），请参阅 How to compile Tensorflow with SSE4.2 and AVX instructions? 但这可能比它值得的更多努力。